Fabrication of CuFeO@HNT modified GCE electrode material as an effective electrocatalyst for sensitive and selective detection of TBHQ.

Copper ferrite (CuFeO) is a semiconducting nanocomposite which has spinel structure and Halloysite nanotube (HNT) is an eco-friendly nanotube that has aluminosilicate structure. In this work, we prepared CuFeO@HNT with the help of ultrasonic method. The structural and surface morphology of the composite was confirmed by the XRD, XPS, SEM and EDX analysis.

A Theoretical Analysis of Nonlinear Phenomena in the Reaction-Diffusion Model under Unsteady-State Conditions in the Poly-Methyl Orange (PMO) Layer of a Non-enzymatic Biosensor for the Species Cholesterol Present in Blood.

This work investigates the behavior of the poly methyl orange (PMO) layer in a nonenzymatic biosensor for detecting cholesterol in the blood. The study focuses on the nonlinear phenomena observed in the reaction-diffusion model within the unsteady-state regime. The current study utilizes the approximate analytical Laplace transform technique to solve second-order nonautonomous coupled nonlinear partial differential equations (PDEs) along with its associated initial and boundary conditions.

Machine Learning-Driven Structural Optimization of a Bistable RF MEMS Switch for Enhanced RF Performance.

In the rapidly advancing digital era, the demand for miniaturized and high-performance electronic devices is increasing, particularly in applications such as wireless communication, unmanned aerial vehicles, and healthcare devices. Radio-frequency microelectromechanical systems (RF MEMS), particularly RF MEMS switches, play a crucial role in enhancing RF performance by providing low-loss, high-isolation switching and precise signal path control in reconfigurable RF front-end systems. Among different configurations, electrothermally actuated bistable lateral RF MEMS switches are preferred for their energy efficiency, requiring power only during transitions.

A highly selective indole-based sensor for Zn, Cu, and Al ions with multifunctional applications.

A wide range of chemosensors has been developed for detecting specific metal ions at trace levels, attracting considerable research interest. However, despite the significant role of indole-based molecules in the biological domain, only a few chemosensors incorporating this moiety have been reported. In this work, a novel indole-based receptor [R = ()-3-((((1-indol-4-yl)methyl)imino)methyl)benzene-1,2-diol], was synthesized and characterized using single-crystal X-ray diffraction, NMR, IR, and ESI-MS techniques.

Fluorine-Substituted Rhenium(I) Metallocycle: A New Class of Potent Anticancer Agent.

Fluorine and nonfluorine substituted rhenium(I) metallocycles, fac-[{Re(CO)}(μ-SO)(L1)] (C1) and fac-[{Re(CO)}(μ-SO)(L2)] (C2), respectively, were synthesized using Re(CO), sodium bisulfite, fluorobenzimidazolyl/benzimidazolyl-based ditopic nitrogen-donor ligands (L1/L2), and toluene-acetone via coordination-driven one-pot solvothermal approach. The metallocycles were characterized using elemental analysis, ESI-TOF-MS, ATR-IR, NMR, and PXRD methods. The molecular structures of C1·9(DMSO), C1·(toluene)(acetone), and C2·(toluene) were determined using SCXRD analysis.

Catalyst-free synthesis of 1,2,3-triazole-N-oxide derivatives using -butyl nitrite: a novel strategy and synthetic applications.

A simple, metal-free method has been developed to synthesize novel 1,2,3-triazole-N-oxide derivatives. In this reaction, -BuONO serves as a NO source, with environmentally friendly solvents such as EtOH and HO employed as additives. Control experiments provided valuable insights into the reaction mechanism.

Serendipitous pathway to naphtho[2,1-]furan and its thioether enabled by triflic acid and thiol-mediated reaction.

We have developed a selective method for 2-phenyl naphthofuran and thio-ether derivatives using hydroxy naphthofuranone and thiols in triflic acid. Product selectivity is controlled by altering the reagent equivalents. This broad-scope, acid-mediated strategy enables diverse functionalization, overcoming regioselectivity challenges and providing a valuable tool for medicinal chemistry and organic synthesis.

Diabetic retinopathy detection via deep learning based dual features integrated classification model.

BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images.ObjectiveThe physical diagnostics for this condition was time-consuming and prone to fault.

Cost-efficient design and optimization of robotic assembly lines using a non-dominated sorting genetic algorithm framework.

The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods.

Prediction of clinical risk factors in pregnancy using optimized neural network scheme.

Women should be aware of prenancy related health issues. A user-friendly model is developed in which the patients can use as well as clinicians to determine the risks associated with foetal development inside the womb, birth weight, whose effects are typically linked to the mother through biological relationships. Recent advances in computer vision and artificial intelligence offer new techniques for automated evaluation of medical images across a variety of fields, including ultrasound (US) images.

A simplified approach for efficiency analysis of machine learning algorithms.

The efficiency of machine learning (ML) algorithms plays a critical role in their deployment across various applications, particularly those with resource constraints or real-time requirements. This article presents a comprehensive framework for evaluating ML algorithm efficiency by incorporating metrics, such as training time, prediction time, memory usage, and computational resource utilization. The proposed methodology involves a multistep process: collecting raw metrics, normalizing them, applying the Analytic Hierarchy Process (AHP) to determine weights, and computing a composite efficiency score.

Efficient photocatalytic degradation of organic pollutants using α-SnWO with g-CN nanocomposites for wastewater remediation.

Wastewater management has become necessary in this industrialized era to meet the water needs of the world. Wastewater is one of the major crises in depletion of the environment. Photocatalysis is considered as the best way to remove pollutants.

Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system.

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals.

Trade-off between training and testing ratio in machine learning for medical image processing.

Artificial intelligence (AI) and machine learning (ML) aim to mimic human intelligence and enhance decision making processes across various fields. A key performance determinant in a ML model is the ratio between the training and testing dataset. This research investigates the impact of varying train-test split ratios on machine learning model performance and generalization capabilities using the BraTS 2013 dataset.

Improving Efficiency of Brain Tumor Classification Models Using Pruning Techniques.

Background: This research investigates the impact of pruning on reducing the computational complexity of a five-layered Convolutional Neural Network (CNN) designed for classifying MRI brain tumors. The study focuses on enhancing the efficiency of the model by removing less important weights and neurons through pruning.

Objective: This research aims to analyze the impact of pruning on the computational complexity of a CNN for MRI brain tumor classification, identifying optimal pruning percentages to balance reduced complexity with acceptable classification performance.

Visible-light promoted oxidative annulation of 2-naphthols with phenylglyoxal monohydrates toward hydroxy-naphthofuranone and its derivatives.

A highly efficient and innovative method involving base-mediated oxidative annulation between 2-naphthols and phenylglyoxal monohydrate under visible light irradiation has been successfully developed. This method leads to the formation of oxygen-containing heterocyclic compounds, particularly hydroxy-naphthofuranone derivatives, encompassing a unique quaternary carbon center. An X-ray diffraction study has unambiguously confirmed the structure of one such derivative.

Efficient agricultural pest classification using vision transformer with hybrid pooled multihead attention.

Accurate pest classification plays a pivotal role in modern agriculture for effective pest management, ensuring crop health and productivity. While Convolutional Neural Networks (CNNs) have been widely used for classification, their limited ability to capture both local and global information hinders precise pest identification. In contrast, vision transformers have shown promise in capturing global dependencies and enhancing classification performance.

Extraction and characterization of novel fibers from Tecoma stans Linn bark for use as reinforcement in polymer composites.

Embarking on a pioneering investigation, this study unravels the extraordinary qualities of Tecoma stans Fibers (TSFs), freshly harvested from the rachis, establishing them as prospective reinforcements for biocomposites. Delving into their intricate characteristics, TSFs exhibit a unique fusion of physical resilience, with a density of 1.81 ± 0.

Synthesis and Photophysical Properties of Bipolar Compound: Triphenylamine/Diazafluorene-Carbazole.

Carbazole and triphenylamine, are two well-known hole transporting units that are attached to electron transporting unit 4,5-diazafluorene in a fascinating way to bring out non-planar configuration of a molecule. The synthesized compound exhibits good thermal stability (T > 515 °C) and high glass transition temperature (T, 191 °C). Optical bandgap (E) obtained from solid state absorption spectra was calculated to be 2.

Investigation of g-CN/Ce(WO) Nanocomposites for the Removal of Basic Dyes.

Herein, we have synthesized pristine and g-CN-assisted Ce(WO) via a facile hydrothermal method. The structure was confirmed with the standard JCPDS card. g-CN encapsulated the crystal and reduced the size.

Detection and mitigation of coordinated cyber-physical attack in CPPS.

Cyber-Physical Power System (CPPS) refers to a system in which the elements of the internet and the physical power system communicate and work together. With the use of modern communication and information technology, grid monitoring and control have improved. However, the components of a cyber system are extremely vulnerable to cyberattacks via cyber connections due to inadequate cyber security measures.

Extraction of lignocellulosic fiber and cellulose microfibrils from agro waste-palmyra fruit peduncle: Water retting, chlorine-free chemical treatments, physio-chemical, morphological, and thermal characterization.

In this paper, lignocellulosic fibers and cellulose microfibrils (CMFs) were extracted from palmyra fruit peduncle waste and investigated as naturally derived cellulosic materials for their potential use as reinforcement materials in composite applications. The physicochemical, mechanical, and thermal properties of the extracted fiber were studied. Physical and morphological analysis results revealed an extracted fiber diameter of 82.