In this study, we demonstrate the enhanced spectral calibration method for 1.3 μm spectral-domain optical coherence tomography (SD-OCT). The calibration method using wavelength-filter simplifies the SD-OCT system, and also the axial resolution and the entire speed of the OCT system can be dramatically improved as well. An externally connected wavelength-filter is utilized to obtain the information of the wavenumber and the pixel position. During the calibration process the wavelength-filter is placed after a broadband source by connecting through an optical circulator. The filtered spectrum with a narrow line width of 0.5 nm is detected by using a line-scan camera. The method does not require a filter or a software recalibration algorithm for imaging as it simply resamples the OCT signal from the detector array without employing rescaling or interpolation methods. One of the main drawbacks of SD-OCT is the broadened point spread functions (PSFs) with increasing imaging depth can be compensated by increasing the wavenumber-linearization order. The sensitivity of our system was measured at 99.8 dB at an imaging depth of 2.1 mm compared with the uncompensated case.

Complemented with the coupling of the most sensitive and challenging interaction between terrestrial hydrology and atmosphere, the Bhagirathi-Alaknanda basin of the Garhwal Himalaya requires advanced dynamic and most comprehensively coupled atmospheric-hydrological models for simulation of streamflows. WRF-Hydro model which is enhanced by integrating most advanced set of hydrologic physics parameterization accounting for lateral water flow occurring on the land surface is the most compatible for this basin. This paper illustrates the development and the calibration of WRF-Hydro model through construction of flow matrices for different seasons.

In this work, Deep learning techniques such as Convolutional Neural networks (CNN) and Transfer Learning are used to detect and identify Fighter aircraft or jets. A dataset consisting of 21 different aircraft with 20000 images is being processed using the above algorithms. CNN works on the principle of "pooling," which progressively reduces the spatial size of the model to decrease the number of parameters and computations in the network. CNN's are widely used for image detection in different domains, including defense, agriculture, business, face recognition technology, etc. Transfer learning is a machine learning method where a model created for a task is reused as the initial point for a model on a second task. Transfer learning is related to issues such as multi-task learning and concept drift and is not only an area of study in deep learning. The dataset is processed and uses python libraries such as pandas, seaborn, sci-kit- learn, etc., to find any pre-trained patterns and insights. Data is separated into train and test datasets with 80-20 percent of total data, respectively. A model is built using the TensorFlow library for CNN. The metric used is "accuracy." A transfer learning model is also built to compare the accuracy results and adopt the best-fitting one

The study focused on the operational efficiency of e-trikes and motorized tricycles as public utility vehicles, leading to the analytical comparison of the data gathered. The comparative analysis on both tricycles reveals that e-trikes are more efficient and safer, consume zero fuel, and almost double in passenger capacity when compared to the motorized tricycle. Further, e-trike can enter narrower streets or alley easily for having lesser width. On the other hand, motorized tricycles are faster and can reach higher kilometrage and with a shorter length, it has no problems on approaching slopes and bumps. M/M/1 was used to evaluate the service performance of the electrical and motorized tricycles in the terminal. The results show that the e-trike terminal service rate is more efficient than that of the motorized terminal where commuters spent lesser time waiting to be served. The study recommends the need to understand further the effects, advantages, and disadvantages of both tricycles as regard the planned implementation in the mode of public transportation system. Additionally, further study may be needed focusing on the economic and environmental sustainability, and the use of renewable resources to contribute on the sustainable development and increase operational efficiency of PUVs.

Due to the decrease in plant quality and productivity, plant diseases seem to be responsible for significant economic losses in the world. As a result, farmers nowadays consider plant disease prediction to be an important area of research. To help an accurate prediction of plant disease, numerous techniques have been detailed in the literature. To highlight the many issues with current approaches for problem-solving predictions, we will evaluate various literary works that are focused on plant disease prediction in the agricultural industry. Based on several variables, including different datasets, year of publication and journals, performance metrics, and other considerations, the analyses of various approaches are enhanced in this case, and include the advantages and disadvantages based on the analysis of the methods. Finally, the paper concludes by discussing future research areas and difficulties in improving prediction performance for the plant disease prediction techniques used in the growing agricultural process.

Using Piezoelectric Materials, Power Harvesting from Aircraft Body Power harvesting has been a key problem for many years, especially \when taking into consideration cost and reducing system disturbances. The main objective of this research is to investigate the possibility of developing such a system using piezoelectric components. One notable characteristic of this material is its capacity to generate electricity from applied pressure. Power harvesting has long been a significant issue, particularly when costs and reducing system disruptions are taken into account. The primary goal of this study is to determine whether such a system may be created utilizing piezoelectric components. One noteworthy quality of this material is its ability to produce electricity when pressure is applied.

The important problem to address in nuclear industries involves mitigation of the radioactive and corrosion products of nuclear reactor components. Even though physical and chemical methods lend a helping hand in solving this problem, the dominant method is by carrying out chemical cleaning. But this process results in the metal ions in complex state, not viable for treatment. To render the metal ions in the free state advanced oxidation process using oxidizing agents like ozone, Hydrogen peroxide, electro flotation, supercritical oxidation etc can be resorted . The decontaminating agents used are picolinic acid, EDTA, ascorbic acid, NTA etc. EDTA complex the metal ions but generate secondary waste with them. In our work, treatment of generated decontaminated waste using EDTA as decontaminant has been dealt with. From the literature, we understand that attempts have been made to degrade ethylene diamine tetraacetic acid using Fenton’s reagent in the presence of light [1]. This will facilitate the release of free radioactive ions for further treatment. In addition to chemical oxidation of EDTA, photochemical oxidation in presence of Zirconia and titania which generates electrons and positive ions for further oxidation has been reported [2]. In this paper, we have made an attempt to absorb the EDTA on zirconia loaded on white cement, study the adsorption characteristics which is the first step for photodegradation using UV light. Zirconium oxide was synthesized by the sol-gel method using Zirconium isopropoxide to water ratio 1:2 which resulted in powder by this process. This Powder was characterized for physical and chemical parameters before immobilizing with cement for adsorption. The system containing cement bonded to Zirconia (Zir-Cem) was subjected to removal of EDTA by adsorption. Since the zirconia powder as generated was found to have a high surface area compared to heat-treated (470oC and 720oC resulting in monoclinic and tetragonal crystalline forms) the as prepared zirconia was found to be the best candidate for efficient adsorption.

Computational fluid dynamics (CFD) simulation of Magnus Lift -Driven wind turbines provide different results depending on the method of wind power capture and the nature of the turbine. The Magnus Lift -driven wind turbines, which would normally have cylindrical blades rotating either about a vertical or horizontal axis, reveals interesting CFD results. For instance, the blade aspect ratio is critical in determining the performance of the Magnus WT. The power coefficient generated by Magnus WT at low tip-speed ratio clearly justifies that the turbine would perform optimally in urban environment. This review paper focuses on these Magnus Lift -driven wind turbines, by analyzing the research results in the literature review section. The results section contains the simulation outcome based on various CFD approaches. The conclusion cites the gaps in research. More importantly, the paper reviews the factors affecting the efficiency of the Magnus wind turbine such as drag coefficient, surface roughness effect, and wind velocity.

Our objective is to study, analyze and draw inferences on the movement of the stock prices of Indian pharmaceutical companies solely based on the COVID-19 pandemic in India. We specifically targeted pharmaceutical stocks because their share price is more directly dependent on the COVID-19 pandemic than companies in other sectors. As the demand for the primary products sold by pharmaceutical companies, i.e., medicines, is directly dependent on the COVID-19 pandemic, a common hypothesis is that the stock prices of pharmaceutical companies at a given time are significantly contingent upon the COVID-19 pandemic situation. We have tested this hypothesis by calculating the correlation between pharmaceutical stock prices and COVID-19 variables that measure the severity and provide an outline of the COVID-19 pandemic. The COVID-19 variables we have considered provide information regarding covid cases, deaths, testing, vaccination, positivity rate, virus reproduction rate, and government restrictions to counter the spread of the virus. Furthermore, as human emotion plays a significant part in deciding the share prices, we have considered public fear and awareness by considering the frequency by which the terms “Covid 19” and “Covid medicines” are searched on Google. We have considered the stock prices of 19 companies that contribute to the Nifty Pharma index as the target values upon which the impact of the COVID-19 pandemic is tested. We have selected the covid fields that have the most significant impact on the pharmaceutical stock prices and then calculated the correlation between the Covid fields and the stock parameters. The data we have considered for our study belongs to the period from 15th March 2020 to 17th February 2022. Link to the Github repository with the code for the presented research: https://github.com/Atishaysjain/Predictive-Analysis-Project.

This paper introduces an adaptive sliding mode controller design based on fuzzy compensation for efficient robotic manipulator tracking control. This work introduces design of Adaptive Fuzzy Controller based on sliding control principles for Robotic Manipulators. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. The fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real-time by adaptive laws, to approximate unknown nonlinearities. This makes it easier to direct the nonlinear system's output to follow a specific trajectory. An adaptive control algorithm based on the adaptive fuzzy model is created using the Lyapunov approach. Both the chattering and the stable performance are assured.

Today, organizations are using IoT devices to accurately collect real data and make better business decisions to increase customer satisfaction. The data collected should be stored and stored in a well-designed storage system, which encourages companies to review their data storage infrastructure. The company needs to store data created by the Internet of Things, and that data grows exponentially, forcing IoT to think about cloud storage for data storage. Security issues are a major concern when handling and processing data in DI and cloud environments. Secure integration of IoT and cloud computing, and introduced a model to ensure this integration. The secure database of any IoT operating system was suffers from poorly protected read and write functions, which limits data storage on any IoT platform. In addition, clouds can provide space to store a wide variety of data that plays an important role in the world of cyber security. However, large centralized systems operating in the cloud are also very vulnerable due to their power, so they can be transformed into a kind of double-edged sword. In this paper, we propose a novel secure lightweight authentication scheme for data storage (SLA-DS) in IoT and cloud server. The SLA-DS integrates IoT and cloud technology combination which mainly focuses on security issues.

Social networking is the most common way of communication nowadays. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As the number of users on social media keep increasing, the amount of data about the users are available on the network is also increasing. Attacks on these networks are currently at an all-time high which can be by Phishing attacks, Botnets, Sybil Attack, Profile Cloning, Spam, Denial of service to name a few of them. There are a number of threats possible on social networks. Data in social networks must be protected from various types of cyber-attacks. The main requirement is providing security to such networks. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As and when security is being provided to these networks, attacks are also evolving. Cyber-attacks are becoming complex which means that sometimes the threat for which the solution needs to be found is unknown. Threats are becoming automated, hence, using less efficient algorithms for cyber security is not the optimal solution. Hence, machine learning is used to support cyber security to social networks. A framework is built which comprises of the steps such as Data Collecting, Data Preparing, Applying Machine Learning Techniques, Post-processing by applying domain specific knowledge to build a secure system for social networks using machine learning techniques.

The proposed work focuses on the design of a class F power amplifier for 433 MHz which can be used for RFID applications. Power amplifier requires high efficiency and low power dissipation, especially in wireless base stations. In this work, class F amplifier is employed to achieve high efficiency by wave shaping the drain terminal waveforms with suitable harmonic termination networks. At the drain terminal, the voltage and current waveform is shaped into square and half sinusoidal waveforms by employing a harmonics control circuit. The designed class F power amplifier using Advanced Design Software (ADS) simulator has yielded the output power of 44.5 dBm in harmonic balancing for the given input power of 30 dBm at the fundamental frequency with the gain of 14.5 dBm. It was evident from the work that the added harmonic control circuit has suppressed all other harmonics except the third harmonic frequency.

The Internet of Things (IoT) is a worldwide network of "smart gadgets" that can interact with people and other systems. It can sense and connect to their surroundings. Air pollution is a significant problem nowadays. The present monitoring techniques have poor accuracy, sensitivity and need for laboratory testing [1]. We have proposed a three-level air pollution monitoring system to address these issues with current regulations. Gas sensors, the Arduino Integrated Development Environment (IDE), and a Wi-Fi module form the foundation of the IoT kit. Gas sensors gather information from their surroundings and send it to the Arduino IDE. It uses the Arduino Wi-Fi module to send IDE data to the cloud. We’ve also created the Android application IoT-Mobair, which enables users to access the pertinent cloud-based air quality data [3]. When the user arrives at their location, the pollution level is graded throughout the trial, if it is too high an alarm is displayed. Air quality data may also be used to predict future air quality index (AQI) values

The word SMART is popular in everyday activities which is meant for city, road, vehicles and home through the integration of IOT (Internet of Things) and ICT (Information and Communication Technology). It is possible by using the above, our everyday activities can be monitored and recorded using advanced devices in our work environment. It is suggested to have such tools in the education institutes to have better classroom and lab infrastructure for teaching and learning environments. Now a day’s many technologies exist and our aim is to integrate all the existing and new technologies to develop an embedded system application to make the classroom to be more smart and automated. In this context we will study how to design and develop the class room ambience measurement using AI technique. The machine is so smart by identifying the empty chairs and calling individual persons and occupy the place near to others so that the fan, light usage can me minimum which is possible through intelligent device and its prototype model is proposed here.

Vandalism is an illegal act of cannibalism or change of face to a private or public property by human beings for re-sale of parts or to punish the property owner. Initial research findings on transformer Vandalism detection have fallen short of human image recognition of the vandal in real-time but only does detection of activities after the damage is done or as it occurs. Automated real-time systems using sensor feed to a trained deep learning model is a new transformer vandalism detection approach with capabilities of three-dimensional image learning, extracting important image features automatically and temporal output prediction. This paper aims at distinguishing the human object entering a zoned transformer area without permission to take away or modify the established infrastructure, so that the Vandal can be arrested before causing any damage to the transformer. The researchers identified a multiplicative hybrid model combining convolutional neural networks and long short-term memory for application to vandalism problem to detect the image of a vandal as it enters a restricted transformer installation site. The image recognition accuracy can be improved by tuning the model hyper-parameters and the specific hyper-parameters considered in this research work are number of model layers and epochs. The human object is distinguishing by applying the image features taken with Image sensor to a trained deep learning model. The hybrid deep learning method increases the output prediction accuracy from the input data and lowers computational processing complications due to a reduced data volume through pooling. The system is trained and validated using ImageNet dataset. Results achieved by five layers and sixty epochs is 99% recognition accuracy. The performance of the system with an increased number of layers and epochs to five and sixty respectively was the best result as compared with lower layers and epochs. Further increase of these parameters resulted to system overfitting.

Image segmentation is crucial for computer vision. Visual segmentation simplifies image analysis. Detecting and dividing highways is a major difficulty in aerial traffic monitoring, autonomous driving, and border surveillance. This is tough. Image segmentation traditional algorithms are unsuccessful, according to the literature. Segmentation of the semantic field divides an image into semantically relevant components and assigns each to a class. Deep convolutional neural networks accurately segregate semantics. In deep learning, a convolutional neural network utilizes an input image to rate the relevance of various things. This work used convolutional neural networks to recognize and segment roads in aerial photos. SegNet and DeepLabv3+ use Vgg16 and ResNet-18 pre-trained models for road recognition and segmentation from aerial photos. SegNet based on Vgg16 produced high accuracy, whereas DeepLabv3+ based on Resnet-18 was efficient in terms of accuracy and training time. The suggested system designs use MATLAB. Learning and operational phases are included in the algorithm for image segmentation. In MATLAB, convolutional neural network analyses tagged aerial photographs. This work trains a convolutional neural network to accurately extract road features from aerial images. This system identifies roads and automobiles quickly for traffic monitoring.

Currently, the presence of wood is becoming increasingly scarce. In addition, the recognition of wood is still using wood experts, who basing their judgments on the characteristics that can be seen by eye directly such as color, texture and so on. However, wood experts are still few and have a disadvantage that the results obtained are still not sufficiently accurate and time consuming. The purpose of this research is to develop Indonesian commercial woods classification system based on GLCM and k-Nearest Neighbor. Procedures of the wood classification system includes image acquisition using a digital camera, then a preprocessing steps by converting the original image to grayscale image and sharpening the image, after that do texture feature extraction using Gray Level Cooccurrence Matrix (GLCM) with the parameters used are Contrast, Correlation , Energy, Entropy, and Homogeneity, at each direction that are 0°, 45°, 90°, 135°,and the last step is the classification using the k-Nearest Neighbor (k-NN). The testing results show that the testing data can be classified accurately 100% is a testing data derived from the training database with k = 1. In general, the greater the value of k then the classification success rate decreases.

This present paper details a ‘fuzzy logic soft computing technique’ of designing a controller for a buck converter operating at low voltages. The dc – dc buck converters find applications in solar energy technologies where they are used in solar control charging systems, battery charging systems, drones, in the automotive industry where there is integration of the engine and dc-dc motor drives resulting in hybrid electric drives or vehicle’s. Conventional controllers that is the proportional derivative control (pid) have dominated the control engineering field from the time of invention, however, these controllers present increased overshoot and a long time to achieve steady-state conditions. In this regard since fuzzy logic has intelligent capabilities, then a fuzzy logic soft computing-based controller is proposed in this paper. A mathematical analysis of the dc-dc buck design is also given and the modeling of the fuzzy logic control algorithms that avoids mathematical analysis is also discussed. Fuzzy control is one of the soft computing tools that consists of the observation made by people and their decision based on non-numerical information is also discussed. The performance analysis of the fuzzy controller is determined under constant load and variable decreasing voltage conditions. The analysis and simulation of the dynamic response in closed-loop frequency response are conducted in the Simulink platform in Matlab. The proposition of control is executed on a dc-dc step down converter for an input of 12V. This proposed fuzzy logic control technique provides a dc – dc buck converter system with increased overall operating efficiency and a very stable output.

At present, the revolution brought by deep learning based technologies in the field of computer vision gaining momentum in the world of artificial intelligence. In particular, the best models for retrieving common images today are based on features generated by deep convolutional neural networks (DCNNs). However, this great success was expensive. A comprehensive amount of tagged data had to be collected, followed by model design and training. Meanwhile, a transfer-of learning approach has been developed that avoids this costly step by applying a sophisticated, pre-trained generic DCNN model to completely different data domains. With the use of transfer learning, it becomes possible to use deep CNN models for small datasets with better retrieval performance with respect to handcrafted feature based retrieval methods. In this paper a deep CNN based model has been proposed which uses concept of transfer learning and achieves good classification accuracy.

Currently, the way that smart cars operate is that they have sensors all around the vehicle to identify and learn about adjacent objects. However, the precision of these sensors frequently has a relatively short range. Reliable long-distance data collection is becoming increasingly important as autonomous driving systems gain in popularity. The suggested remedy for this problem is a gadget that would be mounted on every moving vehicle. This gadget would transmit data about the car (size, speed, acceleration, position, heading, etc.) and receive data about nearby cars with comparable equipment. This eliminates the need for intelligent vehicles to guess what other vehicles are doing around them. Vehicles can instead get this information directly from other vehicles on the road and spend more resources monitoring/tracking non-vehicular objects and feed their information directly to an intelligent system for further complex decision making. This report mainly consists of collecting data for M2M communication and analyzing data for V2V based protocols; establishing simple V2V communication with software tool and sample data; code for V2V communication using CCA with the help of AoDV; design and simulation of hardware architecture in its application. The simulation is performed on an open-source software which is created on its latest release. The test cases run is in a simulated environment of nodal communication representing clusters of vehicles on the radar interacting with each other. These nodes have reactive protocols such as Co- operative Collision Avoidance (CCA) and Ad-Hoc On Demand Distance Vectoring (AoDV). These aim at providing early warning and message routing using unicast and multicast. The results presented in this report show that the through-put, network-life, energy consumed and distance between the nodes are crucial factors that help route message passing between nodes in a given Wireless Sensor Network (WSN). The results obtained from the project discuss WSN communication using AoDV and CCA, along with this the hardware application is built on the idea to transfer vehicle information such as acceleration and displacement using the ESP-32 control module to establish communication that validates the message transmission between these modules in offline mode.

We have proposed a novel way of symmetric encryption utilizing interleaving salting, Binary Tree traversal, and an Artificial Neural Network in a sequential manner. We have presented the use of an Artificial Neural Network as an invertible(reversible) mathematical function so that decryption of the encrypted output may be possible. Knowledge of the encryption pipeline, salt, and neural network weights is required for decryption. As the same set of values is required for encryption and decryption, our proposed approach is a type of symmetric-key algorithm. Each user will have a unique key. Thus if a key attributing to a particular user is compromised, the integrity of the data of the remaining users will still be maintained. Our approach can be utilized to encrypt text data such as messages, documents, and letters. The encryption process consists of interleaving salting, creation of binary tree by considering the input as its level order traversal, and passing the preorder and inorder traversal of the constructed binary tree as input to the Artificial Neural Network. The output of the Artificial Neural Network would be the encrypted data. Decryption would require determining the input of an Artificial Neural Network from the output, hence solving multiple sets of linear equations and constructing a binary tree from its preorder and inorder traversal. We have then analyzed the variation of performance with the change in the input string size. The codebase of our proposed approach is publicly available at https://github.com/Atishaysjain/Symmetric-Encryption-using-ANN-and-Binary-Trees.

Sustainability in the pharmaceutical integrated supply chain ecosystem is understood as a balance between environmental, social, and economic pillars. Therefore, indicators for sustainability development in pharmaceutical industries therefore must cover all three dimensions of sustainability. The traditional supply chain was more about reducing costs and maximizing revenue and profit. Now the corporate goal is also about considering the environmental and social impact of their products and services journey through the entire value chain — from plan to source, to make, to deliver, to the service domain. Sustainability as part of corporate responsibilities is now mandatory to be mentioned as part of the broader commitment to safeguarding the planet. This multiple qualitative case study aimed to investigate the role of sustainability in the efficient pharmaceutical supply chain. Sustainability is also the key driver for the long-term growth of a pharmaceutical company. The study involved interviewing various pharmaceutical managers with proven strategies for implementing sustainability-related strategies. The theory of constraint was used as the conceptual framework for this qualitative multiple case study. Data from interviews and supporting documents were analyzed using data triangulation to discover themes. Three main themes emerged from data analysis: (a) known or unknown constraints, (b) business operational model change, and (c) training and building sustainability capability. Seven key strategies were developed pertain to these three themes. These themes were identified based on interview data inputs and documentation provided by participants. Identified themes and strategies are summarized in the system model. Researchers and industry leaders can utilize themes and strategies to identify constraints, risks, and issues in the current system and work towards a sustainable supply chain integrated ecosystem.

Many different checks, rules, processes, and technologies work together to keep cloud-based applications and infrastructure safe and secure against cyberattacks. Data security, customer privacy, regulatory enforcement, and device and user authentication regulations are all protected by these safety measures. Insecure Access Points, DDoS Attacks, Data Breach and Data Loss are the most pressing issues in cloud security. In the cloud computing context, researchers looked at several methods for detecting intrusions. Cloud security best practises such as host & middleware security, infrastructure and virtualization security, and application system & data security make up the bulk of these approaches, which are based on more traditional means of detecting abuse and anomalies. Machine Learning-based strategies for securing cloud infrastructure are the topic of this work, and ongoing research comprises research issues. There are a number of unresolved issues that will be addressed in the future.

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

Under sub-critical flow conditions, the presence of blockage (solid wastes and weeds) upstream of box coverage and the scours pattern downstream of the coverage was explored in this study. The upstream blocking was simulated using 48 runs in an artificial trapezoidal cross-section with three square box coverages of side dimensions 8.80, 10.40, and 12.90 cm, four water flows of 2, 5, 8, and 11 L/s, and four blockage ratios of 0, 10, 20, and 30% relative to the coverage cross-section area. To estimate the scour hole characteristics, a 2.00 m long, 0.60 m bed width, and 0.30 m deep sand basin filled with D50 = 0.50 mm bed material was constructed directly downstream of the coverage outlet. In each run, the water level, velocity, and scoured hole parameters downstream of the coverage were measured. According to the analysis, the Non-blocked coverage has less scour depth and length than the partially blocked coverage, where the maximum scour depths and length of the Non- blocked coverage for cases 1, 2, and 3 at the discharge of 11 l/s were 73, 72, and 17 % respectively, and 77.56, 77.34, and 83.66 % respectively relative to the maximum scour depth and length of partially blocked coverage at the discharge of 11 l/s and blockage ratio 30 %. The depth and length of the scour hole downstream coverage are increased with the increment of the coverage's blockage ratio and discharge and are reversely proportional to the inlet area of coverage. Increased relative scour depth and length by 3.60 percent and 11.80 percent respectively, by increasing the downstream Froude number of flow (Frd) by 0.01 while the coverage’ relative wetted area (Ar) is constant. The study suggested applying coverage with a suitable area and water discharge, and protection techniques downstream of the coverage to reduce the influence of coverage and blockage on the open channel's hydraulic efficiency and the scour pattern downstream of the coverage. Also, install a trash rack upstream of the covering and remove aquatic weeds and solid wastes periodically upstream and inside the coverage.

One of the main instrument used in the microwave communication laboratory is analyzer. Particularly, network analyzer, which is used for testing the RF components. Here in this paper the vector network analyzer design study and measurement analysis is discussed. The introduction to Vector Network Analyzer (VNA), working, types, blocks, function, measurements and specifications are presented as revisiting the technologies. As a DUT, a fractal antenna is connected to VNA and its results are presented.

Humans' most powerful tool is their mental wellness. Individuals' well-being can be impacted by poor mental health. This paper focuses on a smart technical solution to the problem of mental health issues detection related to the stress, sadness, depression, anxiety etc. which if not handled efficiently may further lead to a severe problem. The paper deals with the designing of an automated smart system using social media posts, that will help mental health experts to successfully identify and understand about the mental health condition of social media users. That can be done based on text analysis of rich social media resources such as Reddit, Twitter posts. The implementation of the system is done using Natural Language Processing (NLP) methods, machine learning and deep learning algorithms. The models are trained using a prepared dataset of social media postings. With this automated system the mental health experts can able to detect the stress or some other emotions of social media uses in a very earlier as well as faster way. The proposed system can predict five emotional categories: 'Happy', 'Angry', 'Surprise', 'Sad', 'Fear' based on machine learning (Logistic Regression, Random Forest, SVM), deep learning Long Short-Term Memory (LSTM) and BERT transfer learning algorithms. All the applied algorithms are evaluated using confusion matrix, the highest accuracy and f1 score achieved is more than 90%, which is better than the existing human emotion detection systems.

Testing the software is an important part of the Software Development Life Cycle. In present day, the software industry’s focus is on developing quality applications. In order to save time and costs when evaluating a program, most testers have moved from manual testing to automated testing. A large variety of software testing tools, open-source as well as commercial, are available in the market. It is quite a challenging task to select a testing tool that fits best to their software, as the choices keep getting wider. With more choices comes also the increased spectrum of software testing tool features with cost variations. Web application tools help the developers to test their software quite comfortably and are widely used today. With the integration of testing tools and web browsers, testing has become modular. When choosing an appropriate software testing tool, there are many factors which come into play to make this decision apt for a particular software to be tested. Testing tools are either automated or manual. The tool selection is done on parameters which best suit the tester. The purpose of this study is to identify and compare the popular testing tools and provide a review based on parameters which are suitable. This paper provides a comparative review of features of open source and commercial testing tools in a tabular format, based on these different parameters. It also lists down descriptions and features of various testing tools so that users and developers can opt for the appropriate tools based on their demands and requirements.

Al-Cu-Al2O3 / Gr Nano compound was successfully prepared by metallic powder method, 6 samples were prepared with different weight ratios of graphene, 10% copper and 2.5% Al2O3 and proportions of 0.5, 1, and 1.5 graphene were prepared, the samples were run in a mill from Ceramic in a ratio of 1:5 powder into balls for 35 hours, which helped to break the aluminum and copper particles and reduce their size, which helps in the process of homogeneous mixing of the compound. The samples were compressed at 60 bar pressure and sintered in a vacuum at 550 and 565 degrees Celsius for 60 minutes. Sintering at 550°C proved to be more suitable for the mixture. Two identical sets of samples were prepared. Both SEM were used to investigate the microstructure and components of the sintered nanocomposites. Relative density, hardness, electrical conductivity and thermal conductivity study. The rolling process of the samples was carried out with a percentage of 35% of the sample size successfully at a temperature of 480 degrees Celsius, and this led to an improvement in the density of the samples and hardness and an increase in the diffusion of the reinforces, which led to an improvement in electrical conductivity by 16: 25% and a better improvement in thermal conductivity. Improved up to 1.5% graphene by weight after rolling and 1% graphene before rolling.

Egypt faces a major problem out of the abundance in in the inherited buildings, that causes great loss of historic values. The author focusses on identifying the preservation needs for reconciling energy efficiency, achieving the new function in its best performance. The research goal is to retrieve the heritage by managing the optimal change occurred as a sustainable landmark. The research consists of three parts; the first part contains the introduction, recognition of a preservation methods. Second part is the theoretical part that discuss the preservation of inherited buildings. Third part shows the operation methods for sustainable reusing by design builder assessment tool, for being sufficient in addressing the Egyptian heritage according to NOUH principles, to upgrade the indoor environmental quality and upgrade the energy performance. The paper suggests different interventions internally, and externally within minimal additions, by applying transparent solar panels inside the inherited Empain Baron museum.

The research interest in the use robotics for education purposes has increased greatly in the last few years. However, no much consideration has been made to the benefits that the robots have in delivering content in STEM education. Educational robots have been used to support learning of STEM subjects but in the informal learning environment at different levels of education. This review assesses benefits of use of educational robots in teaching of these subjects to learners’ attitude towards the subjects and problem solving skills. In this research 25 papers were selected for the purpose of review through a process of search and review. The papers selected were analyzed based on similarity in their findings and mainly on the benefits educational robot activities towards teaching and learning of STEM subjects. The review reveals that robotic activities employed in education play an important role in enhancing STEM interest and also promoting problem solving skills. These benefits are greater to primary school learners than primary school learners and are realized greatly when the duration of experiment is not extended for longer durations. From the review it was noted that the robots have a greater impact in boys than in girls. The robots being multidisciplinary in nature can be utilized in teaching various subjects at different levels of education. The conclusions of this review will be useful as reference for future research in this field of study.

There are so many tasks being automated nowadays. Likewise, the automatic room temperature and health monitoring system is a process to monitor heart beats using pulse sensor and the body temperature of patient. [3] There is also an automated operation to control the room temperature by closing and opening of windows in old age homes, hospitals, factories, etc. [5] Using IoT the heart beat of patent is monitored, room temperature and patient's body temperature is continuously monitored. [6] The temperature can also be adjusted accordingly. These features can also be manually controlled through a mobile application from anywhere. As the data's are gathered and uploaded to a server the temperature of a closed environment can be easily retrieved. The appliances that are connected like fan and air conditioner will automatically adjust to the temperature [1] in and out of the room according to the patient's body. Thus, this project helps us to control the room (closed environment) temperature without manpower. [1]

Both experimental and finite element analysis (FEA) was used to study the seismic response of reinforced concrete (RC) interior slab-column connection made with pyramid-shaped drop panel subjected to vertical and horizontal loads. The dimensions of the models at “¼ ” linear scale for laboratory testing and FE Analysis (FEA) are derived from rules for dimensions of column drops, given a prototype “9.60m” grid and a slab thickness of “320mm”. Lab specimens were tested with the drops (flat slab, rectangular and pyramid-shaped) facing up, with loadings (vertical down and horizontal in grid direction) applied by jacks towards the top of a central projecting “150mm” square column. One flat slab (“80mm” thick no drop), tested to failure under vertical load (80kN), provided values for setting variables used in the FEA. The remaining 5 lab specimens (1 flat and 1 each rectangular and pyramid-shaped 40mm and 30mm drop thicknesses), under a fixed vertical load (40kN), were tested to failure by increasing a moment at the column slab junction applied by a horizontal load to the column “500mm” above the slab. FEA results for the same conditions compared closely with experimental results. The pyramid-shaped drop models, with equal thickness to the rectangular drop models at the column faces (drops of “40mm” and “30mm”), exhibit similar maximum force resistances to the rectangular drop models. However, these resistances were achieved in the pyramid drops at higher maximum deflections – deflections being measured downwards at column centerline one half of slab thickness away from the face. A parametric study was conducted by FEA, at constant load, in vertical steps (10kN; 25kN 55kN), calculating deflections under increasing horizontal load. Calculations were made on the following definitions: Energy absorption is represented by the area under the deflection vs horizontal load curves; Ductility is the ratio of deflection at maximum to deflection at yield, and Stiffness and Overstrength factors are the slopes of the deflection load diagrams in the elastic and plastic zones respectively. Both drop type models exhibit significantly improved performance compared to the models without drops. The pyramid-shaped drop models exhibited improved energy absorption, ductility, and stiffness and overstrength compared to the rectangular drop models of the same column face thickness.

Optimal estimation of the intrinsic parameters of photovoltaic cells requires the use of meta-heuristics to increase their efficiency. This paper highlights the estimation of unknown parameters of a PV cell and module. For this purpose, the meta-heuristic optimization algorithm based on the Honey Badger Algorithm (HBA) principle is used. The simulation results via MATLAB prove that this algorithm has a good convergence. Indeed, the root mean square error (RMSE) is 9.8602×10-4, 9.8602×10-4, 2.4251×10-3, 1.7298×10-3 and 1.6783×10-2 for the single diode, dual diode, Photowatt-PWP201, Schutten Solar STM6-40/36 and the STP6-120/36 module respectively. Furthermore, the curves representing the current-voltage and power-voltage characteristics of the calculated unknown parameters versus those of the practical data measured from a PV cell/module datasheet coincide. The proposed algorithm can therefore be classified in the literature as one of the optimal parameter extraction techniques.

The research is about developing of prototype Humidity control unit of a chicken chick Banda for maximum reduction in energy wastage and ensuring conducive environmental condition for bird’s growth and development using the proportional integral differential (PID) controller and the particle swarm optimization (PSO) technique for comparison purposes. The PSO stated here in is a stochastic optimization method working on the movement of swarm so as to achieve convergence. The study is achieved through designing of a prototype of the humidity environment controller to achieve two states or conditions that is for the controlled case and for the uncontrolled case. Environmental humidity control is achieved using a programmed Arduino and the DC FAN. The process is then designed using the MATLAB simulation software operating at the Simulink model designing platform. The same design is connected to the PID controller and then also tuned using the PID tuning platform on the Matlab. The same design is implemented on the workspace using particle swarm optimization method and it is then run to see the system behavior in terms of settling time, rising time and peak overshoot. The major reason of the study is to demystify the myth that one can only use conventional PID controller techniques in performance improvement and that there is a better method which can similarly be used with better results and cheaper. Most poultry farmers are stack with their old ways of achieving good performance therefore the results of this work will be an eye opener for them to embrace new techniques in the market The presented particle swarm optimization techniques shows impressive performance in terms of the settling time, rise time and over shoot.

A smart city permits the viable usage of assets and better administration of assets for the residents. Consistent advances are being seen in the field of the Internet of Things (IoT) to intensify the convenience and nature of the foundation. With the advancement of the foundation in metropolitan areas, the number of vehicles has also increased significantly in recent years, causing issues with traffic congestion and street security. IoT has helped solve different issues every day concerning street security, parking spaces, and traffic congestion. Parking spaces can be difficult to detect, particularly during the pinnacle hours in significant metropolitan urban communities, which can be extremely disorganized. In this project, we plan to introduce an altered plan of an Internet of Things (IoT) empowered smart parking system to tackle the parking issue in the city. The system includes an on-location organization of the various sensors, which are utilized to recognize the accessibility of the parking spaces and send data to the server about the equivalent. A real-time web link will be given that permits an end client to take a look at the accessibility of a parking space and book it for a given time frame. A parking executive system can be planned and sent to all smart and future urban communities with the help of sensor systems and IoT innovation. This will save the client's time and diminish the congestion undeniably.

Tungsten carbide is one of the ceramic materials characterized by high hardness. It has many uses in manufacturing, including cutting tools, die inserts and other parts that need materials with high mechanical wear resistance. In this study, tungsten carbide was reinforced with alumina and different ratios of graphene to improve its mechanical properties. The BSE mode used the electronic imaging device (SEM) to study the powders and manufactured sample's microstructure. The densification, hardness, and toughness of fabricated specimens were evaluated. The results proved that the density of samples was decreased by adding alumina and graphene due to their low density. The samples' toughness was improved due to the addition of nickel, where no cracks were established from the hardness test. The hardness was increased by adding 2.5 wt % Al2O3 and different percentages of graphene up to 0.9 wt %.

This project proposes effective work to increase the power quality in the power system by using a Unified Power Quality Conditioner (UPQC) The advanced use of power electronic devices introduces harmonics in the power system which creates a problem in the quality of power delivered. Although several methods are there for improving power quality standards, including the use of active and passive filters, and Hybrid filters have been developed, these methods face issues and are ineffective due to the growing number of applications. The UPQC is an advanced technique for mitigating voltage and load current supply fluctuations. In this project the combination of series active filter and shunt active filter is studied. UPQC is used to reduce the power quality issues like harmonics and sag. The shunt active filter and series active filter performs the simultaneous elimination of current and voltage problems. The project work presents the working of the UPQC filter in such a way that the harmonics are reduced.

The Pandemic COVID-19 outbreak has significantly affected all sections of life, including a substantial reduction in economic development and production, from industrial activities to tourism and automobile congestion. During this phase, the maximum human activities were restricted, but COVID-19 came out as a blessing for the environment. Globally reported that all the environmental variables have improved since the pandemic outbreak, including water and air quality and water quality while minimizing the restrictions for wildlife even in urban areas. India has always been a hotspot of pollution, with rising air quality index (AQI) readings in all large cities due to its vast population, traffic congestion, and polluting industries. However, after the lockdown announced during the pandemic, air quality started improving, and Other environmental factors, such as the water quality of rivers, started to improve. This paper reviewed the studies conducted to define the improvement in India's air and water quality during the lockdown period. Different tools such as remote sensing technologies and onsite real-time monitoring are used in many studies to monitor India's air and water quality during this period.

Compared to the other classic supply chain, the pharmaceutical supply chain has many critical challenges, such as cold chain transportation, temperature monitoring, long lead time, and counterfeiting prevention. Pharmaceutical products, such as medicines, drugs, vaccines, and specialty treatments, work as intended within a specific specified temperature. Below +25°C (controlled temperature), +2°C to +8°C (temperature-sensitive products), -20 °C to -40 °C (negative temperature), and -70 °C (ultra-low temperature) are some of the precise and regulated storage thresholds for various types of pharmaceutical products. The cold chain includes production, transport, and storage. It requires a reliable infrastructure to maintain a precise temperature when transporting from manufacturers to patients. This multiple qualitative case study aimed to investigate the role of the Internet of Things (IoT) in the pharmaceutical cold chain. The study involved interviewing various pharmaceutical managers with proven digital strategies for implementing IoT-based digital enablers. The theory of constraint was used as the conceptual framework for this qualitative multiple case study. Data from interviews and supporting documents were analyzed using data triangulation to discover themes. Two main themes emerged from data analysis: (a) Known or unknown constraints in the current cold chain system and (b) implementation of IoT-based digital enablers. Six key strategies were developed pertain to these two themes.

The brand new outbreak of Coronavirus poses a primary threat and has been declared a global public fitness emergency. entire world is trying to stop the virus but no efficient device and approach is there to govern it. Tracking a patient's fitness remotely is genuinely essential, especially for patients affected by a long term disease. This necessitates the creation of a single platform where consumers may monitor data in real time. This paper discusses health monitoring systems that can be implemented with market sensors and allow patients to be tracked without needing to visit a doctor. Doctors need constant updates on the patient's health- related measures such as blood pressure, heart rate, and temperature in such crucial situations. For this type of situation, an IOT-based system can provide automation that keeps doctors up to current at all times via the internet .Heart disease has become a major concern in recent decades, and many individuals have died as a result of various health issues As a result, cardiac disease must be treated with caution. This disease can be averted if the ECG signal is studied or monitored early on. So here's the deal: An AD8232 ECG Sensor and an Arduino with an ECG Graph are used to monitor the heart rate. The ARDUINO- UNO board is used as a microcontroller, and the Cloud computing concept is used in this system. In this design, we'll interface an AD8232 ECG Sensor to an Uno and use a digital plotter or the Programming IDE to observe the Ecg.

One of the essential components of Recommender Systems in Software Engineering is a static analysis that is answerable for producing recommendations for users. There are different techniques for how static analysis is carried out in recommender systems. This paper drafts a technique for the creation of recommendations using Cosine Similarity. Evaluation of such a system is done by using precision, recall, and so-called Dice similarity coefficient. Ground truth evaluations consisted of using experienced software developers for testing the recommendations. Also, statistical T-test has been applied in comparing the means of the two evaluated approaches. These tests point out the significant difference between the two compared sets.

This paper proposes the comprehensive review on the different resonant converter topologies, different modelling techniques, and different control techniques are being used in electric vehicle application. This paper also discusses the merits and demerits of different modulation/control techniques. The performance of variable frequency control technique can be improvised using SSPM is discussed in this paper. Optimal projectory control method has a quick transient response than SSPSM followed by SSOC, then PSM, and finally by VF controller are reviewed in detail in this study. Cyclic averaging is an accurate alternative method for state variable, this approach is used for time domain analysis of resonant dc-dc converter has been emphasized in this paper. Reverberation would be beneficial when series and parallel resonance converters combined are explained in this paper. LLC topology would be best suitable for electric vehicle applications are discussed and structure of the resonant converters, power efficiency, compatibility and its suitable applications are presented in this paper. A detailed study of modelling techniques to address the increasing demand for electric vehicles are presented.

Electric Vehicles (EV) are becoming more popular in present scenario because it can be easily charged using charger. Thus, the chargers play a vital role in EV vehicle. Hence, many distinct types of EV charging technologies have been developed so far. This paper reviewes an effective and quick charging approach which extends the life cycle of a battery with high charging efficiency. This article also presents about characteristics of charger in terms of converter topologies, modulation schemes and control algorithms.

The new outbreak of Corona virus poses a major threat and has been declared a global public health emergency. Whole World is trying to stop the virus but no efficient tool and strategy is there to control it. Monitoring of Patient’s health remotely is really important especially for patients suffering from a long-term disease. Vital signs such as pulse rate, Body temperature etc., needs to be monitored regularly as they are the primary indicators of a human’s health. Also, Elderly people gets benefited by making less visits to the hospitals for regular check-up. Therefore, we intend to bring in a GSM based health monitoring system for patient’s which provides security to patient’s health. Health monitoring is a technology to enable monitoring patient’s health outside clinical settings. The system measures the heartbeat and body temperature of patient and then the immediate information will be sent to the registered number.

Road pavement is a supporting factor for national development, especially in the distribution of trade in goods and services as well as the movement of human mobility. Road maintenance needs to be done regularly so that the road is always in good condition, but the weather and road loads are the things that cause road damage. Road damage is generally categorized into cracks, alligator cracks and potholes. The purpose of this research is to utilize image processing to detect and classify the types of road damage. The steps involved include: image acquisition with a digital camera, conversion of RGB images into grayscale images, image normalization, selection of damage points, counting histogram bins, determining damage bins, calculating noise with image morphology (closing and opening) using a disk element structure of size 5, calculating radial vector and finally classifying road damage using the K-NN (K Nearest Neighbor) method with 3 classes and a K value of 11. The image from the classification results is then calculated the level of damage based on the category according to the SDI (Surface Distress Index) provisions, where the level of crack damage is seen from the width of the crack, the alligator crack is seen from the percentage of damaged area compared to the segment under review and the pathole is seen from many holes. The test used 597 images consisting of 95% training data and 5% test data, the results obtained that the accuracy of this research reached 83%.

A simple and efficient algorithm is proposed for solving the economic dispatch problem of power system with valve point discontinuities employing a particle swarm optimization based approach. Evolutionary methods such as GA and PSO are known to perform better than conventional gradient based optimization methods for non convex optimization problems. This reactive power management in economic load dispatches plays a vital role in improving power quality of the system. The power compensation and economic load dispatches is major problem in distribution network. The power is maintain the state of the UPQC (Unified power quality conditioner). The UPAC controlled by the STATCOM or DSTATCOM. The performance of the proposed method has been compared with conventional PSO. The effectiveness of the algorithm has been tested on a test system having three generating units.

Construction and rehabilitation of water control structures in Egypt considered as an important project, as it influences the usage of water resources in Egypt which become limited resources due to water scarcity existed in the last decades. Also these projects helps in the optimization of water resources, Moreover it helps in the growth of agricultural and industrial sector. Project Risk Management (RMP) is considered as a vital and important tool in decision making, thus RMP used as a planning management system to detect risks affecting project deliverables; such as cost and time target. This research shows how to optimize the deliverables for construction of box culverts in Egypt, through a well-defined risk management framework and real case study for a certain project executed in the last decade. Finally, this study shows how to calculate cost and time contingency for these projects through an integrated risk management technique. Finally this study shows hazard risk identification and assessment for these type of projects. The conclusion of this study show that the cost contingency needed to resolve different risk factors arise in the shown case study is to increase the estimated budget with average value 11.50 percent on the total estimated budget, as well as the time contingency is found with average value 16.00 percent to be added over the total original baseline schedule of the construction project.

The goal of this study is to assess the rehabilitation of Bahr Awlad Mohamed canal.The canal was rehabilitated by installing lining trapezoidal cross-sections. Before and after the rehabilitation process, a field investigation, hydraulic and hydrographic measurements, and a mathematical model (Sobek – 1D) were conducted. According to the findings, the canal's rehabilitation improved average water velocity from 0.18 m/s to 0.32 m/s, the average Manning roughness coefficient from 0.065 m-1/3s to 0.020 m-1/3s, the water surface slope from 17 and 50 cm/km to 2 and 6 cm/km, the average areas of cross-sections reduced from 3.37 m2 to 3.07 m2, the average water width was reduced from 5.84 m to 4.83 m, the water losses were decreased by approximately 27.31 % from the canal's inlet discharge. Additionally, the water level fulfilled the requirements for water distribution, the canal banks were restored and widened, the hydraulic efficiency of the study canal was improved, alleviating downstream farmers' concerns and conserving water that could be utilized for irrigation, and saving time and maintenance cost. Finally, canal rehabilitation necessitates periodic maintenance, appropriate maintenance equipment, and a strategy of canal preservation. In addition, other canals in Egypt must be improved to determine the irrigation network's water-saving potential.