Investigation on Optical, Thermal, and Fluorescence Studies of Iron Nickel Magnesium Tetraoxide (FeNiMgO) Nanocomposites for Light-Emitting Diode Devices.

The iron nickel magnesium tetra-oxide (FeNiMgO) nanocomposites (NCs) first reported in this article were synthesized using the sol-gel method. For investigation using powder X-ray diffraction (PXRD), the presence of a cubic structure is confirmed. In Raman spectroscopy, the vibrational modes are investigated.

Multi-Modal Lung Cancer Detection Using Pyramidal Cascade Neuro-Fuzzy Fractional Network.

Lung cancer detection (LCD) is a process of identifying an occurrence of lung cancer (LC) or irregularities in the lungs. Early detection of lung cancer is crucial for improving patient survival and enabling effective treatment. Computed Tomography (CT) images and Positron emission tomography (PET) are employed for screening and detecting LC.

Hamamelitannin from Hamamelis virginiana Attenuates Ethanol-Induced Oxidative and Inflammatory Responses in Danio rerio Larvae.

Alcoholic liver disorder (ALD) is one of the most prevalent hepatic ailments worldwide, with oxidative stress and inflammation playing a vital role in disease progression. The current study intended to assess the anti-inflammatory nature of Hamamelitannin (HAM), a gallotannin from Hamamelis virginiana barks, which was predicted to possess anti-inflammatory properties based on in-silico docking analysis. To further explore its effects, we examined the therapeutic effect of HAM against ethanol-mediated inflammation using an in-vivo zebrafish larvae model.

Enhancing diesel engine efficiency with waste cooking oil biodiesel and nano additives for sustainable fuel applications.

This study investigates the effect of incorporating nano-additives Cerium oxide (CeO), Zinc oxide (ZnO), and Titanium oxide (TiO) at 25 ppm concentration into biodiesel derived from waste cooking oil, with the objective of improving diesel engine performance and reducing exhaust emissions. Experiments were carried out on a single-cylinder four-stroke diesel engine under varying load conditions. Among the tested blends, the B20 + TiO (25 ppm) mixture exhibited a modest reduction in brake thermal efficiency (2.

CrossFormerGAN: enhancing pedestrian trajectory prediction in autonomous driving through advanced attention mechanisms and transformer architectures.

Pedestrian trajectory prediction is essential for autonomous driving systems, aiming to foresee pedestrian movements and improve safety by anticipating their future positions and paths. Traditional methods often fail to capture the full complexity of pedestrian behavior due to their limited ability to account for subtle gestures, environmental factors, and social interactions, which critically affect movement patterns. This lead to the system making incorrect predictions, potentially leading to unsafe driving decisions.

Walrus Optimization-Enhanced ResNet-50 for AI-Driven Renal Malignancy Prediction with Occlusion Sensitivity-Based Interpretation.

Objective: Main goal is to optimize the hyperparameters of ResNet-50 using the Walrus Optimization Algorithm (WaOA) to enhance classification performance for renal malignancy detection. The study aims to compare the WaOA-optimized ResNet-50 with conventional deep learning models, evaluate its effec-tiveness through various performance metrics, and integrate Occlusion Sensitivity Analysis to ensure model interpretability and transparency in AI-driven medical diagnosis.

Methods: A total of 12,446 abdominal CT images were collected from multiple hospitals in Dhaka, Bangladesh, comprising four diagnostic categories: cyst (3,709 images), normal (5,077), stone (1,377), and tumor (2,283).

AI-Powered Skin Lesion Diagnosis using Whale Optimization Algorithm Enhanced ResNet 50 for Cancer Prediction.

Objective: The primary objective of this study is to enhance the accuracy and efficiency of binary skin lesion classification by optimizing the ResNet-50 convolutional neural network using the Whale Optimization Algorithm (WOA). This involves fine-tuning key hyperparameters such as learning rate, weights, and biases to improve predictive performance.

Methods: This study compares five CNN architectures: AlexNet, GoogleNet, VGG16, Resnet 50, and WOA-optimized Resnet 50.

Corrigendum to "Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features" [MethodsX 15 (2025) 103491].

[This corrects the article DOI: 10.1016/j.mex.

Ensemble deep learning model for early diagnosis and classification of Alzheimer's disease using MRI scans.

BackgroundAlzheimer's disease (AD) is an irreversible neurodegenerative disorder characterized by progressive cognitive and memory decline. Accurate prediction of high-risk individuals enables early detection and better patient care.ObjectiveThis study aims to enhance MRI-based AD classification through advanced image preprocessing, optimal feature selection, and ensemble deep learning techniques.

An optimized multi-scale dilated attention layer for keratoconus disease classification.

Introduction: Keratoconus (KCN) is a progressive and non-inflammatory corneal disorder characterized by thinning and conical deformation of the cornea, resulting in visual impairment. Early and accurate detection is crucial to prevent disease progression. Conventional diagnostic methods are time-consuming and depend on expert evaluation.

Dietary Fermentation with sp. and sp. Modulates Rumen Transcriptomic and Microbiota Profiles in .

Animal feed made from fermented agricultural residues using sp. sp. has received significant attention.

Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.

This article presents a reproducible machine learning methodology for the early prediction of Alzheimer's disease (AD) using clinical and behavioural data. A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance (accuracy: 93.9 %, F1-score: 91.

Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments.

Cybersecurity has often gained much popularity over the years in a fast-evolving discipline, as the number of cybercriminals and threats rises consistently to stay ahead of law enforcement. Recently, cybercriminals have become more complex with their approaches, though the underlying motives for conducting cyber threats remain largely the same. Classical cybersecurity solutions have become poor at identifying and alleviating evolving cyber threats.

Phase Evolution of High-Entropy Stannate Pyrochlore Oxide Synthesized via Glycine-Assisted Sol-Gel Synthesis as a Thermal Barrier Coating Material.

High-entropy ceramics have gained wider attention due to their structural integrity and stability, which can be used in various functional applications. Especially, high-entropy oxides exhibit excellent thermal stability, particularly at high temperatures. Thermal barrier coating materials must demonstrate good thermal stability without any phase transformation or phase separation, which is critical in aerospace and energy conversion applications.

A novel approach for profiling of nature-inspired and bioactive proteins and peptides isolated from selected medicinal plants and their implications in Alzheimer diseases by in-silico analysis.

This study reported the profiling and the in-silico analysis of the therapeutic potential of proteins/peptides (for Alzheimer disease) isolated from Tinospora cordifolia, Evolvulus alsinoides, Centella asiatica and Convolvulus pluricaulis. The proteins/peptides were extracted by using four different pH based buffer solutions. The trypsin digested proteins/peptides were analyzed by LC-MS/MS based peptide mass fingerprinting which showed the presence of high number of proteins/peptides involved in regulating the oxidative stress.

Optimizing credit card fraud detection with random forests and SMOTE.

In the financial world, Credit card fraud is a budding apprehension in the banking sector, necessitating the development of efficient detection methods to minimize financial losses. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques.

Advancements in nanofibers and nanocomposites: Cutting-edge innovations for tissue engineering and drug delivery-A review.

Nanofibers and nanocomposites have emerged as critical materials in biomedical applications, particularly for tissue engineering and medication delivery. These sophisticated nanomaterials are at the forefront of study because of their distinct features, which improve biocompatibility, mechanical strength, and functional adaptability. This review examines the most recent advances in nanomaterial production, characteristics, and applications, demonstrating their transformational promise in medicinal treatments and tissue engineering procedures.

Hybrid AI models for thermal imaging and analysis of neurological disorders using thermoplasmonics.

This paper describes a novel Artificial Intelligence-based approach to predict and analyse heat distribution in multi-tiered tissue structures by using plasmonic nanoparticle enhanced Multi-Spectral Thermal Imaging (MSTI). The optimization problem combines biophysical simulation with innovative machine learning techniques to improve the thermal mapping and analysis of biological tissues. The described technique uses gold (Au) and silver (Ag) nanoparticles of sizes 25-35 nm, being characteristic of their thermoplasmonic properties and capable of obtaining high-resolution thermal images through multi-spectral imaging.

A reinforcement learning approach for real-time adjustment of thermal imaging parameters in thermoplasmonic applications.

The application of thermoplasmonics faces challenges related to precise temperature control distribution for managing heat in heterogeneous materials. A hybrid Silicon Carbide (SiC) and Aluminium (Al) paste was developed for effective temperature control in thermoplasmonic heating-based applications. The thermal images of this hybrid paste of SiC-Al is examined for multimodal parameters to estimate the plasmonic heat.

Multi-modal fusion in thermal imaging and MRI for early cancer detection.

Early detection of cancer relies on precise imaging that captures both structural and metabolic details, critical for identifying small yet significant tissue anomalies. Thermal imaging detects temperature changes linked to increased metabolic activity in cancerous tissues, while Magnetic Resonance Imaging (MRI) provides high-resolution soft tissue contrast. However, traditional single-modality imaging techniques can lack sufficient sensitivity or anatomical context when used alone.

Hybrid AI models for predicting heat distribution in complex tissue structures with bioheat transfer simulation.

The accurate prediction of thermal behaviour in biological tissues is critical for various medical treatments, including hyperthermia, thermal ablation, and tissue engineering. This paper presents a novel deep learning-enhanced bioheat transfer model that integrates a Fractional Legendre wavelet approach to predict thermal effects in engineered tissue constructs precisely. The model incorporates a multi-phase analysis considering key properties such as blood perfusion, thermal conductivity, and metabolic heat generation.

Music interventions and obstructive sleep apnea: a brain connectivity analysis.

Background: The blockage in the upper airway that occurs, while sleeping is represented as obstructive sleep apnea (OSA). This seem to be a major issue which cause breathing difficulties also increases the risk of severe complications, such as heart attacks and strokes. Therefore, in this proposed study the impact of OSA using brain connectivity analysis under various conditions such as Neelambari, Kapi, and no music has been investigated.

Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis.

Periodontal disease, or periodontitis, is a chronic inflammatory condition affecting the tissues supporting teeth, with epigenetic mechanisms such as DNA methylation, histone modifications, and RNA molecules playing a crucial role in its progression. Histone deacetylase (HDAC) inhibitors have shown potential in treating inflammatory diseases by modulating gene expression to suppress inflammation and promote tissue regeneration. Machine learning models, particularly Kolmogorov-Arnold Networks (KANs), provide an advanced solution for predicting drug-gene associations, offering superior accuracy, efficiency, interpretability, and scalability compared to traditional Multi-Layer Perceptrons (MLPs).

Protective Effect of Artemisinin Against Luperox Induced Oxidative Stress and Insulin Resistance via Pi3k/Akt Pathway in Zebrafish Larvae.

Oxidative stress plays a critical role in the development of insulin resistance (IR), a key factor in metabolic disorders such as diabetes. Plant active ingredients play a crucial role in protecting organisms from environmental stressors and have shown promising therapeutic potential against various metabolic disorders. Artemisinin (ART), a sesquiterpenoid with a lactone ring obtained from the herb Artemisia annua, exhibits promising therapeutic properties.