Green synthesis of strontium stannate nanorods using extract of Juniperus communis L.: Structural characterization and evaluation of antibacterial, antifungal, and antioxidant activity.

Strontium stannate nanorods (SrSnO NRs) were synthesized in the present study via a green, sustainable, and cheap method with leaf extract from Juniperus communis L. UV-visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray analysis (EDAX) were performed to investigate the SrSnO NRs. The particle size distribution (PSD) of SrSnO NRs characterized by using dynamic light scattering (DLS) analysis.

Integrating AI with Advanced Hyperspectral Imaging for Enhanced Classification of Selected Gastrointestinal Diseases.

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient contrast often limits its effectiveness, making it challenging to distinguish between healthy and unhealthy tissues, particularly when identifying subtle mucosal and vascular abnormalities.

Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning.

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.

Unlocking the potential of photocatalysts: Recent advances in layered double hydroxide and future outlook.

Layered double hydroxides (LDHs) have emerged as a promising class of photocatalysts with remarkable properties for diverse energy and environmental-related applications. This review offers insights into recent advances in LDH-based photocatalysts, focusing on their synthesis methods, structural properties, and photocatalytic performance. The unique structure of LDHs, characterized by positively charged metal hydroxide layers and intercalated anions, presents opportunities for tailoring their properties to enhance photocatalytic performance.

Fabrication and comprehensive experimental evaluation of surfactant-activated PEDOT:PSS/SnO thin films deposited via spin coating for advanced sensing applications.

This research investigates the fabrication of surfactant-mixed tin oxide (SnO) nanostructured thin films on a fluorine-doped tin oxide (FTO) substrate via hydrothermal synthesis, focusing on their structural, morphological, optical, and electrical properties for sensor applications. To examine the effect of surfactant concentration, cetyltrimethylammonium bromide (CTAB) was incorporated at varying weight percentages (0%, 6%, 11%, 16%, and 20%), resulting in five distinct sensor samples, labelled SnO-1, SnO-2, SnO-3, SnO-4, and SnO-5, respectively. X-Ray Diffraction (XRD) analysis confirms a tunable crystallite size from 12.

Self-feedback delay induces extreme events in the theoretical Brusselator system.

We present a study of the theoretical Brusselator model with time-delayed self-feedback, demonstrating its ability to induce extreme events when delays in reaction processes significantly influence subsequent dynamics, with and without diffusion. Stability analyses reveal the mechanisms driving this behavior with respect to the delay time. The occurrence of extreme events is validated using various numerical and statistical tools, including phase portraits, time series, probability distribution functions, return periods, and spatiotemporal evolution.

CTAB modified SnO PEDOT PSS heterojunction humidity sensor with enhanced sensitivity stability and machine learning evaluation.

This study presents the development of a high-performance resistive humidity sensor based on a cetyltrimethylammonium bromide (CTAB)-assisted tin oxide (SnO₂) nanostructured thin film integrated with a Poly(3,4-ethylenedioxythiophene): Poly(styrenesulfonate) (PEDOT: PSS)/SnO₂ heterojunction. The sensor design incorporates CTAB at varying weight percentages (0%, 6%, 11%, 16%, and 20%) during the hydrothermal synthesis of SnO₂ to regulate crystal growth, morphology, and surface area. The sample with 20 wt% CTAB (SnO-5) exhibited a flower-like stacked nanostructure, confirmed via field emission scanning electron microscopy (FESEM), which significantly enhanced water molecule adsorption and charge transport pathways.

Direct estimation of amylose and amylopectin in single starch granules by machine learning assisted Raman spectroscopy.

Starch is a fundamental carbohydrate with nutritional and physicochemical properties governed by relative proportions of amylose and amylopectin. Variations in amylose-to-amylopectin ratio significantly influence starch digestibility, texture, glycemic response and dietary fiber functionality. However, conventional techniques such as iodine binding, enzymatic assays and chromatographic separation are often destructive, time-consuming and unable to provide spatially resolved molecular information.

Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning.

The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification.

Novel Snapshot-Based Hyperspectral Conversion for Dermatological Lesion Detection via YOLO Object Detection Models.

: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characteristics, potentially delaying diagnosis. A novel spectrum-aided vision enhancer (SAVE) that transforms standard RGB images into simulated narrowband imaging representations in a single step was proposed.

Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks.

Wireless Sensor Networks (WSNs) used in modern applications like environmental monitoring, smart cities, and healthcare systems depend on accurate sensor node localization. However, attaining accurate localization is challenging due to dynamic environmental conditions. Varying network densities and the interdependence of parameters such as anchor ratio, transmission range, and node density increase the Average Localization Error (ALE) in WSNs.

Machine learning-based model to predict von Mises stress and chip reduction coefficient developed during dry turning of EN36C steel.

This study comprehensively investigates the determination of chip reduction coefficient (CRC) and von Mises stress (VMS) during dry turning of Nickel-Chromium case-hardened steel (EN36C), renowned for its high surface hardness and core toughness. Machining parameters, including cutting speed (36-100 m/min), feed rate (0.49-0.

Robust Brain Tumor Detection and Classification From Multichannel MRI Using Deep Learning.

Brain tumor detection and classification from multichannel magnetic resonance imaging (MRI) using deep learning techniques for an accurate detection and classification of brain tumors from multichannel MRI are essential for guiding effective treatment strategies and improving patient outcomes. Traditional methods often struggle with handling large volumes of MRI data, leading to limitations in both efficiency and reliability. This study aims to develop a robust approach for brain tumor detection and classification by leveraging computer vision and deep learning techniques, addressing the limitations of conventional methods.

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.

A Multi-Objective optimization framework for the sustainable machining of Monel 400.

This study explores the synergistic effects of lubrication as well as cooling on the machinability behaviour of the superalloy Monel 400. A comparative assessment was conducted across four machining environments-dry cutting, Minimum Quantity Lubrication (MQL), Cryogenic CO₂, and a hybrid MQL + CO₂ approach. Among these, MQL + CO₂ exhibited superior performance, yielding reductions of 19.

Genetically engineered 3D printed functionally graded-lignin, starch, and cellulose-derived sustainable biopolymers and composites: A critical review.

The integration of plant biotechnology with Fused Deposition Modeling (FDM) is emerging as a transformative approach for sustainable manufacturing. This review explores the application of genetically engineered biopolymers including starch-based, cellulose-based, and lignin-derived materials in 3D printing. Recombinant DNA (rDNA) technology has significantly enhanced the properties of these biopolymers by improving mechanical strength, thermal stability, and printability, making them competitive alternatives to petroleum-based plastics.

A lightweight and secure authentication and privacy protection scheme for internet of medical things.

The advent of smart healthcare technology has provided various benefits, including remote patient monitoring, personalized treatments, and early disease detection. However, transmitting sensitive patient data through IoMT devices raises significant security and privacy concerns. To protect patients' data and smart medical devices, authentication is required.

Technoeconomic and environmental analysis of cryogenic and MQL-assisted machining of Hastelloy X.

The growing significance of superalloys like Hastelloy X, particularly in critical engineering sectors such as aerospace, chemical processing, and selective biomedical equipment (e.g., surgical instruments and medical tooling), underscores the need for advancements in their manufacturing processes.

Smartphone-Aided Colorimetric Assay: Enhanced Peroxidase Mimicking Activity of Quasi Cube-Like 2D-Co-MOF/CN Label-Free Nanozyme for Selective/Visual Recognition of 4,4'-(propane-2,2-diyl)diphenol.

To ensure environmental safety, it is essential to monitor bisphenol A (BPA), as it poses health hazards. In this study, we report a novel, sustainable binary 2D Co-MOF/CN nanozyme that functions as a bipurpose material with both peroxidase-mimic activity and BPA-specific detection capability. The Co sites and sp hybridized carbon demonstrate remarkable Fenton-like reactions, effectively promoting the transformation of 4,4'-diamino-3,3',5,5'-tetramethylbiphenyl (TMB) into oxidized TMB.

Capsule Endoscopy: Current Trends, Technological Advancements, and Future Perspectives in Gastrointestinal Diagnostics.

Capsule endoscopy (CE) has revolutionized gastrointestinal (GI) diagnostics by providing a non-invasive, patient-centered approach to observing the digestive tract. Conceived in 2000 by Gavriel Iddan, CE employs a diminutive, ingestible capsule containing a high-resolution camera, LED lighting, and a power supply. It specializes in visualizing the small intestine, a region frequently unreachable by conventional endoscopy.

Synthesis of 2D Tungsten disulphide WS for biosensing: a unique perspective on emerging applications.

Two-dimensional (2D) materials have garnered momentous consideration owing to their inimitable structural and physiochemical properties, enabling diverse technological applications. Tungsten disulfide (WS), a prominent transition metal dichalcogenide, exhibits exceptional characteristics such as a tunable bandgap, large surface area, and strong biocompatibility, making it highly suitable for biosensing applications. This review explores various WS synthesis techniques, including mechanical exfoliation, sonication, and chemical exfoliation, highlighting their impact on nanosheet quality and scalability.

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.

Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation.

Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion management enables drivers to bypass heavily trafficked areas and reducing pollutant emissions.

Personalized learning in hybrid education.

The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students.

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Background: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue to determine the severity of the disease and guide treatment decisions.