Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments.

The increasing reliance on Human-centric Internet of Things (H-IoT) systems in healthcare and smart environments has raised critical concerns regarding data integrity, real-time anomaly detection, and adaptive access control. Traditional security mechanisms lack dynamic adaptability to streaming multimodal physiological data, making them ineffective in safeguarding H-IoT devices against evolving threats and tampering. This paper proposes a novel trust-aware hybrid framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Variational Autoencoders (VAE) to analyze spatial, temporal, and latent characteristics of physiological signals.

RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions.

Non-Orthogonal Multiple Access (NOMA) has emerged as a prominent technique for enhancing spectral efficiency in beyond-fifth-generation (5G) and sixth-generation (6G) wireless systems. However, its performance is highly dependent on channel conditions, necessitating robust spectrum-sensing methods. This study proposes a novel Recurrent Neural Network (RNN)-based Bidirectional Long Short-Term Memory (RNN-Bi-LSTM) model to enhance the spectral performance of NOMA under various channel conditions, particularly the Rician and Rayleigh fading channels.

Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.

The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years.

A novel approximation of underwater robotic vehicle controller exploiting multi-point matching.

This proposed work is presenting the approximation of higher-order (HO) underwater robotic vehicle (URV) controller with the help of multi-point matching technique by incorporating greywolf optimization algorithm (GWOA). The performance of URV system is affected by external and internal dynamics. The proper momentum of URV system is achieved by designing a controller.

Physically secure and fog-enabled lightweight authentication scheme for WBAN.

Wireless Body Area Networks (WBANs) are vital for healthcare, fitness monitoring, and remote patient care by means of combining sensors and wearable technologies for data collection and transmission. However, ensuring secure communication in WBANs remains a critical challenge and is generally insecure against the manipulation of data, breaches of privacy, and unauthorized access. Current authentication methods are vulnerable to security risks and have a significant computational burden.

Spatial epidemiology of dengue and chikungunya in Karnataka using GIS-based analysis.

Background: Dengue and chikungunya are major vector-borne diseases with significant public health concerns. Understanding their spatial distribution is crucial for effective disease control and prevention strategies.

Objective: To analyse the spatial patterns and trends of dengue and chikungunya in Karnataka from 2021 to 2024 using GIS-based methods to identify high-risk districts.

Advances in the Synthetic Approaches to β‑Secretase (BACE-1) Inhibitors in Countering Alzheimer's: A Comprehensive Review.

Alzheimer's disease is a progressive, irreversible, neurodegenerative disease, i.e., characterized by the presence of amyloid plaques, hyperphosphorylated tau protein (hyper p-tau), neural damage, etc.

Improved photoinduced charge carrier separation through the built-in electric field in a rationally designed Ag-ZnMnO/exfoliated g-CN, a plasmonic p-n junction for solar-driven reduction of dissolved Cr(VI).

Sensitization of wide bandgap semiconductors by coupling with a low-band-gap semiconductor to improve photoinduced charge carrier separation by the built-in electric field is one of the attractive approaches to develop an efficient photocatalyst. Here we present the development of Ag-ZnMnO/exfoliated g-CN (Ag-ZMO/ECN), a novel photocatalyst designed to remove an inorganic pollutant Cr(VI) under direct solar light irradiation. The enhanced performance of Ag-ZMO/ECN is attributed to efficient charge separation, facilitated by the formation of a p-n junction at the interface of narrow-bandgap p-type ZMO and n-type ECN, and the localized surface plasmon resonance (LSPR) effect of the deposited Ag nanoparticles.

Advancing food preservation with quercetin-based Nanocomposites: Antimicrobial, antioxidant, and controlled-release strategies - A review.

The global food waste crisis, driven by rapid spoilage and oxidative degradation, substantiates the urgent need for sustainable packaging innovations. Though mechanically robust, conventional petroleum-based polymers contribute significantly to environmental pollution due to their non-biodegradability. Quercetin (Qr), a bioactive flavonoid with potent antimicrobial and antioxidant properties, has emerged as a promising component in next-generation active packaging.

Computational optimization of 3D printed bone scaffolds using orthogonal array-driven FEA and neural network modeling.

Today, orthopedic surgeons have been continuously focusing on bone tissue engineering for regenerating damaged bone through the use of biomimetic scaffolds and innovative materials. Hence, this study presents a comprehensive investigation into the optimization of PLA + 3D printed lattice scaffolds for bone tissue engineering applications, emphasizing the role of geometric configuration and processing parameters on mechanical performance. Three distinct lattice geometries such as Lidinoid, Diamond, and Gyroid were developed with varying wall thicknesses (1.

Multi-band high-frequency antenna for satellite, automotive radar, and 6G communication.

This paper presents the design and development of a compact multi-band high-frequency antenna tailored for millimeter-wave applications, particularly within the 6G frequency range. The antenna features a small footprint of 10 × 12 × 1.5 mm and is designed using advanced electromagnetic simulations in CST Microwave Studio.

Developing rejuvenation strategies and artificial flood mitigation plans for Indrani River: a case study.

Artificial flooding of rainwater is most common in urban areas due to various reasons, such as improper drainage systems, obstruction of natural drainage by building constructions, and encroachment of stormwater nallahs. Flash floods lead to significant losses, disrupt transportation, and cause inconvenience to the public. Udupi, characterized by its porous lateritic strata, undulating topography, and proximity to the sea, experiences artificial flooding during the peak monsoon season in its low-lying areas, primarily due to the overflow of the Indrani River, which is also a potential water resource for Udupi, Karnataka.

Tracking soil health and potentially toxic elements (PTEs) across land-use types using physico-chemical, magnetic, and geochemical proxies: a case study from Manipal, Southwestern India.

This study investigates spatial variations in soil quality across five distinct land-use types-forest, roadside, agricultural, industrial, and residential-in the Manipal region of southern India. Ten surface soil samples from each land-use category were analysed for physico-chemical properties, geochemical composition, and environmental magnetic parameters to evaluate soil degradation and identify potential pollution sources. Marked variations were observed in electrical conductivity, pH, and salinity, with industrial and roadside soils exhibiting elevated values indicative of anthropogenic impact.

Adaptive deep SVM for detecting early heart disease among cardiac patients.

Heart attack is one of the most common heart diseases, which causes more deaths worldwide. Early detection and continuous monitoring are essential in reducing the death rate caused by heart diseases. Machine learning gives a promising solution for early and accurate heart disease detection by analyzing the data from healthcare devices.

Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches.

This paper proposes a scalable and autonomous malware detection and defence architecture in software-defined networks (SDNs) that employs federated learning (FL). This architecture combines SDN's centralized management of potentially significant data streams with FL's decentralized, privacy-preserving learning capabilities in a distributed manner adaptable to varying time and space constraints. This enables a flexible, adaptive design and prevention approach in large-scale, heterogeneous networks.

Reinforcement learning-based topology optimization for generative designed lightweight structures.

This study presents an AI-driven generative design framework for creating lightweight, manufacturable mechanical structures. It integrates topology optimization with deep reinforcement learning, specifically the Proximal Policy Optimization (PPO) algorithm, to learn optimal material layouts within a defined design space. The model adheres to strict engineering constraints, including Von Mises stress (≤ 300 MPa) and displacement (≤ 0.

Sustainability communication on social media in the last decade: A review of research perspectives from Asia.

The past decade has been marked by increased discussions on sustainability; some of the prominent accelerators being, mounting natural disasters, vocal climate activism, and the UN Agenda 2030 for Sustainable Development. The interest has crossed over from deliberations in global summits to grassroots-level communication. Additionally, the literature points to the increasing usage of social media in communicating sustainability with an increase of interest in this area among Asian researchers.

Challenges and needs of nurse researchers on research publication in health universities of Karnataka, India: a cross-sectional survey.

Background: Effective dissemination of scientific knowledge through publication is vital for advancing nursing and healthcare. However, nurse researchers often encounter challenges that impede their ability to publish in reputable journals.

Aim: To identify the perceived challenges and support needs related to research publication among nurse researchers at health universities across Karnataka, India.

Enhanced mechanical, thermal, and wear performance of halloysite nanotube infused carbon fiber epoxy composites.

This work explores the mechanical, thermal, and tribological characteristics of carbon fabric reinforced epoxy (CF-Ep) composites filled with halloysite nanotubes (HNT). The mechanical properties were evaluated, including hardness, interlaminar shear strength (ILSS), tensile strength, and flexural strength. The enhanced curing and even dispersion of HNTs in the epoxy matrix were validated by DSC, FTIR, and SEM measurements.

Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.

The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants.

Advances in the management of ocular anterior segment diseases using biomaterials-based drug delivery systems.

The eye is an essential sense organ and drug delivery to the eye is a challenging task due to protective barriers that hinder drug penetration. Over 90 percent of treatments for eye diseases are topical, but frequent administration over extended periods can lead to toxicity and compliance issues. Over the years, extensive research has been aimed at developing drug delivery systems that enhance drug bioavailability at the target site while minimizing side effects.

Review on Aging Behavior and Durability Enhancement of Bamboo Fiber-Reinforced Polymer Composites.

This review article focuses on the long-term durability challenges associated with bamboo fiber-reinforced polymer composites when subjected to various environmental aging conditions such as water immersion, hygrothermal fluctuations, ultraviolet (UV) radiation, soil burial, and refrigerated storage. The primary issue addressed is the degradation of mechanical and structural performance of bamboo fiber-reinforced polymer composites due to moisture absorption, fiber swelling, and fiber-matrix interface deterioration. To mitigate these aging effects, the study evaluates and compares multiple strategies, including chemical and physical fiber surface treatments, filler additions, and fiber hybridization, which aim to enhance moisture resistance and mechanical stability.

Design and analysis of capacitive pressure sensor with L shaped electrode for enhanced sensitivity.

Accurate pressure measurement is essential across a wide range of fields, including engineering, healthcare, and transportation. This paper presents the design and development of a novel capacitive pressure sensor featuring an extended measurement range and a large pressure application area compared to the parallel plate capacitor configuration. The main innovation lies in the shape and structure of the L shaped electrode which enhances the operational range in terms of the displacement, increased surface area.

Gut microbiota profile in newly diagnosed pulmonary tuberculosis patients: an exploratory pilot study in southern India.

Emerging evidence suggests the link between pulmonary tuberculosis (PTB) and gut microbiota dysbiosis. This is the first study from the southern Indian population that characterized the gut microbiota of PTB patients using 16 S amplicon sequencing. The analysis revealed a significant reduction in gut microbial diversity among PTB patients, with particularly lower alpha diversity (Chao1 index, p ≤ 0.

Region-wise landmarks-based feature extraction employing SIFT, SURF, and ORB feature descriptors to recognize Monozygotic twins from 2D/3D Facial Images.

Background: In computer vision and image processing, face recognition is increasingly popular field of research that identifies similar faces in a picture and assigns a suitable label. It is one of the desired detection techniques employed in forensics for criminal identification.

Methods: This study explores face recognition system for monozygotic twins utilizing three widely recognized feature descriptor algorithms: Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented Fast and Rotated BRIEF (ORB)-with region-specific facial landmarks.