Advanced Microscopic Characterization and Antimicrobial Applications of Green-Synthesized Zinc Oxide Nanoparticles in Clinical Wound Healing Bandages.

The green synthesis of nanoparticles using plant extracts has garnered significant attention as a reliable and sustainable method for producing functional nanomaterials. Among these, zinc oxide (ZnO) nanoparticles are extensively studied for their potential biological applications. In this study, ZnO nanoparticles were synthesized through a green synthesis approach using an aqueous extract of Musa acuminata leaves and zinc acetate as precursors.

Temporal fusion transformer-based strategy for efficient multi-cloud content replication.

In cloud computing, ensuring the high availability and reliability of data is dominant for efficient content delivery. Content replication across multiple clouds has emerged as a solution to achieve the above. However, managing optimal replication while considering dynamic changes in data popularity and cloud resource availability remains a formidable challenge.

Insights into dopaminergic agent cabergoline-induced DNA-destabilisation through spectroscopic, thermodynamic and molecular docking/metadynamics simulation studies.

Investigating the interaction of therapeutic drugs with DNA is crucial for assessing the possibility of drugs inducing significant DNA alterations when administered. The dopaminergic drug cabergoline's (CBG) interaction with CT-DNA was investigated using multi-spectroscopic, calorimetric, electrophoresis, and computational approaches. The UV-Vis and fluorescence spectral results certified the complexation of CBG with CT-DNA.

An optimal peer selection for peer-to-peer video content distribution using fuzzy linear programming approach.

The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework is introduced that uses fuzzy linear programming (FLP) to address the inherent uncertainties in peer selection. The primary motivation for the use of FLP lies in its capability to handle the imprecision and vagueness that are characteristic of dynamic P2P environments.

Advanced financial security system using smart contract in private ethereum consortium blockchain with hybrid optimization strategy.

In traditional stock market, the global processing framework shares the data to various mediators like financial and government institutions. The institutional firms needs to handle with large number of data in the system and cooperates with others to provide the trades on the stock exchange platform, and consistently buy-sell orders pass through various parties before settlement. It involves a complex chain of intermediaries, has several drawbacks like weak transparency, long lead times for financial settlements, and a single point of failure.

Analysis of energy, exergy, and emissions in a diesel engine powered by cotton silk seed oil biodiesel with varying injection timings.

To mitigate the exhaustion of hydrocarbon fuels and the rise of pollutants, one can use biofuels in diesel engines for power generation. This study examines the possibility of enhancing the performance and reducing the pollutions of a compressed ignition engine using methyl ester made from cotton silk seed oil. This study aimed to assess the energy, energy efficiency, and emissions (3E) of the Kirloskar engine operating at 1800 rpm.

Effect of heat treatment on tribological behavior of direct metal laser sintered alloy 718.

The main aim of this work is to enhance the wear performance of the direct metal laser sintered (DMLS) alloy 718 by solution treatment aging (STA) method at room temperature (RT) (28°C) and 400°C in dry sliding conditions. The effect of microstructure, phase analysis, and microhardness on the wear behavior and the influence of STA on the specimen at elevated temperatures were studied. The microstructure revealed the presence of melt pool boundary (MPB) in untreated DMLS alloy while recrystallized grains were observed in the STA-treated alloy.

An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images.

Background And Objective: One of the illnesses with most significant mortality and morbidity rates worldwide is lung cancer. From CT images, automatic lung tumor segmentation is significantly essential. However, segmentation has several difficulties, such as different sizes, variable shapes, and complex surrounding tissues.

Classification of motor imagery EEG with ensemble RNCA model.

Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements.

Investigation into the impact of acetylene on performance and emission characteristics of a compression ignition engine using a blended biodiesel of ethanol and tamanu oil.

Due to the significant increase in transportation, traditional fossil fuels utilised in internal combustion engines will only be accessible for a limited duration. Additionally, the harmful pollutants produced by these fuels, including CO, NO, unburned hydrocarbons, smoke, and a small amount of particulate matter, have a severe negative impact on the environment. Although biodiesel proves efficient without necessitating engine modifications, its performance is hindered by its higher viscosity.

Co-pyrolysis of furniture wood with mixed plastics and waste tyres: assessment of synergistic effect on biofuel yield and product characterization under different blend ratio.

Pyrolysis of waste furniture wood, mixed plastics and waste tyres was examined separately and in different combinations from the perspective of improved value products and energy production. The effect of different combinations of furniture wood, plastics and tyres on the product distribution during co-pyrolysis was analyzed. The experimental work throughout this study was performed at a temperature of 500 °C.

A novel approach for heart disease prediction using hybridized AITHO algorithm and SANFIS classifier.

In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendations from medical professionals. However, the existing machine learning classifiers suffer from computational complexity and overfitting problems, which reduces the classification accuracy of the diagnostic system.

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

Background: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction.

Investigation on Luminescence Properties of Dy Ions Doped NaBaBO Phosphors for Optoelectronic Applications.

A series of Dy ions doped NaBaBO phosphors with different dopant concentration was synthesized by solid state reaction. The phase purity was checked by X-ray diffraction analysis and the functional groups present in as prepared phosphors was investigated with the help of FTIR spectral analysis. Under 386 nm excitation, the photoluminescence spectra exhibit three emission bands around 482 nm, 574 nm and 664 nm due to F→H (J = 15, 13, 11) transions respectively.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis.

Revolutionizing heart disease prediction with quantum-enhanced machine learning.

The recent developments in quantum technology have opened up new opportunities for machine learning algorithms to assist the healthcare industry in diagnosing complex health disorders, such as heart disease. In this work, we summarize the effectiveness of QuEML in heart disease prediction. To evaluate the performance of QuEML against traditional machine learning algorithms, the Kaggle heart disease dataset was used which contains 1190 samples out of which 53% of samples are labeled as positive samples and rest 47% samples are labeled as negative samples.

Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.

Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer based Dual Convolutional Neural Networks is employed for detecting and classifying the osteoarthritis patients. The new Gaussian Aquila Optimizer (GAO) is devised to include Gaussian mutation at the exploitation stage of Aquila optimizer, which results in attaining the best global optimal value.

Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication.

The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other industries such as the Internet of Things, broadcast services, and so on. Energy efficiency, scalability, resiliency, interoperability, and high data rate/low delay are the primary requirements and obstacles of 5 G cellular networks.

A hybrid deep CNN model for brain tumor image multi-classification.

The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks.

A novel fuzzy programming approach for piece selection problem in P2P content distribution network.

Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives.

DLTN-LOSP: A Novel Deep-Linear-Transition-Network-Based Resource Allocation Model with the Logic Overhead Security Protocol for Cloud Systems.

Cloud organizations now face a challenge in managing the enormous volume of data and various resources in the cloud due to the rapid growth of the virtualized environment with many service users, ranging from small business owners to large corporations. The performance of cloud computing may suffer from ineffective resource management. As a result, resources must be distributed fairly among various stakeholders without sacrificing the organization's profitability or the satisfaction of its customers.

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks.

The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs.

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model.

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage.

Cashew nut shell oil as a potential feedstock for biodiesel production: An overview.

Biodiesel outperforms diesel in emissions and engine performance. They burn efficiently in diesel engines and are eco-friendly. Since cashew nut shell liquid (CNSO) is waste, commercial biodiesel production from it should be profitable.