The present study investigates the development and performance evaluation of bamboo reinforced geopolymer concrete as a sustainable alternative to conventional reinforced concrete. Geopolymer concrete is formulated using industrial by-products activated with alkaline solutions, while bamboo is utilised as renewable tensile reinforcement to reduce reliance on steel. The research examines compressive strength, flexural behaviour, bond interaction and durability characteristics based on experimental procedures and

supporting secondary data. The findings indicate that bamboo reinforcement enhances flexural strength, crack control and ductility of geopolymer concrete, while the matrix provides adequate compressive strength and improved resistance to environmental deterioration. The composite material demonstrates promising structural performance along with significant sustainability benefits through reduced embodied carbon and utilisation of renewable resources. The study highlights the importance of bamboo treatment and bond optimisation for achieving reliable structural behaviour and supporting the adoption of eco-friendly construction materials in sustainable infrastructure development.

The speed of sound (U), density (ρ), and viscosity (η) of binary liquid mixtures of tetrachloroethylene with cyclohexanone, as well as their pure components, were measured across varying mole fractions at four temperatures: 303.15 K, 308.15 K, 313.15 K, and 318.15 K. These experimentally determined physical properties were subsequently used to calculate thermo-acoustic parameters, including excess molar volume (VE), excess free length (LfE), excess Gibbs free energy (∆GE), and excess enthalpy (HE). The observed trends in these parameters were analysed to understand the nature and strength of intermolecular interactions present within the binary mixtures. Additionally, the experimentally measured speed of sound was compared with values predicted by established theoretical models to evaluate their applicability to the system. The models that showed the closest agreement with the experimental data highlighted the presence of specific molecular interactions, such as hydrogen bonding and dipole–dipole interactions, between the components. Further, Fourier Transform Infrared (FTIR) spectroscopy provided detailed insights into the molecular interactions, confirming the presence of structural and chemical effects influencing

Today’s classrooms highlights the complexity of increasing diverse learners, projecting the urgent need for research-driven wellbeing framework for inclusive educational setting, targeting SEN and Academically backward students. This abstract proposes a strength-based Social-Emotional Learning (SEL) model, drawing inspiration based on inclusive education theory, positive–mind programming psychology, energy ‘word’ medicine, to support an inclusive framework, aligned with global SEL competencies standards such as CASEL and OECD Learning Compass 2030. The implementation of proposed model progress from deficit-oriented perspectives to learners strength integrating guided vocabulary-building tasks, real-life problem solving, strength identification, circle-time discussions, scaffolded behaviour-modelling activities overall building skills, focusing on five major domains self-awareness, self-management, social awareness, relationship skills, responsible decision-making. By adopting a quasi-experimental mixed-methods, research reveals, in inclusive education the significant gains in self-awareness, classroom engagement, and emotional resilience. Students demonstrate improved confidence and reduced behavioral disruptions. Adopting SEL principles, educators can bridge the gap between clinical SEN support and mainstream SEL curricula. SEL is the crucial element for fostering holistic wellbeing in inclusive education system, creating safe learning space, minimizing conduct issues and building supportive academic environment. The research study enriches entire education system aligning with international standards offering universal analysis, forecasting allround academic and holistic development.

A comprehensive analysis of the total energy distribution within an integrated design for sustainable agricultural production in coastal climatic conditions. The system configuration consists of evacuated tube collector, absorption cooling system, thermal energy storage, solar still, and greenhouse operating on humidification-dehumidification principles, provides a fundamental model for evaluating energy flow and utilization of the interconnected subsystems. Thermodynamic modelling, based on energy and mass balance along with inverse technique of Levenberg-Marquardt method, evaluates greenhouse temperature, vapor pressure deficit, and overall performance under varying solar irradiance between 275–972 W/m² for typical ambient conditions of Indian coastal areas. The obtained results presented, stable internal temperatures of 24–28°C and relative humidity of 60–90%, with VPD maintained in the optimal 0.5–1.2 kPa range for crops such as tomatoes and cucumbers by optimizing air (60–90 kg/s) and water (≥0.7 kg/s) mass flow rates. The greenhouse system demonstrates that 90% evapotranspiration water recovery, continuous yearround operation without fossil fuels, and enhanced concentration yield (~1 m³/day per 100 m²) attained through the heat integration. The integrated design addresses, key challenges using groundwater/saline agriculture with water scarcity, overheating, and energy intensity for minimizing the energy losses and enabling scalable, low emissions in food production.

Lattice-based cryptography has emerged as a leading framework for securing communication in the post-quantum era, supported by strong worst-case hardness guarantees. This study focuses on two central constructions, Ring Learning with Errors (Ring-LWE) and Learning with Rounding (LWR), and examines their mathematical foundations, implementation behavior, and performance characteristics. While Ring- LWE benefits from well-established and conservative security reductions, along with broad applicability across cryptographic primitives, its reliance on discrete Gaussian sampling introduces computational overhead and potential side-channel vulnerabilities. In contrast, LWR replaces stochastic noise with deterministic rounding, thereby simplifying implementation and improving efficiency, particularly on resource-constrained platforms, albeit under comparatively newer and less conservative hardness

assumptions. Both schemes are implemented and benchmarked across multiple parameter sets, enabling a systematic comparison of key generation, encryption, and decryption costs. The experimental results highlight the complementary strengths of these approaches and suggest that hybrid constructions may effectively combine the strong theoretical guarantees of Ring-LWE with the practical efficiency of LWR.

Machine learning evaluation has found its way into the world’s most important fields, such as medicine and healthcare, which is a crucial shift in our way of thinking about solving the complex clinical issues. This development is necessary to the healthcare industry, as medical information is highly complicated in its structure and the analysis of such data is challenging by its essence. The approach to medical research can make a great difference in the field of medical study, and the way in which kidney disease is identified is one of the most important areas. The kidney-related diseases that present a challenge in terms of diagnosis include Chronic Kidney Disease (CKD), Acute Kidney Injury (AKI), and UTI-related renal dysfunctions, which have complex clinical behaviors and data heterogeneity. Ensemble learning, which involves the combination of several classifiers, has demonstrated high accuracy and strength compared to conventional single models in the prediction of these diseases. This is a review of 30+ recent studies (2019-2025) on bagging, boosting, stacking, and voting ensemble methods on kidney diseases. Overall, bagging has been shown to be common in CKD prediction with notable accuracy, boosting demonstrated effective in heterogeneous AKI datasets, and stacking ensembles, though underutilized, exhibit the highest accuracy and generalizability across datasets. The review discusses methodological trends, comparative results, and future research paths to optimize ensemble models for renal disease diagnostics and prognosis.

Behavioral Economics is a fascinating and dynamic field that pushes the boundaries of traditional economic theory by challenging the notion that individuals always behave rationally to maximize their utility. By weaving together insights from psychology, cognitive science, and classic economic principles, behavioral economics sheds light on the often perplexing ways in which our biases, emotions, and social influences lead us to make seemingly irrational choices. Behavioral Economics has wide range of applications viz. Public Policy and Governance, It’s truly fascinating how governments harness the power of behavioral insights to foster positive social behaviors! Thanks to AI, we can enhance savings plans and investment strategies through more effective nudging. Behavioral Economics is also impacting Sustainable Decision-Making and Behavioral Interventions and that is towards more environmentally friendly concerns. Implementing green defaults, like automatically choosing sustainable energy providers, could really encourage proenvironmental behavior. Artificial Intelligence (AI) is revolutionizing the landscape of sustainable development with its incredible potential to transform our world. By incorporating AI-driven solutions

Artificial intelligence (AI) is changing modern bioprocessing by offering new ways to design, monitor, and optimize complex biological systems. Traditional tools such as mechanistic models and Design of Experiment (DoE) often fall short when faced with the nonlinear behaviour and variability that are common in fermentation, cell culture, and purification processes. O, the other hand, Machine Learning (ML), Deep Learning (DL), Evolutionary Algorithms, Reinforcement Learning (RL), and Digital Twins (DT) can uncover patterns, predict outcomes, and guide decision-making with far greater accuracy. This review highlights how these AI approaches are being applied across upstream processing, downstream purification, real-time monitoring, and scale-up and put forwards some industrial case studies, including AI-assisted COVID-19 vaccine development by Pfizer and Moderna, CHO fed-batch optimization with titer

increase of up to 48%, data-driven chromatography prediction with R² value of 0.90, GA-based feeding strategies, and AI based soft sensors for E. coli, that illustrate the practical benefits now seen in industry. The paper also puts forward some major challenges and discusses emerging trends such as autonomous digital twins, multi-omics integration, and IoT-enabled monitoring. Overall, these developments show how AI is steadily becoming a core enabler of faster, more consistent, and more efficient biomanufacturing.

Cloud computing has made it easier for many industries to use computing resources whenever they need them and to scale up when demand increases. However, modern cloud systems have become very complex because they rely on containers, microservices, serverless functions, and edge devices. Managing these systems through manual effort or fixed rule-based automation is becoming increasingly difficult. Agentic Artificial Intelligence (Agentic AI) can transform how cloud systems are managed. Unlike traditional AI,

which requires step-by-step human instructions, Agentic AI can observe the system, understand what is happening, plan actions, execute them, and improve based on feedback. This paper discusses how combining Agentic AI with cloud-native technologies can support cloud systems that can organize themselves, recover from failures, and scale automatically. It also examines the key design choices, security and trust concerns, real-world use cases, and future research directions. Overall, the paper presents Agentic AI as an important foundation for next-generation autonomous cloud platforms.

Large language models (LLMs), diffusion-based image generators, speech synthesizers, code assistants, and multimodal foundation models are examples of generative AI systems that are now deeply integrated in scientific research, creative work, and decision-making. Understanding why these models produce specific outputs has become crucial for safety, accountability, trust, debugging, and regulatory compliance as they continue to acquire autonomy and agency. However, explainability for generative AI is significantly

more challenging than explainability for traditional discriminative models, because the generation unfolds over time, the output space is open-ended, and the model internals operate in high-dimensional spaces. Explainable Generative AI (XGAI) is the new interdisciplinary field examined in this paper. The paper defines the scope and fundamental concepts of XGAI, presents a taxonomy of interpretability techniques for generative models, examines technical, social, and regulatory challenges, and outlines future research directions toward transparent and controllable generative systems. This work argues that explainability for generative AI requires coverage of intent, process, uncertainty, and provenance and cannot be reduced to local feature attribution alone.

Climate change has become a substantial national security problem with considerable geopolitical ramifications for Bangladesh. Bangladesh, as a highly climate-vulnerable nation, confronts increasing sea levels, extreme weather phenomena, and resource scarcity, all of which intensify socio-economic fragility and internal instability. This paper examines the convergence of climate change, national security, and geopolitical dynamics, highlighting how environmental degradation exacerbates existing vulnerabilities and cultivates regional tensions. The study emphasises significant security problems, such as climate-induced

displacement, food and water shortages, and the intensification of transboundary water conflicts with India. The arrival of displaced persons in metropolitan areas leads to overcrowding, unemployment, and social unrest, hence straining governing systems. Moreover, unresolved water-sharing accords concerning rivers like as the Teesta and Ganges exacerbate geopolitical tensions, potentially undermining regional peace. The research analyses Bangladesh’s climate adaptation strategies, including the Bangladesh Delta Plan 2100 and the National Adaptation Plan, highlighting issues pertaining to governance, financial limitations,

and regional collaboration. Research indicates that Bangladesh should include climate risks into national security frameworks, improve regional water diplomacy, and bolster resilience via sustainable urban and rural development. Moreover, obtaining international climate funds and promoting global collaboration are essential for mitigating climate-related security risks. This research highlights the pressing necessity for an integrated strategy that connects environmental sustainability with national and regional security frameworks to alleviate the extensive impacts of climate change on Bangladesh’s stability and geopolitical

position.

Digital Education is a very important part of the modern education system. The main objective of Digital Education is to expand education by using various types of technology. Digital Education plays an important role in making education accessible to all of the learners by utilizing various basic technologies and emerging technologies. Digital Education provides the opportunity to study any subject from anywhere, at any time. Various Government and private organizations are taken different initiatives to implement

Digital Education for the learning of the students. Digital Education can be utilized to give education to a large number of learners at a time. It can also utilized for the skill development and training purposes. Various Organizations and departments under Government of India have taken various initiatives to promote this Digital Education. The purpose of this paper is to know about the concept of Digital Education and Massive Open Online Course - MOOC. The paper discussed about various Digital Education Initiatives in India. The paper make a case study on SWAYAM as the MOOC initiative. Different Opportunities and Challenges to implement Digital Education are also discussed in this paper.