A Comprehensive Review of D2D Communication in 5G and B5G Networks

The evolution of Device-to-device (D2D) communication represents a significant breakthrough within the realm of mobile technology, particularly in the context of 5G and beyond 5G (B5G) networks. This innovation streamlines the process of data transfer between devices that are in close physical proximity to each other. D2D communication capitalizes on the capabilities of nearby devices to communicate directly with one another, thereby optimizing the efficient utilization of available network resources, reducing latency, enhancing data transmission speed, and increasing the overall network capacity. In essence, it empowers more effective and rapid data sharing among neighboring devices, which is especially advantageous within the advanced landscape of mobile networks such as 5G and B5G. The development of D2D communication is largely driven by mobile operators who gather and leverage short-range communications data to propel this technology forward. This data is vital for maintaining proximity-based services and enhancing network performance. The primary objective of this research is to provide a comprehensive overview of recent progress in different aspects of D2D communication, including the discovery process, mode selection methods, interference management, power allocation, and how D2D is employed in 5G technologies. Furthermore, the study also underscores the unresolved issues and identifies the challenges associated with D2D communication, shedding light on areas that need further exploration and developmen

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Time-slot based architecture for power beam-assisted relay techniques in CR-WSNs with transceiver hardware inadequacies

Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively

Customer prioritization for medical supply chain during COVID-19 pandemic

During COVID-19, the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand. This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals, pharmacies, and retail stores as its customers. Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers. A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customer-selection criteria. These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’ domain-related knowledge using Analytical Hierarchy Process. Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty, commitment, brand awareness, brand image, sustainable behavior, and risk. Subsequently, Multi Criteria Decision Analysis has been performed to prioritize the customer-selection criteria and customers with respect to selection criteria. Three experts with seven and three and ten years of experience have participated in the study. Findings of the study suggest large hospitals, large pharmacies, and small retail stores are the highly preferred customers. Moreover, findings of prioritization of customer-selection criteria from both Principal Component Analysis and Analytical Hierarchy Process are consistent. Furthermore, this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision. Unlike traditional supply chain problems of supplier selection, this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria

Antioxidant, antimicrobial studies and characterisation of essential oil, fixed oil of Clematis graveolens by GC-MS

The GC-MS, antimicrobial and antioxidant activity of Clematis graveolens was assessed to explore its medicinal importance. Medicinal importance of its genus plants encourages us to undertake the comprehensive investigation of the essential oil and fixed oil of the leaves and stem. GC-MS analysis of essential and fixed oils showed the presence of many compounds in the leaves and stem parts of the plant like 2,2 dimethoxy butane (15.16%) flouroethane (45.14%) undecane (5.16), 1,2-benzenedicarboxylic acid (18.35), 3,8,12-tri-O-acetoxy-7-desoxyingol-7-one (12.74), propanoic acid, 2-(3-acetoxy-4,4,14-trimethylandrost-8-en-17-yl)- (9.14) and vitamine E acetate (4.38). The antimicrobial activity of the essential and fixed oil was resolute by disc diffusion and MIC (Minimum inhibitory concentration) assay and plant showed potent activity. Furthermore the antioxidant potential of essential and fixed oil was assessed by the DPPH, Reducing power and by percentage inhibition in linoleic acid system

Pathogenesis and Immunohistochemical Studies of Caprine Pleuropneumonia in Experimentally Infected Goats

This study was designed to evaluate the pathogenesis of caprine pleuropneumonia (CPP) in the experimentally inoculated goats with Mycoplasma mycoides subspecies Capri (Mmc). For this purpose, 12 goats (Group B) were inoculated with bacterial isolates of Mmc while four goats were kept as untreated control (Group A). Clinical signs of the disease were recorded twice daily. Two goats from group B were sacrificed on weekly basis to demonstrate gross pathological lesions in different organs. Tissue samples from lungs, trachea, liver, heart, kidney, spleen, and small intestines were preserved for histopathological studies. The lungs and lymph nodes were preserved to demonstrate the antigen in tissue by using immuno- histochemical technique. The disease was successfully reproduced in all infected goats with severe manifestation. The clinical signs and gross lesions of the disease were mild at the beginning and became severe at the third and fourth weeks and then progressed to moderate and chronic forms. The histopathological lesions characteristic of CPP were found in all the organs. Antigen of Mmc was detected in tissue sections of lungs and lymph nodes. In conclusion, the disease was efficiently reproduced in experimental animals that showed acute septicemic form with lethal outcome

High vacuum fractional distillation (HVFD) approach for quality and performance improvement of Azadirachta indica biodiesel

Biodiesel offers an advantage only if it can be used as a direct replacement for ordinary diesel. There are many reasons to promote biodiesel. However, biodiesel cannot get wide acceptance until its drawbacks have been overcome including poor low temperature flow properties, variation in the quality of biodiesel produced from different feedstocks and fuel filter blocking. In the present study, a much cheaper and simpler method called high vacuum fractional distillation (HVFD) has been used as an alternative to produce high-quality refined biodiesel and to improve on the abovementioned drawbacks of biodiesel. The results of the present study showed that none of biodiesel sample produced from crude Azadirachta indica (neem) oil met standard biodiesel cetane number requirements. The high vacuum fractional distillation (HVFD) process improved the cetane number of produced biodiesels which ranged from 44–87.3. Similarly, biodiesel produced from fractionated Azadirachta indica oil has shown lower iodine values (91.2) and much better cloud (−2.6 °C) and pour point (−4.9 °C) than pure Azadirachta indica oil. In conclusion, the crude oil needs to be vacuum fractioned for superior biodiesel production for direct utilization in engine and consistent quality production

Brain tumor classification in MRI image using convolutional neural network

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models

Production and characterization of biodiesel from tamarind seed oil

Biodiesel production from food graded oils is not economical due to high prices. Almost 80-85% cost of biodiesel is due to feedstocks. And currently used non-edible, waste oils are not capable of large scale biodiesel production. Therefore, search for other non-edible oil bearing feedstocks is need to be continued. The present investigation is an attempt to use non-edible tamarind seed oil as an inexpensive, sustainable and potential feedstock for biodiesel synthesis. Tamarind seed oil was converted to biodiesel by acid (HCl), base (KOH) and enzyme (immobilized lipase) catalysed transesterification. Tamarind seed biodiesel was found to have low iodine value (26-32) and high cetane number (65-68). Pour point values of biodiesel were ranged from -0.3 to -4.2. The components of produced biodiesel were evaluated by gas chromatographic-mass spectroscopic analysis

Customer prioritization integrated supply chain optimization model with outsourcing strategies

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains’ dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains