An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.

Alzheimer's disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data.

SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare.

Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption.

A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).

Management of environmental impacts of fossil fuel use in refugee camps through transition to renewable energy infrastructure: Case studies in Uganda and Bangladesh.

Many refugee camps exist for decades but associated infrastructure needs are only planned for the very short term, including provision of power. This study advocates a shift in approach to sustainable electrification of essential services in refugee camps for lighting, refrigeration, health, water, education, alongside camp operations. Qualitative and quantitative surveys were conducted in refugee camps in Uganda and Bangladesh which assessed the electrical supply needs across such categories.

Selective lead ion sensing via cotton pad-based NaEDTA capped AuNPs: a smartphone-assisted colorimetric method.

Herein, NaEDTA-capped AuNPs specifically designed for selective smartphone-assisted colorimetric detection are synthesized and characterized. NaEDTA-capped AuNPs was synthesized and characterized by UV-Visible spectroscopy, ATR, Raman, XRD, SEM and EDX. The calculated activation energy of the produced nanoparticles was 0.

Layered double hydroxide modified bismuth vanadate as an efficient photoanode for enhancing photoelectrochemical water splitting.

Photoelectrochemical (PEC) water splitting has attracted significant interest as a promising approach for producing clean and sustainable hydrogen fuel. An efficient photoanode is critical for enhancing PEC water splitting. Bismuth vanadate (BiVO) is a widely recognized photoanode for PEC applications due to its visible light absorption, suitable valence band position for water oxidation, and outstanding potential for modifications.

Optimization of cutting temperature and surface roughness in CNC turning of Ti-6Al-4V alloy using response surface methodology.

This study investigates the optimization of cutting conditions for machining titanium alloy (Ti-6Al-4V) using Response Surface Methodology (RSM), with the goal of minimizing tool-chip interface temperature and surface roughness. The research focuses on key cutting parameters to investigate the most effective combinations for enhancing surface finish and reducing thermal impact during machining. The present study deals with the dry turning of Ti-6Al-4V alloy with carbide alloy inserts in a way to utilize the Analysis of Variance (ANOVA) to develop predictive models for minimum surface roughness and optimum temperature.

Influence of irrigation with oxygen plasma treated metal contaminated water on plant growth.

This study aimed to evaluate the effects of plasma treated metal contaminated water, used for irrigation, on plant growth. Zinc (Zn) is a commonly used metal that can enter the environment through industrial processes. It may be released as particles into the atmosphere or discharged as wastewater into waterways or the ground.

Optical soliton solutions of the M-fractional paraxial wave equation.

This research used a modified and extended auxiliary mapping method to examine the optical soliton solutions of the truncated time M-fractional paraxial wave equation. We employed the truncated time M-fractional derivative to eliminate the fractional order in the governing model. The few optical wave examples of the paraxial wave condition can assume an insignificant part in depicting the elements of optical soliton arrangements in optics and photonics for the investigation of different actual cycles, including the engendering of light through optical frameworks like focal points, mirrors, and fiber optics.

Water, sanitation, handwashing, and nutritional interventions can reduce child antibiotic use: evidence from Bangladesh and Kenya.

Antibiotics can trigger antimicrobial resistance and microbiome alterations. Reducing pathogen exposure and undernutrition can reduce infections and antibiotic use. We assess effects of water, sanitation, handwashing (WSH) and nutrition interventions on caregiver-reported antibiotic use in Bangladesh and Kenya, longitudinally measured at three timepoints among birth cohorts (ages 3-28 months) in a cluster-randomized trial.

Extraction and incorporation of cellulose microfibers from textile wastes into MXene-enhanced PVA-borax hydrogel for multifunctional wearable sensors.

Conductive hydrogel has drawn great concern in wearable sensors, human-machine interfaces, artificial intelligence (AI), health monitoring, et al. But it still remains challenge to develop hydrogel through facile and sustainable methods. In this work, a conductive, flexible, bendable, and self-healing hydrogel (PBCM) composed of polyvinyl alcohol (PVA), borax, cellulose microfibers (CMFs), and MXene nanosheet was fabricated by a simple and efficient strategy.

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators.

Unraveling the interplay between cardiovascular diseases and alcohol use disorder: A bioinformatics and network-based exploration of shared molecular pathways and key biomarkers validation via western blot analysis.

Clinical observations indicate a pronounced exacerbation of Cardiovascular Diseases (CVDs) in individuals grappling with Alcohol Use Disorder (AUD), suggesting an intricate interplay between these maladies. Pinpointing shared risk factors for both conditions has proven elusive. To address this, we pioneered a sophisticated bioinformatics framework and network-based strategy to unearth genes exhibiting aberrant expression patterns in both AUD and CVDs.

Antifungal Potentials of Asian Plants: Ethnobotanical Insights and Phytochemical Investigations.

Introduction: Ethnomedicinal plants in Asia offer a promising, low-side-effect alternative to synthetic drugs for treating fungal infections, one of the most widespread communicable diseases caused by pathogenic fungi. Despite being underexplored, the region's rich plant diversity holds the potential for developing effective antifungal drugs. Research is increasingly focused on bioactive compounds from these plants, which show strong antifungal properties and may serve as leads for new drug development.

Nonlinear dynamic wave properties of travelling wave solutions in in (3+1)-dimensional mKdV-ZK model.

The (3+1)-dimensional mKdV-ZK model is an important framework for studying the dynamic behavior of waves in mathematical physics. The goal of this study is to look into more generic travelling wave solutions (TWSs) for the generalized ion-acoustic scenario in three dimensions. These solutions exhibit a combination of rational, trigonometric, hyperbolic, and exponential solutions that are concurrently generated by the new auxiliary equation and the unified techniques.

Key degradation mechanisms of perovskite solar cells and strategies for enhanced stability: issues and prospects.

Perovskite materials have garnered significant attention within a very short period of time by achieving competitive efficiency. In addition, this material demonstrated intriguing optoelectronic properties and versatile applications. Although they have confirmed amazing efficiency in solar cells at the laboratory scale, mass commercial manufacturing of perovskite solar cells (PSCs) is still a problem due to their poor longevity.

Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection.

Objective: Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture.

Methods: We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset.

Therapeutic Efficacy Studies on the Monoterpenoid Hinokitiol in the Treatment of Different Types of Cancer.

Hinokitiol (HK), a monoterpenoid that naturally occurs in plants belonging to the Cupressaceae family, possesses important biological activities, including an anticancer effect. This review summarizes its anticancer potential and draws possible molecular interventions. In addition, it evaluates the biopharmaceutical, toxicological properties, and clinical application of HK to establish its viability for future advancement as a dependable anticancer medication.

Solving the maximum cut problem using Harris Hawk Optimization algorithm.

The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning problem, it is one of the most challenging combinatorial optimization-based problems, and tons of application areas make this problem highly admissible. Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO).

Improved dielectric performance of graphene oxide reinforced plasticized starch.

High dielectric constants with less dielectric loss composites is highly demandable for technological advancements across various fields, including energy storage, sensing, and telecommunications. Their significance lies in their ability to enhance the performance and efficiency of a wide range of devices and systems. In this work, the dielectric performance of graphene oxide (GO) reinforced plasticized starch (PS) nanocomposites (PS/GO) for different concentrations of GO nanofiller was studied.

Nature's defense against emerging neurodegenerative threats: Dynamic simulation, PCA, DCCM identified potential plant-based antiviral lead targeting borna disease virus nucleoprotein.

The rare zoonotic Borna disease virus (BDV) causes fatal neurological disease in various animals, with a high mortality rate exceeding 90% in central Europe. However, unlike most viruses, it establishes persistent infections within the host cell nucleus, hindering treatment. As successful BDV treatments remain elusive, the researchers turned to a computational approach, utilizing molecular docking, ADME/T, post-docking MMGBSA, MD simulation, DCCM, and PCA to identify promising phytochemical drug candidates targeting the BDV Nucleoprotein (PDB ID: 1N93).

SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.

Background And Objective: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).

Methods: Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity.

The Glycopeptide PV-PS A1 Immunogen Elicits Both CD4+ and CD8+ Responses.

Background/objectives: The MHCII-dependent, CD4+ T-cell zwitterionic polysaccharide PS A1 has been investigated as a promising carrier for vaccine development because it can induce an MHCII-dependent CD4+ response towards a variety of tumor-associated carbohydrate antigens (TACAs). However, PS A1 cannot elicit cytotoxic T lymphocytes through MHCI, which may or may not hamper its potential clinical use in cancer, infectious and viral vaccine development. This paper addresses PS A1 MHCI independence through the introduction of an MHCI epitope, the poliovirus (PV) peptide, to establish an MHCI- and MHCII-dependent vaccine.

Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework.

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.