Fiber-based hybrid composite materials are used for variety of industrial purposes going on from automotive to many engineering fields such as structural, aerospace because of unique properties compared over conventional materials. Fiber based hybrid composites have greatly long-drawn-out o varying applications in today's automotive industry reason behind is due their light weight, high strength, stiffness and ease of repair. This work aims to create a novel type of hybrid composite made by reinforcing S-glass (satin type) & carbon (twill form) in an epoxy matrix. Pure and hybrid composites are made by manual laying with 0° orientation of each fiber layer and the action has been tested with different combinations of each layer of S-glass and carbon and finally with a hybrid subjected to different mechanical stresses. The fiber matrix for the hybrid is created in a 50:50 ratio. Pure samples of fiberglass and carbon fiber-epoxy composites were compared to hybrid samples. The results showed that the hybrid samples outperformed the pure forms of the composite in mechanical tests, due to the presence of carbon fiber on the end faces of the sample, which offers the hybrid form's superior mechanical properties.

In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an AI-based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset using the AI-based m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, We use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model's performance is validated and compared using different measures.

Optical burst switching (OBS) is an optical switching technology; it uses an optical fiber's high bandwidth potential to transfer huge amounts of data in the form of huge packets, which are more commonly referred to as bursts. High burst loss brought on by numerous burst contentions is a significant problem in OBS. An intelligent fuzzy offset time algorithm (FOT) algorithm is suggested to overcome this problem. This study proposed Intelligent Fuzzy Offset Time (FOT) algorithms that adjust offset time (OT) in accordance with network and traffic conditions. The fuzzy input for FOT is made up of three parameters: burst size, distance, and time that burst spent in queuing. The suggested algorithm is assessed versus the Intelligent OT algorithms using the Five defuzzification techniques (Centroid (CM00), Bisector (BM04), largest of maximum (LM02), smallest of maximum (SM03), and mean of maximum (MM01) when Maximum (M) accumulation technique is used, when using Algebraic Sum (S) aggregation methods (Centroid (CS00), Bisector (BS04), largest of maximum (LS02), smallest of maximum (SS03), and mean of maximum (MS01). Simulation results have shown that FOT LM02, FOT LS02, FOT SM03 and FOT LS02 have effects on reducing BLR (burst loss ratio) and E2E (End-2-End) delayed respectively when compared to other defuzzification techniques algorithms. FOT LM02 and FOT SM03 can be used to intelligently adjust the offset parameter using the incoming traffic load and the three parameters.

When natural occurrences affect populated areas and destroy local infrastructure and population, they are referred to as natural disasters and cause pain and deprivation. India is one of the world's regions that experiences disasters the most frequently because of its physiographic and meteorological circumstances. Natural disasters have been more frequent during the past ten years or more, notably in India. Increased population, urbanization, industrialization, development in high-risk areas, environmental degradation, and climate change may all contribute to increased vulnerability to catastrophe risks. Humanitarian operations are started as soon as a disaster occurs with the goal of assisting victims quickly in a variety of ways, such as rescuing those who are affected or stranded, gathering and disposing of corpses, allocating resources, providing food aid, shelter, and medical care, and reopening access to remote areas. Delays in delivery or relief during humanitarian efforts can result in lives lost. Therefore, as it guarantees the seamless flow of products and services in a convoluted supply chain, logistical efficiency is a crucial component of humanitarian success. Logistics is essential to the effectiveness and responsiveness of major humanitarian initiatives like health, food, shelter, water, and sanitation. It acts as a link between catastrophe preparedness and response as well as between procurement and distribution. Calamities, crises, plagues, and destructive actions can all be categorized as disasters depending on the logistical effort needed. Different sorts of disasters require different approaches to management: Running refugee camps is considerably different from giving the kind of aid needed after a sudden natural disaster or a nuclear accident. Aid offered to help a place develop is different from aid given to deal with famine and drought. The humanitarian supply chain (HSC) is an organization that specializes in planning the distribution and storage of supplies to impacted areas and individuals during emergencies and natural disasters. The complex environments involved in a disaster, quick design, new or unfamiliar intermediaries or participants, and thus, it faces many possible obstacles, make it highly unpredictable and tumultuous. The humanitarian supply chain is made up of many different players, each with a unique attitude, way of acting, and cultural background. In a situation involving India, the humanitarian supply chain involves foreign aid agencies, host governments, the military, local self-governments, regional aid agencies, etc., all of whom have competing interests, mandates, capacities, and logistics expertise. Due to the complexity, it is imperative to analyze key variables in order to create a humanitarian supply chain that meets the needs of donors, beneficiaries, and service providers alike. The history of India demonstrates how a lack of professionalism, cooperation between many parties, and several other cultural variables have turned the field of humanitarian logistics into one with a lot of difficulties and important considerations. When building a supply chain, from sourcing to production, storage, distribution, and all transportation links in between, a sustainable supply chain (SSC) tries to take the environment, the economy, and social and human issues into consideration. The humanitarian supply chain should be integrated with sustainable practices while it is being designed or developed. In order to handle the most difficult situations in the context of natural calamities, a sustainable humanitarian supply chain that has been optimized is necessary. With the aid of cutting-edge technologies like artificial intelligence, blockchains, and machine learning, the domain of sustainable humanitarian supply chains in India is experiencing difficulties and obstacles with plenty of room for improvement. In the context of the natural disasters in India, the article's main focus is on the significance of an effective sustainable humanitarian supply chain. The goal of this article is to identify the obstacles and variables that affect the efficient development of a humanitarian supply chain in India that is flexible enough to fulfill the unique needs of an HSC that are completely different from those of a commercial supply chain.

Today's world is becoming more interconnected due to the current global internet, communication, or ways of doing business that have recently shifted to cloud computing platforms in order to increase their speed and productivity. But such can also be affected by cyber attacks on cloud infrastructure services to be executed on various cloud platforms, increasing the number of attacks on such systems to neutralize any harm caused by a cyber attack on such cloud-based infrastructure. Although network administrators utilize intrusion detection systems (IDS) to detect threats and anomalies, they frequently only make available post-attack ready to act in cyber warfare. If we could predict risky behavior, network administrators or security-enhancing software could intervene before harm was done. Incoming intrusion detection messages should be viewed as a sequence. The fundamental function of an intrusion detection system (IDS) is to distinguish between regular and abnormal network traffic. As a result, robust intrusion detection systems (IDS) using deep learning model are required to find such cyber risk in form of threats and anomalies on cloud based infrastructure.

Driving comfort is important for any class of vehicle drivers. Seating comfort is very important for drivers who are undergoing extended period of driving and its associated side effects include higher risk of back problems, discomfort in neck, shoulder and thighs. A good sitting position and correct posture are important for drivers to reduce various work related injuries. There are several occupational risk factors that may affect the work performance of heavy vehicle drivers that may lead to health issues. One of these issues is the driver’s exposure to Musculoskeletal Disorders (MSD). MSDs are injuries and disorders that affect the human body's movement or musculoskeletal system. This is due to the fact that heavy vehicle drivers are often exposed to vibration, prolonged sitting and other postural factors. Some are related to the actual driving task, traffic situation, individual variability and cabin layout. The musculoskeletal disorders are mostly associated with work postures and movements, repetitiveness and pace of work, force of movement, vibration, temperature, workplace layout and monotonous tasks. And these mentioned risk factors may affect not only the health of the heavy vehicle drivers but may also cause related road accidents. In order to determine the occupational risk factors, a study is required to identify the current condition of heavy vehicle drivers in relation to major risk factors. Furthermore, this study aims to determine the significant factors affecting the occurrence of musculoskeletal disorders using statistical analyses.

Memory circuits such as static random-access memory (SRAM) and dynamic random-access memory (DRAM) form an integral part of system design and contribute significantly to system-level power consumption. Memory operating speeds and power dissipation have become important parameters due to the explosive growth of battery-operated appliances and the increased integration of circuits Hence SRAMs with different topologies are examined in terms of parameters like propagation delay, Static Noise Margin (SNM), corner analysis, and static power dissipation by simulating using versatile tool cadence virtuoso at 180nm technology. Besides, topological performance comparison, the SRAM memory array has also been illustrated from 2×2, 4×4 to 8×8, thereby verifying the read and write modes of operation of SRAM.

Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin biometrics which allows the use of pure biometric modalities and metadata in a feature fusion strategy. The main objective of this paper is to present an overview of biometrics as bordered in the literature with a particular focus on multibiometrics and to propose a model of a multi-origin biometric system using pure biometric and soft biometric modalities in a feature-level fusion strategy. The curvelet transformation and the order statistics are proposed respectively for the extraction the feature of the pure biometric modalities, and for the selection of the relevant feature of each modality in order to ensure a good level of discrimination of the individuals. In this paper, we have presented the overview of biometrics through its concepts, modalities, advantages, disadvantages and implementation architectures. A focus has been put on multi-biometrics with the presentation of a harmonized process for feature fusion. For the experiments, we proposed a global model for feature fusion in a multi-origin system using face and iris modalities as pure biometrics, and facial skin color as metadata. This system and the results will be presented in future work.

Wireless communication technology experienced drastic developments during the last few decades. Meanwhile the reconfigurable antenna had attracted more researchers because of its growing needs in various applications. Reconfiguration was performed to replace numerous antennas with single reconfigurable antenna which is capable of changing the Frequency, Polarization, Radiation Pattern etc. Antenna reconfigurability is attained by deploying several switching mechanisms among them electronic switching mechanisms are commonly used because of its simple integration, reliability, and efficiency. In this paper, review of various implementation techniques for antenna reconfiguration using meta surface are studied. Some of the reviewed factors are: Meta surface is the distribution of electrically small scatters called two-dimensional metamaterial. Metamaterial is an artificially built periodic array structure which consists of subwavelength cells. Some of the reviewed challenges in reconfiguration of the antenna are modeling of reconfigurable reflectarray, low-profile, high gain, unidirectional radiation pattern etc. Finally, some of the features, parameters and fundamental properties for designing reconfigurable antenna were investigated. Based on the study conducted, reconfigurable structure for 5 G application using meta surface is proposed.

Sign language is the most putative language among hearing impaired people. They use non-verbal form of communication that involves hand gestures, head or body movement or facial expressions. Of these hand gestures is more widely used. Automatic Sign Language Recognition (ASLR) System can be used to convert these non-verbal signs into text or sound so that normal people can identify them without learning the sign language. ASLR employs Image Processing and Artificial Intelligence (AI) algorithms for effective conversion from sign to sound or text. This review unveils various image processing and AI steps involved in the conversion process, bringing out important topologies in the Image acquisition, segmentation, feature extraction, classification and detection process and a comparative analysis among various topologies. Attempts have been made to shed light into adoption of alternate design strategies in segmentation and feature extraction that enhance the performance in a complex environment.