Refine Search

New Search

Results: 5,841

(searched for: Requirement and Architecture of Organization Development)
Save to Scifeed
Page of 117
Articles per Page
by
Show export options
  Select all
, Man Wu, Renyuan Zhang,
IEEE Transactions on Circuits and Systems I: Regular Papers, pp 1-14; doi:10.1109/tcsi.2021.3099034

Abstract:
A massive core computing architecture is developed for accelerating arbitrary calculations in fully parallel with high speed and low cost. The proposed architecture is reconfigurable in fine-grained (arbitrary functions), mid-grained (flexible function feature, accuracy, and number of operands), and coarse-grained (organization of cores). By implementing a large scale of novel bisection neural network (BNN) on hardware, the re-configuration is conducted by partitioning entire BNN into any specific pieces without redundancy. Each piece of BNN retrieves the arbitrary function approximately. By reconfiguring the BNN topology in software, we can easily adjust dimensions of the computing kernel without rewiring, and achieve a wide range of trade-offs between accuracy and efficiency in hardware. In this manner, the multi-grained reconfigurable accelerator (MuGRA) is achieved. Since MuGRA is flexible in all grained levels, various configurations for each validation are demonstrated with rich options of performance-cost matrix. From the FPGA implementation results, compared with other traditional function approximation methods, our method provides fewer parameter storage requirements. The comparison against related works proves that our accelerator effectively reduces the calculation latency with slight accuracy loss.
Kevin Weiss, Michel Rottleuthner, Thomas C. Schmidt, Matthias Wählisch
Published: 15 July 2021
Abstract:
Developing an operating system (OS) for low-end embedded devices requires continuous adaptation to new hardware architectures and components, while serviceability of features needs to be assured for each individual platform under tight resource constraints. It is challenging to design a versatile and accurate heterogeneous test environment that is agile enough to cover a continuous evolution of the code base and platforms. This mission is even morehallenging when organized in an agile open-source community process with many contributors such as for the RIOT OS. Hardware in the Loop (HiL) testing and Continuous Integration (CI) are automatable approaches to verify functionality, prevent regressions, and improve the overall quality at development speed in large community projects. In this paper, we present PHiLIP (Primitive Hardware in the Loop Integration Product), an open-source external reference device together with tools that validate the system software while it controls hardware and interprets physical signals. Instead of focusing on a specific test setting, PHiLIP takes the approach of a tool-assisted agile HiL test process, designed for continuous evolution and deployment cycles. We explain its design, describe how it supports HiL tests, evaluate performance metrics, and report on practical experiences of employing PHiLIP in an automated CI test infrastructure. Our initial deployment comprises 22 unique platforms, each of which executes 98 peripheral tests every night. PHiLIP allows for easy extension of low-cost, adaptive testing infrastructures but serves testing techniques and tools to a much wider range of applications.
Vladimir R. Kuzmin, Liudmila V. Massel
Published: 13 July 2021
E3S Web of Conferences, Volume 289; doi:10.1051/e3sconf/202128903003

Abstract:
Nowadays, the problems of the impact of pollutants’ emissions from industrial facilities, which include energy facilities, are attracting more and more attention in the world. Different international and governmental organizations issue decrees and recommendations on pollutants emission reduction. This, in turn, requires technologies and tools to assess the impact of current facilities, develop recommendations for them to reduce the emissions, and perform evaluation of impact for planned facilities. This article discusses a proposed technology for impact assessment of energy facilities on region’s environment, methods that are used by this technology. Also, architecture and main components of the scientific prototype of intelligent decision-making support system to support this technology are provided and results of approbation are shown.
, S. Foote, J. Burgo, J. Prezzama
Inventive Computation and Information Technologies pp 1068-1081; doi:10.1007/978-3-030-80119-9_71

Abstract:
Agile and Development & Operations (DevOps) methodologies are changing the way software is developed and delivered. In DevOps, Day 1 represents when capabilities are delivered and operational demands of the delivered software begin to exist. On Day 2 (and the days following), organizations begin to stress application resilience, scale, agility, security, and more. For large enterprises, operations can be very complex and costly, making it more important for Operational Users and Administrators to be prepared for the agile engagement with developers. Based on experience and lessons learned working with several public sector large organizations, this paper focuses on the “Ops” of DevOps discussing key findings and challenges that operations organizations face in the era of continuous engineering. It offers specific recommendations to empower operators in managing the effective transition and execution of capabilities into operations. The first recommendation is the creation of an Operations Architect to represent the important mission context for operations to developers, focusing on key cross-cutting enablers required to support those operations. A second recommendation is to integrate DevOps into existing IT Service Management processes and systems for the target operational environments. A third recommendation discussed the establishment of Minimal Viable Users with appropriate operational experience to adequately represent users to the development community. A fourth recommendation is to encourage community-based operations collaboration across user groups. A final recommendation discusses empowering operators and end users as “Citizen Coders” to be able to rapidly develop application features and as a way to document requirements and designs used in discussions with full development teams.
, , Alexei Zverovitch, , , , , , , Bernadino Romera-Paredes, et al.
Journal of Medical Internet Research, Volume 23; doi:10.2196/26151

Abstract:
Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
A. Nazarova, D. Mishagina
Bulletin of Belgorod State Technological University named after. V. G. Shukhov, Volume 6; doi:10.34031/2071-7318-2021-6-7-51-61

Abstract:
The architectural appearance of historical cities is transforming over time. The elements and characteristics of the historical urban environment being preserved for future generations support the ideas about the values of the urban landscape and contribute to the continuous development of its artistic image. The protection of architectural and urban heritage is provided by a set of normative, legal documents and documents of socio-economic and spatial planning. At the present stage of the development of domestic legislation, a new "tool" for the comprehensive protection of the historical environment is being developed and implemented. It is the status of "historical settlement", within the boundaries of which the entire set of valuable elements and parameters of the historical environment is subject of protection. To date, the selection criteria and the mechanism for the protection of elements presenting the subject of protection of a historical settlement have not been developed. The article presents the results of a comprehensive study of the skyline organization of the historic center of St. Petersburg. It includes recommendations for improving the system of regulation of urban planning activities in order to preserve the skyline and composition of historical buildings as components of the subject of protection of historical settlements. Based on a comprehensive analysis of the skyline organization of buildings and impact factors, proposals for the protection of the skyline can be included in the system of requirements for land use and urban planning regulations within the boundaries of zones of protection of cultural heritage properties, as well as within the boundaries of the territory of a historical settlement.
, Yuanchun Jiang
Transactions on Petri Nets and Other Models of Concurrency XV pp 70-81; doi:10.1007/978-3-030-78612-0_6

Abstract:
In the background of the globalized interaction needs of civil aviation information services, the definition of System Wide Information Management (SWIM) was proposed by International Civil Aviation Organization (ICAO). However, the issue of interaction between traditional business systems and heterogeneous systems still exists on the SWIM platform. The data adapter is an important functional component to solve this problem, which can not only solve the problems of high coupling among systems, poor compatibility, low sensitivity, non-standard syntax and poor semantics, but also realize the high sharing of information between systems. Based on the SWIM platform, this article redefines SWIM. First, this article analyzes the concept and architecture of SWIM as well as the role and logical architecture of adapters. Then, according to the requirements of the adapter, a design method of the adapter structure is proposed, and the process design methods of the two main functional modules, data transformation and service encapsulation, are proposed respectively, including data standard and service standard. Extensible Markup Language (XML) technology is a platform independent language, 90% of systems support XML, it is self-descriptive and extensible, suitable for storage and transport on the network, therefore it is the preferred language for software development and is important for the design of SWIM data adapter. Finally, the data adapter is designed and implemented.
V. V. Satyanarayana Tallapragada
Handbook of Deep Learning in Biomedical Engineering and Health Informatics pp 281-302; doi:10.1201/9781003144694-11

Abstract:
In recent days, systems are designed to have better classification based on input. The inputs vary based on the application that is intended, viz., retina image for detection of diabetic retinopathy (DR). Statistical data for classification based on various input parameters, but these are not limited. This chapter mainly focuses on the issues of biomedical image processing, having its roots in deep learning (DL). Existing techniques before the evolution of DL have made their mark, but its performance is limited. These techniques fail as the database (DB) size increases. In addition, various constraints need to be considered while calculating the recognition accuracy. Therefore, it is the need of the hour to find feasible solutions for improving the classification accuracy. Primarily these techniques are classified as supervised and unsupervised classification techniques. It is a well-known fact that supervised techniques have apriori data while the latter does not. Obviously, the former results in better accuracy, and the latter does not. In the real world scenario, unsupervised classification is more relevant as the input data tends to vary with time. DL is the fastest-growing field that has already proven its worth in machine learning. The attempt to model large-scale data by the use of various layered networks led to the development of deep neural networks (DNN), providing an application to various fields, but not limited to viz., image classification, 282and pattern recognition. In general, DL has two properties, one is various layers that support non-linear processing, and the other is supervised or unsupervised learning based on the features existing on each of the layers. Before DL development of artificial neural networks (ANNs) has enthralled into science and technology, and from 2006, DL created its impact, and now it’s so deep that still, scientists are learning it. An essential part of DL marks its path into optimization, a task that provides data that best fits transfer function and finds a better fitting curve. Such optimization has various applications in classification and recognition. As in the case of an Iris recognition system, as the DB size increases, the features representing a particular class of Iris must also increase, or else the system tends to fail. Hence, optimization is required to capture optimal features for proper recognition. Another application of DL in classification and recognition is in genomic signal processing. The classification of a large set of genome data is a trivial task, and keeping in mind the classification of such an extensive data set is hectic. The systems so trained and developed must be capable enough to handle such extensive data. DL is one of the applications that help users to find a particular genome code resulting in the identification of a particular disease or ailment from the given more extensive data set. The system tends to become more complicated for the search of a particular genome sequence, given different sequences of particular organism viz., COVID-19. This chapter discusses DL architectures that are used in biomedical image analysis viz., CNN, DBN, and RNN. Architecture is SAE, which has found its application in skin analysis, detection of organs in 4D patient information, segmentation of hippocampus from infant’s brains, optic disc extraction from fundus images. DBN is another architecture that has provided various applications such as segmentation of the left ventricle from heart MRI images, identification, and segregation of various retinal diseases. DBN, DNN, and RNN are different DL architectures that are used in the analysis of the genome sequences viz., finding the splicing patterns in individual tissues, highlighting the pathogenicity of genetic variants, identification of splice junction at DNA level, and understanding of the non-coding genome, identification of miRNA precursor and targets. Protein structure is also predicted using DBN, CNN, and RNN. Various models were developed for identification of structural binding choices, and prediction of binding sites of RBPs, disordered protein, other structures, local backbone positions, the area covered by proteins. Keeping aside the advantages and uses of DL, problems while applying DL algorithms in biomedical applications persist. Besides having achievable 283accuracy and better speed, the computational complexity of these algorithms also increases based on classification methods. Labeling of medical images is a hectic task that requires professional training. Instead, these images have a privacy lock that cannot be used by the general public and researchers. Therefore, getting such data is also a complicated process. Further, getting such a large amount of data is also a trivial task. Still, now metrics for the classification process are under development. The developed metric must also be uniform in assessment for various types of data and techniques that are existing in the networks. With the availability of such large data sets, it is also necessary to analyze the developed model carefully and adapt the model based on the features, properties, or characteristics of the data. As technology is accessing more data, which is accessed by wearable sensors via smartphones, DL helps as a tool for interpreting such data, detection, prognosis, prevention, diagnosis, and therapy.
, Yavor Dankov, Dessislava Vassileva, Martin Kovachev
Published: 8 July 2021
by 10.1007
Inventive Computation and Information Technologies pp 395-402; doi:10.1007/978-3-030-80624-8_49

The publisher has not yet granted permission to display this abstract.
, Alba Alvarez-Franco, Marta Portela, , , Claudio Badia-Careaga, , ,
Published: 8 July 2021
Abstract:
The eukaryotic genome is tightly packed inside the nucleus, where it is organized in 3D at different scales. This structure is driven and maintained by different chromatin states and by architectural factors that bind DNA, such as the multi-zinc finger protein CTCF. Zygotic genome structure is established de novo after fertilization, but the impact of such structure on genome function during the first stages of mammalian development is still unclear. Here, we show that deletion of the Ctcf gene in mouse embryos impairs the correct establishment of chromatin structure, but initial lineage decisions take place and embryos are viable until the late blastocyst stage. Furthermore, we observe that maternal CTCF is not necessary for development. Transcriptomic analyses of mutant embryos show that the changes in metabolic and protein homeostasis programs that occur during the progression from the morula to the blastocyst depend on CTCF. Yet, these changes in gene expression do not correlate with disruption of chromatin structure, but mainly with proximal binding of CTCF to the promoter region of genes downregulated in mutants. Our results show that CTCF regulates both 3D genome organization and transcription during mouse preimplantation development, but mostly as independent processes.
, , Eileen M. Burd, Michael Koval, , Craig M. Coopersmith
Published: 8 July 2021
PLOS ONE, Volume 16; doi:10.1371/journal.pone.0246270

Abstract:
During infectious disease, pathogen load drives inflammation and immune response that together contribute to tissue injury often resulting in organ dysfunction. Pulmonary failure in SARS-CoV2-infected hospitalized COVID-19 patients is one such prominent example. Intervention strategies require characterization of the host-pathogen interaction by accurately assessing all of the above-mentioned disease parameters. To study infection in intact mammals, mice are often used as essential genetic models. Due to humane concerns, there is a constant unmet demand to develop studies that reduce the number of mice utilized while generating objective data. Here, we describe an integrated method of evaluating lung inflammation in mice infected with Pseudomonas aeruginosa or murine gammaherpesvirus (MHV)-68. This method conserves animal resources while permitting evaluation of disease mechanisms in both infection settings. Lungs from a single euthanized mouse were used for two purposes-biological assays to determine inflammation and infection load, as well as histology to evaluate tissue architecture. For this concurrent assessment of multiple parameters from a single euthanized mouse, we limit in-situ formalin fixation to the right lung of the cadaver. The unfixed left lung is collected immediately and divided into several segments for biological assays including determination of pathogen titer, assessment of infection-driven cytokine levels and appearance of cell death markers. In situ fixed right lung was then processed for histological determination of tissue injury and confirmation of infection-driven cell death patterns. This method reduces overall animal use and minimizes inter-animal variability that results from sacrificing different animals for different types of assays. The technique can be applied to any lung disease study in mice or other mammals.
Jozwiak Lech, Stojanovic Radovan
Published: 7 July 2021
by Zenodo
Abstract:
Message from the editors, This Summer School on Cyber-Physical Systems and Internet of Things (SS-CPS&IoT’2021) is continuation of very successful 1st School from 2019. Unfortunately, last year, 2020, we were not able to organize the School because of Covid-19 pandemic. This year we adapted to the situation and managed the event on two tracks, remotely and on site. SS-CPS&IoT’2021 aims at serving the following main purposes: -advanced training of industrial and academic researchers, developers, engineers and decision-makers; academic teachers, Ph.D. and M.Sc. students; entrepreneurs, investors, research funding agents, and policy makers; and other participants who want to learn about CPS and IoT engineering; -dissemination, exchange and discussion of advanced knowledge and project results from numerous European R&D projects in CPS and IoT; -promotion and facilitation of international contacts and collaboration among people working or interested in the CPS and IoT area. The School is open to everybody, but previous knowledge or equivalent practical experience at least at the Bachelor level in engineering (e.g. system, computer, electronic, electrical, automotive, aviation, mechanical, or industrial engineering), computer science, informatics, applied physics or similar is recommended. Industry participation is encouraged. SSCPS&IoT’2021 is not only to follow courses and learn new knowledge on Embedded Systems, CPS and IoT from top professionals, but to meet people, interact and discuss with outstanding researchers, developers, academic lecturers, advanced students, and other participants, collaborate or start collaborations, and meet many talented people who may become employees of your companies as well. Distinguishing features of this advanced traditional Summer School are that its lectures, demonstrations, and practical hands-on sessions are given by top European and Worldwide specialists in particular CPS and IoT fields from industry and academia, delivering very fresh advanced knowledge. They are based on results from numerous currently running or recently finished European R&D projects in CPS and IoT, what gives an excellent opportunity to get acquainted with issues and challenges of CPS and IoT development; actual industrial problems, designs and case studies; and new concepts, advanced knowledge and modern design methods and tools created in the European R&D projects. This year, we had the honor to invite guest lecturer outside Europe, from Huawei, multinational company, leading global provider of information and communications technology (ICT) infrastructure and smart devices. Part of the students and lecturers came from the H2020 project SMART4ALL, “Self-sustained customized cyber physical system experiments for capacity building among European stakeholders”, so it can be said that it was a Joint School of our community with this significant project. SS-CPS&IoT’2021 is collocated with CPSIoT’2021, 9th International Conference on CyberPhysical Systems and Internet-of-Things and 10th Mediterranean Conference on Embedded Computing. The Summer School participants were encouraged to submit their papers to CPSIoT’2021 and MECO’2021, and thus gain additional experience of presenting work in one of the TOP conference in computing. The CPS&IoT’2021 Summer School Program is composed of four days of lectures, demonstrations, practical hands-on sessions, and discussions, as well as free participation in MECO’2021 and CPSIoT’2021 sessions. The topics of the lectures, demonstrations, and practical hands-on sessions cover major CPS applications (focusing on modern mobile applications that require high-performance or low energy consumption, as well as, high reliability, security and safety), computing technology for modern CPS, CPS architectures, development problems and solutions, as well as, design methodologies and design tools for all CPS design phases. In line with the technological challenges caused by the Covid-19 pandemic, part of the lecture was focused on fighting this disaster by using CPSs. There were also lectures from precision agriculture, in fact, Smart Anything Everywhere. Detailed list of the SS-CPS&IoT’2021 presentations including the names of their authors and presenters is provided in the Schedule of the School. Venue of SS-CPS&IoT’2021 was Hotel Budva*****, Budva, Montenegro. Budva is a 3500 years old town located at the Adriatic Sea coast of Montenegro. It is a popular touristic destination, with its charming Old Town, beautiful natural environment, 35 clean sandy beaches, and proximity to many famous touristic attractions as Kotor, Boka Kotorska, Sveti Stefan, Dubrovnik, and several national parks. It is an excellent place to have a summer school in a relaxed and friendly atmosphere. What were the brief data about this year Summer School? We had 70 lecturers and students, coming from over 20 countries around the world. We worked for four days in a 32-hour capacity, that is equivalent to an academic workload of 3 ECTS credits. The Chairmen of the SS-CPS&IoT’2021 express their thanks to all authors and presenters, as well as, to all other people who contributed to the success of the Summer School. We are especially proud on 2nd generation of students who successfully finished School and showed an enviable level of knowledge and interest. We are very grateful to Professor Budimur Lutovac, Publication Chair of CPSIoT’2021 and MECO’2021 helping us to compose these Proceedings, which represents only part of the results carried out by SS-CPSIoT’2021. The Proceedings is given here in form of open access document. We hope to see you again next year, mostly on the spot, in good health and mood. Yours, Lech Jóźwiak Eindhoven University of Technology, The Netherlands Radovan Stojanović University of Montenegro and MECOnet, Montenegro Contributors: Ioannis...
Published: 7 July 2021
Journal of Medical Systems, Volume 45, pp 1-10; doi:10.1007/s10916-021-01751-6

Abstract:
Medical image segmentation has seen positive developments in recent years but remains challenging with many practical obstacles to overcome. The applications of this task are wide-ranging in many fields of medicine, and used in several imaging modalities which usually require tailored solutions. Deep learning models have gained much attention and have been lately recognized as the most successful for automated segmentation. In this work we show the versatility of this technique by means of a single deep learning architecture capable of successfully performing segmentation on two very different types of imaging: computed tomography and magnetic resonance. The developed model is fully convolutional with an encoder-decoder structure and high-resolution pathways which can process whole three-dimensional volumes at once, and learn directly from the data to find which voxels belong to the regions of interest and localize those against the background. The model was applied to two publicly available datasets achieving equivalent results for both imaging modalities, as well as performing segmentation of different organs in different anatomic regions with comparable success.
, Hanlin Zhang, Bo Gong, Siyuan Chang, Meili Lu, , , ,
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Volume 29, pp 1-1; doi:10.1109/tnsre.2021.3095316

Abstract:
Closed-loop deep brain stimulation (DBS) paradigm is gaining tremendous favor due to its potential capability of further and more efficient improvements in neurological diseases. Preclinical validation of closed-loop controller is quite necessary in order to minimize injury risks of clinical trials to patients, which can greatly benefit from real-time computational models and thus potentially reduce research and development costs and time. Here we developed an embedded multi-core real-time simulation platform (EMC-RTP) for a biological-faithful computational network model of basal ganglia (BG). The single neuron model is implemented in a highly real-time manner using a reasonable simplification. A modular mapping architecture with hierarchical routing organization was constructed to mimic the pathological neural activities of BG observed in parkinsonian conditions. A closed-loop simulation testbed for DBS validation was then set up using a host computer as the DBS controller. The availability of EMC-RTP and the testbed system was validated by comparing the performance of open-loop and proportional-integral (PI) controllers. Our experimental results showed that the proposed EMC-RTP reproduces abnormal beta bursts of BG in parkinsonian conditions while meets requirements of both real-time and computational accuracy as well. Closed-loop DBS experiments using the EMC-RTP suggested that the platform could perform reasonable output under different kinds of DBS strategies, indicating the usability of the platform.
Jozwiak Lech, Stojanovic Radovan
Published: 7 July 2021
by Zenodo
Abstract:
Message from the editors, This Summer School on Cyber-Physical Systems and Internet of Things (SS-CPS&IoT’2021) is continuation of very successful 1st School from 2019. Unfortunately, last year, 2020, we were not able to organize the School because of Covid-19 pandemic. This year we adapted to the situation and managed the event on two tracks, remotely and on site. SS-CPS&IoT’2021 aims at serving the following main purposes: -advanced training of industrial and academic researchers, developers, engineers and decision-makers; academic teachers, Ph.D. and M.Sc. students; entrepreneurs, investors, research funding agents, and policy makers; and other participants who want to learn about CPS and IoT engineering; -dissemination, exchange and discussion of advanced knowledge and project results from numerous European R&D projects in CPS and IoT; -promotion and facilitation of international contacts and collaboration among people working or interested in the CPS and IoT area. The School is open to everybody, but previous knowledge or equivalent practical experience at least at the Bachelor level in engineering (e.g. system, computer, electronic, electrical, automotive, aviation, mechanical, or industrial engineering), computer science, informatics, applied physics or similar is recommended. Industry participation is encouraged. SSCPS&IoT’2021 is not only to follow courses and learn new knowledge on Embedded Systems, CPS and IoT from top professionals, but to meet people, interact and discuss with outstanding researchers, developers, academic lecturers, advanced students, and other participants, collaborate or start collaborations, and meet many talented people who may become employees of your companies as well. Distinguishing features of this advanced traditional Summer School are that its lectures, demonstrations, and practical hands-on sessions are given by top European and Worldwide specialists in particular CPS and IoT fields from industry and academia, delivering very fresh advanced knowledge. They are based on results from numerous currently running or recently finished European R&D projects in CPS and IoT, what gives an excellent opportunity to get acquainted with issues and challenges of CPS and IoT development; actual industrial problems, designs and case studies; and new concepts, advanced knowledge and modern design methods and tools created in the European R&D projects. This year, we had the honor to invite guest lecturer outside Europe, from Huawei, multinational company, leading global provider of information and communications technology (ICT) infrastructure and smart devices. Part of the students and lecturers came from the H2020 project SMART4ALL, “Self-sustained customized cyber physical system experiments for capacity building among European stakeholders”, so it can be said that it was a Joint School of our community with this significant project. SS-CPS&IoT’2021 is collocated with CPSIoT’2021, 9th International Conference on CyberPhysical Systems and Internet-of-Things and 10th Mediterranean Conference on Embedded Computing. The Summer School participants were encouraged to submit their papers to CPSIoT’2021 and MECO’2021, and thus gain additional experience of presenting work in one of the TOP conference in computing. The CPS&IoT’2021 Summer School Program is composed of four days of lectures, demonstrations, practical hands-on sessions, and discussions, as well as free participation in MECO’2021 and CPSIoT’2021 sessions. The topics of the lectures, demonstrations, and practical hands-on sessions cover major CPS applications (focusing on modern mobile applications that require high-performance or low energy consumption, as well as, high reliability, security and safety), computing technology for modern CPS, CPS architectures, development problems and solutions, as well as, design methodologies and design tools for all CPS design phases. In line with the technological challenges caused by the Covid-19 pandemic, part of the lecture was focused on fighting this disaster by using CPSs. There were also lectures from precision agriculture, in fact, Smart Anything Everywhere. Detailed list of the SS-CPS&IoT’2021 presentations including the names of their authors and presenters is provided in the Schedule of the School. Venue of SS-CPS&IoT’2021 was Hotel Budva*****, Budva, Montenegro. Budva is a 3500 years old town located at the Adriatic Sea coast of Montenegro. It is a popular touristic destination, with its charming Old Town, beautiful natural environment, 35 clean sandy beaches, and proximity to many famous touristic attractions as Kotor, Boka Kotorska, Sveti Stefan, Dubrovnik, and several national parks. It is an excellent place to have a summer school in a relaxed and friendly atmosphere. What were the brief data about this year Summer School? We had 70 lecturers and students, coming from over 20 countries around the world. We worked for four days in a 32-hour capacity, that is equivalent to an academic workload of 3 ECTS credits. The Chairmen of the SS-CPS&IoT’2021 express their thanks to all authors and presenters, as well as, to all other people who contributed to the success of the Summer School. We are especially proud on 2nd generation of students who successfully finished School and showed an enviable level of knowledge and interest. We are very grateful to Professor Budimur Lutovac, Publication Chair of CPSIoT’2021 and MECO’2021 helping us to compose these Proceedings, which represents only part of the results carried out by SS-CPSIoT’2021. The Proceedings is given here in form of open access document. We hope to see you again next year, mostly on the spot, in good health and mood. Yours, Lech Jóźwiak Eindhoven University of Technology, The Netherlands Radovan Stojanović University of Montenegro and MECOnet, Montenegro Contributors: Ioannis...
Javier Castillo-Seoane, Jorge Gil-Rostra, Victor Lopez-Flores, Gabriel Lozano, Javier Ferrer, Juan Pedro Espinos, Kostya Ostrikov, Francisco Yubero, Agustin R. Gonzalez-Elipe, Angel Barranco, et al.
Published: 5 July 2021
Nanoscale; doi:10.1039/d1nr01937f

Abstract:
The eventual exploitation of one-dimensional nanomaterials yet needs the development of scalable, high yield, homogeneous and environmentally friendly methods able to meet the requirements for the fabrication of under design functional nanomaterials. In this article, we demonstrate a vacuum and plasma one-reactor approach for the synthesis of the fundamental common element in solar energy and optoelectronics, i.e. the transparent conducting electrode but in the form of nanotubes and nanotrees architectures. Although the process is generic and can be used for a variety of TCOs and wide-bandgap semiconductors, we focus herein on Indium Doped Tin oxide (ITO) as the most extended in the previous applications. This protocol combines widely applied deposition techniques such as thermal evaporation for the formation of organic nanowires serving as 1D and 3D soft templates, deposition of polycrystalline layers by magnetron sputtering, and removal of the template by simply annealing under mild vacuum conditions. The process variables are tuned to control the stoichiometry, morphology, and alignment of the ITO nanotubes and nanotrees. Four-probe characterization reveals the improved lateral connectivity of the ITO nanotrees and applied on individual nanotubes shows resistivities as low as 3.5 ± 0.9 x 10-4 Ω·cm, a value comparable to single-crystalline counterparts. The assessment of diffuse reflectance and transmittance in the UV-VIS range confirms the viability of the supported ITO nanotubes as a random optical media working as strong scattering layers. Further ability to form ITO nanotrees opens the path for practical applications as ultra-broadband absorbers in the NIR. The demonstrated low resistivity and optical properties of these ITO nanostructures open the way for their use in LEDs, IR shield, energy harvesting, nanosensors, and photoelectrochemical applications
V. Pot, X. Portell, W. Otten, , O. Monga,
European Journal of Soil Science; doi:10.1111/ejss.13142

Abstract:
Macroscopic models of soil organic matter (SOM) turnover have faced difficulties in reproducing SOM dynamics or in predicting the spatial distribution of carbon stocks. These models are based on a largely inadequate linear response of soil microorganisms to bulk concentrations of nutrients and it is clear that a new approach to SOM modeling is required. Introducing explicit microbial activity and OM reactivity in macroscopic models represents a challenge because of the fine spatial scales at which the processes occur. To get a better grasp on interactions that take place at the micro-scale, a new generation of SOM models have been developed at the spatial scale of the soil micro-environments where microorganisms evolve. They are well adapted to challenge traditional hypotheses about the influence of soil architecture on soil microbial activity. Soil architecture provides the scene for a dynamic spatial accessibility of resources to microbes and the emergence of interactions between the actors of SOM decomposition. In this context, we review microscale models of microbial activity that have been designed for soils and soil analogs. To understand how these models account for spatial accessibility, we have looked in detail at how soil micro-environments are described in the different approaches and how microbial colonies are spatialized in these micro-environments. We present the advantages and disadvantages of the developed strategies and we discuss their limits.
Edwin Z. Crues, Dan Dexter, , , Björn Möller
Published: 3 July 2021
by 10.1080
Journal of Simulation pp 1-21; doi:10.1080/17477778.2021.1945962

The publisher has not yet granted permission to display this abstract.
Jaak Tepandi, Carmen Rotuna, Giovanni Paolo Sellitto, Sander Fieten,
Transactions on Petri Nets and Other Models of Concurrency XV pp 141-163; doi:10.1007/978-3-030-79851-2_8

Abstract:
The Once-Only Principle requires the public administrations to ensure that citizens and businesses supply the same information only once to the Public Administration as a whole. Widespread use of the Once-Only Principle has the potential to simplify citizens’ life, make businesses more efficient, and reduce administrative burden in the European Union. The Once-Only Principle project (TOOP) is an initiative, financed by the EU Program Horizon 2020, to explore the possibility to enable the cross-border application of the Once-Only Principle by demonstrating it in practice, through the development of selected piloting applications for specific real-world use cases, enabling the connection of different registries and architectures in different countries for better exchange of information across public administrations. These piloting ICT systems are designed as a result of a pan-European collaboration and they adopt a federated model, to allow for a high degree of independence between the participating parties in the development of their own solutions. The main challenge in the implementation of an OOP solution is the diversity of organizations, procedures, data, and services on all four main levels of interoperability: legal, organizational, semantic, and technical. To address this challenge, TOOP is developing and testing the TOOP Reference Architecture (TOOPRA) to assist organizations in the cross-border implementation of the OOP. The paper outlines the TOOPRA users, principles, and requirements, presents an overview of the architecture development, describes the main views of TOOPRA, discusses architecture profiling, and analyses the TOOPRA sustainability issues.
Won-Hyeok Park, Dong-Pil Park,
Journal of Information Display pp 1-8; doi:10.1080/15980316.2021.1947403

Abstract:
The development of large-area organic light-emitting diode (OLED) displays requires a highly efficient tandem device architecture and an easily processable charge generation layer (CGL) with a low voltage drop and high optical transparency. In this study, we investigated and applied a doped organic n-CGL/p-CGL using thermal vacuum deposition in tandem OLED devices. A doping concentration of 1.0 wt.% for Li in 4, 7-Diphenyl-1, 10-phenanthroline (BPhen) was optimal for the n-CGL with 8 wt.% for 2-(7-dicyanomethylene-1,3,4,5,6,8,9,10-octafluoro-7H-pyrene-2-ylidene)-malononitrile (NDP-9)-doped N,N-bis(4-methylphenyl)benzenamine (TAPC) as a p-CGL. Maximum luminous efficiencies of 42.5 and 63.4 cd/A and a 4,000 cd/m2 current density for the target luminance values of 11.2 and 6.5 mA/cm2 were demonstrated for double-stack and triple-stack tandem blue phosphorescent OLED devices, respectively. Implementing these highly efficient tandem device structures will improve the overall lifetime of OLED displays by lowering their operating current density at the target luminance.
Alireza Miremadi, Mir Mohammad Ali Golchobian, Omidreza Ghanadiof
European Journal of Business and Management Research, Volume 6, pp 55-64; doi:10.24018/ejbmr.2021.6.4.932

Abstract:
Traditionally, Iran was looking for development and either in era of oil revenues or before that, it has always faced serious mismatches in realization of plans. The results of studying activities in developmental organizations show that most of the activities in IRANIAN organizations are providing non-financial sources, assisting the technology development, and developing concentrated investment. In this research, we will review organization development types and factors that impact this, especially in IRANIAN organizations. Also, in this study, we evaluate the relation between civil development companies and export development companies. companies and export development companies.
Adam Pietrobon, Sean P. Delaney, Carole Doré, Julien Yockell-Lelievre, Lisa Julian, William L. Stanford
Tumor Biology, Volume 81, pp 2974-2974; doi:10.1158/1538-7445.am2021-2974

Abstract:
Renal angiomyolipomas (R-AMLs) are hamartomatous kidney tumors which stain positively for markers of adipocytic, vascular, smooth muscle, and melanocytic lineages. These lesions possess loss of function mutations in either TSC1 or TSC2, which are canonical negative regulators of mTORC1 signaling. To date, there exists no in vitro or in vivo model which faithfully recapitulates the architectural and molecular complexity of R-AMLs. Considering these lesions can be detected very early in life- even congenitally- we hypothesized they arise as a consequence of aberrant tissue development. To test this hypothesis, we generated TSC1-/- and TSC2-/- mutants in four human pluripotent stem cell (hPSC) backgrounds using CRISPR/Cas9 genome engineering. Wild type hPSCs differentiated into renal organoids express markers of the glomerulus, proximal and distal tubules, in a topology that resembles human nephron patterning. Remarkably, wild type renal organoids downregulate mTORC1 signaling compared to adjacent undifferentiated cells. In contrast, both TSC1-/- and TSC2-/- hPSCs exhibit substantial lineage infidelity upon differentiation, staining positively for adipocytic and melanocytic markers which are absent in matched wild type controls. Additionally, knockout lines formed nodular growths with disorganized architecture, resembling the hamartomatous organization of R-AML lesions. These lesions exhibit hyperactive mTORC1 signaling, consistent with human pathology. Together, these data suggest three primary findings: loss of TSC1/2 drives lineage infidelity; TSC1/2 may be required for architectural organization of the kidney parenchyma; and a developmental approach to R-AML modelling may best recapitulate the human disease. Citation Format: Adam Pietrobon, Sean P. Delaney, Carole Doré, Julien Yockell-Lelievre, Lisa Julian, William L. Stanford. Loss of TSC1 or TSC2 drives lineage infidelity and hamartoma formation in a renal organoid model of angiomyolipoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2974.
Michael E. Steffey, Ying Qu, Andrew Karalewitz, Sumithra Urs, Julia Kirshner, Scott Wise
Tumor Biology, Volume 81, pp 2643-2643; doi:10.1158/1538-7445.am2021-2643

Abstract:
In order to predict efficacy and toxicity of compounds advancing in the oncology drug discovery pipeline, and to minimize requirements for multiple animal studies, the development of in vitro assays that recapitulate in vivo conditions is increasingly important. While high throughput screening favors 2D assays, both for cost and relative speed in culturing and plating, they do not recapitulate the tumor architecture and the microenvironment that is more comparable to in vivo models. We now report this has been accomplished using zPREDICTA's organ-specific three-dimensional (3D) culture models which support long-term culture of carcinoma cells. Physiologic extracellular matrix (ECM) components are critical in maintaining the physical and spatial microenvironment as well as tumor phenotypes in reconstructed bone marrow (r-Bone) and mouse breast (r-mBreast) models. The r-Bone ECM allows for co-culture of tumor cells with mesenchymal stem cells, fibroblasts, lymphoid, myeloid, and CAR-T cells. The data here describes clear differences in the in vitro pharmacology of several standard of care agents in 2D, in adherent or suspension cultures, compared to the recapitulated 3D tumor microenvironment of the human hematopoietic compartment or the mouse breast ductal epithelium. For example, we find that cisplatin has a 10-30 fold higher IC50 in 4 different murine cell lines (MMTV-PyMT, 4T1, EMT6, and E0771) when cultured in the r-mBreast model than when those cells are adhered to the polystyrene in a 2D culture. Cytotoxicity assays such as CellTiter-Glo® can also be performed in a high throughput format in both 96 and 384-well 3D assay formats and we demonstrate equivalent bortezomib IC50 values in RPMI 8226 cell r-Bone cultures in both plate types. Additionally, cells can be isolated from the matrix after test agent exposure and analyzed by an appropriate downstream application, such as flow cytometry, where we show anti-CD19 CAR-T cell killing of CD19+ NALM6 cells in r-Bone cultures. These 3D models represent new early opportunities to screen test agents in a platform that is designed to more closely mimic in vivo tissue microenvironments and serve as a more relevant tool for multi drug screening and as a prerequisite to animal studies. Citation Format: Michael E. Steffey, Ying Qu, Andrew Karalewitz, Sumithra Urs, Julia Kirshner, Scott Wise. Human and mouse reconstructed plate-based 3-dimensional culture assays that mimic the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2643.
Setyo Budi Hartono, Wahab Zaenuri, Fania Mutiara Savitri, Dessy Noor Farida, Yuyun Ristianawati, Stie Totalwin
JIAFE (Jurnal Ilmiah Akuntansi Fakultas Ekonomi), Volume 07; doi:10.34204/jiafe.v7i1.2915

Abstract:
Abstrak: Penelitian ini ditujukan pada anggaran dalam bentuk intangible asset (sumber daya manusia) dan tangible asset (aset tetap dan persediaan) yang diprediksi dapat mempengaruhi intellectual capital, kinerja keuangan sekarang dan mendatang, serta indikator kinerja utama. Alokasi anggaran sebagai baromater prioritas dalam mengembangkan intellectual capital ditujukan untuk memenuhi performa keuangan bagi indikator kinerja utama organisasi. Populasi yang juga menjadi sampel yaitu unit dan fakultas pada UIN Walisongo Semarang sebanyak 30 unit. Metode pengambilan sampling menggunakan teknik sampel jenuh yang mengambil seluruh populasi. Data yang digunakan adalah data sekunder berupa laporan tahunan dan laporan pencapaian indikator kinerja utama tahun 2019-2020. Analisis data menggunakan path analysis. Hasil penelitian ini adalah alokasi APBN tahun 2019 UIN Walisongo hanya terfokus pada tangible asset sebesar 82%, sementara 18% dialokasikan untuk intangible asset. Intangible asset tidak berpengaruh secara terhadap semua hubungan, hanya tangible asset saja yang dapat mempengaruhi intellectual capital secara langsung dan kinerja keuangan sekarang secara tidak langsung. Berdasarkan hasil penelitian ini menunjukkan perlu dilakukan audit sumber daya manusia sehingga dapat ditetapkan alokasi kebutuhan anggaran bagi intangible asset-nya. This research is aimed at the budget in the form of intangible assets (human resources) and fixed assets and inventories that are predicted to affect intellectual capital, current and future financial performance, as well as key performance indicators. Budget allocation as a priority barometer in developing intellectual capital is aimed at meeting financial performance for the organization's main performance indicators. The population that is also a sample is 30 units and architecture at UIN Walisongo Semarang. The sampling method uses a saturated sample technique that takes the entire population. The data used is secondary data in the form of annual reports and performance indicator reports for 2019-2020. Data analysis using path analysis. The results of this study were that the 2019 State Budget allocation of UIN Walisongo only focused on tangible assets by 82%, while 18% was allocated for intangible assets. Intangible assets do not affect all relationships, only tangible assets can directly affect intellectual capital and current financial performance indirectly. Results Based on this research, it is necessary to conduct an audit of human resources so that they can determine the allocation of budget requirements for intangible assets.
Muthu Krishnan.S, Angelin Ranjithamani.D, Deepa .C
International Journal on Cybernetics & Informatics, Volume 10, pp 137-144; doi:10.5121/ijci.2021.100216

Abstract:
Online event management system is an online event management system software project that serves the functionality of an event manager. The system allows registered user login and new users are allowed to register on the application. The system helps in the management of events, users and the aspects related to them. This proposed to be a web application. The project provides most of the basic functionality required for an event type e.g. [technical,Non-technical events,etc]. College students are advanced in thinking, who are senior and specialized professionals trained by the nation. They have independent thinking skills, and often have unique views and opinions on events. In this paper, an index is constructed to solve the difficulty of monitoring the ideological dynamics of college students, since the ideological dynamics of college students is difficulty to be captured. In particular, a visualization management information system is developed to monitor the ideological dynamics of college students based on big data. This system is based on the B/S architecture, which uses the SQL Server database. This system realizes the functions of index modification, document entry, word segmentation statistics, index correlation, keyword search, index analysis, document analysis.
Robin Khosla,
ACS Applied Electronic Materials, Volume 3, pp 2862-2897; doi:10.1021/acsaelm.0c00851

Abstract:
Over the decades since ferroelectricity was revealed, ferroelectric materials have emerged as a cornerstone for a wide spectrum of semiconductor technology and electronic device applications, particularly in state-of-the-art complementary metal oxide semiconductor (CMOS) logic circuits and digital information storage media. Recent unprecedented advancements and future perspectives on integrating ferroelectric materials, particularly with high-κ dielectrics for electronic devices, are weighed. The emphasis is on (i) application (logic and memory); (ii) ferroelectric materials (organic, inorganic, and two-dimensional (2D)); (iii) device structures (metal/ferroelectric/metal (MFM), metal/ferroelectric/semiconductor (MFS), metal/ferroelectric/insulator/semiconductor (MFIS), and metal/ferroelectric/metal/insulator/semiconductor (MFMIS)); and (iv) next-generation electronic devices (negative capacitance field effect transistors (NC-FETs), ferroelectric RAM (FeRAM), ferroelectric field effect transistors (FeFETs), and ferroelectric tunnel junctions (FTJs)). In NCFETs, the ferroelectric layer serves as a negative capacitor so that the channel surface potential can be amplified more than the gate voltage. Hence, devices can overcome the “Boltzmann tyranny” and operate with a steep subthreshold swing < 60 mV/dec and supply voltage < 0.5 V. Thus, NC-FETs would be more suitable for high-speed logic operations, scalability, low-power, and cost-effectiveness, targeting applications such as 14T-type CPU registers and 6T-type cache static random access memory (SRAM). Ferroelectrics also opens a path to solving the problems associated with technology scaling due to the unique structural and electronic properties. Ferroelectric memories are anticipated to be in different flavors based on optimum performance, cost, and end-user requirements. Herein, we deliberate on the exciting possibilities for the development of device structures such as one-transistor one-capacitor (1T-1C)-type FeRAM with fast access time (1014 cycles), and moderate data retention being considered as a strong contender for volatile dynamic random access memory (DRAM), while, for nonvolatile memory applications, 1T-type ferroelectric gated transistors, called FeFETs with nondestructive readout, fast access time (∼109 cycles), and high retention time (>10 years) have the potential to compete with embedded solid-state drives (SSDs). Finally, the FTJs with three-dimensional cross-point architecture are strong contenders for high-density niche storage applications to interchange with low-cost per bit external hard disk drives. We conclude with a brief survey of recent ferroelectrics advances and potential futuristic comparisons for next-generation computing and storage device applications so the field may expand and pave the way for high-volume manufacturing of semiconductor technology down to the sub-5 nm node over the coming years.
Published: 29 June 2021
Ardeola, Volume 68, pp 445-460; doi:10.13157/arla.68.2.2021.fo1

Abstract:
The next reform of the EU Common Agricultural Policy (CAP) for the period 2021-2027 (currently extended to 2023-2030) requires the approval by the European Commission of a Strategic Plan with environmental objectives for each Member State. Here we use the best available scientific evidence on the relationships between agricultural practices and biodiversity to delineate specific recommendations for the development of the Spanish Strategic Plan. Scientific evidence shows that Spain should (1) identify clear regional biodiversity targets and the landscape-level measures needed to achieve them; (2) define ambitious and complementary criteria across the three environmental instruments (enhanced conditionality, eco-schemes, and agri-environmental and climate measures) of the CAP's Green Architecture, especially in simple and complex landscapes; (3) ensure that other CAP instruments (areas of nature constraints, organic farming and protection of endangered livestock breeds and crop varieties) really support biodiversity; (4) improve farmers' knowledge and adjust measures to real world constraints; and (5) invest in biodiversity and ecosystem service monitoring in order to evaluate how the Plan achieves regional and national targets and to improve measures if targets are not met. We conclude that direct assessments of environmental objectives are technically and economically feasible, can be attractive to farmers, and are socially fair and of great interest for improving the environmental effectiveness of CAP measures. The explicit and rigorous association of assessments and monitoring, relating specific environmental indicators to regional objectives, should be the main criterion for the approval of the Strategic Plan in an environmentally-focused CAP 2023-2030.—Díaz, M. et al. (2021). Environmental objectives of Spanish agriculture: scientific guidelines for their effective implementation under the Common Agricultural Policy 2023-2030. Ardeola, 68: 445-460.
International Journal of Molecular Sciences, Volume 22; doi:10.3390/ijms22136997

Abstract:
We developed a multi-channel cell chip containing a three-dimensional (3D) scaffold for horizontal co-culture and drug toxicity screening in multi-organ culture (human glioblastoma, cervical cancer, normal liver cells, and normal lung cells). The polydimethylsiloxane (PDMS) multi-channel cell chip (PMCCC) was based on fused deposition modeling (FDM) technology. The architecture of the PMCCC was an open-type cell chip and did not require a pump or syringe. We investigated cell proliferation and cytotoxicity by conducting 3-(4,5-dimethylthiazol-2-yl)-2,5-dphenyltetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays and analysis of oleanolic acid (OA)-treated multi-channel cell chips. The results of the MTT and LDH assays showed that OA treatment in the multi-channel cell chip of four cell lines enhanced chemoresistance of cells compared with that in the 2D culture. Furthermore, we demonstrated the feasibility of the application of our multi-channel cell chip in various analysis methods through Annexin V-fluorescein isothiocyanate/propidium iodide staining, which is not used for conventional cell chips. Taken together, the results demonstrated that the PMCCC may be used as a new 3D platform because it enables simultaneous drug screening in multiple cells by single point injection and allows analysis of various biological processes.
Monjur Ahmed, Krassie Petrova
Published: 28 June 2021
Secure Edge Computing pp 51-64; doi:10.1201/9781003028635-5

Abstract:
Cloud computing and other cloud-based computing platforms (e.g., fog computing and edge computing) are recent and popular computing approaches. Due to the nature, architecture and infrastructure of cloud-based computing, it is important to acknowledge the importance of cloud data security, privacy and compliance in an organizational context. This chapter presents a discussion of the key factors an organization is required to address in order to develop a cybersecurity strategy aligned with its organizational and business goals. The specific architectural contexts of cloud computing and cloud-based computing are considered from an information security and privacy point of view. The discussion in this chapter explores the key organizational, human and technological factors associated with data and information security, privacy and compliance, from the perspective of an organization that is adopting cloud-based computing.
Olaonipekun Oluwafemi Erunkulu, , Caspar K Lebekwe, Modisa Mosalaosi, Joseph Chuma
IEEE Access, pp 1-1; doi:10.1109/access.2021.3093213

Abstract:
The mobile demands and future business context are anticipated to be resolved by the fifth-generation (5G) of mobile communication systems. It is expected to provide an utterly mobile device, connected society, and support the demanding services of various use cases (UCs). This is intended to meet the demand requirement by providing services at tens of Gbps in terms of data rates, higher mobility range, lower latencies, and massive connectivity density devices per square kilometer. A comprehensive and up-to-date survey of the different developed and proposed use cases is presented in this paper. The first part of the paper presents the overview of the new 5G Architecture by introducing new features such as the new radio interface (New Radio), an overview of the 5G Core Network, minimum requirements, and the Radio Access Network, 5G spectrum requirements and other fundamentals of the network. Secondly, a detailed review of the developed and proposed use cases for 5G communications by the standards development organizations (SDO) and other key players in mobile communication is provided. Thirdly, we went ahead to propose spectrum bands for the deployment of the various use cases based on the low-, mid-, and high-band spectrum and further classified the use cases with respect to their relevance and family, identifying the IMT-2020 test environments and the usage scenarios derived by the 3GPP, fourthly, the channel capacity and the bandwidth of the spectrum was studied, simulated and compared to ascertain the spectrum proposed in this paper for each UC family. Hence, this paper serves as a guideline for understanding the usage scenarios for the future 5G deployment in various environments. This would allow system developers to design and implement 5G channel characterization models specific to the usage scenarios to meet the system requirements.
Multimedia Tools and Applications pp 1-28; doi:10.1007/s11042-021-11158-7

Abstract:
The novel coronavirus outbreak has spread worldwide, causing respiratory infections in humans, leading to a huge global pandemic COVID-19. According to World Health Organization, the only way to curb this spread is by increasing the testing and isolating the infected. Meanwhile, the clinical testing currently being followed is not easily accessible and requires much time to give the results. In this scenario, remote diagnostic systems could become a handy solution. Some existing studies leverage the deep learning approach to provide an effective alternative to clinical diagnostic techniques. However, it is difficult to use such complex networks in resource constraint environments. To address this problem, we developed a fine-tuned deep learning model inspired by the architecture of the MobileNet V2 model. Moreover, the developed model is further optimized in terms of its size and complexity to make it compatible with mobile and edge devices. The results of extensive experimentation performed on a real-world dataset consisting of 2482 chest Computerized Tomography scan images strongly suggest the superiority of the developed fine-tuned deep learning model in terms of high accuracy and faster diagnosis time. The proposed model achieved a classification accuracy of 96.40%, with approximately ten times shorter response time than prevailing deep learning models. Further, McNemar’s statistical test results also prove the efficacy of the proposed model.
Published: 26 June 2021
by MDPI
Sensors, Volume 21; doi:10.3390/s21134379

Abstract:
Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the hierarchy levels based on their dimensional features. The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures. Toward this target, we develop the DLSTM model in auto-encoder (AE) fashion and take full advantage of the hierarchical architecture for better time series forecasting. DLSTM-AE works as an alternative approach to traditional and machine learning approaches that have been used to manipulate hierarchical forecasting. However, training a DLSTM in hierarchical architectures requires updating the weight vectors for each LSTM cell, which is time-consuming and requires a large amount of data through several dimensions. Transfer learning can mitigate this problem by training first the time series at the bottom level of the hierarchy using the proposed DLSTM-AE approach. Then, we transfer the learned features to perform synchronous training for the time series of the upper levels of the hierarchy. To demonstrate the efficiency of the proposed approach, we compare its performance with existing approaches using two case studies related to the energy and tourism domains. An evaluation of all approaches was based on two criteria, namely, the forecasting accuracy and the ability to produce coherent forecasts through through the hierarchy. In both case studies, the proposed approach attained the highest accuracy results among all counterparts and produced more coherent forecasts.
Luciano E. Chiang,
Journal of Medical Devices, Volume 15; doi:10.1115/1.4051246

Abstract:
In this article, we present a clinically validated invasive emergency mechanical ventilator developed in Chile called VEMERS UC. It has been clinically tested and validated in intubated Covid-19 patients. Once the pandemic hit Chilean soil in March 2020, it was quite clear that the number of mechanical ventilators available would not be enough. As in other parts of the world many initiatives sprung, most of them naively simple. Chilean medical societies joined engineering specialists and agreed early on in an organized and regulated open process for validating emergency mechanical ventilators, thus allowing for rapid development but with the required functionality, reliability, and safety features. VEMERS UC was one of a few that completed successfully all stages of the validating process, the final test being on five critically ill intubated Covid-19 patients for eight hours each. VEMERS UC is based on an electropneumatic circuit architecture, and its components are all low cost, off-the-shelf pneumatic, and electronic products easily obtained in industrial markets. It works in continuous mandatory volume control mode. The novel technical features of VEMERS UC are discussed here as well as the results obtained in each stage of the validating process. The validating process carried out in Chile is noteworthy by itself, and it could be used as an example in other developing countries. Furthermore, VEMERS UC can be used as a guiding design reference in other countries as well, since this design has already been thoroughly tested in human patients and has proven to work successfully.
Svitlana Cherepanova
Filosofiya osvity. Philosophy of Education, Volume 26, pp 90-99; doi:10.31874/2309-1606-2020-26-2-6

Abstract:
Modern reformation-educational processes are influenced by digital technologies, electronic communications networks, media-art practices, etc. Hence we get the actuality of creative potential of art for pedagogical activity as the concept of philosophy of education. Human consciousness inherents organic interdependence theoretically-cognitive (knowledge, ideas, comprehension of the boundary principles of human existence, culture, procedure of philosophical reflection) and social-psychological (feelings, will) elements. Author’s perennial experience incorporates interactive forms of artistic knowledge activation of pedagogical specialties students: preparations and guided tours by students (museums, architecture of Lviv, etc.), developing skills to conduct dialogues about art and education of the countries which languages are taught in pedagogical institution (Ukraine, the United Kingdom, Germany, France, Spain). In accordance personal and pedagogical experience of intersubjective communication, existential and cultural self-determination is enriched. In the system of philosophy of education, art is designed to harmonize human existence, to balance the sensory-emotional and rational-intellectual spheres of consciousness. The spread of electronic media requires a thorough study of their impact on humans, given the cognitive problems of communication technologies, information and computer systems, digitalization. A variety of artistic phenomena form a holistic system. Moreover, beliefs are knowledge that has passed through the world of feelings and human will. An open humanitarian space, new dialogical, communicative, cultural opportunities for interaction of nature-man-culture-society-universe, the universal nature of self-organization of human life, education of intersubjective cultural communication between carriers of different types of worldview, values, spiritual traditions is methodologically important.
Prathamesh Churi, Ambika Vishal Pawar, Amir A. Abdulmuhsin
International Journal of Organizational Analysis; doi:10.1108/ijoa-11-2020-2486

Abstract:
Purpose Focusing on the Indian context, with the increase in the amount of data and its analysis in health-care knowledge management (KM), the privacy concerns rise which results in loss of trust of an individual in e-health-care systems. Privacy issues in health care, specific to India, are caused by prevalent complacency, culture, politics, budget limitations, large population and infrastructures. Because of these factors, data security requires a backseat that allows easy access to confidential information. Furthermore, the prevalent culture affects health-care disclosure in India. In many cultures, disclosing sensitive personal health-care data is considered ill mannered. This leads to discrepancies in the recorded health-care data and a decrease in the level of treatment meted out. The results and statistics of treatments given do not match the records because of inaccurate data reporting. With the significant rise in the analysis and use of technology in health-care KM systems, it is important to understand the perception of KM in terms of its use and awareness about data sharing in the KM system. The purpose of the paper is to measure the perception of privacy issues in the context of Indian healthcare management systems. Design/methodology/approach To measure the perception of the use of the KM system, a set of 20 questions was circulated with a sample size of 337 which includes health-care researchers, doctors, practitioners and patients. The questions focused upon the use, share the sensitive health data in the KM platform. All the demographic information such as age, sex, religion, occupation is recorded. The privacy of the individual is maintained while circulating the questionnaire. The usage of health KM system and its privacy is measured through means and t-test. Findings The results of the t-test were found positive. This research study finds that the privacy factor is important among the Indians to share the information with the KM repository. It is also found that medical practitioners or data custodians are not much serious about sensitive data is being stored for analysis. From the statistical perception of usage of KM and its privacy, new architecture and privacy guidelines were suggested which can be considered in future research. Research limitations/implications From the literature review, the questionnaire has developed which can help policymakers and hospital administrators collect information about KM processes in health-care organizations, and this can result in higher performance of health organizations. The privacy factor can also be included in typical health KM architecture ensure that while knowledge acquisition process, privacy of individual or organization can be maintained. Social implications KM enhances the value of corporations and business industries through knowledge production, distribution and provides reliable access to the knowledge resources. KM in health care can comprise a confluence of formal methodologies and techniques to facilitate the creation, identification, acquisition, development, preservation, dissemination and finally the utilization of the various facets of a health-care enterprise’s knowledge assets. According to IBM Global executive report in the year 2012, the entire health-care system has changed from diseases-centric to patient-centric. India is emerging in terms of revenue and employment in the health-care field. The advances of information and communication technology help the health-care sector streamline for data structure and access and health analytics. Originality/value In India, the KM is frequently used in health-care industries majorly by health-care practitioners and professionals. As health-care data and knowledge are considered to be sensitive, the privacy of an individual while using the data cannot be compromised. The proposed empirical work will provide a solution in determining the main barriers of implementing privacy policies that need to be solved first and to ensure effective implementation of KM in the health care of India.
Priyanka Tyagi
Published: 23 June 2021
Pragmatic Flutter pp 251-256; doi:10.1201/9781003104636-16

Abstract:
This chapter sheds light on the importance of managing states in a Flutter application. Needless to say, that any real-world application requires to manage their application states sooner or later. A simple app needs only one screen in the beginning. However, many more screens keep adding to the app to support the new functionalities and features. These additional screens may need to know the state of the application at a given time. When it comes to building Flutter applications where everything is a ‘widget’, the deeply nested widget trees start to build up quickly. The widgets in widget-trees might need to share the application's state and pass their state to other widgets. It becomes crucial to handle the widget's state sharing or ‘State Management’ appropriately and efficiently to avoid the scaling issues that can lead to technical debts. In Flutter development, the architecture patterns and state management are used interchangeably. However, architecture patterns help to streamline code organization into separate layers to segregate responsibilities. There are many options to manage the state of the application in the Flutter applications. The Flutter app states are categorized into two types: Local (or Ephemeral) State and Application State.
Published: 22 June 2021
by MDPI
Polymers, Volume 13; doi:10.3390/polym13132041

Abstract:
The fabrication of 3D scaffolds is under wide investigation in tissue engineering (TE) because of its incessant development of new advanced technologies and the improvement of traditional processes. Currently, scientific and clinical research focuses on scaffold characterization to restore the function of missing or damaged tissues. A key for suitable scaffold production is the guarantee of an interconnected porous structure that allows the cells to grow as in native tissue. The fabrication techniques should meet the appropriate requirements, including feasible reproducibility and time- and cost-effective assets. This is necessary for easy processability, which is associated with the large range of biomaterials supporting the use of fabrication technologies. This paper presents a review of scaffold fabrication methods starting from polymer solutions that provide highly porous structures under controlled process parameters. In this review, general information of solution-based technologies, including freeze-drying, thermally or diffusion induced phase separation (TIPS or DIPS), and electrospinning, are presented, along with an overview of their technological strategies and applications. Furthermore, the differences in the fabricated constructs in terms of pore size and distribution, porosity, morphology, and mechanical and biological properties, are clarified and critically reviewed. Then, the combination of these techniques for obtaining scaffolds is described, offering the advantages of mimicking the unique architecture of tissues and organs that are intrinsically difficult to design.
Omri Heifler, Ella Borberg, Nimrod Harpak, Marina Zverzhinetsky, Vadim Krivitsky, Itay Gabriel, Victor Fourman, Dov Sherman,
Published: 22 June 2021
ACS Nano, Volume 15, pp 12019-12033; doi:10.1021/acsnano.1c03310

Abstract:
In order to reduce medical facility overload due to the rise of the elderly population, modern lifestyle diseases, or pandemics, the medical industry is currently developing point-of-care and home medical device systems. Diabetes is an incurable and lifetime disease, accountable for a significant mortality and socio-economic public health burden. Thus, tight glucose control in diabetic patients, which can prevent the onset of its late complications, is of enormous importance. Despite recent advances, the current best achievable management of glucose control is still inadequate, due to several key limitations in the system components, mainly related to the reliability of sensing components, both temporally and chemically, and the integration of sensing and delivery components in a single wearable platform, which is yet to be achieved. Thus, advanced closed-loop artificial pancreas systems able to modulate insulin delivery according to the measured sensor glucose levels, independently of patient supervision, represent a key requirement of development efforts. Here, we demonstrate a minimally invasive, transdermal, multiplex, and versatile continuous metabolites monitoring system in the subcutaneous interstitial fluid space based on a chemically modified SiNW-FET nanosensor array on microneedle elements. Using this technology, ISF-borne metabolites require no extraction and are measured directly and continuously by the nanosensors. Due to their chemical sensing mechanism, the nanosensor response is only influenced by the specific metabolite of interest, and no response is observed in the presence of potential exogenous and endogenous interferents known to seriously affect the response of current electrochemical glucose detection approaches. The 2D architecture of this platform, using a single SOI substrate as a top-down multipurpose material, resulted in a standard fabricated chip with 3D functionality. After proving the ability of the system to act as a selective multimetabolites sensor, we have implemented our platform to reach our main goal for in vivo continuous glucose monitoring of healthy human subjects. Furthermore, minor adjustments to the fabrication technique allow the on-chip integration of microinjection needle elements, which can ideally be used as a drug delivery system. Preliminary experiments on a mice animal model successfully demonstrated the single-chip capability to both monitor glucose levels as well as deliver insulin. By that, we hope to provide in the future a cost-effective and reliable wearable personalized clinical tool for patients and a strong tool for research, which will be able to perform direct monitoring of clinical biomarkers in the ISF as well as synchronized transdermal drug delivery by this single-chip multifunctional platform.
, Wayne Loschen, Richard Wojcik, Rekha Holtry, Monika Punjabi, Martina Siwek, Sheri Lewis
JMIR Public Health and Surveillance, Volume 7; doi:10.2196/26303

Abstract:
Background The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats. Objective This study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it. Methods The features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users. Results The resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse. Conclusions With emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.
Xuekai Xiong, Ruya Liu, Vijay Yechoor, Pradip Saha, Ke Ma
Published: 21 June 2021
Diabetes, Volume 70; doi:10.2337/db21-200-lb

Abstract:
The adipogenic progenitor transformation into mature adipocytes requires dissolution of intracellular actin cytoskeleton, and the cytoskeleton-MRTF/SRF signaling blocks adipogenesis. The circadian clock confers temporal control in metabolism, with tissue-intrinsic clock circuits contributing to metabolic homeostasis. In the current study, we identify a novel clock-MRTF/SRF regulatory axis that suppresses beige adipogenesis that is required for whole-body glucose metabolism. Key components of the cytoskeleton-MRTF/SRF signaling cascade display circadian oscillations in beige fat depot mediated by Bmal1 transcriptional activity. Genetic loss- or gain-of-functions of Bmal1 in adipogenic precursors markedly altered actin cytoskeleton organization, with silencing of Bmal1 inhibiting F-actin formation and MRTF/SRF activity whereas its forced expression augmenting actin architecture. Furthermore, we show that Bmal1 circadian control of the MRTF/SRF pathway drives beige thermogenic capacity in vivo. Prrx1-Cre-mediated beige fat-selective Bmal1 ablation induced beige depot browning with augmented mitochondrial metabolism, resulting in improved whole-body insulin sensitivity and resistance to obesity. Conversely, beige fat-specific overexpression of Bmal1 suppressed beige thermogenic program leading to impaired glucose tolerance and adipose expansion. Mechanistically, we show that Bmal1 deficiency enhanced beige precursor differentiation and thermogenic induction. Collectively, our findings uncover a temporal regulation of the cytoskeleton-MRTF/SRF signaling that modulates beige thermogenic capacity to maintain metabolic homeostasis. Disclosure X. Xiong: None. R. Liu: None. V. Yechoor: None. P. Saha: None. K. Ma: None. Funding National Institutes of Health (DK112794, DK097160-01); American Heart Association (17GRNT33370012)
Published: 20 June 2021
by MDPI
Entropy, Volume 23; doi:10.3390/e23060783

Abstract:
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).
Sandeep Dalal, Kamna Solanki
Green Internet of Things for Smart Cities pp 1-21; doi:10.1201/9781003032397-1

Abstract:
Latest technologies and the upheaval of Internet of Things (IoT) have fueled research in almost all areas related to human beings, disseminating anytime, anywhere, any device information access to human beings in different ways. This phenomenal research in recent decades aims to synergize human, data, processes and things, organizations, places, services, and facilities together in exceptional ways. Although IoT offers ample benefits to human society, still the process of manufacturing, utilization, implementation, and distribution of IoT services and devices requires voluminous energy and resources, ultimately producing ever- increasing toxic electronic waste. To mitigate the potential negative impacts of the latest scientific developments in the area of IoT on human society and environment, it has become mandatory to effectively deal with the threats and challenges posed by IoT. These challenges mainly include enhanced energy consumption, generation of electronic waste, emission of greenhouse gases (mainly CO2), utilization of non-biodegradable materials for IoT devices, usage of non-renewable and natural raw materials. This situation has created a need to move towards Green IoT (G-IoT), a future technological enhancement of IoT associated with green technology and the green economy. Green IoT is aimed towards bringing notable improvements in environmental and human well-being so as to make the world smarter using sustainable technological developments. G-IoT is the need of the hour and it carries the potential to completely transform human society along with improving environmental health. G-IoT is the latest technology that emphasizes developing solutions towards eliminating or mitigating the negative influences on people’s health and the environment. It basically focuses on two aspects. The first aspect is oriented towards designing IoT computing devices, communications protocols, and networking architectures that are energy efficient. The second aspect is oriented towards leveraging IoT technologies to reduce emissions of greenhouse gases, radiation, and pollution. Using the technological advancements in IoT enabling technologies, G-IoT has great capability to strengthen environmental and economic sustainability. This chapter presents an overview of the need and importance of green technologies and green processes in sustainable development and building a smart world. It goes on to explore the principles and applications of G-IoT in the growth of the society by examining its potential to enhance the quality of human life, economic growth, the environment, and green global modernization.
Melek Akın, Ahmet Öztopal, Ahmet Duran Şahin
Published: 18 June 2021
Abstract:
<p>As is known, wind is a renewable and non-polluting energy resource. In addition, there is no transportation problem in wind energy and it does not require very high technology for electricity generation. Wind turbines are used for electricity generation from kinetic energy of wind. In the point of power curves of these turbines, wind speed must be a certain band. Generally, they do not generate electricity cut-in wind speed that is between 0 and 4 m/s and cut out wind speed that is over 20-25 m/s. Over cut-out values cause breaking down of wind turbines, because high wind speeds create extra mechanical loads on them. Therefore, maximum/extreme winds and their estimation and prediction carry weight in terms of energy generation.</p><p>New European Wind Atlas (NEWA) is the project, within the scope of ERANET+ Program, and the attendants are Belgium, Denmark, Germany, Latvia, Portugal, Spain, Sweden, and Turkey. The aim of NEWA is to present a new wind atlas to the wind industry. In this project, the physical model used for obtaining wind speeds is a numerical weather prediction model named Weather Research and Forecasting (WRF).</p><p>One of the methods, which are developed by imitating of biological properties of living forms in a virtual environment, is Artificial Neural Networks (ANNs). Stimulations taken from the environment by using sense organs are transmitted to brain whereby neurons in a body and brain makes a decision towards these stimulations. That is the working form of ANNs. Moreover, ANNs can be thought as a black box, which processes given data and produces outputs against inputs. Furthermore, they are a method of Artificial Intelligence.</p><p>In this study, maximum wind speeds of 4 different wind farms in Turkey were estimated by using a downscaling method based on ANNs and wind data which were produced in grid points of NEWA Project. Besides that, 8 different levels (10, 50, 75, 100, 150, 200, 250, and 500 m) for each wind farm were considered. As a result of determining the best ANN architectures with sensitivity analysis, it was seen that Levenberg-Marquardt Backpropagation (trainlm) approach as a training algorithm and 9 neurons in each layer are common traits of best ANN architectures. In addition, 50 m for 2 wind farms and 10 m with 75 m for others were determined as an optimum downscaling levels. Moreover, according to downscaling results, correlation values were calculated around 0.80.</p><p><strong>Key Words: </strong>ANN, Downscaling, Maximum wind, NEWA, Turkey, Wind farm.</p>
Galina Aidarova, Aidar Aminov
Published: 18 June 2021
E3S Web of Conferences, Volume 274; doi:10.1051/e3sconf/202127401008

Abstract:
New trends in the social life at crucial points mean applying to the past experience and looking for new development models. COVID-19 has marked a global transition to a new architecture and urban planning paradigm of the environment in accordance with the sanitary, hygienic requirements and rational forms. In accordance with the current challenges it becomes necessary to reevaluate the concepts of urbanism and disurbanism redefining urban planning, existing typology, structural and functional organization as well as to search for new ways of architecture and urban development. Urban structures and sociology are expected to be reconsidered leading to reduced capacity of all public buildings, disappearance of some of them and replacement by recreation zones. Inexhaustible ideas and resources of past design approaches may be featured in the buildings styles. We could predict appearance of significant signs of new ethics in the new aesthetics which will mark the arrival of the third global «superstyle» which features have been already seen in the rigid construction approaches, in the social movements activities. Methods of architecture education are expected to be modified: in particular, the importance of advanced techniques in the educational process will increase and teamwork in the architecture projects will became vital.
Yu-Long Li, Xiao-Ning Cheng, Tong Lu, ,
Frontiers in Cell and Developmental Biology, Volume 9; doi:10.3389/fcell.2021.671887

Abstract:
Syne2b/nesprin-2 is a giant protein implicated in tethering the nucleus to the cytoskeleton and plays an important role in maintaining cellular architecture. Epiboly is a conserved morphogenetic movement that involves extensive spreading and thinning of the epithelial blastoderm to shape the embryo and organize the three germ layers. Dynamic cytoskeletal organization is critical for this process, but how it is regulated remains elusive. Here we generated a zebrafish syne2b mutant line and analyzed the effects of impaired Syne2b function during early development. By CRISPR/Cas9-mediated genome editing, we obtained a large deletion in the syne2b locus, predicted to cause truncation of the nuclear localization KASH domain in the translated protein. Maternal and zygotic syne2b embryos showed delayed epiboly initiation and progression without defects in embryonic patterning. Remarkably, disruption of Syne2b function severely impaired cytoskeletal organization across the embryo, leading to aberrant clustering of F-actin at multiple cell contact regions and abnormal cell shape changes. These caused disintegration of the epithelial blastoderm before the end of gastrulation in most severely affected embryos. Moreover, the migration of yolk nuclear syncytium also became defective, likely due to disorganized cytoskeletal networks at the blastoderm margin and in the yolk cell. These findings demonstrate an essential function of Syne2b in maintaining cytoskeletal architecture and epithelial integrity during epiboly movement.
Meera Madhu, Remya Ramakrishnan, Vishnu Vijay,
Chemical Reviews; doi:10.1021/acs.chemrev.1c00078

Abstract:
Inspired by the high photoconversion efficiency observed in natural light-harvesting systems, the hierarchical organization of molecular building blocks has gained impetus in the past few decades. Particularly, the molecular arrangement and packing in the active layer of organic solar cells (OSCs) have garnered significant attention due to the decisive role of the nature of donor/acceptor (D/A) heterojunctions in charge carrier generation and ultimately the power conversion efficiency. This review focuses on the recent developments in emergent optoelectronic properties exhibited by self-sorted donor-on-donor/acceptor-on-acceptor arrangement of covalently linked D–A systems, highlighting the ultrafast excited state dynamics of charge transfer and transport. Segregated organization of donors and acceptors promotes the delocalization of photoinduced charges among the stacks, engendering an enhanced charge separation lifetime and percolation pathways with ambipolar conductivity and charge carrier yield. Covalently linking donors and acceptors ensure a sufficient D–A interface and interchromophoric electronic coupling as required for faster charge separation while providing better control over their supramolecular assemblies. The design strategies to attain D–A conjugate assemblies with optimal charge carrier generation efficiency, the scope of their application compared to state-of-the-art OSCs, current challenges, and future opportunities are discussed in the review. An integrated overview of rational design approaches derived from the comprehension of underlying photoinduced processes can pave the way toward superior optoelectronic devices and bring in new possibilities to the avenue of functional supramolecular architectures.
, Ian Walker, Thomas Speck
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.711942

Abstract:
Editorial on the Research Topic Generation Growbots: Materials, Mechanisms, and Biomimetic Design for Growing Robots Plants are the dominant life form on the planet, accounting for over 80% of its biomass (Thompson, 2018). Plants are adapted to and thrive in virtually all environments, both natural and human-adapted, across the globe. In achieving this widespread presence, plants exhibit a significant range of structures and operational strategies. On the one hand, many key aspects of plant biology remain imperfectly understood, and the possibilities for plant-inspired engineering remain largely unexplored. On the other hand, increasing interest in plant-inspired research can be observed in architecture and technology in general over the last decades (cf. Speck and Speck 2019). More recently, plants have also started to represent models in robotics (Mazzolai et al., 2010; Lastinger et al., 2019; Sadeghi et al., 2020; Wooten et al., 2018), especially for the design of systems that have to deal with unstructured environments and require advanced capabilities of soft interaction, adaptation, and self-morphing. With this view, the goal of this special issue is to illustrate the potential of identifying principles from plant growth and movement suitable for engineering, and the adaptation of those principles to the new emerging field of “growing” robots, or Growbots. The field of robotics has expanded rapidly over the past 25 years. Important advances in robotic design, planning, locomotion, and manipulation have been inspired and driven by insights gained from biology, notably in the structure and behavior of animals. However, to date very little attention has been paid by roboticists to the multitude of “existence proofs” provided by plants. In this Research Topic, which is based on the contributions presented at the 2019 Robotics Science and Systems (RSS) workshop “Generation GrowBots” (June 22, 2019 in Freiburg, Germany), we present a research topic of nine articles focused on the intersection of robotics and plant biology. The articles are authored by a highly interdisciplinary group of domain experts, bringing together natural scientists and engineers, including experts in material science, soft robotics, plant biology, and architecture to present new scientific discoveries on plants and technological advances relevant to continuum, soft, adaptable, and growing robots. Collectively, the articles are representative of the current state of the art in the emerging area of plant-inspired robotics. Trends, frontiers and potential applications for a variety of high-tech sectors are discussed. Under the Research Topic “Generation GrowBots” contributing authors discuss the science and technologies of the new field of plant-inspired robotics and growing robotics, exploring the materials, mechanisms and behavioral strategies as the basis of a new paradigm for robot mobility inspired by the moving-by-growing ability of plants. Plants show unique capabilities of endurance and movement by growth. Growth allows plants to strongly adapt the body morphology to different environmental conditions, and to move in search for nutrients and light or for protection from harmful agents. Because of these features, together with plant biologists and materials scientists, engineers are deeply investigating the biomechanics, materials, energy efficiency mechanisms, and behavior of a variety of plant species, to take inspiration for the design of multi-functional and adaptable technologies, and for the development of a new class of low-mass, low-volume robots endowed with new and unprecedented abilities of movement. With their capability to better challenge unstructured and extreme environments, soft, self-morphing, growing machines will have potential applications in a variety of sectors, including the exploration and monitoring of archaeological sites, unknown/challenging terrestrial or extra-terrestrial areas, as well as novel technological systems for the advancement of future urban architectures. The topics of the nine articles in the present issue on “Generation GrowBots” vary in focus, but all address the overall theme of plant-based movement and its potential adaptation to robots. Two articles (Gallentine et al.; Geer et al.) introduce new robotic structures based on curling structures in fruit awns and climbing plants. The two examples cover a huge size range. The biomimetic robotic manipulator presented by Geer et al. is inspired by the ultrastructure of the cell wall of awns showing a helical cellulose fiber arrangement which allows for humidity driven awn movement. The concepts for transfer to motile structure in robots presented by Gallentine et al. are based on the macroscopic structure and movement of liana stems and tendrils and the finding that many climbing plants use curling and/or twining of their stems or tendrils for stiffening (braided stems) or securing attachment (tendrils). They show that these systems represent interesting models for new types of climbing plant-inspired soft robots. The nature of movement in plants, and the consequent implications for plant-inspired robots, are considered by Frazier et al., and models of plant growth aimed at implementation in robots are presented by Porat et al.. These two contributions prove that for a successful transfer of motion principles and movements in plants to soft robots and other types of soft machines, a thorough analysis of these movements in plants using a combination of experimental and modeling approaches are a prerequisite. Without a basic and quantitative understanding of the form-structure-function relation of the plant organs used as concept generators for moving GrowBots the potential of plant-inspired approach cannot fully be used. Realizations of vine-inspired growing robots are described in (Blumenschein et al.), with review on recent work on robots that...
Yi Liu, , , , Elena P Sorokin, Nick van Bruggen, ,
Published: 15 June 2021
eLife, Volume 10; doi:10.7554/elife.65554

Abstract:
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.
Published: 14 June 2021
Kybernetes; doi:10.1108/k-06-2020-0384

Abstract:
Purpose To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity. Findings The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations. Research limitations/implications This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core. Practical implications AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms. Social implications The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting. Originality/value The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.
International Journal of Molecular Sciences, Volume 22; doi:10.3390/ijms22126336

Abstract:
Background: Preclinical drug development studies rarely consider the impact of a candidate drug on established metastatic disease. This may explain why agents that are successful in subcutaneous and even orthotopic preclinical models often fail to demonstrate efficacy in clinical trials. It is reasonable to anticipate that sites of metastasis will be phenotypically unique, as each tumor will have evolved heterogeneously with respect to gene expression as well as the associated phenotypic outcome of that expression. The objective for the studies described here was to gain an understanding of the tumor heterogeneity that exists in established metastatic disease and use this information to define a preclinical model that is more predictive of treatment outcome when testing novel drug candidates clinically. Methods: Female NCr nude mice were inoculated with fluorescent (mKate), Her2/neu-positive human breast cancer cells (JIMT-mKate), either in the mammary fat pad (orthotopic; OT) to replicate a primary tumor, or directly into the left ventricle (intracardiac; IC), where cells eventually localize in multiple sites to create a model of established metastasis. Tumor development was monitored by in vivo fluorescence imaging (IVFI). Subsequently, animals were sacrificed, and tumor tissues were isolated and imaged ex vivo. Tumors within organ tissues were further analyzed via multiplex immunohistochemistry (mIHC) for Her2/neu expression, blood vessels (CD31), as well as a nuclear marker (Hoechst) and fluorescence (mKate) expressed by the tumor cells. Results: Following IC injection, JIMT-1mKate cells consistently formed tumors in the lung, liver, brain, kidney, ovaries, and adrenal glands. Disseminated tumors were highly variable when assessing vessel density (CD31) and tumor marker expression (mkate, Her2/neu). Interestingly, tumors which developed within an organ did not adopt a vessel microarchitecture that mimicked the organ where growth occurred, nor did the vessel microarchitecture appear comparable to the primary tumor. Rather, metastatic lesions showed considerable variability, suggesting that each secondary tumor is a distinct disease entity from a microenvironmental perspective. Conclusions: The data indicate that more phenotypic heterogeneity in the tumor microenvironment exists in models of metastatic disease than has been previously appreciated, and this heterogeneity may better reflect the metastatic cancer in patients typically enrolled in early-stage Phase I/II clinical trials. Similar to the suggestion of others in the past, the use of models of established metastasis preclinically should be required as part of the anticancer drug candidate development process, and this may be particularly important for targeted therapeutics and/or nanotherapeutics.
Page of 117
Articles per Page
by
Show export options
  Select all
Back to Top Top