An Energy conserving routing scheme for wireless body sensor nanonetwork communication

Current developments in nanotechnology make electromagnetic communication possible at the nanoscale for applications involving body sensor networks (BSNs). This specialized branch of wireless sensor networks, drawing attention from diverse fields, such as engineering, medicine, biology, physics, and computer science, has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, terahertz frequency bands with limited transmission range, and so on, in designing protocols for wireless nanosensor networks. This work proposes an improved performance scheme of nanocommunication over terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains - a new energy-efficient forwarding routine for electromagnetic communication in wireless nanonetworks consisting of hybrid clusters with centralized scheduling; a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing; and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a nano-sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communications; multisensor fusion at the fusion node enhances the link quality of the transmission

Towards a heterogeneous mist, fog, and cloud based framework for the Internet of Healthcare Things

Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems

Design of the Organic Rankine Cycle for High-Efficiency Diesel Engines in Marine Applications

Data Availability Statement: Data available on request due to privacyCopyright © 2023 by the authors. Over the past few years, fuel prices have increased dramatically, and emissions regulations have become stricter in maritime applications. In order to take these factors into consideration, improvements in fuel consumption have become a mandatory factor and a main task of research and development departments in this area. Internal combustion engines (ICEs) can exploit only about 15–40% of chemical energy to produce work effectively, while most of the fuel energy is wasted through exhaust gases and coolant. Although there is a significant amount of wasted energy in thermal processes, the quality of that energy is low owing to its low temperature and provides limited potential for power generation consequently. Waste heat recovery (WHR) systems take advantage of the available waste heat for producing power by utilizing heat energy lost to the surroundings at no additional fuel costs. Among all available waste heat sources in the engine, exhaust gas is the most potent candidate for WHR due to its high level of exergy. Regarding WHR technologies, the well-known Rankine cycles are considered the most promising candidate for improving ICE thermal efficiency. This study is carried out for a six-cylinder marine diesel engine model operating with a WHR organic Rankine cycle (ORC) model that utilizes engine exhaust energy as input. Using expander inlet conditions in the ORC model, preliminary turbine design characteristics are calculated. For this mean-line model, a MATLAB code has been developed. In off-design expander analysis, performance maps are created for different speed and pressure ratios. Results are produced by integrating the polynomial correlations between all of these parameters into the ORC model. ORC efficiency varies in design and off-design conditions which are due to changes in expander input conditions and, consequently, net power output. In this study, ORC efficiency varies from a minimum of 6% to a maximum of 12.7%. ORC efficiency performance is also affected by certain variables such as the coolant flow rate, heat exchanger’s performance etc. It is calculated that with the increase of coolant flow rate, ORC efficiency increases due to the higher turbine work output that is made possible, and the condensing pressure decreases. It is calculated that ORC can improve engine Brake Specific Fuel Consumption (BSFC) from a minimum of 2.9% to a maximum of 5.1%, corresponding to different engine operating points. Thus, decreasing overall fuel consumption shows a positive effect on engine performance. It can also increase engine power output by up to 5.42% if so required for applications where this may be deemed necessary and where an appropriate mechanical connection is made between the engine shaft and the expander shaft. The ORC analysis uses a bespoke expander design methodology and couples it to an ORC design architecture method to provide an important methodology for high-efficiency marine diesel engine systems that can extend well beyond the marine sector and into the broader ORC WHR field and are applicable to many industries (as detailed in the Introduction section of this paper).This research received no external funding

Using Bayesian Networks and Machine Learning to Predict Computer Science Success

Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links AQ1 inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with ≈91% accuracy. This could help to increase throughput as well as student wellbeing at university

Impact of Organizational Culture on Employee's Career Salience: An Empirical Study of Banking Sector in Islamabad, Pakistan

Abstract This study was conducted to inspect the relationship between organizational culture and employee &apos

Inhibition of histone methyltransferase EZH2 in Schistosoma mansoni in vitro by GSK343 reduces egg laying and decreases the expression of genes implicated in DNA replication and noncoding RNA metabolism

Background: The possibility of emergence of praziquantel-resistant Schistosoma parasites and the lack of other effective drugs demand the discovery of new schistosomicidal agents. In this context the study of compounds that target histone-modifying enzymes is extremely promising. Our aim was to investigate the effect of inhibition of EZH2, a histone methyltransferase that is involved in chromatin remodeling processes and gene expression control; we tested different developmental forms of Schistosoma mansoni using GKS343, a selective inhibitor of EZH2 in human cells. Methodology/Principal findings: Adult male and female worms and schistosomula were treated with different concentrations of GSK343 for up to two days in vitro. Western blotting showed a decrease in the H3K27me3 histone mark in all three developmental forms. Motility, mortality, pairing and egg laying were employed as schistosomicidal parameters for adult worms. Schistosomula viability was evaluated with propidium iodide staining and ATP quantification. Adult worms showed decreased motility when exposed to GSK343. Also, an approximate 40% reduction of egg laying by GSK343-treated females was observed when compared with controls (0.1% DMSO). Scanning electron microscopy showed the formation of bulges and bubbles throughout the dorsal region of GSK343-treated adult worms. In schistosomula the body was extremely contracted with the presence of numerous folds, and growth was markedly slowed. RNA-seq was applied to identify the metabolic pathways affected by GSK343 sublethal doses. GSK343-treated adult worms showed significantly altered expression of genes related to transmembrane transport, cellular homeostasis and egg development. In females, genes related to DNA replication and noncoding RNA metabolism processes were downregulated. Schistosomula showed altered expression of genes related to cell adhesion and membrane synthesis pathways. Conclusions/Significance: The results indicated that GSK343 presents in vitro activities against S. mansoni, and the characterization of EZH2 as a new potential molecular target establishes EZH2 inhibitors as part of a promising new group of compounds that could be used for the development of schistosomicidal agents

Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required