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Socially Responsible Educational Research in Pursuit of the Sustainable Development Goals: Bridging Scholarship, Society, and Global Commitments
(Book Rivers Publications, Lucknow, India, 2026) Bushra Sumaiya
Educational research today is situated within a world marked by rapid technological change, persistent inequalities, and an escalating urgency to achieve the Sustainable Development Goals (SDGs). Against this backdrop, socially responsible research emerges as both a moral imperative and a scholarly necessity. This chapter examines how responsibility is conceptualised, practiced, and institutionalised within educational research, drawing on diverse theoretical, methodological, and policy-oriented perspectives. It argues that socially responsible research is anchored in values of equity, reflexivity, participation, and responsiveness to societal needs. Foundational scholarship highlights how responsibility extends beyond technical rigour, requiring researchers to remain critically aware of their positionalities, ethical commitments, and the broader socio-cultural contexts that shape knowledge production.
Building on this foundation, the chapter explores the growing alignment between educational research and the SDGs, particularly through Education for Sustainable Development (ESD), institutional policies, and curriculum reforms. These developments illustrate how higher education systems are increasingly expected to foster sustainability, global citizenship, and community engagement. The chapter also examines how leadership education, citizenship frameworks, and teacher professional development contribute to cultivating socially responsible mindsets among learners and educators. Through such interconnected efforts, educational systems become active contributors to sustainable futures.
The role of universities is further highlighted as they evolve into socially responsible institutions capable of integrating sustainability into research, governance, and community partnerships. Methodological innovations and technological advancements—ranging from design-based research to artificial intelligence and ICT for development—offer powerful tools for advancing socially responsive scholarship. Taken together, these perspectives demonstrate that socially responsible research is not a peripheral aspiration but a transformative framework that can shape the future of educational inquiry. The chapter concludes by outlining future possibilities for strengthening the integration of responsibility and sustainability within research ecosystems, thereby reinforcing education’s role in global development.
Deciphering the Genetic Basis of Complex Diseases
(STANZALEAF PUBLICATION, 2025) Vishal Agarwal
Complex diseases, including diabetes, cardiovascular disorders, cancer, and neurological conditions, result from intricate interactions among numerous genetic and environmental factors. Unlike monogenic diseases, complex disorders involve polygenic contributions, making the identification of causal variants particularly challenging. With the advent of high-throughput technologies such as genome-wide association studies (GWAS), whole-genome sequencing (WGS), and integrative multi-omics, researchers have made substantial progress in identifying genetic variants associated with disease phenotypes. However, a significant proportion of heritability
remains unexplained, and many identified variants lie in non-coding regions, complicating functional interpretation. This paper explores the methodologies used to uncover genetic contributions to complex diseases, discusses findings from recent literature, highlights the significance of functional genomics and systems biology, and proposes future directions for more inclusive and mechanistically insightful studies. Bridging the gap between genetic discovery and clinical application remains a central goal of current genomic research.
Software Requirements: Advanced Approaches and Techniques: An Optimization of Semantic Similarity Measurement
(LAP Lambert Academic Publishing, 2025) Farooq Ahmad, Mohammad Faisal
Software Requirements: Advanced Approaches and Techniques" is a book focused on improving methods for measuring the semantic similarity between words and sentences in software requirements documents. It addresses challenges in understanding and interpreting software requirements accurately, especially when documents contain complex, ambiguous, or vague language. The book proposes techniques to enhance the comparison of words, phrases, and sentences, using methods such as natural language processing (NLP), machine learning, and other computational tools. By optimizing semantic similarity measurement, the book aims to improve communication between stakeholders, ensure clearer requirements, and more efficient software design and implementation. A novel hybrid method proposes by this book combines knowledge-based and corpus-based approaches and considers not only semantic information such as lexical databases, word embeddings, and corpus statistics, but also implicit word order information. It also proposes a framework using a novel hybrid methodology for identifying similarities in software requirements of the same domain to improve reusability and identify the correct requirements.
Advancing Pandemic Prediction With big Data and Machine Learning
(LAP Lambert Academic Publishing, 2025) Mirza Ghazanfar Beg, Mohammad Faisal, Sandeep Kumar Nayak
This book has presented the development of a prediction technique in big data analytics, incorporating both unsupervised and supervised learning perspectives. It has addressed the challenges of handling large volumes of complex data and provided insights into the prediction process. The research findings highlight the performance and applicability of the developed technique in various domains, showcasing its potential for practical implementation. The book contributes to the field of big data analytics by advancing the understanding of prediction techniques and providing recommendations for further research to enhance their capabilities. It has showcased the potential of these techniques to extract meaningful patterns, make accurate predictions, and generate valuable insights from vast and diverse datasets. The research outcomes open up new opportunities for organisations to harness the power of big data and make data-driven decisions that can drive innovation, efficiency, and success.
Digital Flood Analytics
(LAP Lambert Academic Publishing, 2025) Mohammed Siddique, Tasneem Ahmed
This groundbreaking work by Dr. Siddique presents cutting-edge methodologies for flood monitoring and prediction using satellite images. Drawing from extensive research conducted in Uttar Pradesh, India, the book offers a comprehensive framework for developing Flood Monitoring and Early Warning Systems (FMEWS) through advanced image processing and machine learning techniques. Readers will master the implementation of various classification methods, time-series analysis, and change detection techniques using Sentinel-1 satellite images, enabling them to identify flood-prone areas and predict flood patterns with unprecedented accuracy. The book bridges critical research gaps by introducing innovative approaches to flood assessment, including Random Forest and KNN-based classification, and deep learning models. Whether you're a researcher, practitioner, or student in computer science or environmental sciences, this practical guide will equip you with the tools and knowledge needed to develop robust flood monitoring systems that can help save lives and protect communities.