Advances in cellulosic natural fibre-reinforced polymer composites: Properties, additive manufacturing and hybridisation - A review.

This review critically examines the rapidly advancing field of cellulosic natural fibre-reinforced polymer (NFRP) composites, with a particular emphasis on material innovation aligned with sustainability and environmental responsibility. The review presents a systematic analysis of recent literature evaluating the mechanical, thermal, water absorption, wear, and machining characteristics of NFRP composites, as well as the influence of advanced processing approaches such as additive manufacturing. Special attention is given to the structure-property relationships and hybridisation strategies employed to address limitations such as relatively lower mechanical performance and durability compared to synthetic fibre composites.

Green Chemistry-Assisted Synthesis of Metal Nanoparticles and Fabrication of Microstructurally Engineered Conductive and Endurable M@PEO Functional Films.

The present research reports the synthesis of poly-[ethylene oxide]-based composite films (500 μm) containing metal nanoparticles (NPs) [Ag ( ∼ 6 nm), Cu ( ∼ 25 nm), and Fe ( ∼ 35 nm)] as the mobile phase. The novelty of the study is in the corroboration of a plausible mechanism for the generation of metal NPs through green synthesis using herbal extracts of (Tea) and (Neem). Density functional theory (DFT) is used to optimize the phytoreductants present in both biosources, wherein the reducing and/or stabilizing functional entities are primarily hydroxyl groups (-OH).

Design of optimal LMI-LQR based PI controller for modular three-phase quadratic boost converters in wind energy conversion systems to mitigate current harmonics.

The article proposes an innovative single-stage modular three-phase Power Factor Correction (PFC) converter featuring fewer current controllers. This technique leverages a practical DC-DC Quadratic Boost Converter (QBC) for PFC in Wind Energy Conversion Systems (WECS). An equivalent circuit with the non-ideal QBC is established and its small-signal model is derived, followed by a cascade control of inner current loops and outer voltage loops.

Convolutional Neural Network Fused With Recurrent Network for ECG-Based Detection of Hypertrophic Cardiomyopathy.

Background: Hypertrophic Cardiomyopathy (HCM) affects the left ventricle of the heart, leading to thickening of the ventricular wall and potentially life-threatening conditions, such as atrial fibrillation, cardiac failure, and sudden death. Early and accurate detection of HCM from Electrocardiogram (ECG) signals is critical for reducing mortality risk. However, most existing methods fail to simultaneously capture spatial and temporal patterns in ECG data, resulting in reduced diagnostic reliability.

A lightweight hybrid DL model for multi-class chest x-ray classification for pulmonary diseases.

Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and efficiency. Timely and precise diagnosis of these diseases proves to be invaluable for effective treatment procedures.

Advances in nanoparticle-mediated cancer therapeutics: Current research and future perspectives.

One in six deaths worldwide is caused by cancer, making it a major global health concern. Despite their effectiveness, traditional treatment approaches such as radiation therapy, chemotherapy, and surgery frequently have negative side effects and high costs. New approaches, such as gene therapy, are promising but are hampered by high costs and accessibility problems.

Lumbar Spinal Stenosis Detection from Sagittal and Axial MR Images using Hybrid of Deep Kronecker Network and SpinalNet.

Purpose: The main purpose of this research is to develop an automated and accurate method for detecting Lumbar Spinal Stenosis (LSS) using both sagittal and axial MRI images. The study addresses the challenge of differentiating LSS from similar conditions such as herniated disks, aiming to facilitate quick diagnosis with less dependence on expert interpretation.

Method: This research proposes a Hybrid Deep Kronecker Spinal Network (DKN_Spinal) for LSS detection using sagittal and axial Magnetic Resonance Imaging (MRI) images.

Biofilms and oral health: nanotechnology for biofilm control.

Dental biofilms are complex microbial communities enclosed by a self-produced extracellular matrix, leading to dental caries, periodontitis, and other oral diseases. These biofilms are often resistant to conventional antibiotics and result in persistent infections that negatively impact oral health. Recent advances in nanotechnology have demonstrated nanoparticles as a promising therapeutic alternative for controlling dental biofilms.

A Joint Multimodal User Authentication-based Privacy Preservation with Disease Prediction Framework in Modern Healthcare System Using Multi-Scale Cross Attention-based ResNet.

Background/introduction: The disease prediction process plays a crucial part in a person's life "to lead a healthy life." The sudden spread of the data mining approach has generated the disease forecasting system. Secure transfer of medical data and effective storage is the major difficulty faced by recent healthcare management.

Emerging Trends in Chemosensor Technologies for Zn²⁺: Fluorescence and Colorimetric Approaches.

Zinc ions (Zn²⁺) are essential to a wide range of biological, environmental, and industrial functions, creating a strong demand for detection techniques that are both highly sensitive and selective. In recent years, there has been notable progress in the development of fluorescent and colorimetric chemosensors specifically designed for Zn²⁺ detection. These sensor systems offer several key advantages, including fast response times, ease of visual observation, excellent selectivity, and low detection thresholds, making them well-suited for real-time analytical applications.

Recent Progress in Thiazole Based Chemosensors for Toxic Ions: Mechanisms, Strategies, and Future Perspectives.

Thiazole derivatives have emerged as powerful scaffolds in the design of chemosensors due to their excellent photophysical properties, structural versatility, and strong coordination capabilities with various toxic ions. Recent advances have demonstrated significant improvements in the development of selective and sensitive chemosensors targeting environmentally and biologically hazardous ions such as Hg⁺, Pb⁺, Cd⁺, As⁺, CN⁻, and F⁻. This review highlights the latest progress in the design and application of thiazole/benzothiazole-based chemosensors, focusing on their sensing mechanisms-including photoinduced electron transfer (PET), intramolecular charge transfer (ICT), and fluorescence resonance energy transfer (FRET)-as well as strategic modifications to enhance selectivity, sensitivity, and water solubility.

Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling.

Load balancing is one of the significant challenges in cloud environments due to the heterogeneity, dynamic nature of resource states and workloads. The traditional load balancing procedures struggle to adapt the real-time variations which leads to inefficient resource utilization and increased response times. To overcome these issues, a novel approach is presented in this research work utilizing Spiking Neural Networks (SNNs) for adaptive decision-making and Temporal Graph Neural Networks (TGNNs) for dynamic resource state modeling.

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.

Molecular characterization and information entropies of chevron-like graphene nanoribbons with chemical applications.

Carbon-based nanomaterials, such as graphene and graphene nanoribbons (GNRs), have attracted researchers because of their optoelectronic properties. One of the most intriguing properties of GNRs is their tunable bandgap. Unlike inherently metallic graphene sheets, GNRs can exhibit a bandgap, a crucial property for electronic devices.

Harnessing algal biomass for renewable energy and biofuel production: Current strategies and future insights.

The energy crisis and environmental complications worldwide are due to the depletion of fossil fuels. The continuous utilization of fossil fuels leads to air pollution, emission of greenhouse gases and global warming. Biofuels are promising alternatives to fossil fuels and are eco-friendly.

Evaluation of synergistic effect of Zerumbone with Cisplatin on anti-angiogenic potential in zebrafish model.

Angiogenesis plays a critical role in cancer progression, highlighting the need for effective anti-angiogenic therapies. Zerumbone (ZER), a phytochemical compound, is known for its anti-angiogenic and anti-carcinogenic effects in various cancer cell lines. Cisplatin (CIS) is a widely used chemotherapeutic agent that effectively inhibits tumor growth.

Chloroquine as a potential anticancer agent for triple-negative breast cancer: effects on MDA-MB-231 cells.

Breast cancer is a major cause of mortality among women worldwide, with triple-negative breast cancer (TNBC) presenting a particularly serious clinical challenge due to its low survival rates and high likelihood of recurrence. Despite the availability of several treatment options, more effective and targeted therapies are urgently needed. Chloroquine (CQ), a well-known anti-malarial drug, has recently emerged as a potential anti-cancer agent and chemo sensitizer when combined with other treatments.

A secure medical image encryption technique based on DNA cryptography with elliptic curves.

Health services and telemedicine have proven to be an important area for information protection in research, especially with medical services and smart health care applications. In these systems, medical imaging protection are important not only for clinical diagnosis, but also to protect the very sensitive and confidential patient data. With progress in imaging technologies and biomedical processing algorithms, the amount of image data increases rapidly.

Improvement of vibration and acoustic properties of woven jute/polyester composites by surface modification of fibers with various chemicals.

In response to the growing demand for lightweight and sustainable materials, the integration of natural fibers into polymer matrix composites has become very important. To improve the compatibility between hydrophilic natural fibers and matrices, surface modification has proven to be a crucial step. Therefore, this study advanced this technique by modifying the surfaces of woven jute mats with 1% of sodium hydroxide (NaOH), chromium sulphate (CrSO), and sodium bicarbonate (NaHCO).

Optimization of robotic spray painting trajectories using machine learning for improved surface quality.

The production process needs spray painting particularly within automobile manufacturing since product painting accuracy establishes product quality. The combination of hand spray techniques produces intricate designs as well as small quantity needs yet industrial robots excel at painting large industrial product orders. Taguchi Design of Experiments (DoE) is used to investigate the effect of six process variables which included spray distance along with pressure, temperature, humidity level, speed and viscosity rate.

Molecular chemosensors for hazardous anions (AcO, CN and F): progress in fluorescent and colorimetric detection strategies.

This review provides a comprehensive analysis of recent advancements in the development of molecular chemosensors for the detection of hazardous anions such as acetate (AcO), cyanide (CN), and fluoride (F), with a particular focus on fluorescent and colorimetric detection strategies. It examines the fundamental principles of molecular recognition and sensor design, highlighting how specific binding interactions and structural tuning contribute to enhanced selectivity and sensitivity. The review discusses innovative synthetic approaches and the integration of various signaling mechanisms that enable rapid and reliable detection in complex matrices, addressing both the challenges of interference and the need for real-time monitoring in environmental, industrial, and biomedical applications.

Ag-Embedded Hollow Poly(-phenylenediamine)-Reinforced NiOOH-BiOCl Hybrid-Structured Nanomaterials for Highly Sensitive Dual Electrocatalysis.

The rational development of electrodes for electrochemical sensors has wider application prospects in the fields of wearable electronics, environmental monitoring, and medicine. However, it is highly challenging to construct an electrode with high sensitivity and selectivity due to the complex analytical matrices and low concentrations of the targeted compounds. In this study, we rationally designed the Ag-embedded hollow poly(-phenylenediamine)-reinforced NiOOH-BiOCl (h-APNB) electrocatalyst for dual electrocatalysis toward a biosphere pollutant of 4-nitrophenol (4-NP) and a biomolecule of uric acid (UA).

Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis.

The rapid spread of SARS-CoV-2 has highlighted the need for intelligent methodologies in COVID-19 diagnosis. Clinicians face significant challenges due to the virus's fast transmission rate and the lack of reliable diagnostic tools. Although artificial intelligence (AI) has improved image processing, conventional approaches still rely on centralized data storage and training.

Review on Carbon-Based Micro and Nano Electro-Mechanical Systems for Biotechnological Application.

The combination of carbon-based nanoelectromechanical systems (C-NEMS) and carbonbased microelectromechanical systems (C-MEMS) has become a promising new direction in biotechnology with a wide range of applications that could significantly improve medical research and healthcare. These carbon-based materials, which are highly suited for a variety of biotech applications, include graphene and carbon nanotubes (CNTs). They have special qualities including large surface area, superior electrical conductivity, and biocompatibility.

Fluorescent and Colorimetric Methods for Mercury Sensing: Trends and Applications.

Mercury contamination poses a profound threat to both environmental integrity and human well-being due to its extreme toxicity and strong bioaccumulative properties. Consequently, there is an increasing imperative for detection methodologies that exhibit exceptional sensitivity, selectivity, and efficiency in identifying mercury ions (Hg⁺) with rapid response times. Among the diverse array of analytical techniques, fluorescent and colorimetric sensors have emerged as highly promising due to their operational simplicity, remarkable sensitivity, and capability for real-time monitoring.