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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.

Oxy-fuel carbon capture in power plants is a relatively new concept aiming at reducing carbon dioxide emissions from the plants. This is achieved by burning the fossil fuel using oxygen as oxidizer with no nitrogen, thereby rendering the exhaust gases very rich in carbon dioxide (after condensing water vapor by cooling), which facilitates its capture for environmental or commercial purposes. Despite the worldwide interest in oxy-fuel carbon capture, its progress is at risk given the large energy needed to separate oxygen from air in order to provide the oxidizer, thereby hindering further progress of this concept toward large-scale applications. This paper focuses on alleviating this drawback of oxy-fuel combustion by making it more attractive through combining it with another concept, namely magnetohydrodynamic (MHD) power generators. The end product is a power plant operating on a combined cycle composed of a topping MHD ultra-high-temperature cycle with direct electricity extraction from plasma, followed by a bottoming steam cycle with conventional turbo-generators. Different design aspects and simplified technical analysis for the MHD generator are presented.

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.

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.

Background: Breast cancer (BRCA) is the most frequently diagnosed cancer in women, with a rise in occurrences and fatalities. The field of BRCA prediction and diagnosis has witnessed significant advancements in recent years, particularly emphasizing enhanced computer-aided digital imaging techniques, and has emerged as a powerful ally in the prediction of BRCA through histopathology image analysis. A number of approaches have been suggested in recent years for the categorization of histopathology BRCA images into benign and malignant as it examines the images at cellular level. The histopathology slides must be manually analysed which is time consuming and tiresome and is prone to human error. Additionally, different laboratories occasionally have different interpretation of these images. Methods: This paper focuses on implementing a framework for Computer-Aided digital imaging technique that can serve as a decision support. With recent advancements in computing power the analysis of BRCA histopathology image samples has become easier. Stain normalization (SN), segmentation, feature extraction and classification are the steps to categorize the cancer into benign and malignant. Nuclei segmentation is a crucial step that needs to be taken into account in order to establish malignancy. These are considered essential for early diagnosis of BRCA. A unique method proposed for BRCA prediction is put forward. To maximize the prediction accuracy, the suggested method is integrated with machine learning (ML) techniques and clinical data is used to evaluate the suggested approach. Results: This strategy is adaptable to many cancer types and imaging techniques. The suggested technique is applied to clinical data and is integrated with logistic regression and K-Nearest Neighbor resulting in accuracy of 92.10% and 86.89% respectively for BRCA histopathology images. Conclusion: The objective of this work is to validate the proposed model which takes input as feature pattern for a given label. For the collected clinical samples, the model is able to classify the input as benign or malignant. The proposed model worked efficiently for different BC datasets and performed classification task successfully. Integrating mathematical model (MM) with ML model for interpreting histopathology BRCA is a potential area of research in the field of digital pathology.

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Methodology: However, the Internet-of-Things (IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results: This study also contributes in two ways to the ongoing effort to investigate securityprivacy concerns in the circumstance of smart cities for healthcare purposes. Conclusion: Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.