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Bacterial biofilm inhibition activity of ethanolic extract of hemidesmus indicus
Multi-drug resistance is one of the biggest nightmares in the field of healthcare today. Adding on to this, some bacteria like Staphylococcus aureus and Pseudomonas aeruginosa have the ability to form biofilms. These essentially are large colonies of bacteria that are held together by polysaccharides and other biomolecules which in turn facilitate in their adherence to solid substrate both natural and synthetic. This further creates a life-threatening implication leading to nosocomial infections like pneumonia, Urinary tract infections (UTI), etc. increasing the co-morbidities and mortality of critically-ill patients. The combination of antimicrobial resistance, ability to form biofilms and threat of nosocomial infections calls for a need to investigate newer, safer alternatives. Plant based medicaments have been used for centuries and they are a great alternative to synthetic drugs. In the present study, ethanolic extracts of Hemidesmus indicus was evaluated against clinically-important multi-drug resistant organisms. Percentage biofilm inhibition of plant extracts of Hemidesmus indicus by crystal violet assay method. Triplicate analysis was done and data obtained was statistically interpreted using Microsoft Excel. Alcoholic extracts of Hemidesmus indicus exhibited significant biofilm inhibitory activity against the common bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus subtilis. Further, isolation of the chief active constituent responsible for Anti-biofilm activity is in process. 2020, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved. -
Influence of employees' perception on the use of flexible work arrangements
The study aims to explore the factors that influence the perception of employees on the usability of flexible work arrangements and to predict whether those factors induce them to opt for such flexible practices. The data was collected from 239 Indian employees working across different sectors of the country. The study employed a quantitative approach for data collection by using a structured questionnaire consisting of close-ended questions. The data was analyzed using factor analysis, binomial logistic regression and Analysis of Variance on SPSS Statistics 25. The study identified five major factors that influenced the employees perception about using flexible work options. Among them two factors namely, FWA perquisites and FWA anxiety were found significant in predicting the employees use of flexible work options. Further, it was found that married employees recognized strong benefits from using flexible options. This study contributes to the existing literature by unveiling the mindset of Indian employees towards flexible work arrangement and suggests that the employers, society and the government should create favorable environment for deploying flexible work practices. 2020 IJSTR. -
Fire Safety Challenges in Electrical Shafts: A Case of Fire Accident at High Rise Residential Building in Bengaluru, India
Fire safety in high-rise residential buildings is a complex issue, especially when it comes to electrical shaft fires. These enclosed vertical shafts help the rapid spread of smoke, toxic gases and heat across different floors and pose a high risk to the occupants and affect the evacuation process. However, the actual life fire outbreaks show that the existing measures are still wanting as far as evacuation safety is concerned. These issues are addressed in this research through the use of performance-based fire design (PBFD) to assess fire behaviour and evacuation patterns in high-rise buildings. The study then simulates using PyroSim and Pathfinder simulation software, the visibility of smoke, reduction of visibility, CO and CO2 levels, temperature changes, and congestion of the evacuation corridors. Unlike other studies, this paper combines both fire development and occupants response to give a holistic approach to the issue of evacuation challenges. One of the findings of this research is the analysis of the ASET and RSET whereby it was found that the current provisions in fire safety do not ensure a safe exit. It was established that RSET is much higher than ASET, which points to a severe lack in current fire safety solutions. Simulations incorporating NBC-2016 (National Building Code-2016) provisionssuch as fire-rated doors, sprinklers, and mechanical ventilationdemonstrate improved outcomes, reducing RSET from 1,272 seconds to 746 seconds. However, persistent challenges such as congestion, bottlenecks, and hazardous gas levels highlight the need for enhanced fire safety strategies. This research offers practical recommendations to improve evacuation effectiveness through better ventilation, compartmentalization, and advanced suppression systems. The findings contribute to risk mitigation strategies, expand the knowledge base for fire safety, and provide a foundation for improving fire safety regulations in high-rise buildings. 2025 by authors, all rights reserved. -
Demographic Determinants of Fire-Safety Behavior in High-Rise Residential Buildings: A Survey-Based Behavioral Analysis from Bengaluru, India
This study explores the role of demographic and experience-based parameters for fire safety behavior among residents of high-rise residential apartment buildings in the city of Bengaluru, a metropolitan capital in India. Data were gathered through a questionnaire-based survey among 262 residents. Multiple regression analysis was used to assess the correlation among demographic parameters and behavioral responses during evacuation. The results show that age (R2 = 0.154, p = 0.004), presence of vulnerable household members (R2 = 0.137, p = 0.022), and prior fire experience (R2 = 0.157, p = 0.004) are statistically significant predictors of fire-safety behavior. In contrast, gender (R2 = 0.117, p = 0.073), educational qualifications (R2 = 0.109, p = 0.136), and chronic health conditions (R2 = 0.121, p = 0.500) do not exhibit significant associations. Cross-tabulation analysis further indicates that residents who have received fire-safety training prioritize immediate evacuation, whereas untrained residents display delay behaviors. By providing empirical behavioral evidence from an Indian metropolitan context, this study highlights the demographic heterogeneity in evacuation behavior and supports the integration of behavioral realism into performance-based fire safety design for high-rise residential buildings. (2026), (Dr D. Pylarinos). All rights reserved. -
Personal fableness and perception of risk behaviors among adolescents
Adolescence is a crucial period where one tends to identify who they are as an individual. However, as a teenager is struggling to find his/her place in this world, it is also a time where they are prone to engaging in risk behaviors, which tend to have an extreme psychological impact. The objective was to explore the experiences of an adolescent who engages in risk behaviors and to understand their level of personal fables. The study was a qualitative design with content analysis with semi-structured interviews of ten male adolescents aged 16-18 years. The major findings of the study indicated that adolescents pattern of thinking revolves around the fact that they are invincible and invulnerable. Furthermore, adolescents are aware of the risks they are putting themselves through and how in the process they are hurting others. The implications of the study are to conduct more life skill programs in schools; greater awareness has to be created on the impact and harmful effects of such behaviors. 2018, Indian Journal of Public Health Research and Development. All rights reserved. -
THE MEDIATING EFFECT OF HEEDFUL INTERRELATING ON SELF DETERMINATION AND THRIVING AT WORK AMONG UNIVERSITY FACULTY MEMBERS; [EL EFECTO MEDIADOR DE LA INTERRELACI ATENTA EN LA AUTODETERMINACI Y EL PROSPERAR EN EL TRABAJO ENTRE LOS MIEMBROS DEL PROFESORADO UNIVERSITARIO]; [O EFEITO MEDIADOR DA INTER-RELAO CUIDADA NA AUTODETERMINAO E NO PROSPERO NO TRABALHO ENTRE MEMBROS DO FACULDADE UNIVERSITIA]
Objective: The objective of this study is to empirically examine the mediating effect of heedful interrelating on the direct effect of self-determination and thriving at work among university faculty members. Theoretical Framework: The organismic human integration philosophy forms the theoretical underpinning for the study. The conceptual model is built by integrating self-determination theory (SDT) with the theory of heedful interrelating. Method: Following an explanatory research design, data from 396 university faculty members PAN India was used to test the conceptual model with the PLS-SEM bootstrapping technique. Results and Discussion: The findings validate a significant direct influence of self-determination on thriving at work. Furthermore, there exists a significant mediation effect of heedful interrelating between self-determination and thriving at work. Through causal mediation, it is interpreted that self-determined and autonomously motivated behaviors, stemming from the satisfaction of universal basic psychological needs of autonomy, competence, and relatedness, play a pivotal role in fostering heed-based behavior within an individual. Research Implications: This empirical study validated the organismic integration theory of human nature in the academic sector through the positive direct effect. Implications for the sample of university faculty members suggest the use of heedful interrelating during group tasks through the dimensions of contributing, representing, and sub-ordinating. Originality/Value: This study makes significant original theoretical contributions to the SDT literature and to the SDT puzzle, firstly, by adding heed as a novel indicator to self-determination theorys relatedness dimension and secondly, by validating the role of heedful interrelating in bridging the dialectic gap within the self-determination theory. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
Can Heedful Interrelating Be a Self-empowering Approach to Thwart Maladaptive Workplace Functioning? An Integrative Literature Review
Maladaptive workplace functioning hinders task completion, work-goal attainment and collaborative interactions, thereby affecting the optimal utilisation of human capital, learning outcomes and organisational sustainability. By integrating self-determination theory, the theory of heedful interrelating and the socially embedded model of thriving at work, this study proposes a conceptual model as a self-empowering approach to thwart maladaptive functioning. A five-stage integrative literature review was conducted to examine the available knowledge base, critically review and synthesise selected literature on heedful interrelating to locate a knowledge gap and bring forth a new way of thinking to address employee workplace maladaptive functioning. A sample of ten empirical articles was consolidated to gauge the antecedents and outcomes of heedful interrelating. The originality of the study lies in bridging the dialectic gap of self-determination theory, introducing heed as a novel factor to the relatedness dimension and employing an intelligent review methodology to propose a practical workplace solution to maladaptive functioning. The Author(s) 2025. -
A Study on Enhancing E-Governance Applications Through Semantic Web Technologies
International Journal of Web Technology, Vol-1 (2), pp. 53-59. ISSN-2278-2389 -
Impact of Goal Divergence, Unbalanced Dependence and Miscommunication on Marketing Channel Satisfaction
Purpose: Marketing channel satisfaction is a critical factor influencing the efficiency and long-term sustainability of distribution networks. However, conflicts arising from goal divergence, unbalanced dependence, and miscommunication often disrupt channel relationships, affecting overall satisfaction levels. This study examines the impact of these three conflict-inducing factors on marketing channel satisfaction, drawing insights from empirical research conducted in the fast-moving consumer goods (FMCG) sector. FMCG sector is considered as the barometer of any economy because of its wide reach to both the urban and rural market. Research design, data and methodology: Using a structured survey and statistical analysis, the study identifies the extent to which goal misalignment, power imbalances, and communication breakdowns contribute to dissatisfaction among channel members. Results: The findings highlight that goal divergence leads to reduced cooperation, unbalanced dependence fosters opportunistic behaviour, and miscommunication exacerbates misunderstandings, collectively diminishing channel satisfaction. The study contributes to the literature on channel conflict management and offers practical implications for businesses seeking to enhance collaboration, trust, and efficiency in their marketing channels. Conclusions: The study explores how the marketing channel members like distributors, wholesalers and retailers can reduce distribution channel conflict and enhance marketing/distribution channel satisfaction. This is still important even though online selling and e-commerce has become the order of the day. This study is very relevant in the field of distribution science. The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Impact of Restrictive Trade Policies, Channel Conflict and Uncertain Business Environment on Marketing Channel Satisfaction in FMCG Sector
Purpose: This study examines the influence of restrictive trade policies, channel conflict, and uncertain business environments on marketing channel satisfaction in the Fast-Moving Consumer Goods (FMCG) sector. It explores how external policy interventions and environmental volatility shape channel dynamics and satisfaction levels. Design/Methodology: A structured questionnaire was administered to channel members in the FMCG sector in Kerala. Data were analyzed using correlation and regression techniques to assess the direct effects of restrictive trade policies and channel conflicts. Data from 189 wholesalers and 262 retailers in Kerala in the FMCG Sector, was analyzed using Structural Equation Modeling (SEM). Results: The results indicate that restrictive trade policies and channel conflicts significantly and negatively impact channel satisfaction. Additionally, the uncertain business environment exacerbates these negative effects. Practical Implications: The findings provide actionable guidance for both policy-makers and marketing channel managers in the FMCG sector. Conclusions: This study contributes to marketing channel literature by integrating policy, conflict, and environmental perspectives into a single framework. It underscores the importance of understanding how macro-level restrictions and uncertainties interact with micro-level channel dynamics in shaping satisfaction, particularly in emerging market contexts. This study is very relevant in the field of distribution science. The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Technological Interventions in Unpaid Care Work and Gender Dynamics
Unpaid care work, frequently centered on women, is an important yet neglected component of the world economy. This study strives to address the potential and flexibility that technology brings in curbing gender disparities, along with the bodily burden associated with unpaid care work. An examination is made for smart home devices, telehealth solutions, and caregiving applications that will further be evaluated for their effectiveness in minimizing the time and effort invested in unpaid care responsibilities. Through existing theoretical frameworks, empirical evidence, and case studies, this paper aims to determine how technological innovation can more effectively redistribute care work between genders and enhance the economic value of unpaid care to further improve gender equality. For instance, in Japan, smart home appliances such as automated pill dispensers and remote monitoring devices have become crucial solutions to a caregiving burden largely imposed on women. Telemedicine services similar to these have transported rural India from its unfavorable health care situation, thereby significantly shortening the time women spend on activities related to health care. Caregiver applications have assisted in the United States in achieving an equal distribution of caregiver responsibilities between male and female caregivers. Sophisticated robotic assistants in South Korea may fill gaps in the workforce while tending to older populations; thus, potentially minimizing housework hours for women. The education systems operating online across Sub-Saharan Africa enable girls to juggle learning with caring effectively and hence strike long-lasting gender parity. Such socio-economic advantages were purely garnered through wearable health monitors in Europe that eased family members burdens while experiencing economic benefits at a broader level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
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Predictive Analytics for Stock Market Trends using Machine Learning
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. -
Digital Forensics Investigation for Attacks on Artificial Intelligence
The new research approaches are needed to be adopted to deal with security threats in AI based systems. This research is aimed at investigating the Artificial Intelligence (AI) attacks that are malicious by design. It also deals with conceptualization of the problem and strategies for attacks on Artificial Intelligence (AI) using Digital Forensic tools. A specific class of problems in Adversarial attacks are tampering of Images for computational processing in applications of Digital Photography, Computer Vision, Pattern Recognition (Facial Mapping algorithms). State-of-the-art developments in forensics such as 1. Application of end-to-end Neural Network Training pipeline for image rendering and provenance analysis, 2. Deep-fake image analysis using frequency methods, wavelet analysis & tools like - Amped Authenticate, 3. Capsule networks for detecting forged images 4. Information transformation for Feature extraction via Image Forensic tools such as EXIF-SC, Splice Radar, Noiseprint 5. Application of generative adversarial Networks (GAN) based models as anti-Image Forensics [8], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of Digital Forensics. The Electrochemical Society -
Pattern Reconfigurable Antennas for Wireless Applications: A Review of Design Techniques and Advances
This literature survey investigates advances in pattern reconfigurable antennas that take advantage of Meta-surface (MS) technology over other techniques. The study explores how these reconfigurable antennas are transforming next-generation communication systems, addressing critical applications such as 5G/6G networks and smart wireless environments. Key research observations include the ability of MS-based designs to achieve dynamic beam steering, crucial to meeting the diverse requirements of future communication systems. The paper also identifies challenges such as design complexity, power efficiency, integration with existing systems, and scalability for practical deployments. By highlighting these advances and addressing open challenges, this survey aims to provide information on the potential of MS-enabled reconfigurable antennas to shape the future of wireless communication technologies. 2025 IEEE. -
Pattern Reconfigurable Antenna Design Using Amc Array for Enhanced 5G Performance
This paper introduces a microstrip patch antenna integrated with a novel artificial magnetic conductor (AMC) to reconfigure the antenna radiation pattern for 5 G sub- 6 GHz applications. By adopting AMC technology, the proposed antenna exhibits beam steering, pattern reconfiguration, and enhancement in gain, suitable for the high data rate demands of 5 G networks. The design process involves the evolution of antenna elements, the design of AMC unit cell (AMC-UC) to operate at the desired 3.75 GHz, and the development of an AMC integrated antenna that is suitable to control the antenna radiation pattern, achieving significant enhancements in signal directionality and minimizing interference. The simulation results demonstrate improved performance parameters, such as return loss and gain, highlighting the potential of AMC-assisted reconfigurable antennas in advancing 5G network coverage and capacity. This work provides valuable information on the achievement of versatile and efficient antenna designs for next-generation wireless communications. 2025 IEEE. -
Enhancing fabric quality with AI-based defect detection systems
In summary, there is a necessity to use AI-based defect detection systems in fabric quality improvement especially in the process of textile production. These sophisticated solutions eliminate the requirement for time-consuming and error-prone traditional manual procedures, and thus not only speed up the inspection but guarantee a higher quality of the products. -
AI- and ML-driven intelligent design of digital twins
Digital twins (DTs), or virtual copies of real-world systems, have changed and improved many industries in terms of monitoring, analysis, and optimization in real time. Artificial intelligence (AI) and machine learning (ML) together have significantly enhanced the functionalities of DTs so that they become more efficient and versatile decision-making and process improvement tools. The production and application of DTs most importantly rely on AI and ML. Such technologies allow integration and analysis of very large amounts of data from various sources and provide an overview of the physical system. The personnel involved in the company may gain deeper insights into overall business processes and identify changes that would remain unknown when applying the traditional methods, based on the employment of the capabilities of AI-based integration and data analysis. An essential example of ML use cases in the framework of DTs is predictive maintenance. Any ML algorithm can resort to historical data and immediate sensor data to predict potential failures of application equipment and propose a repair schedule, significantly reducing operational downtime and refining the distribution of resources. The AI-powered optimization and simulation methods can give organizations the possibility to consider numerous scenarios and identify the most effective ways to resolve complex issues. The DTs are AI-enabled and can detect and decide on the fly, which allows them to react to changing conditions instantly and prevent some of the issues before they happen. In addition, AI-powered predictive analysis and risk management allow the firms to go a step ahead and address the potential problems in advance by developing effective risk reduction strategies. DTs are mainly constructed with AI and ML in various industries. In the context of manufacturing and Industry 4.0, DTs play an important role in optimizing production and increasing the quality control standards. Urban planners use the DTs to strategize building smart cities, while healthcare professionals use them for medical diagnosis and planning. In the aerospace and auto industries, DTs are beneficial in improving the product development, testing, and other maintenance processes. This chapter focuses on the smart creation of DTs with the help of AI and ML technology. The discussion will also dive into the complex mechanism behind building advanced, digital replicas of physical systems, particularly the support of the AI and ML in the advancement of their usefulness and precision. The chapter begins with the discussion of the role of data integration and analysis in the creation of a DT. This section shows how AI and ML algorithms facilitate the seamless combination of different sources of data into one and reach a much more dynamic and detailed similitude of the physical member. The chapter illustrates how these technologies can convert raw information into valuable information, which makes the DT capable of replicating the real-world situations and behaviors quite dramatically. Moreover, the chapter addresses the profound role of AI and ML in the optimization and simulation of DTs. We observe how these advanced technologies are able to give more precise predictions and process the decision-making and testing of even complex scenarios. The chapter focuses on how AI-enabled optimization methodologies and AI-based simulations driven by ML are broadening the opportunities of DTs, thus driving innovation in a number of verticals. 2026 Elsevier Inc. All rights reserved. -
DIGITAL FORENSICS ANALYSIS FOR ADVERSARIAL ATTACKS IN AI-BASED SYSTEMS
Digital forensics investigation is a field of science that focuses primarily on the recovery and examination of devices. The digital forensics or cyber forensics was primarily used for computer-based forensics. Artificial intel ligence (AI for short) is a group of computational models that use the black box-based technology of the neural network (NN) such as classification, prediction, and optimization tasks for various applications, such as computer vision, medical imaging, natural language processing, and autonomous vehicles. Cyber Forensics such as robotics, deals with a class of problems in which computational models are manipulated due to intentional malfunction such as adversarial attacks, back-end kind of attacks. This study is aimed to investigate attacks on AI. It also describes conceptual attack strategies using AI and digital forensics tools. The main purpose of this work is to provide a comprehensive analysis of malicious AI-based attacks and different types of classifiers. Two main factors were considered when testing attacks on AI-based systems: (1) Use AI to create inputs that reveal different attacks, or (2) Adjust attacker-specific attacks from different threat models and parameters. 2026 by Apple Academic Press, Inc. -
A comparative heat transfer analysis of rectangular fin through LTE and LTNE model
The objective of this research is to compare the thermal performance of rectangular porous fins through the Local Thermal Equilibrium and the Local Thermal Non-Equilibrium models. The thermal interactions between the solid and fluid phases are represented by two distinct energy equations in the Local Thermal Non-Equilibrium model. Whereas, heat transfer is governed by a single energy equation in the Local Thermal Equilibrium model. The governing equations describing the temperature distribution inside the fin system are developed using basic heat transfer principles. To enhance thermal conductivity and total effectiveness of heat transmission, the fluid phase of water is amalgamated with Al2O3 and TiO2 nanoparticles. The governing nonlinear ordinary differential equations are nondimensionalized, and the RungeKutta Fehlberg fourth-fifth order (RKF45) method is employed to solve these equations numerically. The accuracy and dependability of the obtained solution are confirmed by comparing it with previous findings. The influence of pertinent parameters on the thermal characteristics of the permeable fin is depicted graphically, and the rate of heat transfer is analyzed by Response surface methodology. It has been determined that, for the capturing of phase-wise thermal variations, Local Thermal Non-Equilibrium model performs better, particularly in permeable media with no heat conduction differences. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025.