Technology-bAsed cardiac rehabilitation therapy (TaCT) for women: Intervention implementability, usability, engagement and acceptability in a middle-income setting.

Background: Despite the benefits of cardiac rehabilitation (CR), women are under-represented, especially in lower-income settings. Technology may be leveraged to tailor CR to better engage women, but this has never been tested in a middle-income country. This study assessed the implementability, usability, engagement, and acceptability of Technology-bAsed Cardiac rehabilitation Therapy (TaCT) in women with cardiovascular disease (CVD) in a middle-income country.

Systematic growth of non-epitaxial ZnO shell over Ln-Li co-doped YO phosphor core.

Here, Ln-Li co-doped YO@ZnO core-shell heterostructures were synthesized by three different techniques - intermediate layer conversion method, a hydrothermal method, and an interlayer mediated hydrothermal method. The synthesis procedure is optimized based on the thickness and compactness of the developed shell. The growth kinetics and synthesis mechanism of each adopted method have been explained in detail using XRD, FESEM, TEM, SAED, and EDX characterization techniques.

Novel dual responsive embelin functionalised ZnO nanomaterials amplify DNA damage and induce apoptosis via pERK1/2/p53 pathway in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor prognosis and chemoresistance. Nano-bioconjugates, due to their enhanced surface-to-volume ratio, offer significant potential in cancer therapy. In this study, we synthesized ZnO nanoparticles (NPs) using solution combustion method and exhibited a particle size range of 20-70 nm as confirmed by TEM analysis.

Identification, purification and functional characterization of a thermostable marine chitinase for potential fungal control via chitin degradation mechanism.

The growing prevalence of treatment-resistant Candida species highlights an urgent need for innovative antifungal therapies. The current range of antifungals, limited to polyenes, azoles, and echinocandins, are becoming insufficient due to the rise of resistance, including cross-resistance among fungal strains. Marine environment is an underexplored reservoir of unique enzymes which can be extremophilic.

Plastic pollution in the Ganga River: Modeling the town-level impact of population and waste management systems using region-specific parameters.

Plastic pollution in riverine environments poses a growing threat to ecosystems and human well-being, with the Ganga River emerging as a critical hotspot. This study introduces a novel town-level modeling framework that integrates population dynamics and heterogeneity in waste generation and management factors including collection, treatment, and recycling efficiencies, in the Ganga River basin to estimate riverine plastic waste (RPW). Model predictions were validated using the field data showing waste generation estimates within ±10 %.

Development of a novel method for recovery of intracellular biopolymer polyhydroxyalkanoate from by regulating a lambda phage lytic system.

Polyhydroxyalkanoates (PHAs), biodegradable biopolyesters produced by bacteria, offer an eco-friendly alternative to synthetic plastics. However, current methods of recovering intracellular PHA require complex cell lysis steps, which contribute to high production costs. In this study, we developed a novel approach for efficient PHA recovery by engineering an autolytic strain of using the lytic gene system from lambda (λ) phage.

AI-Integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based systems.

Solar photovoltaic (PV) systems, especially in dusty and high-temperature regions, suffer performance degradation due to dust accumulation, surface heating, and delayed maintenance. This study proposes an AI-integrated autonomous robotic system combining real-time monitoring, predictive analytics, and intelligent cleaning for enhanced solar panel performance. We developed a hybrid system that integrates CNN-LSTM-based fault detection, Reinforcement Learning (DQN)-driven robotic cleaning, and Edge AI analytics for low-latency decision-making.

Enhanced Physico-Chemical and Electrical Characteristics of Hydrothermally Synthesized NiTiO via Tungsten Doping for Gas Sensing Applications.

Herein, tungsten-doped nickel titanate (WNiTiO3) nanoparticles are synthesized using hydrothermal route and investigated physicochemical and gas sensing properties. The effect of W-dopant concentrations on the physicochemical and electrical characteristics of NiTiO3 is systematically investigated. X-ray diffraction analysis revealed a consistent decrease in crystallite size, accompanied by an increase in dislocation density and micro-strain with higher W doping levels.

Effect of various heat treatment methods and optimization of their parameters on mechanical properties of AISI 4140 steel.

AISI 4140 steel is one of the important category in the steels with wide range of applications including but not limited to automotive, general machinery, oil and gas industry. In the current study, an effort is made to understand the effects of heat treatment parameters, such as heat treatment temperature and holding time, on the mechanical properties of AISI 4140 steel, and to optimize these parameters to obtain the superior combination of mechanical properties. The three important heat treatments which are used in this study are annealing, normalizing and oil quenching.

Explainable machine learning for comprehensive characterization of poly (6-(Ethoxybenzothiazole acrylamide)) resin for removal of Th(IV), As(V), and Hg(II) ions from aqueous solution.

Adsorption is a promising technique with significant potential for water purification. In this context, the present study examines the adsorption efficiency of poly(6-(ethoxybenzothiazole acrylamide) (PEBTA) in removing high-valent metal ions from aqueous environments, such as Th(IV), As(V), and Hg(II). Structural and chemical characterization of PEBTA is carried out by FT-IR, H-NMR, C-NMR, TGA, SEM, and EDAX.

Green Synthesis of Lanthanum Oxide Nanoparticles Using Coleus Barbatus: Annealing Effect on Gas Sensing and Antimicrobial Efficacy Applications.

Lanthanum oxide (LaO) nanoparticles (NPs) were synthesized via a green co-precipitation method using Coleus barbatus leaf extract as a reducing and capping agent, with CTAB as a stabilizer. X-ray diffraction (XRD) confirmed a hexagonal polycrystalline structure with an average crystallite size of 16 nm, further supported by Raman spectroscopy. Calcination temperature played a critical role: at 600 °C, incomplete precursor decomposition was observed, while 800 °C treatments led to oxygen vacancies and peak broadening.

Synthesis and photophysical studies of new fluorescent naphthalene chalcone.

This study reports the base-catalyzed Aldol condensation route for synthesizing a new naphthalene chalcone derivative (C1). The molecular structure of C1 was ascertained by FTIR, NMR (H and C NMR) spectroscopy, and mass spectrometry. Optical properties were explored through UV-Visible absorption and photoluminescence spectroscopy.

Bauhinia monandra derived mesoporous activated carbon for the efficient adsorptive removal of phenol from wastewater.

The quality evaluation of the activated carbon made from agricultural waste is investigated in the adsorption of phenol. Adsorbent was prepared through the activation of orthophosphoric acid, producing a structure that is porous with a surface area of 1090 m/g. Elemental composition analysis confirmed that the carbon composition is 78.

Experimentally validated finite element model for mechanical and fracture characteristics of SiCN thin films under different loads.

In this work, SiCN thin films were deposited on p-Si (100) substrate using a thermal Chemical Vapor Deposition (CVD) process. The mechanical behavior of the thin film was characterized using the nanoindentation technique, where the load was varied from 1 to 4 mN, to understand the influence of load variation on the load-displacement response. Additionally, an experimentally validated FE model, incorporating an elast-plastic material response of the thin film, was developed to understand localized stress distribution and fracture behavior.

Baseline assessment of microplastic contamination in agricultural soils from the coastal stretches of Karnataka and Goa, Southwestern India.

Microplastic (MP) contamination in terrestrial ecosystems-particularly in agricultural soils-remains an underexplored environmental concern in India, an agriculture-driven economy. This study assessed MP contamination in paddy fields across 15 sites in Udupi (Karnataka) and Goa, with soil samples collected from the surface and at three subsurface depths (10, 20, and 30 cm). MPs were extracted using density separation, identified via stereomicroscopy and ATR-FTIR, and further characterized using SEM-EDS to examine surface morphology and elemental composition.

Audio signal analysis using a modified forward-forward algorithm with enhanced segmentation for soil pest detection.

The presence of pests in soil costs the agriculture industry billions of dollars every year since it reduces crop yields and raises preventive costs. The pest detection in soil is vital for maintaining healthy crops, optimizing pest management, and ensuring economic and ecological sustainability. There are several invasive and non-invasive methods available for pest detection, where invasive methods are costly as well as time-consuming compared to the non-invasive methods.

ASD-HybridNet: A hybrid deep learning framework for detection of autism spectrum disorder.

Current diagnostic methods for autism spectrum disorder (ASD) are based on subjective behavioral assessments, which present challenges to an accurate and early diagnosis. This paper proposes a hybrid deep learning framework, ASD-HybridNet, which integrates both region of interest (ROI) time series data and functional connectivity (FC) maps derived from functional magnetic resonance imaging (fMRI) data to improve ASD detection. Experiments on the ABIDE dataset demonstrate the effectiveness of the proposed method compared to existing approaches.

Smartphone Application-Based Rehabilitation Program for Older Adults With Type 2 Diabetes Mellitus: Insights From the Design, Development, and Validation.

Background: Older adults facing chronic health challenges may experience significant advantages from smartphone technologies. This study aimed to design, develop, and validate a smartphone application to deliver rehabilitation programs to older adults with type 2 diabetes mellitus.

Methods: This study consisted of three phases: (1) Assessment of need, (2) Design and development of the smartphone application, and (3) Evaluation of the smartphone application by content validation.

Machine-learning-based artificial intelligence tools for the diagnosis of tropical fevers: a systematic review and meta-analysis protocol of diagnostic test accuracy.

Introduction: Recent advancements in diagnosing tropical fevers increasingly use artificial intelligence (AI). These innovations focus on diagnosing single or multiple diseases, significantly reducing the global burden of tropical fevers. This protocol helps to identify the key factors required for a systematic review of AI-based machine learning (ML) diagnostic test accuracy-based studies to obtain a view on the pooled performance of different types of available tools.

Microwave assisted catalytic co-pyrolysis of banana peels and polypropylene: experimentation and machine learning optimization.

The growing accumulation of agricultural and plastic waste poses serious environmental challenges, necessitating sustainable and efficient valorization strategies. This study investigates the microwave-assisted catalytic co-pyrolysis of banana peels and polypropylene, using graphite as a susceptor and potassium hydroxide as a catalyst. Experiments were conducted by varying biomass and plastic quantities and microwave power levels to study their effects on product yields and thermal performance.

Process intensification of anaerobic digestion for biohydrogen and methane production from crude glycerol and dairy wastewater using cavitation techniques.

This study investigates methane production from the mono-digestion of dairy wastewater (DWW) and hydrogen production from the co-digestion of DWW and crude glycerol (CG), both of which are abundantly available in India. In this study, ultrasonication (US) and hydrodynamic cavitation (HC) were employed as pretreatment methods for DWW prior to mono and co-anaerobic digestion, with the aim of enhancing methane and hydrogen production. The results show that these methods significantly improve methane yield, offering a sustainable solution for efficient bioenergy recovery from organic waste.

A Questionnaire Dataset on Perceived Stress in Indian Higher Education Students during Emergency Remote Learning.

Background: The COVID-19 pandemic led to a sudden shift to Emergency Remote Learning, significantly impacting students' mental health. This study visualizes and analyses various stressors contributing to stress levels among university students during Emergency Remote Learning and explores how different factors from environmental and instructional mediums contribute to their perceived stress.

Method: Data was collected through a cross-sectional survey using the Modified Perceived Stress Scale and an additional set of 20 Likert scale items on Emergency Remote Learning.

Proton-coupled pseudocapacitive behavior of a manganese oxide-decorated nitrogen-rich COP for sustainable high-performance energy storage devices.

A hierarchical hybrid material (MnO@COP) with dual charge storage capabilities was created by synthesizing a triazine-based covalent organic polymer (COP) that is rich in nitrogen functionalities and integrating it with MnO nanoparticles. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), Brunauer-Emmett-Teller (BET) studies, and X-ray photoelectron spectroscopy (XPS) demonstrated a distinct architecture: MnO nanoparticles were uniformly embedded in a stable, porous COP matrix. MnO loading caused a modest decrease in surface area, but the composite still had the mesoporosity needed for quick ion diffusion.

Enhanced complex network influential node detection through the integration of entropy and degree metrics with node distance.

Complex networks play a vital role in various real-world systems, including marketing, information dissemination, transportation, biological systems, and epidemic modeling. Identifying influential nodes within these networks is essential for optimizing spreading processes, controlling rumors, and preventing disease outbreaks. However, existing state-of-the-art methods for identifying influential nodes face notable limitations.