Market awareness is a critical determinant of organizational competitiveness in dynamic business environments. This study examines the level of market awareness among employees of Mithra Agencies Pvt. Ltd., a leading Maruti Suzuki dealership in Hyderabad. Using a descriptive research design, primary data were collected from 100 employees through structured questionnaires, supplemented by secondary data from company records and published literature. The study analyses awareness of products, market trends, competitor activities, customer preferences, and internal communication effectiveness. Findings reveal moderate to high awareness levels but identify gaps in training frequency, competitor analysis, and access to market intelligence. The paper concludes that strengthening training programs, communication systems, and market research practices can significantly enhance organizational responsiveness and performance.
Mental health disorders such as stress, anxiety, and mental fatigue have become increasingly prevalent in modern society due to rapid technological advancements, increased workload, and prolonged screen exposure. Continuous stress without proper monitoring can lead to severe physical and psychological health problems. Traditional stress detection methods rely on manual observation, questionnaires, or clinical diagnosis, which are often timeconsuming and unsuitable for real-time monitoring. This paper proposes a real-time mental health monitoring system that utilizes multimodal artificial intelligence techniques combined with webcam-based facial analysis and Internet of Things (IoT) physiological sensing. The system collects facial expressions through a webcam and physiological parameters such as heart rate and body temperature through IoT sensors. Machine learning and deep learning algorithms process the collected data to identify stress patterns and classify stress levels accurately. The system generates alerts when abnormal stress levels are detected and stores data for further analysis. The proposed system improves detection accuracy by integrating multiple data sources and enables continuous monitoring of mental health conditions. The system can be implemented in workplaces, educational institutions, healthcare facilities, and remote monitoring environments to support early stress detection and preventive healthcare.
Transformer bushings and air-end insulators are essential components in high-voltage electrical power systems, providing both electrical insulation and mechanical support for conductors passing through grounded equipment. Conventional bushings are generally manufactured using porcelain ceramic materials due to their high dielectric strength and good mechanical properties. However, porcelain insulators possess inherent limitations such as high weight and brittle fracture behavior, which make them susceptible to mechanical failure under impact loading, environmental stress, and operational disturbances. These failures can lead to equipment damage, power outages, and increased maintenance costs in substations. This study focuses on the analysis and optimization of transformer bushings to improve their impact resistance and structural reliability. The mechanical behavior of conventional porcelain bushings is investigated under different loading conditions including internal pressure, bending load, and impact forces. A mathematical analysis is performed to evaluate the stress distribution and structural response of the bushing structure. Furthermore, the potential replacement of porcelain material with polymer-based composite materials such as glass fiber reinforced polymers is explored to enhance impact resistance while reducing overall weight. Finite Element Analysis (FEA) is proposed to simulate stress, deformation, and failure behavior under various operating conditions. The results of this study are expected to contribute to the development of lightweight, durable, and impact-resistant transformer bushings suitable for modern high-voltage power systems.
This paper presents the design, hardware implementation, and experimental validation of a low-cost Automatic Power Factor Controller (APFC) integrated with Internet of Things (IoT)-based real-time remote monitoring. The system employs zero-crossing detection (ZCD) circuits for both supply voltage and load current to compute the displacement power factor continuously on an Arduino Uno (ATmega328P) microcontroller. Based on the measured phase lag between voltage and current zero crossings, the controller automatically switches up to two relaycontrolled capacitor banks to compensate reactive power and maintain power factor at or above 0.95 lagging. An ESP32 Wi-Fi module receives electrical parameter data from the Arduino over UART and serves a responsive HTML5 web dashboard accessible from any browser on the local network without internet dependency. The system also features a dual-mode operation — fully automatic capacitor switching and manual override via push buttons — with a 16×2 LCD for on-panel display. Experimental results confirm power factor improvement from 0.823 to 0.994 under mixed inductive-resistive loading at 230 V, 50 Hz, representing a 78.0% reduction in reactive power demand. The complete prototype cost is approximately INR 2,500, offering a practical and scalable solution for small industries and educational institutions.
Employee attrition has become a significant challenge for organizations worldwide, affecting productivity, employee morale, and overall business success. Employee turnover not only disrupts business operations but also imposes significant financial costs in terms of recruitment, training, and lost expertise. Organizations facing high attrition rates often struggle to maintain team stability, meet project deadlines, and ensure consistent knowledge retention. Moreover, a consistent cycle of new employee onboarding disrupts team synergy and knowledge transfer, further complicating operational efficiency. This research investigates the causes and consequences of employee attrition, highlighting its impact on organizational performance. Drawing insights from HR professionals, the study explores strategies for mitigating attrition through improved engagement, leadership development, and effective retention techniques. Factors such as salary dissatisfaction, workplace stress, limited career growth, and managerial inefficiencies have emerged as key contributors to attrition. The study emphasizes the importance of fostering positive work environments, implementing structured career development programs, and enhancing leadership practices to improve retention rates. Additionally, understanding individual employee needs, offering tailored growth opportunities, and promoting a sense of belonging are identified as crucial factors in improving retention. By examining case studies from prominent organizations such as Infosys and Alembic Pharmaceuticals, this research highlights practical examples of how companies have successfully addressed attrition through strategic interventions. Infosys, for example, achieved improved employee retention by enhancing career growth opportunities, implementing mentorship programs, and introducing flexible work arrangements. Similarly, Alembic Pharmaceuticals reduced its high attrition rate by restructuring its employee engagement programs, improving workplace recognition strategies, and introducing mentorship initiatives. These real-world examples demonstrate how strategic interventions, when effectively implemented, can reduce attrition and strengthen organizational performance. The findings aim to provide actionable insights for organizations seeking sustainable growth. By understanding the factors that contribute to employee turnover, businesses can develop effective policies that prioritize employee well-being, engagement, and long-term commitment. Implementing proactive measures, such as mentorship programs, leadership training, and personalized career advancement plans, can greatly reduce turnover rates. Additionally, organizations that actively promote work-life balance, mental health support, and recognition programs often experience lower attrition rates. Through this comprehensive approach, organizations can mitigate attrition’s negative effects while fostering a motivated and dedicated workforce poised for success. By investing in employee satisfaction and organizational culture, businesses can create an environment where employees feel valued, motivated, and committed to the organization's growth.
The loss of upper-limb functionality significantly affects an individual's ability to perform everyday activities, creating a growing demand for affordable and accessible prosthetic solutions. This paper presents the design and implementation of a low-cost Prosthetic Twin System capable of real-time motion replication using embedded systems and wireless communication technologies. The developed system consists of two identical prosthetic hand units in a master–slave architecture. User input is provided through a smartphone interface communicating with the master unit via ESP32, while motion commands are transmitted wirelessly to the slave unit using the ESP-NOW protocol for real-time synchronization. The prosthetic hand utilizes an underactuated tendon-driven mechanism with MG996R servo motors and cable-driven actuation through 3D printed ABS finger structures. The system integrates a PCA9685 PWM driver, INA219 current sensor, OLED display, and LiPo battery management subsystem. Experimental testing demonstrated synchronized finger flexion, extension, and basic grasping with 20–40 ms communication latency and stable operation within a 5–10 metre range. The prototype demonstrates the feasibility of a physical digital twin architecture for prosthetic applications using commercially available low-cost components.
Leadership styles including transformational, transactional, servant, and autocratic approaches play a vital role in determining organizational effectiveness, employee productivity, engagement, job satisfaction, and long-term sustainability. This study examines the influence of these leadership styles on organizational performance indicators and workplace culture. The findings indicate that transformational and servant leadership styles significantly enhance employee engagement, productivity, and the development of a positive organizational culture that supports sustained success. In contrast, transactional leadership is shown to be more effective in achieving short-term performance outcomes but contributes less to long-term cultural development. These insights highlight the importance of adaptive and peoplecentered leadership in achieving sustainable organizational success.
The increasing dependence on digital infrastructure, cloud computing, and distributed systems has significantly exposed enterprises to sophisticated cybersecurity threats such as ransomware attacks, data breaches, and unauthorized access. Traditional cybersecurity solutions often rely on static security mechanisms that are unable to detect emerging threats in real time. Recent advancements in Artificial Intelligence (AI), Blockchain technology, and Cloud Computing have introduced new opportunities to enhance cybersecurity through intelligent threat detection, decentralized data management, and scalable infrastructure.This survey paper reviews existing research on the integration of AI, Blockchain, and Cloud Computing for enterprise cybersecurity. The study analyzes key methodologies, architectural frameworks, and security mechanisms proposed in recent IEEE research papers. The survey categorizes existing solutions based on technology integration, security features, and system performance. It also identifies research gaps related to scalability, interoperability, and real-time threat detection in distributed enterprise environments.The findings indicate that integrated cybersecurity frameworks combining AI, Blockchain, and Cloud Computing provide improved data integrity, enhanced threat detection accuracy, and secure communication in enterprise systems. The survey concludes that hybrid cybersecurity architectures represent a promising direction for developing secure and resilient enterprise security solutions.
In today’s highly competitive and dynamic business environment, organizations increasingly recognize employees as strategic assets rather than mere operational resources. Talent Management (TM) and Performance Management Systems (PMS) have emerged as critical human resource practices for attracting, developing, motivating, and retaining high-performing employees. This research paper examines the concepts, processes, and effectiveness of Talent Management and Performance Management Systems in organizational settings. The study focuses on competency-based talent management practices, performance evaluation mechanisms, leadership development, and reward systems. The study adopts a descriptive research design using both primary and secondary data. Primary data were collected through a structured questionnaire administered to employees from selected organizations, while secondary data were sourced from journals, books, reports, and online databases. Statistical tools such as percentage analysis, mean scores, and correlation analysis were applied for data interpretation. The findings reveal that competency-based talent management significantly improves employee performance, leadership readiness, and retention. Effective PMS ensures goal alignment, continuous feedback, and fair reward distribution. The study concludes that integrating TM and PMS strategically enhances organizational effectiveness and sustainability. Recommendations are provided for strengthening competency frameworks, leadership pipelines, and performance-linked reward systems.
ABSTRACT: The rapid adoption of generative artificial intelligence (GenAI) in healthcare has introduced transformative opportunities for patient engagement, clinical decision support, and administrative efficiency. Large language models (LLMs), when integrated with electronic health records (EHRs) and ancillary systems via secure healthcare APIs, can enable advanced conversational interfaces for patients, clinicians, and researchers. However, these integrations pose critical challenges related to data privacy, interoperability, and security. Sensitive patient records governed by HIPAA, GDPR, and other regulatory frameworks must be protected from unauthorized disclosure, particularly in scenarios where LLMs risk overexposure of data or manipulation through prompt injection attacks. This research explores a layered architecture for securing LLM access to healthcare APIs, focusing on three core areas: (1) limiting LLM data visibility through controlled API responses, (2) implementing robust defenses against prompt injection and adversarial queries, and (3) ensuring interoperability across heterogeneous systems via HL7–FHIR transformations. The proposed framework emphasizes zero-trust access models, de-identification techniques, and standardized data governance while highlighting practical use cases such as AI-enabled patient portals and clinical chatbots. Through conceptual modeling, threat analysis, and system design, this paper outlines best practices for balancing usability, interoperability, and compliance in GenAI-driven healthcare ecosystems.
ABSTRACT: The influx of Fiber-to-the-x (FTTx) networks all over the world has amplified the necessity to have efficient, precise, and scalable network design approaches. The planning of fiber networks traditionally has been based on the computer-Aided Design (AutoCAD) tools that are mostly manual, geometry-based and subject to human error. Although useful in small scale project development, these approaches cannot meet the growing complexity and data demands of the contemporary broadband deployment. The result is a paradigm shift in the industry toward design frameworks that are based on Geographic Information System (GIS) and data-driven design frameworks. Although the recent developments have been made, there is a considerable gap in research in the systematic comparison of the conventional CAD based work flows and intelligent, attribute-based GIS methods, especially on the efficiency of automation, splice loss modeling, and accuracy of capacity planning. This paper will seek to fill this gap by critically assessing the performances and results of the two design paradigms. The study uses comparative methodology of analysis, which incorporates literature synthesis, workflow benchmarking and simulation-based assessment. The significance of smart, data-driven design models is measured by the key performance indicators, such as design accuracy, design planning time, error rates, splice loss margins, and network scalability. The results prove that GIS-based and attribute-driven planning is much better than traditional CAD methods regarding automation, accuracy, and scalability. The intelligent design systems lower the planning time, provide less splice loss uncertainties, and allow proactive capacity prediction via data analytics and optimization algorithms. In practice, the research offers viable information to the network operators and planners who want to streamline their design procedures. Its findings help in strategic decision making to move towards smart planning platforms that ultimately would result in a high level of deployment efficiency, lower operation costs and the long run sustainability of a network.
Artificial Intelligence (AI) has emerged as a powerful tool in social media analytics, enabling organizations to process large volumes of data, anticipate consumer behavior, and improve marketing campaign performance in real time. This study investigates the influence of AI on key social media analytics functions, including trend identification, sentiment analysis, predictive insights, and campaign optimization. A quantitative research design was employed, with data collected from 200 marketing professionals through structured questionnaires. The collected data were analyzed using SPSS to examine the relationship between AI adoption and campaign effectiveness, trend detection accuracy, and marketing decision-making efficiency. The results reveal that AI adoption significantly enhances trend identification, improves campaign targeting, and contributes to higher marketing returns on investment. The study underscores the strategic importance of AI in shaping future social media marketing practices while also drawing attention to ethical and operational considerations associated with its use.
Brand preference plays a decisive role in shaping consumer buying behavior, particularly in highinvolvement product categories such as decorative paints. In the Indian context, increasing urbanization, rising disposable incomes, and intensified competition have made branding a critical differentiator. The present study examines consumer brand preferences toward decorative paints in the Greater Hyderabad region, focusing on the influence of brand image, promotional strategies, and perceived product value on purchase decisions and customer satisfaction. Primary data were collected from 200 respondents using a structured questionnaire. The findings indicate that established brands enjoy stronger consumer preference due to consistent quality, effective advertising, and reliable service support. The study concludes that branding, supported by quality and competitive pricing, significantly enhances customer satisfaction and long-term brand loyalty in the decorative paint market.
Recruitment and selection are among the most critical functions of human resource management, as they directly influence organizational performance, employee efficiency, and long-term sustainability. An effective recruitment and selection system ensures that organizations attract competent candidates and select individuals whose skills, attitudes, and values align with organizational objectives. This study focuses on examining the recruitment and selection practices followed at Maruti Suzuki India Limited, one of India’s leading automobile manufacturing companies, with the aim of evaluating their effectiveness and identifying areas for improvement. The research analyzes various aspects of the recruitment and selection process, including sources of recruitment, methods of candidate screening, selection techniques, and employee perceptions regarding fairness, transparency, and efficiency. Both primary and secondary data were used for the study. Primary data were collected through a structured questionnaire administered to employees across different departments, while secondary data were gathered from company records, human resource manuals, academic journals, textbooks, and relevant websites. The collected data were analyzed using appropriate statistical tools such as percentages and simple graphical techniques to draw meaningful inferences. The findings of the study indicate that Maruti Suzuki India Limited follows a systematic and well-structured recruitment and selection process, which helps the organization attract qualified candidates and place them in suitable job roles. Employees largely perceive the recruitment and selection procedures as fair and transparent, contributing to higher levels of job satisfaction and organizational commitment. However, the study also identifies certain areas where improvements can be made, such as expanding recruitment channels and enhancing the efficiency of selection procedures. The study concludes by offering practical suggestions to strengthen recruitment and selection practices and support sustained organizational growth.
Ruskin Bond, renowned Indian writer is praised for his creative abilities to eloquently convey the innocence of infancy and the spirit of nature. His short stories illustrate the profound impact of nature on the experiences and lives of his young heroes. A close bond between nature and childhood was envisioned by romantic poets like William Wordsworth and William Blake. Wordsworth idealizes juvenile innocence in nature, which he describes as a reassuring haven that provides respite and renewal. Blake contrasts the harsh reality of life with the innocence of childhood, frequently use nature as a metaphor for two diametrically opposed moods. Bond explores the value of nature in his books by showing how different parts of the world provide a backdrop for his characters' adventures. They examine the ways in which the unique flora and animals of the Himalayan region influence the characters' decisions and perspectives as they travel. They also concentrate on Bond's depiction of childhood, highlighting qualities like the children' callowness, strength, and curiosity. This essay explores how the youthful protagonists in Bond's novels navigate the difficulties of a rapidly evolving world and discover solace and wisdom via their encounters with the natural world. His narrative conveys a strong environmental message in addition to capturing the sweet and innocent parts of infancy. It inspires readers to respect, preserve, and live in harmony with nature.
The hybrid work model, which combines remote and on-site work arrangements, has emerged as a dominant organizational practice in the post-pandemic era. Organizations across sectors have adopted hybrid work to balance operational efficiency with employee well-being. This paper examines the impact of the hybrid work model on employee performance, focusing on productivity, job satisfaction, work–life balance, collaboration, and organizational commitment. Using a structured questionnaire administered to employees from IT, education, and service sectors, data were collected from 300 respondents. Statistical tools such as descriptive analysis, correlation, and regression analysis were applied to test the proposed hypotheses. The findings reveal that the hybrid work model has a significant positive impact on employee performance, particularly through improved work–life balance and job satisfaction. The study contributes to the growing body of literature on new work models and provides practical implications for managers and policymakers
The metal industry is one of the most capital-intensive and volatile sectors of the Indian economy, closely linked with global commodity cycles, infrastructure growth, and macroeconomic conditions. Investors in metal stocks are exposed to substantial fluctuations in returns due to price volatility, regulatory changes, and demand–supply imbalances. This research paper conducts an in-depth risk–return analysis of selected metal companies listed on the National Stock Exchange (NSE), namely JSW Steel Ltd, Tata Steel Ltd, Hindalco Industries Ltd, Hindustan Zinc Ltd, and Coal India Ltd. The study uses daily stock price data for a three-month period from January 1, 2024 to March 31, 2024. Quantitative tools such as average return, variance, standard deviation, and coefficient of variation are employed to evaluate performance and volatility. The empirical results reveal that all selected companies generated negative average returns during the study period, indicating bearish market sentiment. However, significant differences in risk levels were observed across companies. Hindustan Zinc Ltd exhibited the lowest volatility, while Coal India Ltd recorded the highest risk. The study provides meaningful insights for investors, portfolio managers, and policymakers by highlighting sector-specific risk characteristics and emphasizing the importance of risk-adjusted investment decisions in the metal sector.
ABSTRACT: The global energy landscape is undergoing a profound transformation, driven by the dual imperatives of decarbonization and digitalization. The integration of intermittent renewable energy sources, the proliferation of distributed energy resources (DERs), and the emergence of new load types like electric vehicles (EVs) are pushing traditional, centralized power grids beyond their operational limits. In response, the concepts of the Intelligent Energy Grid (IEG) and the Autonomous Power System (APS) have emerged as critical paradigms for a sustainable, resilient, and efficient energy future. This research paper presents a comprehensive investigation into the architectural, technological, and operational foundations of IEGs and APSs. We begin with a detailed literature survey that traces the evolution from the traditional grid to the smart grid and now to the intelligent, autonomous grid. The core contribution of this work is a novel, integrated framework that synergizes a multi-agent system (MAS) for decentralized control with a hierarchical analytics stack for system-wide intelligence. This framework is designed to enable real-time self-optimization, selfhealing, and adaptive protection. Our methodology involves the development of a high-fidelity co-simulation testbed integrating PowerFactory for electrical domain simulation, MATLAB for control algorithm development, and MQTT brokers for communication emulation. We model a benchmark IEEE 33-bus distribution system modified with high penetration of solar PV, wind generation, and EV charging stations. The results of our simulation analysis demonstrate the efficacy of the proposed framework. In scenario testing, the system successfully contained a fault through autonomous reconfiguration, reducing the number of affected customers by 85% compared to a traditional grid response. Furthermore, the MAS-based energy market achieved a 22% reduction in locational marginal prices during peak renewable generation hours by efficiently utilizing local DERs. The results also highlight critical challenges, including communication latency and cybersecurity threats, which can destabilize the system if not adequately mitigated. This study concludes that the transition to IEGs and APSs is not merely a technological upgrade but a fundamental architectural shift. It requires the deep integration of advanced sensing, communication, and artificial intelligence to create a grid that is not only smarter but truly cognitive and self-sufficient, paving the way for a fully decentralized and resilient energy ecosystem.
ABSTRACT: Enterprise middleware serves as the foundational integration layer for mission-critical systems across regulated industries such as healthcare, insurance, and financial services. These industries operate under strict regulatory, security, and availability constraints, where system failures or data breaches can have severe legal and operational consequences. Traditional middleware platforms, often designed for on-premises and monolithic environments, face growing challenges related to scalability, vulnerability exposure, manual compliance processes, and operational rigidity. This paper proposes a comprehensive framework for Enterprise Middleware Modernization and Secure Cloud Automation tailored specifically for regulated environments. The framework integrates security-first architecture principles, automated compliance engineering, vulnerability management, and high-availability design patterns. By synthesizing academic research, regulatory requirements, and real-world enterprise implementation experiences, this study demonstrates how modern middleware architectures can enhance system resilience, improve compliance posture, and enable scalable digital transformation while minimizing operational risk
ABSTRACT Expansive soils are considered problematic for civil engineering applications due to their low strength, high compressibility, and poor bearing capacity, which limit their use as foundation or pavement subgrade materials. To overcome these limitations, this study investigates the stabilization of expansive soil using gypsum and Microbially Induced Calcite Precipitation (MICP) as strengthening agents. The experimental program was carried out in three stages. In the first stage, gypsum, an industrial by-product, was incorporated into the soil in varying proportions of 2%, 4%, 6%, 8%, and 10% by dry weight. The treated samples were tested for Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR). The results revealed a significant improvement in strength with increasing gypsum content up to 4%, beyond which a slight reduction was observed. Thus, 4% gypsum was identified as the optimum content for stabilization. In the second stage, MICP treatment was applied by introducing bacterial solutions in concentrations of 2 ml, 4 ml, 6 ml, 8 ml, and 10 ml under optimum moisture conditions. The treated samples were tested for Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR). The results revealed a significant improvement in strength with increasing MICP content up to 4ml-MICP, beyond which a slight reduction was observed. Thus, 4ml-MICP was identified as the optimum content for stabilization. In the final stage, 4% gypsum and MICP (4 ml MICP concentration) were combined and applied to new soil samples to evaluate the combined effect on strength enhancement. However, the results showed a decrease in strength in the UCS test. The comparative analysis indicated that the individual treatments performed better depending on the curing period. Overall, the study demonstrates that gypsum provides a more sustainable improvement when compared to MICP, whereas MICP offers a more cost-effective solution. Both gypsum and MICP are environmentally friendly stabilizers suitable for enhancing the performance of expansive soils in geotechnical and pavement applications
