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The present work aims to perform a thermoeconomic assessment of a battery pack integrating a novel compact coolant system through which Al2O3, TiO2, CuO, and Cu nanofluids flow at different Reynolds numbers and concentrations. The inlets in the north and west and the outlets in the south and east directions are found to be the best configuration since the least number of batteries exhibits higher temperatures. A nanofluid-based cooling system offers an improvement of 15 K in battery core temperature when compared to the pack without any coolant system at 200 s; however, a reduction of 0.3 K is noticed when compared to water. CuO nanoparticles perform better at a low concentration of 2%, whereas Cu particles have an advantage over other nanoparticles at a concentration greater than 2%. An economic analysis of the nanofluid has also been performed, eradicating the idea of using Cu–water nanofluid in the coolant system owing to its significantly high cost. Though the cost of the CuO and Al2O3 nanofluid is 13 times lower than Cu, using pure water as the coolant is recommended since there is a marginal reduction of 0.1–0.3 K in the battery pack temperature when water is replaced by nanofluid.

COVID-19 (Corona Virus Disease of 2019) is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) virus. This disease has significantly impacted every aspect of people's lives, including their work style, leisure activities, and use of technology. Additionally, due to psychological factors or other reasons, there has been a surge in deaths from cardiovascular failure during the pandemic. As COVID-19 is a silent killer whose symptoms only become visible after significant damage has been done, constant monitoring of heart parameters is crucial to address this issue. This paper explores the emerging trends in monitoring vital signs such as the electrocardiogram (ECG), heart rate, respiration rate (breaths), related sensors, remote sensor organization, and telemedicine innovations. Furthermore, this paper discusses the potential application of non-contact radar-based remote monitoring for vital sign monitoring of affected patients.

Graphene oxide, a derivative of graphene, has recently emerged as a promising nanomaterial in the biomedical field due to its unique properties. Its potential as a nanocarrier in the treatment of Alzheimer's disease represents a significant advancement. This abstract outlines a study focused on utilizing graphene oxide to reduce the toxicity of Alzheimer's proteins, marking a revolutionary approach in treatment strategies. The pathological features of Alzheimer’s disease, primarily focusing on the accumulation and toxicity of amyloid-beta proteins, have been described in this review. These proteins are known to form plaques in the brain, leading to neuronal damage and the progression of Alzheimer's disease. The current therapeutic strategies and their limitations are briefly reviewed, highlighting the need for innovative approaches. Graphene oxide, with its high surface area, biocompatibility, and ability to cross the blood-brain barrier, is introduced as a novel nanocarrier. The methodology involves functionalizing graphene oxide sheets with specific ligands that target amyloid-beta proteins. This functionalization facilitates the binding and removal of these toxic proteins from the brain, potentially alleviating the symptoms of Alzheimer's disease. Preliminary findings indicate a significant reduction in amyloid-beta toxicity in neuronal cell cultures treated with graphene oxide nanocarriers. The study also explores the biocompatibility and safety profile of graphene oxide in biological systems, ensuring its suitability for clinical applications. It calls for further research and filing patents for its translational potential and benefits of this nanotechnology paying the way for a new era in neurodegenerative therapy.

The impact of different diesel-ethanol-methanol-butanol (DEMB) blends on the spray and combustion characteristics of a single-cylinder Diesel engine has been investigated. For this study, commercially available software called Diesel-RK that can predict the spray and combustion parameters has been utilized. Some ex-periments have also been conducted using D100 (100% pure diesel by volume) fuel at a fixed speed of 1500 rpm at peak load while maintaining the same operating conditions as the simulation. The predicted results have been validated against the experimental results obtained with D100. The results of the simulation were found to be in reasonably good agreement with those of the experiment. The analysis of the simulated results shows that the heat release rate, ignition delay and peak cyl-inder pressure increase for all quaternary blends, whereas the peak combustion temperature decreases at low load and increases at higher load. In terms of spray characteristics, the investigations show that quaternary alcohol blends shorten spray tip penetration and increase spray cone angle. Furthermore, as the propor-tion of ethanol and methanol in the DEMB blends increases, the atomized fuel droplets become smaller in diameter and the sauter mean diameter of the blends gradually drops. The authors also suggest that the quaternary blends of this pre-sent investigation have a higher potential to be used as a next-generation fuel in Diesel engine.

The textile industry plays a major part in the economy of the Kingdom of Saudi Arabia (KSA). However, the environmental impact of textile dyeing and wastewater discharge has become a growing concern in the region. This study addressed this issue by identifying and characterizing azo dye degrading enzymes that can be used in bioremediation strategies. Six enzymes, namely Thiol reductase, Thiol peroxidase, Alkene reductase, NADH-oxidoreductase, Oxidoreductase, and Sulfite reductase, were identified through a literature review and used as queries in BLASTp to search for homologous enzymes from Bacillus cereus, Brevibacillus brevis, Bacillus acidicola, and Paenibacillus alvei. The physicochemical characteristics and subcellular distribution of these enzymes were determined using online tools. Phylogenetic analysis was performed to investigate the evolutionary connection of these enzymes across different bacterial species. Additionally, gene structure and motif analysis were conducted to gain insights into functional motifs and gene organization of these enzymes. Domain prediction and protein–protein interaction analysis were carried out to identify conserved domains and potential protein interactions. The outcomes of this study offer valuable understandings on prospect of azo dye degrading enzymes for bioremediation strategies in the KSA textile industry, which is in agreement with the future Vision 2030 strategy. The identified enzymes and their homologs from other microbial genomes represent promising candidates for further experimental validation and utilization in bioremediation processes. Moreover, they contribute to the development of effective bioremediation strategies for the textile industry in the KSA region. Overall, this study enhances our understanding on azo dye degrading enzymes and their potential uses in the textile industry, particularly in the context of KSA.

Background: The quality of water directly or indirectly impacts the health and environmental well-being. Data about water quality can be evaluated using a Water Quality Index (WQI). Computing WQI is a quick and affordable technique to accurately summarise the quality of water. Objective: The objective of this study is to find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages in different geographic locations, to predict the quality of water, and to identify low-quality water before it is made accessible for human consumption. Methods: To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine, and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the quality of water. Methods used in handling these features include five steps that are data preprocessing with min-max normalization, finding WQI, using feature correlation to identify parameter importance with WQI, application of supervised machine learning regression models such as Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately. Results: The examination of the testing model revealed that RF regression and MLR algorithms performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively. Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93, and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang. Conclusion: This study explores a method for predicting water quality that combines easy and feasible water quality measurements with machine learning. The stack model classifier performed best for multiclass classification, according to this study. To ensure that the highest quality of water is given throughout the year, information from this study will motivate researchers to look into the underlying root causes of the quality variations.

: Blockchain technology is increasingly attracting young people because it is so well adapted to the digital age. A decentralised data management system is necessary for the blockchain idea in order to store and share data and transactions throughout the network. This study investigates various types of risks associated with blockchain technology. The research covers different aspects of blockchain, including the architecture, consensus mechanism, smart contracts, and underlying cryptographic algorithms. It also examines the risks associated with the adoption and implementation of blockchain in various industries, such as finance, healthcare, and supply chain management. : Moreover, this study identifies several types of risks, including technical risks, such as scalability, interoperability, and security, as well as non-technical risks, such as regulatory compliance, legal liability, and governance issues. This study also discusses the potential impact of these risks on blockchain-based systems and the strategies that can be used to mitigate them.

Introduction: Cloud computing has revolutionized how individuals and businesses engage with data and software, turning the internet into a powerful computing platform by centralizing resources. Despite its benefits, there's a challenge in safeguarding sensitive information stored externally. Cryptography faces threats, particularly chosen-ciphertext attacks aiming for secret keys or system information. While more common in public-key encryption, these attacks are less frequent in symmetrically coded systems. Security efforts include validating system resilience and continuous improvement, which are vital in countering evolving threats like adaptive chosen ciphertext attacks. Methods: In the evaluation model, stringent measures emphasize robust encryption for system security. Despite the planning, no ciphertext attack guarantees success, necessitating adaptive security protocols. Adaptive attacks like CCA2 expose vulnerabilities, enabling attackers to manipulate ciphertexts persistently. Results: We observe an average gain of 65% for the decryption algorithm. Efforts focus on strengthening security. The flawed 32-bit key-based encryption in the modified Cramer-Shoup structure undergoes remediation. Conclusion: Conventional uncertainties validate resilience, emphasizing continuous evaluation and enhancement to counter evolving threats.

Biogenic and anthropogenic feedstocks can be converted into high-quality and hydrogen-rich synthesis gas through high-pressure entrained flow gasification. However, robust pilot-scale process data is essential for the optimisation, design and scale-up of this process. Therefore, this study conducted pilot-scale experiments, developed balancing and equilibrium models for performance analysis and derived input and validation data for CFD models. The experiments were carried out at the bioliqEntrained Flow Gasifier plant using mixtures of ethylene glycol or beech wood pyrolysis oil with glass beads, thermal inputs of up to 5 MW and operating pressures of 40 bar. The cooling screen was recoated before the experiments to ensure well-defined heat transfer conditions. The data from on-line measurements and off-line analyses was evaluated with emphasis on the synthesis gas condition before quenching, the heat extraction from the inner reactor chamber and the carbon conversion. The results show that the balancing model provides consistent and accurate predictions and the equilibrium model is able to track the generated process data. Specifically, the balancing predictions are accurate if the solution of CO$_2$ in the quench water is accounted for, if undetected intermediates are described as lost carbon and lost atomic hydrogen and if further chemical reactions in the quench water are avoided by appropriate operating conditions.