Study of thermal performance and melting behaviour of a novel paraffin wax-carbon quantum Dots PCMs.

Thermal energy storage with phase change materials (PCMs) is emerging as a key solution in addressing the global energy crisis, driving innovation in energy storage and management systems. This work numerically investigates the thermal performance and melting behavior of a novel composite PCM composed of paraffin wax (PW) dispersed with different weight percentages of carbon quantum dots (CQDs) in order to validate the experimental results. Latent heat curves for the prepared composite PCMs were generated numerically using computational fluid dynamics (CFD), with the input values provided from experiments and they seem to support the pattern of the experimental curves.

An assessment of optimizing biofuel yield percentage using K-fold integrated machine learning models for a sustainable future.

Accelerating population and modernization has triggered a steady rise in energy demand and a significant rise in household waste, particularly municipal solid waste. In this context, waste-to-energy conversion has emerged as a sustainable solution. This study aims to maximize biofuel production yield using biomass-based banana peel catalyst waste by optimizing process parameters through machine learning models integrated with k-fold cross-validation.

IoT enabled health monitoring system using rider optimization algorithm and joint process estimation.

The timely detection of abnormal health conditions is crucial in achieving successful medical intervention and enhancing patient outcomes. Despite advances in health monitoring, existing methods often struggle with achieving high accuracy, sensitivity, and specificity in real-time detection. This work addresses the need for improved performance in health monitoring systems in real time sensor data.

Grid tied hybrid PV fuel cell system with energy storage and ANFIS based MPPT for smart EV charging.

This paper presents the comprehensive design, simulation, and experimental validation of a grid-tied hybrid renewable energy system tailored for electric vehicle (EV) charging applications. The proposed system integrates photovoltaic (PV) panels, a proton-exchange membrane fuel cell, battery storage, and a supercapacitor to ensure reliable and efficient power delivery. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm is employed to enhance PV power extraction under dynamically varying environmental conditions.

An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks.

Intelligent Transportation Systems (ITS) necessitate scalable, real-time, and adaptive traffic flow prediction models to enhance urban mobility and alleviate congestion. Conventional Graph Neural Network methodologies encounter difficulties in managing extensive road networks, long-range temporal relationships, and computing efficiency for real-time applications. An innovative deep learning framework named Meta Temporal Hyperbolic Quantum Graph Neural Networks that integrates hyperbolic embeddings, meta learning, quantum graph, Neural Ordinary Differential Equation (NODEs) to improve the ITS Performance.

Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights.

Biodiesel presents a favourable economic outlook and environmental benefits, yet it faces limitations such as diminished calorific value and suboptimal combustion characteristics. Recent research focuses on enhancing biodiesel performance using nanoparticles and thermal barrier coatings. This study investigates non-edible biodiesel from Momordica seed oil, tested on a single-cylinder diesel engine.

Production and utilization of hydrogen enriched fifth generation biofuel in LTC engines with reformed EGR.

The increasing global demand for cleaner energy solutions highlights the need to combine hydrogen with advanced biofuels for use in low-temperature combustion (LTC) engines. This study experimentally investigates the performance and emissions of an LTC engine fueled with six test fuels: Diesel, Citronella biofuel, three hydrogen-enriched blends (H20, H40, H60) and H40E (H40 + 10%R-EGR). Among the tested fuels, the H40 blend demonstrated the highest brake thermal efficiency (BTE), surpassing that of conventional diesel.

Integrated deep learning framework for driver distraction detection and real-time road object recognition in advanced driver assistance systems.

Most accidents are a result of distractions while driving and road user's safety is a global concern. The proposed approach integrates advanced deep learning for driver distraction detection with real-time road object recognition to jointly address this problem. The behaviour of a driver is categorized into physical and visual distraction and cognitive distraction using Convolutional Neural Networks (CNN's) and transfer learning in order to achieve greater accuracy while also consuming lesser computational resources.

Investigating SnOx/Graphene Oxide heterostructure for methane sensing and its application as a tunable light absorber for optoelectronic devices.

This study investigates the optical and electronic properties of SnOx/Graphene Oxide (SnOx/GO) heterostructures, focusing on their sensitivity and selectivity to methane adsorption and its tunable light absorption capabilities across different wavelength ranges. By categorizing SnOx/GO heterostructures into four types based on the oxygen mole fraction (x) of SnOx, notable differences are observed in their light absorption, extinction coefficient, and reflectance. Among these, Type-C heterostructures demonstrate the highest absorption coefficient (~1.

Transformer based models with hierarchical graph representations for enhanced climate forecasting.

Accurate climate predictions are essential for agriculture, urban planning, and disaster management. Traditional forecasting methods often struggle with regional accuracy, computational demands, and scalability. This study proposes a Transformer-based deep learning model for daily temperature forecasting, utilizing historical climate data from Delhi (2013-2017, consisting of 1,500 daily records).

Exhaust manifold performance enhancement using nano fluids a design and CFD investigation for four stroke petrol engines.

This paper presents a trivial approach in which the temperature of the exhaust manifold decreases due to the changes in the fuel composition. The authors have defined Nano fluids as a modern class of fluids that consist of suspended solid Nanoparticles in fluids. Also, they have shown that noise reduction is possible with Nano fluids.

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images.

Blockchain based electronic educational document management with role-based access control using machine learning model.

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways.

Genetic and bioactive functionalization of bioinks for 3D bioprinting.

3D bioprinting is revolutionizing tissue engineering and regenerative medicine by enabling the precise fabrication of biologically functional constructs. At its core, the success of 3D bioprinting hinges on the development of bioinks, hydrogel-based materials that support cellular viability, proliferation, and differentiation. However, conventional bioinks face limitations in mechanical strength, biological activity, and customization.

Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks.

Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation.

Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.

Clinical Document Classification (CDC) is crucial in healthcare for organizing and categorizing large volumes of medical information, leading to improved patient care, streamlined research, and enhanced administrative efficiency. With the advancement of artificial intelligence, automatic CDC is now achievable through deep learning techniques. While existing research has shown promising results, more effective and accurate classification of long clinical documents is still desired.

An ECG signal processing and cardiac disease prediction approach for IoT-based health monitoring system using optimized epistemic neural network.

The rising prevalence of cardiac diseases necessitates advanced IoT-driven health monitoring systems for early detection and diagnosis. This study presents an efficient ECG-based cardiac disease prediction framework leveraging a multi-phase approach to enhance computational efficiency and classification accuracy. The Convolutional Lightweight Deep Auto-encoder Wiener Filter (CLDAWF) is employed for signal preprocessing, while the Quantized Discrete Haar Wavelet Transform (QD-HWT) extracts critical cardiac features, including P-wave fluctuations, QRS complex, and T-wave intervals.

Red edge effect of chalcone derivatives and their application in Bio-sensing.

A series of chalcone-based compounds with varied functional groups were designed and synthesized through green chemistry. Polarity-tuned solvatochromic photophysical studies were thoroughly performed using steady-state absorption and emission spectroscopic techniques. These donor-acceptor structured chalcones were capable of exhibiting excitation dependent fluorescence (EDF), which is widely known as the red edge effect, with a large Stokes shift above 140 nm, making them capable of accessing the yellow to blue region of the spectrum.

Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.

Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of severe consequences and enhance patient outcomes. Predictive methods have been used to estimate the risk factor, but they often have drawbacks such as improper feature selection, overfitting, etc.

Grid connected improved sepic converter with intelligent mppt strategy for energy storage system in railway applications.

In order to increase the utilization rate of regenerative braking energy, reduce the operation cost and improve the power quality of traction power supply system in high-speed railway. This paper presents a grid-connected improved SEPIC converter with an intelligent maximum power point tracking (MPPT) strategy tailored for energy storage systems in railway applications. The proposed system enhances power conversion efficiency and stability by integrating an optimized SEPIC topology with an adaptive MPPT algorithm.

An investigation study on residential buildings for cost overrun.

Construction projects are now widely recognized as being among the most important factors contributing to the overall economic development of a nation. When delays occur in these construction projects, however, both the overall progress of the projects and their profitability are affected. In this investigation, a literature review and a questionnaire survey were used to find the most important factors that cause delays in the Indian construction industry.

Design, Dynamics and Development of Upgraded Tiltable Wing associated Quadcopter through Advanced computational simulations incorporated Bottom-Up Approach.

This paper aims to design a hybrid quadcopter that can be used for multiple detecting applications in which its performance parameters are studied under various maneuverings such as forward and vertical movements based on computational studies. In order to enhance the endurance, the conventional rectangular cross-sectional arm was replaced by airfoil cross sectional arm which helps in reduction of overall drag. The proposed idea is a combination of both tilt wing and tilt rotor configurations to the hybrid unmanned aerial vehicle (HUAV).

Advances in bioink-based 3D printed scaffolds: optimizing biocompatibility and mechanical properties for bone regeneration.

The development of bioink-based 3D-printed scaffolds has revolutionized bone tissue engineering (BTE) by enabling patient-specific and biomimetic constructs for bone regeneration. This review focuses on the biocompatibility and mechanical properties essential for scaffold performance, highlighting advancements in bioink formulations, material combinations, and printing techniques. The key biomaterials, including natural polymers (gelatin, collagen, alginate), synthetic polymers (polycaprolactone, polyethylene glycol), and bioactive ceramics (hydroxyapatite, calcium phosphate), are discussed concerning their osteoconductivity, printability, and structural integrity.

Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.

Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD).

A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.

Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods struggle to capture the intricate temporal and spatial dependencies in EEG data, limiting their precision.