Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments.

As cloud computing becomes increasingly prevalent, the detection and prevention of phishing URL attacks are essential, particularly in the Internet of Vehicles (IoV) environment, to maintain service reliability. In such a scenario, an attacker could send misleading phishing links, potentially compromising the system's functionality or, at worst, leading to a complete shutdown. To address these emerging threats, this study introduces a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model for cloud-enabled IoV infrastructure.

High-Performance Oxidation and Nanomolar Detection of Phenylhydrazine Using a 6-Hydroxyflavone-Based Molecular Electrocatalyst Functionalized Multiwalled Carbon Nanotube in Batch Injection Analysis.

The development of simple and rapid methods for preparing redox-active molecular catalyst-functionalized carbon electrodes for electrocatalytic applications is a significant research area. 6-Hydroxyflavone (HFLA), a naturally occurring flavonoid with known anxiolytic properties, also acts as a noncompetitive inhibitor of cytochrome. This study focuses on the in situ functionalization of multiwalled carbon nanotubes (MWCNTs) with redox-active HFLA, resulting in a modified electrode denoted as GCE/MWCNT@HFLA-Redox, where HFLA-Redox represents the redox-active product of HFLA.

Optimizing forensic file classification: enhancing SFCS with β hyperparameter tuning.

In forensic topical modelling, the α parameter controls the distribution of topics in documents. However, low, high, or incorrect values of α lead to topic sparsity, model overfitting, and suboptimal topic distribution. To control the word distribution across topics, the β parameter is introduced.

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly in its early stages. Traditional computational methods, primarily using basic convolutional neural networks (CNNs), often require extensive pre-processing and struggle with generalizability across diverse clinical environments.

Ultrasensitive detection of carcinogenic chromium(VI) species below the WHO limit using a LaCeO/carbon black screen printed electrode in batch injection analysis.

The widespread industrial use of chromium and its subsequent release into the environment as toxic and carcinogenic hexavalent chromium (Cr(VI)) species pose significant risks to human health and the environment. The World Health Organization (WHO) has established a limit of 50 ppb (960 nM) for Cr(VI) in water samples. Developing simple, selective, and separation-free methods for the direct detection of Cr(VI) species in the environment remains a challenging task.

Suppression of necroptosis-driven cell death and inflammation in hypoxic neuroblastoma (SH-SY5Y) cells by necrostatin-1.

Neuroblastoma (NB) is the most typical malignant extracranial solid tumor in the pediatric population. The advent of drug resistance is an essential deterrent in treating high-risk NB patients despite a multi-modality remedy. Inflammation-induced early neuronal degeneration plays a leading part in the pathogenesis of NB via necroptosis; however, the mechanisms remained cryptic.

AI augmented edge and fog computing for Internet of Health Things (IoHT).

Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of these cloud frameworks right now is their limited scalability, which results in their inability to meet needs.

The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review.

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities.

Enhancing surface detection: A comprehensive analysis of various YOLO models.

Material defects can significantly affect strength, durability and overall quality. Complex backgrounds and variations in steel surface images often hinder productivity and quality in industrial environments. Accurate defect detection becomes difficult due to small target size and unclear features.

Exploring Neighborhood Topological Descriptors for Quantitative Structure-property Relationship (QSPR) Analysis and Entropy Measures of Some Anti-cancer Drugs.

Background: This study investigated many cancer medicines using a wide range of degree sum-based topological indices and entropy. These numerical numbers, commonly referred to as topological indices or molecular descriptors, depict a substance's molecular structure. They have been successfully used to properly reflect different physicochemical properties in a number of Quantitative Structure-Property Relationship (QSPR) and Quanti-tative Structure-Activity Relationship (QSAR) research studies.

Facile Antibody Immobilization on a Redox-Active Thionine-Functionalized Carbon Nanofiber Surface for Rapid Electrochemical Immunosensing of a Bioengineered Malaria Protein Biomarker.

Antibodies that target the histidine-rich protein-II biomarker (HRP-II) are being used in rapid diagnostic tests (RDTs) of malaria. HRP-II levels associated with severe malaria are typically greater than 100 ng mL. Unfortunately, genetic variations within the HRP-II gene can reduce the reliability of these RDTs by affecting both sensitivity and specificity.

Expression, characterization and cytotoxicity of recombinant l-asparaginase II from Salmonella paratyphi cloned in Escherichia coli.

L-asparaginase is a remarkable antineoplastic enzyme used in medicine for the treatment of acute lymphoblastic leukemia (ALL) as well as in food industries. In this work, the L-asparaginase-II gene from Salmonella paratyphi was codon-optimized, cloned, and expressed in E. coli as a His-tag fusion protein.

Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness.

Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.

Background: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients.

Methods: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis.

Synthesis, Crystal Structure, Hirshfeld Surface Analysis, and Computational Approach of a New Pyrazolo[3,4-]isoquinoline Derivative as Potent against Leucine-Rich Repeat Kinase 2 (LRRK2).

Ethyl-2-((8-cyano-3,5,9a-trimethyl-1-(4-oxo-4,5-dihydrothiazol-2-yl)-4-phenyl-3a,4,9,9a-tetrahydro-1-pyrazolo[3,4-]isoquinolin-7-yl)thio)acetate () was synthesized, and its structure was characterized by IR, MS, and NMR (H and C) and verified by a single-crystal X-ray structure determination. Compound adopts a "pincer" conformation. In the crystal, the hydrogen bonds of -H···O, C-H···O, and O-H···S form thick layers of molecules that are parallel to (101).

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies.

Neem biomass derived carbon quantum dots synthesized via one step ultrasonification method for ecofriendly methylene blue dye removal.

This article presents a one-step ultrasonication technique for generating biomass carbon dots (BCDs) from neem bark (Azadirachta indica) powder. The BCDs were characterized using modern techniques such as UV-Vis, FTIR, Raman, XRD, HRTEM, FESEM, EDAX, and Zeta potential analyses. Unlike traditional nanocomposite bed systems, this study utilized BCDs as a liquid-phase adsorbent for the regenerative adsorption of the environmentally harmful dye, methylene blue (MB), through an in-situ precipitation reaction.

Fabrication of zinc oxide nanorods for photocatalytic degradation of docosane, a petroleum pollutant, under solar light simulator.

The use of advanced oxidation processes (AOP) in photocatalysis is critical for treating hazardous chemical compounds in oil-produced water (OPW). ZnO NRs are one of the most important modern and safe photocatalysts and have been easily prepared by a microwave-assisted hydrothermal method and grown on glass substrates. Hexagonal-shaped ZnO NRs and a bandgap energy () of up to 3.

Understanding bacterial pathogenicity: a closer look at the journey of harmful microbes.

Bacteria are the most prevalent form of microorganisms and are classified into two categories based on their mode of existence: intracellular and extracellular. While most bacteria are beneficial to human health, others are pathogenic and can cause mild to severe infections. These bacteria use various mechanisms to evade host immunity and cause diseases in humans.

Synthesis, X-ray crystallography, computational investigation on quinoxaline derivatives as potent against adenosine receptor A2AAR.

Quinoxaline represents one of the most important classes of heterocyclic compounds, which have exhibited a wide range of biological activities and industrial importance in many different fields. In this regard, we have synthetized two new quinoxaline derivatives. Their structures were confirmed by single-crystal X-ray analysis.

Insights into the crystal structure investigation and virtual screening approach of quinoxaline derivatives as potent against c-Jun N-terminal kinases 1.

Quinoxaline derivatives are an important class of heterocyclic compounds in which N replaces one or more carbon atoms of the naphthalene ring and exhibit a wide spectrum of biological activities and therapeutic applications. As a result, we were encouraged to explore a new synthetic approach to quinoxaline derivatives. In this work, we synthesized two new derivatives namely, ethyl 4-(2-ethoxy-2-oxoethyl)-3-oxo-3,4-dihydroquinoxaline-2-carboxylate () and 3-oxo-3,4-dihydroquinoxaline-2-carbohydrazide () respectively.

Novel peptide inhibitors targeting CD40 and CD40L interaction: A potential for atherosclerosis therapy.

Atherosclerosis is a chronic inflammatory disease characterized by plaque build-up in the arteries, leading to the obstruction of blood flow. Macrophages are the primary immune cells found in the atherosclerotic lesions and are directly involved in atherosclerosis progression. Macrophages are derived from extravasating blood monocytes.