Authors - Smita Kalokar, Ritesh Sule, Dinesh Mirkute Abstract - Demographic dividend is an economic theory that suggests a potential for growth when a population has a higher ratio of working-age people. This concept highlights the economic advantages that can arise from shifts in a society's age structure. In India, there has been a growing trend of dual-career couples, where both partners are employed. This phenomenon has emerged as a response to escalating inflation and elevated living standards, necessitating both partners to work concurrently. While this arrangement contributes to financial stability and social standing, it also presents numerous challenges. These challenges encompass psychological strain on the couples, difficulties in child-rearing, struggles with work-life balance, inadequate support from organizations and families when required, insufficient spousal aid in home responsibilities and career development, and disruptions in personal life. Women, who often shoulder the primary responsibility for childcare and family matters, face additional pressure when entering the workforce as they must juggle job duties with familial obligations. The research seeks to investigate the challenges encountered by dual-career couples and their strategies for managing the demands of work and personal responsibilities. The paper examines these issues and suggests various strategies at both individual and organizational levels to address and manage these concerns. This study utilizes a descriptive methodology, drawing upon an analysis of existing scholarly literature and direct observations in the field
Authors - Rivu Bhattacharjee, Ramisa Nawar, Farzana Sadia, Mahady Hasan, Rifat Ara Rouf, M. Rokonuzzaman Abstract - The core idea of DevOps is to integrate development and operations teams to improve software delivery, yet existing Critical Success Factor (CSF) frameworks remain largely untested in emerging economies where resource constraints exist and organizational cultures differ substantially. This study seeks to quantitatively validate and refine existing CSF models within the developing countries’ software industries. A cross-sectional survey of 23 DevOps professionals from leading software firms mapped 19 prior CSFs against perceived project success. Multiple linear regression analysis evaluated model explanatory strength, checked multicollinearity via Variance Inflation Factors (VIFs), and applied two-tailed t-tests to identify predictive factors. The regression model explained 95.6% of the variance in DevOps project success, identifying five key CSFs: regular DevOps events, collaborative team culture, complex performance engineering integration, build automation, and CI/CD pipe-line challenge mitigation. Regular DevOps events, collaborative culture, and streamlined automation emerged as primary success drivers. Results confirm core CSF frameworks while providing empirically grounded recommendations for resource-constrained contexts. Future research should employ larger multi-regional samples and qualitative methods.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho, Ulises Edison Ruiz Mendoza Abstract - The main objective of this study was to create a Geomechanical Zoning Map for a polygon located in the northwestern area of Loja, based on the soil bearing capacity derived from lithological and topographic surveys, at a 1:6000 scale. The lithological map identified six zones with different soil classifications. DCP tests were conducted at six demonstration points, including one comparison point, to evaluate the bearing capacity. Results showed varying soil strengths, with the highest CBR value of 79.36% in the center of the polygon (ML lithology) and the lowest CBR value of 18.1% in the upper left margin (OH lithology). Overall, the soils ranged from good to acceptable in bearing capacity. It was also noted that approximately 70% of the polygon area is already urbanized.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho Abstract - This study presents an integrated geotechnical analysis to evaluate the slope stability of a critical sector located along the lateral road of Ángel Felicisimo Rojas, near the Punzara lagoon in Loja, Ecuador. The site, characterized by steep slopes and weak lithological units such as silty sands, sandy clays, and weathered conglomerates, was identified as highly susceptible to landslides. A comprehensive methodological framework was employed, combining field surveys, geophysical profiling using the dipole-dipole method, laboratory soil classification, and shear strength testing. Mechanical properties such as cohesion and internal friction angle were determined and used to model slope stability under static and pseudo-static conditions using GEO5 software. The initial safety factor was found to be below recommended limits, indicating severe geotechnical risk. Through the evaluation of stratigraphic profiles and failure modes, the study out-lines the potential failure mechanisms and proposes slope reinforcement strategies to mitigate hazards. This research underscores the importance of integrating geological, geomorphological, and geotechnical data to support informed engineering decisions in landslide-prone Andean environments.
Authors - Grant Royd Howard Abstract - Oversight of autonomous organizational systems (AOS) decision making is imperative for the betterment of and the prevention of harm to humanity. Human decision making oversight literature is extensive across many disciplines, but little exists about AOS decision making oversight. This was the research problem addressed, guided by the question, what fundamental concepts are essential for AOS decision making oversight? The question was answered by developing an original conceptual framework. To this end, a conceptual research design was applied to answer the research question. Conceptual research is effective for generating new knowledge using intellectual tools, logical argumentation and assimilation of interdisciplinary empirical research. The framework exposed fundamental oversight concepts for overseeing decision making in AOS, namely governance framework, delegation and autonomy, human involvement, transparency, traceability and explainability, accountability, ethical and legal compliance, risk management, and feedback, adaptation and improvement. The framework is important for all AOS and artificial intelligence (AI) scientists, academics, programmers and organizations for averting vulnerabilities, threats, failures, stagnant models, opaque decisions, auditing difficulties, undetected biases, responsibility vacuums, unclear liability, unethical behavior, ethical violations, erosion of trust, business continuity risk, legal and regulatory non‑compliance, and missed learning opportunities. Furthermore, the framework advances AOS and AI oversight and governance research, establishing fundamental concepts for theory development and knowledge progression in the domain of AOS decision making oversight.
Authors - Shiwani Wagh, Shubham Kishor Kadam, Utkarsha Wanjari Abstract - The coupling of quantum computing and behavioural analytics signals broader opportunities for human resource management (HRM) and, in particular, talent management. In an age where we are using massive, multidimensional data sets simply for workforce planning and retention strategies, traditional HR approaches falter too frequently beneath the weight of such complexity. Moreover, that brings us to the flip side of the coin: quantum computing, the trading depth of behavioural analytics, for qubits for data processing power. This research explores how these technologies con-fluence to provide new solutions for Recruitment, Employee retention, Workforce development, Organisation Planning, etc. By combining predictive analytics and personalized interventions to make treatment decisions, quantum-enhanced behavioural analytics can support a targeted approach to behavioural analytics. Additional applications include pipeline optimization, attrition risk prediction, and alignment of development programs with business goals. On the other hand, while we have these new frontiers, we also have technical limitations, ethical concerns about data privacy, and the potential for biases in decision-making. To address these, they need to be driven by robust governance, ethical frameworks and upskilling of our HR professionals. The potential synergy with this is brought out in this paper, with a recommendation for its ethically implemented use in HR practices. The next frontier in HRM includes quantum computing and behavioural analytics, which will aid in the bolstering of workforce engagement and organizational success through data-driven, human-centred strategies.
Authors - Priyanka Patel, Priyanshi Desai Abstract - This research examines the relationship between Diversity, Equity, and Inclusion (DEI) strategies and organizational performance across global business environments. Through extensive analysis of empirical studies, international corporate case examples, and industry best practices, this study demonstrates that effectively implemented DEI initiatives contribute significantly to enhanced organizational outcomes, including increased innovation, improved financial performance, higher employee engagement, and expanded market reach. The paper addresses implementation challenges with particular focus on leadership accountability frameworks and strategies to prevent diversity fatigue while main- training long-term employee engagement. This analysis provides valuable insights for business leaders seeking to leverage DEI as a strategic advantage while navigating cultural and regulatory differences across inter- national markets.
Authors - Anurag Agarwal, Mahesh Jangid, Prashant Vats Abstract - Breast cancer starts as a problem with breast cells that multiply too fast, forming growths called lumps. Breast cancer commonly appears in milk ducts before advancing to other body parts, if found too late. The sooner doctors find and diagnose breast cancer, the better patients respond to treatment and survive longer. Better breast cancer detection comes from using large collections of imaging data through AI technology, especially machine learning and deep learning. With transfer learning tools and hybrid networks combined with CNNs sophisticated systems achieve strong results in breast cancer evaluation and characterization. Through their use of artificial intelligence, medical researchers are developing better diagnostic tools and creating wider accessible healthcare systems for worldwide needs.
Authors - Rangith B. Kuriakose Abstract - Learning is a complex process shaped by psychological and social factors. Psychological theories examine mental processes like memory and problem-solving, while sociological theories analyze how culture and societal structures influence education. Key psychological perspectives include behaviorism, which emphasizes reinforcement, and cognitivism focusing on knowledge construction and active learning. Vygotsky’s constructivism highlights social inter-action and the Zone of Proximal Development (ZPD), where guidance enhances learning. Sociologically, functionalism (Parsons) sees education as promoting social cohesion, whereas conflict theory (Marx, Freire) critiques its role in perpetuating inequality. Freire’s Pedagogy of the Oppressed advocates for empowering, reflective education. This paper explores cognitivism’s application in higher education, using an Electrical Engineering module as a case study. It identifies learning challenges ("learning knots") and proposes chunking, a cognitivist strategy, as an intervention. A comparative study (control vs. test groups over five tutorials) evaluates the method’s effectiveness in fostering meaningful, sustained learning. The paper bridges theory and practice, offering insights into curriculum design and findings suggest structured knowledge segmentation improves comprehension, demonstrating cognitivism’s practical relevance in technical education.
Authors - Mageshwari A. J., Jothi Prabha R., Karthiga N., Lekshmi R. R. Abstract - Ensuring the quality of printed circuit boards is crucial for the reliability of electronic devices. Traditional manual inspection methods are laborintensive and prone to errors, underscoring the need for automated solutions. This study presents an automated deep learning-based approach for printed circuit board inspection and defect detection by leveraging segmentation and object detection models. The investigation focuses on the performance of the SAM and an integrated YOLO-SAM pipeline. A high-resolution dataset com-prising PCB images annotated with six common defect types—missing hole, mouse bite, open circuit, short, spur, and spurious copper—is collected and preprocessed for training and evaluation. Initial experiments with SAM reveal limitations in segmenting defects without guided prompts, particularly board with missing holes. To address this, YOLO is integrated with SAM to provide localized bounding box prompts, enhancing segmentation accuracy. The YOLO-SAM model is trained for 100 epochs and evaluated using precision, recall, F1-score, mean average precision, Intersection over Union, and Dice score. Results demonstrate that the YOLO-SAM pipeline significantly outperforms standalone SAM, achieving more accurate and robust detection across multiple folds. This two-stage approach offers a reliable solution for automated, high-precision printed circuit boards’ defect analysis.
