The low-velocity impact behavior of glass-fibre-reinforced polymer/polyurethane sandwich composite panels containing different concentrations of iron-oxide nanoparticles was investigated through experimental and numerical force–displacement analysis. Fe₂O₃ nanoparticles were incorporated into the polyurethane region at concentrations of 0, 1, 3 and 5 wt.% to study their influence on impact force, displacement, energy absorption, equivalent stress and equivalent strain. Experimental force–displacement curves were processed to determine peak impact force, loading energy, rebound energy and absorbed energy. Numerical force-displacement curves were generated from the experimental response to compare the behavior of the four material configurations. The experimental peak forces of the panels containing 0, 1, 3 and 5 wt.% Fe₂O₃ nanoparticles were 43.0, 47.1, 50.0 and 62.7 N, respectively. The panel containing 1 wt.% Fe₂O₃ nanoparticles recorded the highest absolute absorbed energy of 0.2561 J. The 5 wt.% panel recorded the lowest rebound energy of 0.0073 J and the highest absorbed-energy percentage of 97.09%. The numerical force-displacement response showed an increase in peak force and a decrease in displacement at peak force with increasing Fe₂O₃ nanoparticle content. The maximum equivalent stress increased from 38.5 MPa for the unfilled panel to 59.2 MPa for the 5 wt.% panel, while the equivalent strain decreased from 1.95% to 1.41%. The results show that Fe₂O₃ nanoparticle reinforcement increased panel stiffness, improved load transfer and reduced impact deformation. The 1 wt.% composition provided the highest absolute energy absorption, whereas the 5 wt.% composition provided the highest peak-force resistance and absorbed-energy percentage.
In this work, we investigated the optical, structural, and electrical properties of aluminum-doped hydrogenated amorphous silicon carbide thin films (a-SiC:H(Al)). The samples were prepared by DC magnetron sputtering with a silicon carbide (6H-SiC) target, where the plasma was generated from a gas mixture of argon and hydrogen. Doping was carried out in situ by co-sputtering aluminum strands symmetrically placed on the target. By varying the number of strands, different samples were obtained.The samples were characterized by optical transmission, scanning electron microscopy (SEM), infrared spectroscopy (FTIR), and electrical measurements (I–T). The incorporation of aluminum atoms into the a-SiC:H matrix affects the optical properties of the films, leading to a decrease in the optical band gap from 2.3 to 1.7 eV. In addition, good agreement was observed between the optical and structural parameters obtained from the different techniques.Electrical measurements clearly reveal that the doping effect enhances the electrical conductivity of doped samples, increasing from 6×10⁻¹¹ to 10⁻⁷ Ω⁻¹·cm⁻¹ compared to the conductivity of undoped samples.
The Zika virus (ZIKV), a mosquito-borne pathogen, has emerged as a major global health concern due to its link with congenital abnormalities and neurological disorders. This paper introduces a novel graph-theoretic framework for modelling ZIKV transmission through its primary vector, Aedes mosquitoes. In this approach, epidemiological compartments of the transmission cycle are represented as nodes, while transitions between human and mosquito states are modelled as edges. The adjacency matrix derived from this graph enables the computation of eigenvalues, from which the basic reproduction number (R0) is estimated. Unlike traditional compartmental models, the graph-based method provides a structural perspective on the interplay between human and vector dynamics. The model was applied to reported Zika cases in Brazil (2015–2016 outbreak), and further validated using infection fractions and population data from Yap, Moorea, Tahiti, and New Caledonia. The Result demonstrate that R0 exceeds unity when infection levels are sufficiently high, while remaining below threshold under lower prevalence conditions. This confirms that the proposed framework reliably captures epidemic thresholds and offers a flexible tool for analyzing region-specific outbreak scenarios.
Background-In-situ gels have been providing a promising approach due to its sustained and controlled delivery of drug offering an incrementation in patient compliances due to target site specific release and reduction in dose frequency, as transition occurs in solution to gel under exposure of physiological condition depending on type of polymer used like temperature, pH, ionic providing a larger target section of diseases on ophthalmic, nasal, vaginal, oral. Aim- The review below aimed benefits of in-situ gel over conventional formulation with the current marketed formulation present. Method- In-situ gel formulation is dependent on the type of polymer used for specific purpose like thermo-responsive, ion-activated, pH-sensitive and the evaluation parameter depending on the methods and the targeted disease pH, gelation time, capacity, viscosity, in-vitro gelation studies using simulated physiological fluid (STF), rheological studies. Future advancement-.In-situ gel have been prepared by 3D printing a recent advancement in hybrid approach allowing the development of bio-links its advantages and application are clearly visible in field of tissue engineering, patient specific implant, wound healing, controlled drug delivery providing a localized therapeutic delivery ,showing a recent advancement in personalized medicine, and polymer science, causing development in stimuli-responsive gel having enhancement in biocompatibility, mechanical strength and drug loading efficiency hence this review below provides an comprehensive overview of different types of in-situ with formulation and mechanism diseases that can be targeted, evaluation parameters with recent and future advancement in-situ gel in personalized medicines. Conclusion- In-situ gel shows a modern and enhanced approach to drug delivery as compared to traditional/conventional formulation
DC–DC boost converters are widely used in modern power electronic systems to step up low input voltage to a higher regulated output. This paper presents a comparative analysis of Single Input Single Output (SISO) and Multi-Input Single Output (MISO) boost converter topologies. The SISO converter, based on inductor energy storage and controlled switching, is simple, cost-effective, and suitable for low-power applications. this work presents a unified comparative investigation of SISO and MISO boost converters under multiple operating conditions using small-signal state-space modeling and MATLAB/Simulink analysis. The proposed study additionally evaluates the dynamic behavior and stability characteristics of hybrid-source MISO configurations incorporating DC, AC, and fuel-cell inputs, thereby providing practical insight for renewable-energy-based power conversion systems.
The systematic efforts to prevent and mitigate the dangers posed by medical devices are one of the principal functions of materiovigilance. MvPI’s inception under the Pharmacovigilance Programme in India was a significant milestone in enhancing the management of medical devices and regulatory intervention for the devices. The paper explores the roles of some members of the multidisciplinary healthcare team (physicians, nurses, biomedical engineers, pharmacists, & technical staff) and the potential device-related risk. The paper also explores the influence of maternal vigilance on the regulatory cycle (recalls & alerts, redesign & modification, & control) on the Medical Device Rules (MDR 2017). The paper also explores the current PMS in India and the positive indicators such as the rise in reporting centres, the growing number of reports submitted digitally, and the use of real-world data. India has made progress on Reporting Centre closure, Reporting Centre underuse, Reporting Centre untraceability, Reporting Centre manufacturer unaccountability, and Reporting Centre poor hospital culture weak Reporting Centre. India’s progress has been compared to that of other countries in order to highlight India’s progress. This comparison shows that India has made progress in reporting. India has made progress on reporting. This paper is focussed on reporting in order to enhance device safety and surveillance in India.
This paper presents the design and implementation of a cost-effective Fire and Smoke Detection System using Arduino aimed at enhancing safety in residential, commercial, and small-scale industrial environments. Fire hazards pose a significant risk to life and property, necessitating the development of reliable early detection systems. The proposed system employs an MQ-2 smoke sensor and a temperature sensor (LM35/DHT11) to continuously monitor environmental conditions. These sensors detect variations in smoke concentration and temperature, converting them into electrical signals that are processed by an Arduino-based microcontroller. The microcontroller compares real-time sensor data with predefined threshold values and activates alert mechanisms such as a buzzer and LED indicators when abnormal conditions are detected. The system is designed to ensure rapid response, high sensitivity, and operational reliability while maintaining low cost and ease of implementation. Experimental testing, including smoke and heat analysis, demonstrates effective performance in detecting fire-related hazards with minimal delay. Furthermore, the modular architecture of the system allows future enhancements such as IoT integration for remote monitoring and automated emergency response. This work highlights the importance of embedded systems in safety applications and provides a practical solution for early fire detection.
Flooded underpasses lead to massive traffic jam, safety risk and mobility failure in the city during downfalls. The traditional pump houses use manual checkups and simple electric indicators which give very little information on pump operation and water level status. Here, this paper will provide a full Smart Underpass Pump System (SUPS) developed with an STM32 microcontroller, multi-sensor monitoring, SMS alerts on GSM, and a cloud-linked dashboard to provide real-time visualization. Systems incorporated to the system include two water level float sensors, a diesel tank float sensor, a YF-S201 flow sensor, a SW-420 sensor that verifies the pump is in operation through vibration, and a DS18B20 temperature sensor. An algorithm of vibration analysis using Windows can easily identify the ON/ OFF switching of the pump, water-level and flow-rate sensor identify the performance of a system under different load conditions. STM32 produces structured JSON telemetry sent over backend services to a MongoDB database allowing live graphs and logs to the Admin, Manager and Workman dashboards. A SIM800L GSM module is a type of module that will give instant SMS notifications whenever the pump is switched ON or OFF. It is tested in experiments with very reliable pump-state detection, a stable telemetry, a speedy SMS delivery, and the stable real-time dashboard performance. The proposed solution provides an affordable field deployable, practical, underpass management solution to the municipality.
Hemovigilance includes an organized overview of monitoring and managing any negative outcomes of blood transfusion along with safeguarding the donors and recipients of blood transfusions. This study articulates systematically the theory, types and ways of putting the hemovigilance systems into practice in micro and macro health institutions. It discusses the role of structured, streamlined, and controlled transfusion safety reporting along with databases and feedback systems. It encompasses the systems of primary donor and recipient hemovigilance, operational and laboratory monitoring, near and other systems for other countries. It analyses current constraints to reporting, definitional inconsistencies, and digital resource deficits, while proposing system effectiveness improvements. It emphasizes system strengthening improvements to artificial intelligence, automatic electronic reporting, donor-recipient system feedback, and definitional globalization, particularly in resource constrained environments. It emphasizes the necessity of improved structured systems to promote transfusion medicine safety and efficient practice globally.
The structural efficiency of diagrid systems has made them a prominent and widely preferred choice in the design of modern tall buildings, particularly due to their ability to effectively resist lateral forces such as wind and seismic loads. Motivated by this structural configuration, the present study introduces a new graph model called the delimited cross graph. In this model, the nodes of the diagrid framework are represented as vertices, while the connecting structural members are represented as edges. This form of representation provides a mathematical framework for analyzing and interpreting structural patterns in a simplified and organized manner. The fundamental structural properties of the delimited cross graph are investigated. Furthermore, the leverage centrality of the vertices is analyzed to determine their relative structural influence within the network and to emphasize the significance of individual vertices in the overall graph structure. These analyses provide a structured approach for evaluating the roles of vertices within the network.
The development of 5G wireless networks necessitates the adoption of advanced waveform techniques that meet stringent performance requirements in terms of spectral efficiency, robustness, and power optimization. This paper presents a comparative performance evaluation of several prominent waveform candidates for 5G systems, including Orthogonal Frequency Division Multiplexing (OFDM), Filtered-OFDM (F-OFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Weighted Overlap and Add (WOLA). The assessment is based on key performance indicators (KPIs) such as Peak-to-Average Power Ratio (PAPR), Power Spectral Density (PSD), Bit Error Rate (BER), and throughput. Simulation results under various channel conditions demonstrate that FBMC offers superior spectral efficiency and throughput, particularly in uncoded scenarios, while WOLA and F-OFDM exhibit notable improvements in sidelobe suppression and BER under channel coding. The findings highlight the trade-offs between complexity, performance, and implementation feasibility, supporting the selection of optimized waveforms for diverse 5G use cases.
Four-dimensional (4D) printing has emerged as a transformative manufacturing paradigm that integrates time as a fourth dimension into three-dimensionally printed structures, enabling programmable shape transformation, autonomous actuation, and adaptive functionality in response to environmental stimuli. This comprehensive review synthesises advances in 4D printing technologies, smart materials, actuation mechanisms, and their convergence with soft robotics and intelligent automation systems over the period 2015–2025. We critically examine stimulus-responsive materials including shape-memory polymers (SMPs), hydrogels, liquid crystal elastomers (LCEs), magneto-active composites, and multi-material systems, comparing their actuation performance, biocompatibility, and scalability across 37 quantitative metrics derived from over 350 peer-reviewed publications. Key application domains surveyed include minimally invasive surgical devices, adaptive soft grippers, wearable exoskeletons, underwater autonomous vehicles, and micro-scale drug delivery systems. We further assess the integration of machine learning, computational design, and bio-inspired principles with 4D printing to achieve closed-loop intelligent soft robotic systems. Despite remarkable progress, persistent challenges in material fatigue, multi-stimulus control, scalable manufacturing, and regulatory compliance continue to impede clinical and industrial translation. This review concludes with a forward-looking roadmap identifying high-priority research directions and projected technology readiness levels through 2030.
Ability to detect fake news with precision and speed is crucial in this age of rapid information dissemination online. Transformer- or sequence-based models don’t always function with full-text information, so you may not be able to utilize them initially or in real time. To overcome obstacles, study demonstrates how to implement a Contextual-Sequential-Ensemble Hybrid (CSE-Hybrid) approach. It uses XGBoost ensemble learning, an attention-gated fusion mechanism, BiLSTM-driven sequential dependency modeling, and BERT-based contextual encoding. The suggested technique includes a new Hybrid Early Detection Mechanism (HEDM) that employs multi-prefix sampling and confidence-based inference to allow for categorization of text inputs that are streaming or broken up. We employed three well-known datasets in this study: AG News, LIAR, and FakeNewsNet. We used a strict 5-fold nested cross-validation method that included bootstrap confidence intervals and statistical significance analysis. The CSE-Hybrid model had problems with blank text, but it did better than baseline approaches on other measures including F1-score and AUC. It all made sense when I used attention heatmaps, t-SNE visualizations, and error location analysis. The time-to-detection statistic is one way to tell how well the model works. It seems to be almost flawless with just 65–75% of the text. We also spoke about ethical deployment tactics, making fair datasets, and other relevant concerns to keep everyone safe and up to date. The CSE-Hybrid model provides a reliable, user-friendly, and context-aware framework for quickly and accurately finding bogus news. The proposed approach employs attention-gated fusion, early-detection learning, and confidence-aware inference, distinguishing itself from rival frameworks that rely on component selection.
Probably one of the most common edible oils which were used globally is that of the sunflower extract that is made by mixing seeds of a plant species referred to as Helianthus annuus with high nutritional value and no limit on application. It contains mainly unsaturated fatty acids like linoleic acid and oleic acid, bioactive compounds like tocopherols, phytosterols and phenolic compounds. The ingredients facilitate its functions as an antioxidant, cardioprotective, anti-inflammatory and skin beneficial. The plot of the current review is focused on the phytochemical and physicochemical analysis of sunflower oil in the light of the chemical composition, quality parameters, and the functional significance. Phytochemical analysis provides evidence on the presence of important bioactive compounds which enhances its therapeutic action as physicochemical parameters such as acid value, iodine value, peroxide value, saponification value, and oxidative stability determines the quality of the product, its purity and shelf life. Detailed characterization of its components is often done using various tools of analyses, such as GC-MS, HPLC, FTIR, and spectroscopic. Also noted by the review is the importance of sunflower oil in food, pharmaceutical, cosmetic and industrial since it possesses nutritional benefits and functionality properties. However, the existence of issues such as oxidation, adulteration, and no standardization are considered to be important barriers. The paper highlights the necessity of conducting high-quality research, creating a better-quality control, and standardization procedures to advance its safety and effectiveness. All in all, sunflower oil is one of the precious natural resources with a huge potential in terms of promoting health and sustainable industrial usage.
Background: Adolescent substance abuse is an escalating public health crisis in India, with initiation predominantly occurring during the second decade of life. Structured educational interventions represent a cost-effective and evidence-aligned strategy to build protective knowledge among this vulnerable cohort. Objectives: To assess baseline knowledge, administer a Structured Teaching Programme (STP), and evaluate its effectiveness by comparing pre-test and post-test knowledge scores among secondary-school adolescents. Methods: A one-group pre-test–post-test pre-experimental design was adopted (N = 300). A validated, bilingual (English–Hindi) Structured Knowledge Questionnaire (SKQ; 42 knowledge items, 5 domains; KR-20 = 0.82; S-CVI/Ave = 0.93) was employed. Data were analysed using paired t-test, descriptive statistics, and Chi-square tests. Results: Pre-test mean score was 14.77 ± 4.32; post-test mean was 34.87 ± 3.94 (mean gain = 20.10 ± 4.68; paired t = 74.28; df = 299; p < 0.001; Cohen’s d = 4.29). Participants with adequate knowledge increased from 8.0% to 73.3%. Conclusions: The STP demonstrated exceptionally significant effectiveness. School-based nurse-facilitated drug-education programmes should be institutionalised as a core component of national adolescent prevention policy.
Mental health disorders amplified by work-related stress, economic insecurity, and social isolation have become a defining public health concern in rural India, especially within agrarian economies that depend heavily on seasonal employment and weather-driven livelihoods. In this evolving landscape, Green Care – a therapeutic paradigm that integrates structured engagement with agriculture, horticulture, and animal husbandry – offers a compelling dual-pathway strategy for simultaneously advancing mental health outcomes and reinforcing sustainable livelihoods. This study rigorously examines the measurable impact of context-sensitive Green Care interventions implemented across Karnataka and Andhra Pradesh using primary data gathered from 270 rural respondents through a carefully designed quasi-experimental framework. The analytical architecture comprises before–after comparative assessment, descriptive statistics, and multivariate regression techniques, enabling a nuanced evaluation of shifts in both psychological and economic dimensions. The empirical results are remarkable: stress levels declined by 47.8%, anxiety by 49.7%, and depression by 52.0%, while social interaction surged by 108.6% and the happiness index improved by 81.0%. On the livelihood front, total household income more than doubled (+113%) and productive employment days increased by 92%. Econometric findings confirm that Green Care participation, household income, and social engagement are all statistically significant negative predictors of mental distress, whereas state-level differences remain non-significant – a finding that strongly reinforces the cross-regional robustness of the intervention. The Composite Green Care Index (GCI), computed at 0.71, corroborates strong overall programme effectiveness. In conclusion, this study establishes Green Care as a scalable, cost-effective, gender-inclusive, and institutionally adaptable model with transformative potential for simultaneously addressing the twin crises of mental health and livelihood insecurity in rural India.
With the rapid advancement of digital technologies, multimedia content such as images has become increasingly accessible and widely distributed. However, this growth has also raised significant concerns regarding the security and privacy of data during transmission and storage. In today’s multimedia-driven world, images play a pivotal role in sectors like business, marketing, and digital promotions. Consequently, protecting image data from unauthorized access is of paramount importance. Image encryption serves as a crucial tool in the domain of information hiding. This article presents an enhanced chaos-based image encryption method that utilizes a 128-bit symmetric key for robust security. The effectiveness of the proposed encryption approach is evaluated using JDK1.7 and benchmarked against existing techniques. Experimental results demonstrate that the proposed scheme achieves higher entropy in the encrypted images, indicating improved security performance over traditional methods.
The growing need for low-carbon masonry materials has led to studies on using construction waste and associated materials as additional cementitious materials. In this study, Brick Waste Powder (BW) is used as a partial substitute for cement paste (cement + water) in masonry mortar. The goal is to enhance sustainability without reducing structural integrity. The optimisation of the 3 key parameters, namely water-to-cement ratio (W/C) of 0.7, 0.8, and 0.9, BW replacement level (RL) of 10%, 20%, and 30%, and the curing period (CP) of 7, 28, and 90 days, is carried out using the Taguchi Technique of analysis. L9 orthogonal array is employed to optimise these parameters. The workability and compressive strength of all mixtures are evaluated against the control mortar of the MM5 grade standard (≥6 MPa), which is normally preferred for masonry applications. All BW-modified mortars satisfied or surpassed the required strength criteria, indicating that BW is an effective cementitious modifier. To clarify the influence of parameters, analysis of variance (ANOVA) revealed that the CP is the most significant factor, accounting for 67.5% of the variation in strength development, followed sequentially by RL and W/C. Furthermore, the Taguchi method indicated that a 0.7 W/C, a 20% BW RL, and a 90-day CP are optimal. This specific combination of parameters provides the highest compressive strength satisfying the required workability. The SEM (Scanning Electron Microscopy) and EDAX (Energy-Dispersive X-ray Analysis) findings confirmed the results obtained from optimisation through the Taguchi technique, demonstrating enhanced packing density and increased formation of C–S–H gel phases at appropriate RL. The findings show that BW can effectively replace up to 20% of the cement paste in masonry mortar without compromising its strength or performance. The use of the cement paste replacement method is a practical solution for developing cost-effective, circular-economy-oriented, and carbon-efficient binder systems in construction.
3D printing has become a state-of-the-art manufacturing technique that goes beyond its original use in prototyping to create working assemblies. This work centers on the development and production of compact load-bearing structures utilizing FDM technology. We have used a commercially available fused deposition modeling (FDM) printer and Iron-PLA material. Thus, the effect of process parameters on the flexural strength and bending angle of Iron-PLA material are determined on the same. The fabricated samples, which were printed with a 100% infill density were measured and tested in accordance with ASTM D790, therefore the results were compared to the original 3D CAD model. The CT3 Texture Analyzer was utilized to perform the three-point bending test. The device is outfitted with a calibrated load cell that can measure forces up to 500 N with a precision of 0.5%. After which, their mechanical qualities were assessed on the basis of the flexural test performed. The results are examined on both the experimental and theoretical methods.
In transition economies where, formal institutions remain underdeveloped and information asymmetry is pervasive, relational networks substitute for market mechanisms in allocating resources and managing uncertainty. This study investigates the impact of firm-level social capital on corporate risk-taking behavior among 338 non-financial companies listed on the Ho Chi Minh Stock Exchange (HOSE) from 2019 to 2024. Our contribution to the literature is twofold. First, we move beyond traditional binary metrics to build a comprehensive, four-dimensional formative index of corporate social capital. This index integrates an automated CSR disclosure score aligned with GRI standards, a biographical locus measure reflecting the CEO’s social network, political connections, and bank connectivity. Second, we address the challenge of causal identification by deploying a dual strategy. Specifically, we pair Two-way Fixed Effects with the Panel Double Machine Learning method to eliminate nonlinear confounding and ensure the robustness of our causal inferences. The empirical evidence suggests that social capital functions as a strategic risk management toolkit with dual roles. While political and bank ties act as institutional buffers that dampen earnings volatility, they simultaneously act as a catalyst for financial expansion. These ties increase leverage capacity by 14.6 percentage points for every standard deviation increase in connectivity. Furthermore, CEO networks and political ties drive a shift toward precautionary cash holdings. Notably, the Panel Double Machine Learning approach uncovers significant effects overlooked by linear models, confirming the material importance of nonlinearity in relational networks. By dismantling the binary view of social capital as either a risk-booster or a risk-reducer, this study positions it as a sophisticated portfolio that stabilizes performance while broadening financial flexibility.
