Automated Podcast Transcription and Topic Segmentation

Abstract

This paper presents an AI-based system for automated podcast transcription and topic segmentation. The system accepts audio uploads, performs speech-to-text transcription, segments content into topics, extracts keywords, generates summaries, and produces PDF reports through a Flask web application. The architecture integrates audio preprocessing, transcription, NLP-based topic segmentation, and report generation to improve accessibility and content discovery. Experimental evaluation demonstrates efficient processing and useful summaries for educational and media applications.

Prediction of Brain Stroke

Abstract

Brain stroke is one of the leading causes of death and long-term disability worldwide, making early detection and timely intervention essential for improving patient outcomes. This project presents a machine learning-based Brain Stroke Prediction system that estimates an individual's risk of stroke using health and lifestyle parameters such as age, gender, hypertension, heart disease, average glucose level, body mass index (BMI), smoking status, work type, residence type, and marital status. The proposed system is developed using Python, Flask, and Scikit-learn, with a Random Forest Classifier trained on a publicly available healthcare dataset to provide accurate and reliable predictions. The application features a simple and interactive web interface that allows users to enter their health information and instantly receive a prediction indicating their stroke risk. To protect user privacy, the system operates without a database, ensuring that no personal information or prediction results are stored. Data preprocessing techniques, including handling missing values, encoding categorical features, and feature transformation, are applied to improve the performance of the prediction model. The trained model is integrated into a Flask-based web application to enable fast and efficient real-time predictions. The proposed system aims to assist healthcare professionals, students, and individuals by providing an accessible tool for preliminary stroke risk assessment. Although it is not intended to replace professional medical diagnosis, it can serve as a valuable decision-support system that promotes early awareness and preventive healthcare. The project demonstrates the practical application of machine learning in healthcare by combining predictive analytics with an easy-to-use web interface, offering a cost-effective, secure, and efficient solution for stroke risk prediction suitable for academic, research, and educational purposes.

Recruitment Challenges in Global Capability Centers (GCCs): An Analysis of India’s Talent Acquisition Landscape

Abstract

Global Capability Centers (GCCs) have transformed themselves from cost-reducing back office departments into strategically focused centers of engineering, artificial intelligence, and enterprise innovation, with India itself having over 1,700 centers employing almost 2 million people and contributing annual revenues of over USD 64 billion. Such fast growth has put immense pressure on the recruiting function. This paper aims to explore the main challenges of recruiting in GCCs, paying particular attention to the Indian environment. To do so, secondary information is used: reports from the industry, studies by consultancies, and scholarly literature related to talent acquisition and employer branding. In total, six interconnected challenges of recruiting in GCCs have been identified, namely the shortage of specialized and new technologies' talent, specifically of generative artificial intelligence, cloud architecture, and cybersecurity specialists; high levels of employee turnover and infant turnover among new joiners; leadership readiness problems preventing the localization of senior management positions; increased competition for employers due to the presence of over 1,700 centers competing for one and the same talent pool; infrastructure and quality problems associated with hiring people in Tier-II and Tier-III cities; and legal, taxation, and data compliance problems associated with international recruiting. The results show that talent recruitment under the GCC approach has moved from being a volume-oriented and cost-oriented process to a capabilities-oriented strategic approach, whereby organizations that do not view talent acquisition as a board-level activity could face delayed projects and increased cost inefficiencies.

Personalized Fitness Guide

Abstract

The rapid growth of artificial intelligence and machine learning has enabled the development of intelligent healthcare and fitness applications that provide personalized recommendations based on individual user characteristics. This research presents a Personalized Fitness Guide, a web-based application developed using Python, Flask, and Machine Learning to assist users in achieving their health and fitness goals through customized guidance. The proposed system integrates four major modules: daily calorie requirement prediction, calorie burn estimation, nutrition analysis, and personalized workout plan generation. User-specific information, including age, gender, height, weight, and activity level, is processed using trained machine learning models to produce accurate and personalized fitness recommendations. The nutrition analysis module provides detailed information about calories, proteins, carbohydrates, fats, vitamins, and minerals, enabling users to make informed dietary decisions. Additionally, the workout recommendation module generates structured exercise plans tailored to the user's fitness objectives and physical profile. The application is implemented using HTML, CSS, JavaScript, and Flask, while Scikit-learn, Pandas, NumPy, and Joblib are used for machine learning model development and deployment. Experimental evaluation demonstrates that the proposed system delivers reliable predictions, fast response times, and an intuitive user experience by integrating multiple fitness management functionalities into a single platform. The system minimizes the need for separate fitness applications and offers an intelligent, accessible, and cost-effective solution for personalized health management. The proposed approach has potential applications in personal fitness, wellness centers, healthcare organizations, and sports training environments, while future enhancements may include wearable device integration, cloud deployment, and advanced deep learning models to further improve prediction accuracy and user engagement.

CODE PULSE — ONLINE CODING PRACTICE PLATFORM

Abstract

Abstract— The rapid growth of the software industry has increased the demand for strong programming and problem-solving skills among students and professionals. Traditional learning methods often provide limited opportunities for practical coding experience and immediate feedback. To address these challenges, CODE PULSE -- ONLINE CODING PRACTICE PLATFORM is proposed as a web-based application that enables users to practice programming problems in an interactive and user-friendly environment. The platform offers features such as user authentication, problem categorization, code submission, automated evaluation, performance tracking, leaderboards, and an administrative management system. It is developed using React.js for the frontend, Spring Boot for the backend, and MySQL for database management, ensuring scalability, security, and efficient data handling. The system helps learners improve coding proficiency, logical thinking, and competitive programming skills through continuous practice and instant feedback. The proposed platform bridges the gap between theoretical learning and practical implementation, making it a valuable tool for academic institutions, training organizations, and aspiring software developers preparing for technical interviews and coding competitions.

AgriSathi: An AI-Powered Smart Farming Assistant and Decision Support System

Abstract

Operational information fragmentation in agriculture remains a significant barrier to productivity, crop yield protection, and economic security for smallholder farmers. Traditional farming methods rely heavily on static calendars and localized, often asymmetrical, information regarding soil health, disease diagnostics, weather patterns, and market pricing. This paper presents AgriSathi, a unified, high-performance web portal designed to address these gaps. Implemented with a FastAPI backend and a reverse-proxied Nginx server, AgriSathi integrates eight core agricultural modules: crop recommendations based on multi-parameter environmental rules, non-invasive leaf disease diagnostics with confidence thresholds exceeding 85%, localized weather forecasting with tailored agronomic advisories, real-time wholesale price tracking across multiple weight classes, and a stateful multilingual voice assistant supporting regional languages (specifically Telugu). The system architecture leverages SQLite for low-latency transaction processing. Preliminary validation demonstrates that the unified interface reduces information retrieval latency and empowers farmers with actionable, data-driven decisions to prevent yield loss and middleman exploitation. Keywords: Precision Agriculture, FastAPI, Convolutional Neural Networks, Multilingual Localization, Leaf Pathology, Mandi Price Index, Agricultural Decision Support Systems.

Machine Learning-Based Life Expectancy Prediction in Developed and Developing Regions

Abstract

Life expectancy is a core demographic and public health indicator reflecting the overall quality of healthcare, living conditions, nutrition, socioeconomic progress, and disease control in a country. However, manually analyzing how dozens of health, social, and economic indicators influence lifespan is complex and time-consuming. This paper presents an interactive, web-based intelligence application developed using the Django framework, SQLite database, and machine learning regression algorithms to forecast life expectancy in developed and developing regions. The system utilizes real-world health and development indicators from the World Health Organization (WHO) and United Nations. We implement and evaluate three regression models: Linear Regression, Random Forest Regressor, and XGBoost Regressor. Preprocessing steps handle missing data, normalize features, and split records into training and test sets. XGBoost achieves superior predictive performance due to its robust gradient-boosting trees, yielding the lowest Mean Absolute Error (MAE) and highest R-squared ( R 2 R 2 ) value. The system is integrated into a Django web platform, allowing users to view data tables, train models, compare performance metrics via interactive visualizations (correlation matrices, scatter plots, violin plots, and radar charts), and predict life expectancy through user-friendly forms.

ASTUDYONTHEWORKPLACESAFETYANDHEALTH MEASURES IN PRS TYRES PVT LTD AT RASIPURAM

Abstract

The workplace safety and health of employees play a significant role in improving productivity, reducing accidents, and creating a healthy working environment. This study aims to evaluate the workplace safety and health measures adopted in PRS Tyres Pvt Ltd, Rasipuram. The research focuses on employees' awareness of safety policies, the availability of safety equipment, health facilities ,emergency preparedness, and management support. Primary data were collected from 120 employees using a structured questionnaire. Secondary data were collected from books, journals, and company records. The findings reveal that the organization provides satisfactory safety measures, but improvements in training and emergency response systems can further enhance employee well-being. The study concludes with practical suggestions to strengthen workplace safety and health practices. Keywords: Work place Safety, Employee Health,Occupational Safety,PRS TyresPvtLtd, Rasipuram

A STUDY ON EFFECTIVE MARKETING STRATEGY & ITS IMPACT MANAKKADU MASALA, SALEM

Abstract

Marketing strategy plays an important role in improving business performance and customer satisfaction. This study aims to evaluate the effectiveness of the marketing strategies adopted by Manakkadu Masala, Salem. The research is based on primary data collected from 120 respondents using a structured questionnaire and secondary data collected from books, journals, and company records. Percentage analysis was used for data interpretation. The study found that product quality, reasonable pricing, promotional activities, and product availability significantly influence customer satisfaction. The study concludes that effective marketing strategies improve customer loyalty and business growth.

Anthills of the Savannah: A Critique of Post-Colonial Leadership and Resistance in Chinua Achebe’s Novel

Abstract

Chinua Achebe’s Anthills of the Savannah (1987) is a searing indictment of post-independence African leadership written after a long literary silence. The novel is set in the fictional West African state of Kangan and shows the degeneration of military rule into dictatorship, exposing the betrayal of the ideals of independence. In the neocolonial system, Achebe examines the theme of corrupting power, the alienation of the elite, sycophancy, and exploitation through the intertwined destinies of three friends – Sam, the military ruler, Ikem Osodi, the revolutionary poet and journalist, and Chris Oriko, the passive intellectual – and Beatrice Okoh, a perceptive woman. The novel is a celebration of the resilience of common people and folk knowledge, represented by the anthills and the folk story of the tortoise and the leopard. It highlights the important role of intellectuals, storytellers, and the ‘New African Woman’ in resisting oppression and in imagining national rebirth. Achebe, using a range of narrative voices, political satire, and Igbo mythology, demonstrates the power of literature as a weapon of social protest and historical memory. Anthills of the Savannah turns out to be a grim diagnosis of Africa’s post-colonial condition, but also a tentative hope for a more inclusive and responsible leadership.

A Study and Analysis of Employee Satisfaction at XASCOM Info Solutions LLP

Abstract

Employee satisfaction is an important factor that influences organizational performance and employee productivity. Satisfied employees contribute positively to the success of an organization by improving efficiency, teamwork, and commitment. This study aims to analyze the level of employee satisfaction at XASCOM Info Solutions LLP. The study focuses on various factors such as salary, working conditions, career growth, employee welfare, and management support. Primary data were collected through a structured questionnaire, and secondary data were obtained from books, journals, and company records. Percentage analysis was used to interpret the collected data. The findings indicate that most employees are satisfied with the work environment and organizational policies. The study also suggests improvements in career development and

A Study on the Workplace Culture and its Impact on Employees’ Retention at Hosur

Abstract

Workplace culture plays a significant role in influencing employee satisfaction, motivation, and retention. A positive work environment encourages employees to remain committed to the organization and improves productivity. This study aims to examine the relationship between workplace culture and employee retention in selected organizations at Hosur. The study is based on primary data collected from 120 employees through a structured questionnaire and secondary data collected from books, journals, company records, and websites. Percentage analysis has been used to analyze the data. The findings indicate that a supportive work environment, effective communication, career development opportunities, recognition, and work-life balance positively influence employee retention. The study concludes that organizations with a healthy workplace culture are more successful in retaining skilled employees.

A STUDY ON SALES PROMOTION TOWARDS SKA DAIRY FOODS INDIA PRIVATE LIMITED AT SALEM

Abstract

Sales promotion is one of the most important marketing tools that helps organizations increase product awareness, attract new customers, and improve sales performance. The present study focuses on the sales promotion activities of SKA Dairy Foods India Private Limited at Salem. The main objective of the study is to analyze the effectiveness of promotional strategies adopted by the company and to evaluate customer satisfaction towards these promotional activities. The study is based on both primary and secondary data. Primary data were collected from 120 respondents using a structured questionnaire, while secondary data were collected from books, journals, company records, and websites. Percentage analysis was used to interpret the collected data. The findings reveal that promotional activities positively influence customer buying behaviour and improve customer loyalty.

A STUDY ON EMPLOYEE RETENTIONAMONGWORKERS IN I MARQUE SOLUTIONSAT CHENNAI

Abstract

Employee retention has emerged as one of the most critical challenges facing contemporary organizations, particularly in highly dynamic sectors like business process outsourcing and IT-enabled services. This research aims to analyze the various factors determining employee retention strategies within | Marque Solutions, Chennai. A sample size of 120 respondents was gathered using a descriptive research design coupled with non-probability convenience sampling. A structured served as the questionnaire primary instrumentfordataacquisition. Statisticalmethodologies, including SimplePercentage Analysis, Chi-Square Test, and Pearson Correlation Analysis, were meticulously applied to interpret the empirical dataset. The statistical findings revealed that while basic compensation packages create a primary baseline, career development opportunities, organizational culture, and professional relationship dynamics play an overwhelming role in fostering long-term organizational commitment. The study highlights that targeted interactive interventions and transparent appraisal frameworks effectively lower turnover intent. Stringent retention strategic models are recommended to nurture a resilient workforce and sustain competitive advantages.

A STUDY ON THE IMPACT OF WORK LIFE BALANCE ON EMPLOYEES PERFORMANCE AT CHRISTY FRIED GRAM INDUSTRY, TIRUCHENGODE

Abstract

In the highly competitive modern industrial ecosystem, maintaining an optimal equilibrium between professional obligations and personal well-being has emerged as an indispensable comerstone for enhancing institutional productivity. This empirical research systematically investigates the critical impact of work-life balance vectors on overall employee performance frameworks with specific reference to Christy Fried Gram Industry across the Tiruchengode region. Employing a descriptive research design, primary quantitative data was successfully gathered from a well-structured sample of exactly 120 active employees via a standardized convenience sampling methodology. Advanced statistical analysis operations, including frequency percentage distribution tests, bivariate Chi-Square hypotheses evaluations, and Pearson product-moment correlation tracking loops, were applied to the dataset. The scientific findings demonstrate that core work-life balance elements-specifically flexible shifts, stress management protocols, health welfare setups, and rapid grievance redressal exercise an overwhelming, mathematically verifiable influence over employee performance and organizational commitment. Bivariate checks established that employee age cohorts and tenure parameters significantly govern work-life satisfaction levels, highlighting strategic focal points for HR technical teams. The study concludes by proposing an optimized corporate framework tailored to compress operational stress and escalate retention benchmarks.

Strategic Partnerships Drive Technology Adoption in Indian Industry

Abstract

This document shows the required format and appearance of a manuscript prepared for SPIE e-journals. The abstract should consist of a single paragraph containing no more than 200 words. It should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (i.e., no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to six keywords should immediately follow, with the keywords separated by commas and ending with a period. Key Words: Indian industry, strategic partnership, corporate partnership, Commercial Alliance, manufacturing sector

Financial Literacy and Inclusion: A Driver of Economic Empowerment in Rural India

Abstract

Abstract: This research paper investigates the pivotal role of financial literacy and financial inclusion in fostering economic empowerment among rural populations in India. Using updated data from RBI's Financial Inclusion Index, NABARD surveys, and NSSO reports, it analyzes the influence of financial awareness and access to services on household income, savings, insurance coverage, and consumption patterns. Tables, graphs, and authentic statistical data enrich the findings. The study aims to offer policymakers a clear framework for enhancing rural economic development through targeted financial strategies. Keywords: Financial Literacy, Financial Inclusion, Rural India, Economic Empowerment, Digital Banking

Unequal Earths: Climate Justice and the Politics of Vulnerability in Select Indian Climate Fiction

Abstract

Climate change is increasingly recognised not merely as an environmental crisis but as a profoundly unequal social phenomenon. The discourse of climate justice foregrounds how marginalised communities disproportionately bear the burdens of ecological degradation despite contributing least to its causes. This paper examines the representation of climate justice and vulnerable communities in select Indian literary texts, including The Hungry Tide (2014), Gun Island (20196), Latitudes of Longing (2019), The Butterfly Effect (2019), and A Guardian and a Thief. Drawing on postcolonial ecocriticism, eco-narratology, and intersectionality, the study explores how Indian fiction narrates environmental injustice through caste, class, gender, and indigeneity. The paper argues that these texts not only depict ecological crises but also critique structural inequalities and advocate for a more inclusive and ethical environmental imagination.

An evaluation of employee engagement rate and employee engagement strategies on deriving organizational succes

Abstract

This study evaluates the employee engagement rate and employee engagement strategies in achieving organizational success. Primary data were collected through a structured questionnaire, and secondary data were obtained from books and journals. The findings indicate a strong positive relationship between employee engagement and organizational success. The study concludes that effective engagement strategies improve employee satisfaction, performance, commitment, and overall organizational growth.

A STUDY ON EMPLOYEES JOB SATISFACTION USING BUSINESS ANALYTICS WITH REFERENCE TO XASCOM INFO SOLUTIONS LLP AT CHENNAI

Abstract

In the hyper-competitive software engineering and knowledge-services vertical, maximizing employee job satisfaction functions as a primary indicator for reducing operational talent drain and driving organizational innovation. This empirical research explores the core determinants of employee job satisfaction inside Xascom Info Solutions LLP, Chennai, by integrating modern business analytics data frameworks. Moving away from standard descriptive HR practices, this investigation combines descriptive percentage distributions, bivariate Chi-Square optimization matrices, and Pearson product-moment correlation tracking to analyze a representative cross-sectional sample of 120 full-time technology professionals. Data was systematically gathered using a structured questionnaire that translates traditional human relations indicators such as technological infrastructure support, compensation health, management transparency, and work-life balance frameworks into discrete analytics variables. The empirical findings indicate that while financial compensation defines the baseline layer, predictive analytics tools demonstrate that career path clarity and data-driven management frameworks exert a dominant influence over long-term retention intent. Bivariate testing confirmed that educational qualifications do not systematically skew employee satisfaction with analytics-driven corporate learning systems. The study concludes by outlining an actionable data-driven talent management architecture designed to optimize operational workplace health. Keywords: Job Satisfaction, Business Analytics, Predictive HR Metrics, Workforce Optimization, Xascom Info Solutions.

A study on the customer satisfaction towards product in thangavelu textile private limited

Abstract

Customer satisfaction plays an important role in the success and growth of every business organization. In the textile industry, maintaining product quality and customer satisfaction is essential due to increasing market competition. This study focuses on analyzing the level of customer satisfaction towards the products of Thangavelu Textile Private Limited at Salem. The study aims to identify customer opinions regarding product quality, pricing, durability, and service.

Design of an FPGA-Based ECG Signal Analysis System

Abstract

ECG signals are very small in amplitude and are easily affected by noise, which can make heart-rate measurement and R-peak detection unreliable. In this paper, a real-time ECG acquisition and processing system is developed using the Nexys A7 FPGA to achieve accurate and fast cardiac monitoring. The ECG signal is collected through surface electrodes and conditioned using the AD8232 module, then digitized with the Artix-7 XADC. A 0.5–40 Hz bandpass filter is applied to remove noise and baseline drift. R-peaks are detected using an adaptive thresholding method combined with an exponential moving average and a refractory period, ensuring reliable detection. The heart rate is calculated from RR-intervals and displayed on the onboard 7-segment display, while the processed ECG signal is converted back to analog for oscilloscope observation. The results show that the FPGA-based system provides accurate ECG processing with low delay, making it suitable for portable and embedded cardiac monitoring applications.

A Study on the Impact of Digital Banking and Fintech Services on Customer Satisfaction with Reference to ESAF Small Finance Bank at Salem

Abstract

The rapid evolution of digital infrastructure has completely transformed the traditional banking landscape, driving financial institutions toward absolute financial inclusion and seamless digital service architecture. This dynamic empirical research systematically investigates the critical impacts of digital banking channels and fintech services on overall customer satisfaction frameworks with specific reference to ESAF Small Finance Bank across the Salem region. Employing a comprehensive descriptive research design, primary quantitative intelligence was successfully gathered from a well-structured sample of exactly 120 active retail banking customers via a standardized convenience sampling methodology. Advanced statistical analysis operations, including frequency percentage distribution tests, bivariate Chi-Square hypotheses evaluations, and Pearson product-moment correlation tracking loops, were applied to the empirical datasets. The scientific findings demonstrate that core digital banking elements specifically transaction security architecture, application processing agility, user-interface accessibility, and rapid customer care support operations-exercise an overwhelming, mathematically verifiable influence over customer satisfaction and institutional trust. Bivariate checks firmly established that customer education profiles and financial parameters critically govern digital adaptation levels, highlighting strategic focal points for bank technical teams. The study concludes by frameworking an optimized, user-centric integrated fintech satisfaction delivery matrix specifically designed to minimize transactional attrition and escalate retention benchmarks.

AquaLens: A Machine-Learning Decision-Support System for Industrial Water Footprint Reduction

Abstract

I'm pursuing an M.Tech in Computer Science

A STUDY ON THE CUSTOMER SATISFACTION TOWARDS AFTER SALES, SERVICE AT SRIRAM BAJAJ SHOWROOM AT RASIPURAM

Abstract

This study focuses on the organizational performance and customer satisfaction of Bajaj Auto. The main objective of the study is to understand the company's services, product quality, employee performance, and customer satisfaction level. Data were collected through questionnaires and customer feedback methods The study analyzes important factors such as product quality, service satisfaction, pricing, and staff behavior.

A Comprehensive Empirical Study on Employees Stress Management in Work Place at Jeyam Multi Speciality Hospital, Mallur, Salem

Abstract

In the modern, high-pressure healthcare sector, managing workplace stress among medical and administrative staff has emerged as a primary challenge linked directly to patient care quality, organizational efficiency, and clinical safety. This extensive empirical research investigates the multi-dimensional workplace stressors and coping mechanisms determining employee profiles within Jeyam Multi Speciality Hospital located at Mallur, Salem. Employing a strict descriptive research methodology, primary quantitative datasets were mobilized from a structured cross-sectional sample size of 120 full-time active hospital staff via non-probability convenience sampling channels across varying emergency and inpatient shifts.

A Study on the Effectiveness of Customer Relationship Management (CRM) Strategies on Customer Loyalty with Reference to ESAF Small Finance Bank at Salem

Abstract

In the highly competitive and dynamically evolving retail banking industry, Customer Relationship Management (CRM) has emerged as a core strategic mandate for ensuring long-term institutional sustainability, market penetration, and customer retention. This empirical study comprehensively. investigates the structural effectiveness of CRM strategies and their direct downstream impact on fostering long-term customer loyalty within ESAF Small Finance Bank situated across branches in Salem, Tamil Nadu. By leveraging a strict descriptive research methodology, primary quantitative responses were compiled utilizing a structured cross-sectional digital questionnaire administered to a sample size of exactly 120 active retail banking customers chosen via non-probability convenience sampling parameters. Advanced statistical tools, including percentage distribution testing, bivariate Pearson Chi-Square verification arrays, and Pearson product-moment correlation tracking loops, were used to examine the underlying behavioral datasets. The analytical outcomes reveal that key transactional metrics specifically digital banking interface ease, service delivery responsiveness, transparency in financial charges, and customized financial solutions exert an overwhelming and highly visible influence over individual customer loyalty vectors. The research concludes by formulating robust and integrated strategic CRM roadmap precisely customized to bolster operational banking efficiency, maximize touchpoint engagement,

LUNA-X: A Hybrid Deep Learning Framework for Automated Lung Nodule Detection, Disease Classification, and Three-Dimensional Pulmonary Visualization from CT Images

Abstract

LUNA-X is a hybrid deep learning framework that couples a ResNet backbone with a self-attention Vision Transformer (ViT) to automate pulmonary nodule detection and classification in CT scans. The system improves clinical decision support by combining detailed local texture analysis with global spatial dependencies, achieving a 95.60% Dice Similarity Coefficient on the LIDC-IDRI dataset for identifying malignancies, pneumonia, and fibrosis.

Impact of Digitalization in Marketing Communication on Consumer Perception in Southern Odisha

Abstract

Digitalization of marketing is an approach used by organizations to brand and coordinate their communication efforts. In the latest couple of years, there has been a quantum growth in the number of internet users and the awareness towards the World Wide Web has increased in India. The various opportunities that it presents have been recognized and companies have started making plans to include internet, e-commerce and e-business in their scheme of things. This paper aims at exploring detailed information on major domains of the dissertation topic by reviewing past research, books and related articles. The study aims to understand the massive contribution of digital marketing in terms of consumer value; the researcher has used stratified probability sampling with a sample size of 200 numbers across Brahmapur, a major commercial city in Southern Odisha. Thus, the present study adopts descriptive research design and undertakes the survey method with questionnaire as a research instrument in order to collect primary data required in the research. The study finds that consumers rely upon more than one medium in order to enhance their brand related knowledge. The study also reveals that main reason for growing importance of Digital marketing is the increasing literacy about internet among people. The major benefits of Digital marketing are its capability of interaction between consumers and marketers followed by availability of wide range of information & ease of shopping. These benefits make Digital marketing superior than traditional marketing. Key Words: Key words: digital marketing communication, consumer perception, DMC, IMC, digitalization.

An Empirical Study on Urban Materialism and Consumer Opportunity

Abstract

Materialism is the ‘devotion to material desires, to the neglect of spiritual matters; a way of life, based entirely upon material interests’ (The Oxford English Reference Dictionary, 1995). The materialism of the consumer represents the degree of attachment a consumer has to worldly possessions. The increased exposure of the consumer to occidental culture through media and in the backdrop of rapid economic growth and transforming societal norms in the urban parts of the country, an empirically oriented analysis was required to understand the details of the consumer materialistic tendencies. The tendency of materialism fetches both novelty and challenges for marketers in order to stabilize the demand and adding values to their products. The study is confined only to Bhubaneswar city and attempted to investigate the significance of gender difference in terms of materialistic tendencies of urban consumers to ensure availability of a valid and reliable means of measuring individual differences. The sample size consisted of 96 respondents under the age of 30, residing in the city of Bhubaneswar. Materialism was measured using the Richins and Dawson (1992), 18 - item, 5 - point, agreement - disagreement scale and subjected to t - test in order to compare materialism among the two independent samples consisting of men and women. The study revealed that in two of the eighteen variables used to measure materialism, there exists a significant difference between the materialistic tendencies of men and women living in urban India. Key-words: urban consumers, wellbeing, Novelty, Materialism, values, satisfaction.

TRAC: AN INTELLIGENT DECOUPLED IAM FRAMEWORK FOR SECURE CLOUD INFRASTRUCTURE

Abstract

The foundation of cloud infrastructure is made up of cloud access control protocols. Industries have been taught to utilize role-based access to provide people permissions for decades. Despite decades of development, role explosions that lead to excessive user privileges continue to plague cloud identity and access management systems. The entire cloud infrastructure may be at risk due to this issue. This study presents an ultra-modern framework based on the fundamental ideas of decoupling and dynamic job assignment to address this problem. Instead of considering roles, the suggested framework bases user access on the tasks that users must do.

Reimagining Educational Assessment through Human–AI Collaborative Intelligence

Abstract

Abstract The evolving educational landscape demands assessment systems that transcend traditional measures of academic achievement and actively contribute to improving learning outcomes. Despite sustained curriculum reforms, a significant proportion of learners continue to progress through school without mastering foundational competencies, resulting in cumulative learning deficits that impede higher-order thinking and problem-solving abilities. Diagnostic assessment, integrated with systematic remediation, offers a learner-centred approach that identifies individual learning gaps and enables targeted instructional interventions. This paper examines the conceptual underpinnings, design, implementation, and educational implications of a comprehensive diagnostic assessment framework developed through collaborative efforts involving curriculum experts, psychometricians, technology specialists, and educators. Drawing upon implementation experiences and contemporary educational research, the paper discusses how diagnostic assessment supports competency-based education, aligns with the vision of India's National Education Policy (NEP) 2020, and contributes to preparing learners for the demands of the twenty-first century. The paper also analyses implementation challenges, including teacher readiness, educational change management, and post-pandemic learning recovery, while proposing future directions for data-informed personalized learning. Keywords: Diagnostic assessment, remediation, competency-based education, psychometrics, formative assessment, NEP 2020, future-ready learners.

Comparative Analysis of Indian Higher Education Rankings: IIRF, Outlook-I Care, and India Today–MDRA

Abstract

This study critically examines three major private, non governmental Indian higher education ranking frameworks: the Indian Institutional Ranking Framework (IIRF), Outlook–ICARE, and India Today–MDRA. As higher education in India expands, rankings have become essential tools for benchmarking institutional performance, guiding student choices, and shaping institutional strategy. Unlike government endorsed systems such as NIRF, these private rankings operate independently, blending objective data with perceptual surveys and emphasizing parameters such as pedagogy, innovation, employer perception, infrastructure, and governance.

Performance Evaluation of Top 5 Large-Cap Mutual Funds in India

Abstract

The acceptance of mutual funds among investors has grown consistently over the years among retail and institutional investors due to the benefits they offer portfolio diversification, professional fund management, and opportunities for long-term capital growth. Assessing the performance of these investment schemes is important for helping investors make well-informed financial decisions. This study evaluates and compares the investment efficiency of choosen large-cap mutual fund schemes operating in India by applying risk-adjusted performance measures. The analysis draws upon secondary information obtained from Net Asset Values (NAVs) associated with five chosen large-cap mutual fund schemes over one-year, three-year, and five-year periods. To measure performance, the study employs widely recognized indicators such as the Sharpe, Treynor indices, and Jensen's Alpha, which assess returns while considering the degree of investment risk. Research also examines key variables including portfolio returns, market returns, the risk-free rate, beta and standard deviation to ensure a detailed assessment of the choosen mutual fund schemes. By adopting a risk-adjusted approach, the study offers a systematic comparison of mutual fund performance over varying investment time frames and provides meaningful guidance for investors, researchers and financial professionals.

“Boundary Regularity of Optimal Transport Maps in the Monge Problem on Riemannian Manifolds”

Abstract

We study the boundary regularity of the optimal transport maps in the Monge problem on Riemannian manifolds. For the quadratic cost induced by the Riemannian distance, we show that the optimal map is Hölder continuous up to the boundary when the source domain has a strictly convex boundary and the ambient manifold has bounded sectional curvature. Our argument is based on a combination of c-convex analysis and barrier constructions adapted to the Fermi coordinates in a neighbourhood of the boundary. We also give explicit counterexamples showing that regularity may fail when the convexity of the boundary is violated. These results generalise classical boundary regularity results of the Euclidean theory to curved geometries and clarify the geometric conditions required for continuity of optimal maps. We discuss applications to geometric PDE's on manifolds with boundary, and further open questions.

Sculpt Studio: An AI-Powered Web Platform for Personalized Fitness Coaching, Scheduling, and Conversational Support

Abstract

Maintaining a consistent and effective fitness routine is difficult for most people without access to personalized coaching. In many settings, users rely on generic workout plans found online or on rigid gym schedules, which do not adapt to individual goals, fitness levels, or availability. While personal trainers can provide tailored guidance, this option is often expensive and difficult to schedule consistently. This paper presents Sculpt Studio, a practical AI-based web platform designed to make personalized fitness coaching accessible and easy to use. The system analyses user profile data, activity history, and stated goals to generate adaptive workout recommendations. Instead of focusing only on recommendation accuracy, the design also considers how easily the guidance can be understood and acted upon by everyday users. The platform combines three core capabilities: an AI-driven workout recommendation engine, an integrated booking and scheduling module for sessions and classes, and a conversational AI chatbot that answers fitness-related queries and assists with navigation. Initial testing shows that recommendations remain consistent across repeated use and that the scheduling and chatbot features reduce the effort required to plan and maintain a routine. Overall, the study highlights that combining AI-driven personalization with a user-friendly design approach can make fitness coaching more accessible, consistent, and scalable.

A Moving-Average Crossover Analysis of the Nifty 50 (2010–2022)

Abstract

Abstract - This study provides a comprehensive, long-horizon empirical evaluation of the 50-day/200-day simple moving-average (SMA) crossover — popularly known as the “Golden Cross” and “Death Cross” — on the Nifty 50, India's benchmark equity index, using 3,077 daily observations from January 2010 to June 2022. Beyond documenting the timing of eleven Golden Cross and eleven Death Cross events, this paper constructs and backtests a full systematic trading strategy built on the crossover rule, benchmarks it against a passive buy-and-hold approach, decomposes performance by market regime and sub-period, tests sensitivity to four alternative moving-average pairings, quantifies transaction-cost drag, and examines volatility dynamics surrounding signal events. The mechanical crossover strategy is found to underperform buy-and-hold substantially on a raw-return basis (4.01% vs. 9.90% CAGR) while achieving materially lower realised volatility (12.54% vs. 22.52% annualised) — a trade-off that nets out to a lower risk-adjusted (Sharpe) ratio for the trend-following approach in this sample. We place these findings in the context of efficient-markets and technical analysis literature, explain the rationale for why the signal always has a time lag and thus cannot be considered a true leading indicator, and describe the implications of the findings both in practice and in the context of academic research using a developed-market signal and data applied to an emerging market that experiences greater price volatility. Key Words: technical analysis, moving average crossover, Golden Cross, Death Cross, Nifty 50, trend following, emerging markets, market efficiency, backtesting.

Rupee-Cost Averaging versus Lump-Sum Investing in a Structurally Bullish Emerging Market: Evidence from the Nifty 50 (2012–2022)

Abstract

Abstract - Systematic Investment Plans (SIPs) are marketed widely to Indian retail investors as a disciplined, risk-reducing alternative to lump-sum investment through periodic fixed-amount purchases, commonly termed rupee-cost averaging. This paper presents a holistic empirical test of that claim using Nifty 50 index data from January 2010 to June 2022. Beyond a single-window comparison, the analysis constructs 91 overlapping five-year and 31 overlapping ten-year rolling windows across the full sample to assess how consistently lump-sum investing outperforms SIP across different starting points; computes annualised volatility, maximum drawdown, and Sharpe ratios for both strategies; stress-tests outcomes using a crash-timed worst-case start date; and decomposes results across two five-year sub-periods. Across the primary January 2012–June 2022 window, lump-sum investing produced a terminal value of ₹49.3 lakh against ₹25.0 lakh for an equivalent-capital SIP, and lump sum outperformed SIP in 97.8% of five-year and 100% of ten-year rolling windows tested. Despite this consistent terminal-value advantage, SIP’s money-weighted annualised return (XIRR) exceeded lump sum’s CAGR in the median rolling window, a result reconciled by the differing amounts of time capital is at risk under each strategy. We discuss why SIP nonetheless retains behavioural and cash-flow-management value for the large share of retail investors who do not have a lump sum available at the outset, and outline the market conditions under which SIP’s relative performance would improve. Key Words: systematic investment plan, rupee-cost averaging, dollar-cost averaging, lump-sum investing, Nifty 50, retail investing, emerging markets, behavioural finance, rolling-window analysis.

Airport Service Quality Assessment: A study of ASQ parameters at major AAI airports (2024-2025)

Abstract

This study offers a data-driven analysis of Airport Service Quality (ASQ) performance at 16 major airports operated by the Airports Authority of India (AAI) during the 2024-2025 period. The research draws on eight quarters of standardized ACI World survey data. Covers 32 service parameters and includes 120 pooled cross-sectional observations. The study isolates the true structural drivers of Overall Passenger Satisfaction by applying correlation analysis, ordinary least squares regression, standard deviation-based volatility modelling, and gap analysis. The analysis reveals that staff interactions dominate as predictors of Overall Satisfaction. Courtesy and Helpfulness of Airport Staff (r = 0.742), Ambience (r = 0.695), and Health Safety (r = 0.708) together explain 54.4% of the variance in a parsimonious OLS model. Meanwhile, Wi-Fi Service Quality and Availability of Charging Stations present a different picture. Their base scores were low in 2024, 3.91 and 4.32, respectively, and they emerged as the highest-volatility, highest-improvement parameters across both years. This points to a pattern of systemic underinvestment in digital infrastructure that is now being corrected, though unevenly. Srinagar (SXR) stood out as a statistically significant underperformer relative to the system mean (t = 4.42, p = 0.022), with a structural deficit concentrated in commercial amenities, entertainment, and digital connectivity. Indore (IDR) and Goa (GOI) anchor the performance frontier and provide replicable benchmarks. A behavioural economics framework distinguishes Hygiene Factors from Delighter parameters. Deficiencies in Hygiene Factors produce disproportionate dissatisfaction, while Delighters generate satisfaction uplift when exceeded. Applied strategically, this framework supports a targeted capital expenditure prioritization matrix. The matrix recommends an infrastructure investment sequence designed to maximize returns on Overall Satisfaction. By the ACI World elasticity estimate, each percentage point of satisfaction gained produces a 1.5x multiplier effect on non-aeronautical revenue.

A STUDY ON IMPACT OF SOCIAL MARKETING ON CONSUMER BUYING BEHAVIOUR INSTANT CHAPTHI PRODUCES TOWARDS ATVE FOODS, AT SALEM.

Abstract

Social media marketing has emerged as one of the most influential promotional tools in the modern business environment. The rapid growth of social networking platforms such as Facebook, Instagram, YouTube, and WhatsApp has significantly changed the way organizations communicate with consumers and promote their products. Businesses increasingly utilize social media marketing to create brand awareness, engage customers, and influence purchasing decisions. In the food industry, social media plays a vital role in shaping consumer perceptions and encouraging product adoption, particularly for convenience food products that cater to the fast-paced lifestyle of modern consumers.

Impact of Digital Automation on Operational Efficiency: The Mediating Role of IT Investment Intensity and the Moderating Role of Firm Size

Abstract

Abstract- Digital automation - comprising artificial intelligence, robotic process automation, enterprise resource planning, cloud computing, and Internet of Things - is changing how businesses manage production, delivery of services, and decision-making processes. In this research paper, a theoretical framework and a testable model have been developed to investigate the effect of firm-level Digital Automation Index (DAI) based on content analysis of corporate disclosures on Operational Efficiency (OE) of Indian listed companies involved in automation-intensive industries such as industrial technology, manufacturing, engineering, automobiles, and electronics. Based on the Resource-Based View and Dynamic Capabilities perspective, the testable model asserts that IT Investment Intensity (ITI) acts as a mediator between DAI and OE, and Firm Size (FS) moderates their relationship. A panel data approach for the years 2020-2025 involving 30-50 listed firms (180-300 observations) has been suggested, using secondary data sources such as annual reports, sustainability reports, and financial statements. This paper describes how Digital Automation Index will be calculated, operationalize all the constructs, provides five hypotheses, and presents a nine-phase statistical analysis method including descriptive statistics, panel diagnostics, fixed or random-effects regressions, mediation and moderation analysis, and robustness tests.Key Words: digital automation, operational efficiency, IT investment intensity, firm size, Industry 4.0, panel data, Indian listed companies.

GenAI HRM Recruitment Article

Abstract

The development of generative artificial intelligence (GenAI) has seen a swift transition from a novel HR pilot study to an operational application, with a dramatic increase in GenAI adoption in human resources management in less than one year. This paper will review how the adoption of GenAI is transforming the quality of hiring and decisions made by managers in the field of human resource management (HRM). In order to do this, I will use evidence from industry benchmarking reports, market research on the HR-technology landscape, as well as scientific literature on algorithmic hiring, technology acceptance, and human-algorithm collaboration. It becomes evident from the evidence below that the application of GenAI provides quantifiable benefits in terms of efficiency improvements throughout the hiring process, including decreases in time-to-hire, cost-per-hire, and resume screening times, as well as increases in quality-of-hire and retention among early hires if used as part of a properly governed workflow. At the same time, the reviewed evidence also uncovers several consistent and even growing risks, including disparate impact created through algorithmic bias in the training datasets and algorithm itself, as well as human-in-the-loop studies suggesting high propensity to replicate the recommendations of biased algorithms. In this paper, I refer to the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and Structuration Theory as explanations for not only what causes adoption but also when the use of GenAI improves versus diminishes the quality of decision-making. The conclusion from this analysis is that the net impact of GenAI on recruitment quality depends on governance decisions made by people rather than the impact of the technology per se.

MRCL-FECG: Morphology–Rhythm Contrastive Representation Learning for Label-Efficient Fetal Arrhythmia Classification

Abstract

Fetal electrocardiogram (FECG) analysis is a valuable method for evaluating fetal cardiac activity and diagnosing abnormal cardiac rhythms in pregnancy. Reliable fetal arrhythmia classification models are, however, hampered by the lack of labeled FECG data because obtaining such data is challenging and time-consuming. Existing deep learning (DL) approaches predominantly rely on supervised learning and may suffer from overfitting and limited generalisation when trained on small datasets. Moreover, fetal arrhythmia is also reflected in a morphological variation of the FECG waveform and temporal abnormalities of cardiac rhythm. Most of the classification methods used, however, only learn these features from a small amount of labelled data, and they fail to explicitly make use of the relationship between the morphology of the waveforms and the rhythm pattern to which they belong. This article introduces a Morphology–Rhythm Contrastive Representation Learning framework (MRCL-FECG) for label-efficient fetal arrhythmia classification to overcome these drawbacks. Pre-processing and segmentation of FECG are performed first to get fixed-length windows, in which cardiac rhythm information is represented by the corresponding RR-interval and Fetal Heart Rate (FHR) sequences. The FECG waveforms are used to extract the morphological representation using lightweight one-dimensional convolutional neural networks (1D-CNNs) and the temporal rhythm using the RR-interval and FHR sequence. The resulting morphology and rhythm representations are projected in a shared feature space that encourages agreement among complementary representations derived from the same cardiac segment, and differentiates the representation of different segments, while being unrelated to one another. The learned representations are then fine-tuned for fetal arrhythmia classification with a few-shot learning method with a small number of labelled fetal data.

AI IN FORECASTING GEOPOLITICAL RISK AND ITS IMPACT ON ESG AND CORPORATE FINANCIAL STRATEGY EVIDENCE FROM THE INDIAN ENERGY SECTOR

Abstract

Abstract – Geopolitical instability has become one of the determinants of business financial decisions; thus, businesses use artificial intelligence (AI) to forecast their risks. This paper aims to find out if the usage of greenwashing, being a proxy for the credibility gap between ESG positioning and actual risk exposure of the firm, has an effect on stock price volatility at 20 energy firms in India, listed on the exchange for the 2019-2023 period (100 firm-year observations). The research investigates the moderating role of board independence and the mediating role of the market-to-book ratio. The sample companies' financial performance is predicted using Artificial Intelligence techniques, including Linear Regression, Random Forest, Gradient Boosting and XGBoost. Out-of-sample predictions are provided for 2024-2026. Panel data tests include panel diagnostics, correlation, variance inflation factor (VIF) test, fixed/random effects regression, Hausman test, Sobel/bootstrap mediation test and moderated regression. It can be seen from the above-mentioned results that the ratio of market to book is a significant predictor of volatility and significantly mediates the effect of greenwashing on volatility, and at the same time board independence is a positive predictor of volatility; the Random Forest model demonstrates the best out-of-sample predictive stability and market to book, as well as firm size, is found to be the most significant predictor of volatility.

Green Banking 5.0: An AI-Driven ESG and Climate Risk Framework for Sustainable Banking in India

Abstract

The transition toward sustainable finance has become a strategic priority for the global banking industry due to increasing climate change risks, stricter environmental regulations, and the growing demand for responsible investment practices. In India, banks are progressively adopting digital technologies and environmentally sustainable financial services; however, the integration of Artificial Intelligence (AI), Environmental, Social and Governance (ESG) analytics, climate risk assessment, and carbon-aware lending into a unified decision-making framework remains limited (1). Existing studies primarily focus on digital banking and paperless operations, with comparatively less attention given to AI-enabled sustainability intelligence and predictive environmental risk management (2). The present study proposes a Green Banking 5.0 framework that integrates AI-driven analytics, ESG evaluation, climate-risk assessment, and carbon-conscious lending to support sustainable financial decision-making in the Indian banking sector. The study employs a descriptive and analytical research design using both primary data collected from 120 banking customers through a structured questionnaire and secondary data obtained from published literature, banking reports, government publications, and sustainability documents (3). Statistical analysis includes percentage analysis, comparative interpretation, and graphical representation to evaluate customer awareness, adoption of green banking services, and perceptions regarding sustainable banking initiatives (4). The findings indicate that customers increasingly prefer digital and environmentally friendly banking services because of their convenience, operational efficiency, and reduced environmental impact. Furthermore, AI-assisted ESG assessment and climate-risk analytics have significant potential to improve credit appraisal, strengthen risk management, and encourage sustainable investment decisions while supporting India's long-term climate and net-zero objectives (5). Despite these opportunities, several challenges remain, including inadequate ESG data availability, cybersecurity concerns, technological infrastructure limitations, and the absence of standardized sustainability reporting frameworks (6). The proposed Green Banking 5.0 framework provides a comprehensive conceptual model that combines digital transformation with sustainable finance and offers practical guidance for banks, regulators, and policymakers seeking to strengthen environmentally responsible financial systems. The study contributes to the emerging literature on AI-enabled sustainable banking by presenting an integrated approach for future-ready banking aligned with Industry 5.0 principles and global sustainability goals (7).

Artificial Intelligence Adoption and Corporate Financial Decision-Making: Examining the Influence of Digital Maturity on Investment, Budgeting, Risk Management, and Strategic Planning in Indian Companies

Abstract

Artificial Intelligence (AI), covering areas like machine learning, natural-language processing, predictive analytics, and increasingly agentic decision-support systems, is gradually becoming an integral part of the finance function of Indian corporations, going far beyond back-office automation to become an integral part of the decision-making process involved in investment analysis, budgeting and forecasting, risk management, and financial planning, to name a few. The proposed paper will develop a conceptual framework and a survey-based model, testing the impact of AI Adoption (AIA) on the Quality of Corporate Financial Decision-Making (FDMQ) among Indian companies. Building on theories from the Resource-Based View, Dynamic Capabilities, and Technology-Organization-Environment models, the proposed research will suggest that the mediating role in the relationship between AIA and FDMQ is played by AI-Enabled Analytics Capability (AEAC), while the moderating role in this relationship belongs to the firm's Digital Maturity (DM). The novelty of this research project compared to the existing literature lies in its intention to collect primary data, using the structured questionnaire to survey the CFOs, controllers, FP&A managers, risk managers, and financial analysts at Indian corporations, suggesting 250-400 survey respondents. In this research paper, an outline of the development and validation process of the four scales - AI Adoption, Analytics Capability, Digital Maturity, and Financial Decision Making Quality, and formulation of five hypotheses have been done along with a nine-step analysis plan using PLS-SEM methodology for the purpose of testing mediated and moderated mediation.

Influence of Perceived Organizational Support on Employee Engagement, Job Satisfaction, and Employee Retention Across Different Generations Working in Indian Organizations Today

Abstract

This study examines the influence of Perceived Organizational Support (POS) on employee engagement, job satisfaction, and employee retention across different generations working in Indian organizations. The research aims to understand how employees' perceptions of organizational support shape their workplace attitudes and retention intentions while identifying generational differences in these relationships. A quantitative research design is proposed, using a structured questionnaire to collect primary data from employees across diverse industries in India. The collected data will be analyzed using descriptive statistics, reliability analysis, correlation, regression, and Structural Equation Modeling (SEM) to examine the direct and indirect relationships among the study variables. The findings are expected to demonstrate that higher perceived organizational support significantly enhances employee engagement and job satisfaction, which in turn strengthen employee retention. The study also anticipates that generational differences influence the magnitude of these relationships, reflecting varying workplace expectations and motivational factors among employees. The research contributes to human resource management literature by providing empirical evidence on the strategic role of organizational support in building an engaged and committed multigenerational workforce and offers practical implications for designing inclusive HR policies that improve employee retention and organizational effectiveness in the Indian context.

Integrated Project Delivery Readiness and Schedule Risk Index (IPDRSRI): A DPMS and Primavera P6-Based Predictive Framework for Engineering, Procurement, Construction, Commissioning, and Contract Closure in IISCO Steel Plant Projects

Abstract

Since large EPC projects for steel almost always experience delays, cost overruns, design blockages, material supply issues, and contractual problems, oversight becomes crucial. Today, most projects monitor progress by percentages, milestones, and historical timeline reviews, which look backward quite effectively but are unaware of upcoming issues such as delayed drawings, shortages of materials, on-site congestion, unaddressed blockers, or conflicts in agreement management. Now, imagine combining Oracle Primavera P6 schedules and DPMS readings into one consolidated metric. Here, all aspects—engineering progress, procurement status, construction, pre-startup, money management, contract conditions, schedule variances, critical path, post-legal activities, and post-completion verification—get integrated into a single indicator representing a project's readiness instead of many separate, growing reports. Instead of relying on speculation, teams see genuine cross-functional synchronization, as each part of the operation remains compartmentalized. Since timing impacts the bottom line, tracking the critical path adds leverage where it is needed most. Added to that is the advantage of a DPMS feeding real-time updates, which then drive predictive behavior. This becomes a physical reality rather than an abstract model driven by multiple interactions simultaneously. Signs of weakness are generally observed relatively late in the design phase, but not in this instance. Here, the data stream originates from DPMS readings for the entirety of the work—the cost structure, blocked areas, engineering designs, and delivery information. If delays occur, they are instantly noted. Rather than waiting until the deadline passes, critical alerts are transmitted in advance of key dates. Easily fractured timelines are clearly identified. All transactional documents, including contracts and invoices, are fed into the model, along with time spent during inspections and engineering task status. Team communications are also processed to track such things as changes in the longest path and delays in shipments and dispatch. Any signs of risk are immediately highlighted. This allows managers to clearly see areas that require attention before the negative impact grows. The use case for the model clearly applies to the broader industrial complex that involves steel production. That can encompass areas where molten metal flows under the rigors of temperatures that rework materials. It extends to facilities that convert molten steel into a solidified form through various steps. Similarly, coke plant operations using coal as input have a direct application. Facilities involved with burning limestone or dolomite may also find its structure suitable for their processes. The framework easily extends to facilities managing significant tonnages of ore, scrap, or flux, as well as their associated auxiliary works.