Knowledge, Innovation and Technology in Scientific Research Vol. 5

Integrating the Sustainable Development Goals into Engineering Education: A PO-Aligned Framework for Sustainable Engineering Learning

Iman Farshchi — 2026-10-15

The integration of the United Nations Sustainable Development Goals (SDGs) into engineering education has gained significant attention globally, yet a critical gap persists between SDG integration and measurable Programme Outcome (PO) attainment under Outcome-Based Education (OBE) frameworks. This research addresses this gap by developing and presenting a comprehensive SDG–PO Integration Framework specifically designed for engineering education in Malaysia, aligned with the Engineering Accreditation Council (EAC) requirements. The framework systematically maps all 17 SDGs against the 11 EAC Programme Outcomes, demonstrating how SDG-integrated engineering lectures can serve as authentic problem contexts for simultaneously developing and assessing multiple POs. Using Geotechnical Engineering as a case study, six lectures were redesigned to explicitly connect geological engineering topics with relevant SDGs while maintaining rigorous alignment with PO assessment requirements. The framework proposes a pedagogical flow model: SDG Context → Authentic Engineering Problem → SDG-integrated Lecture → Problem-Based/Project-Based Learning → PO Development → PO Assessment → PO Attainment Evidence → Continuous Quality Improvement. This research contributes a practical, accreditation-relevant tool that bridges sustainability education with measurable learning outcomes, demonstrating that SDGs are not merely additional sustainability content but rather powerful mechanisms for achieving multiple engineering competencies simultaneously. The framework provides engineering faculties with a structured approach to embed sustainability while strengthening OBE implementation and generating robust evidence for EAC accreditation and continuous quality improvement processes.

From Digital Extension to Intelligent Advisory Systems: The Evolution of Agricultural Knowledge Delivery in the Age of Artificial Intelligence

Nikki Bhardwaj — 2026-10-15

Agricultural knowledge delivery is changing from the electronic distribution of recommendations towards systems that interpret observations, retrieve evidence and generate interactive advice. This transition is frequently presented as a technological progression, although the evidence supporting digital communication, predictive models and generative advisory systems concerns different outcomes and levels of validation. This critical narrative review examines that distinction through literature on agricultural extension, development economics, machine learning, knowledge systems and responsible innovation, covering publications available up to 27 July 2026. Targeted scholarly searching and reference verification informed a thematic, design-sensitive synthesis rather than a pooled estimate of effectiveness. Experimental evidence indicates that digital information can improve selected knowledge, adoption and production outcomes, but null findings and substantial contextual variation challenge claims of universal benefit. Predictive accuracy does not establish the value of a recommended action, while language-model benchmarks and expert assessments remain insufficient substitutes for prospective evidence on farm decisions and welfare. Recent generative artificial intelligence studies demonstrate useful capabilities alongside sensitivity to evaluation task, workflow and outcome definition. Across these literatures, information relevance, farmer resources, intermediary support, commercial incentives and control over data shape whether technical capability becomes practical benefit. The review develops a conditional-capability framework in which an advisory system must establish an adequate decision context, use traceable evidence, communicate uncertainty, support feasible action and retain accountable routes for correction. This framework is a synthesis-derived proposition, not a validated maturity scale. Research priorities include comparative field evaluations, multilingual and out-of-distribution testing, measurement of human oversight costs, gender-sensitive outcome assessment and longitudinal institutional analysis. Intelligent advisory should therefore be understood as a property of an accountable knowledge service rather than of a model alone. Its contribution will depend on strengthening, rather than bypassing, the social and scientific relationships through which agricultural knowledge becomes usable.

Integrated Technologies for Slope Stability Assessment of Open-Pit Coal Mine Slopes: An Indian Perspective

Somesh Sengupta — 2026-10-15

Highwall and overburden-dump instability is a persistent safety and operational challenge in open-pit coal mines because slope performance is governed by interacting geological, geotechnical, hydrogeological, geometric, dynamic, and operational factors. This narrative review synthesizes complementary methods for slope-stability assessment in Indian open-pit coal mining, including geological mapping and discontinuity characterization, laboratory and field characterization, rock-mass classification, groundwater assessment, limit-equilibrium and numerical modelling, and modern deformation-monitoring technologies. Monitoring approaches considered include total stations, GNSS, terrestrial laser scanning, LiDAR, slope-stability radar, time-domain reflectometry, fibre Bragg grating sensors, and IoT-enabled sensing. The review emphasizes that analytical and numerical models are most defensible when calibrated against site observations and updated as new monitoring data become available. A case study from Jayant Open-Pit Coal Mine, Singrauli Coalfield, India, illustrates how investigation, design, drainage control, operational measures, and monitoring can be combined to manage dragline-dump stability. The resulting framework supports risk-informed slope design and adaptive mine-slope management while recognizing the uncertainty inherent in geological conditions, material properties, groundwater behaviour, and model assumptions.

Gyro-Free Attitude Determination for Guided Rockets Using Learned Gravity and GNSS Ambiguity Estimators Combined with Laser Seeker Data

Raul de Celis — 2026-10-15

Aims: This chapter evaluates, in simulation, a gyro-free attitude-determination architecture for a guided rocket. Neural estimators provide the body-axis gravity direction and GNSS carrier-phase integer ambiguities; these estimates are combined with GNSS-derived baselines and, during terminal flight, semi-active laser (SAL) line-of-sight data. The gravity estimate is treated as one reference direction and does not by itself determine full three-axis attitude.

Study Design: A numerical study coupling a high-fidelity flight simulator with supervised learning experiments.

Place and Duration of Study: Aerospace Systems and Transport Research Group, Rey Juan Carlos University (URJC), Madrid, Spain. The satellite scenario covers one full day (24 h) above the URJC campus.

Methodology: Training data came from a MATLAB/Simulink six-degree-of-freedom (6-DoF) model of a canard-steered artillery rocket and from a GNSS signal generator. One network maps accelerometer readings to body-axis gravity (10 000 samples); a family of 35 networks recovers double-difference ambiguities for eight antennas and six satellites (576 000 epochs from 100 runs). Four experimental phases, implemented in TensorFlow/Keras, compared classification with regression, feedforward with recurrent models, and variations in window length, network size, activation, learning rate and number of epochs. The chosen estimators then fed a least-squares attitude solver, which was tested with Monte Carlo simulation and compared with a real-time kinematic (RTK) reference.

Results: Recurrent classifiers were the most accurate ambiguity estimators, reaching about 0.92 during screening and at least 99% on the internal model-development evaluation subset after final training; regression models generally produced smaller-magnitude errors when incorrect. The selected architecture used dense layers of 800, 400, 200 and 100 units plus a 100-unit tanh recurrent layer. Errors clustered both in time and across networks. Using the classification models, mean attitude RMSE values averaged across the tested command amplitudes were 0.55° in roll, 0.48° in pitch and 0.82° in yaw, numerically similar to the RTK reference values of 0.56°, 0.47° and 0.83°. Regression models gave 6.80°, 5.59° and 10.25°. The RTK comparison is descriptive and does not constitute a formal equivalence test.

Conclusion: Within the simulated conditions, learned gravity and GNSS ambiguity estimates, augmented by terminal laser-seeker vectors, produced mean attitude RMSE values below one degree when averaged across the tested command amplitudes for the classification configuration, although some high-amplitude yaw cases exceeded 1°. The findings support the architecture as a proof-of-concept for low-cost, highly dynamic GNC applications; independent holdout evaluation and hardware or flight validation remain necessary before deployment claims can be made.

The Rhizomatic Lifelong Learning Ecology: A Relational Interpretation of LifeComp in the Postdigital Era

Birsel Aybek — 2026-10-15

Purpose: This conceptual chapter develops the Rhizomatic Lifelong Learning Ecology (RLLE) as a relational framework for explaining how competence-related activity takes shape within changing learning relationships across formal, non-formal, and informal settings.

Approach: The chapter develops a selective conceptual synthesis of the LifeComp framework, rhizomatic pedagogy, learning ecology, and postdigital scholarship. It interprets selected rhizomatic dynamics through the nine LifeComp competences, examines the conditions that may enable or constrain participation, and demonstrates the framework’s analytical use through a hypothetical community inquiry.

Conceptual Contribution: RLLE distinguishes four connected analytical functions: ecological arrangements, rhizomatic dynamics, learning processes, and LifeComp configurations. Support, access, and power operate across these functions and shape the opportunities available to participants. The framework shows how revising a learning route, evaluating sources, negotiating participation, and reconnecting after interruption may involve personal, social, and learning to learn competences. It also distinguishes opportunities to exercise these competences from evidence that they have developed or transferred across settings.

Conclusion: RLLE provides a non-prescriptive language for examining lifelong learning through changing relationships among people, purposes, resources, technologies, and settings. It supports interpretation across educational, workplace, and community contexts while keeping differences in participation and influence visible. Empirical research is required to examine the framework’s usefulness across settings and over time.

Artificial Intelligence and Machine Learning for Intelligent Vehicular Ad Hoc Networks

Kusum Dalal — 2026-10-15

Vehicular ad hoc networks (VANETs) must sustain communication among rapidly moving vehicles and infrastructure despite short-lived links, heterogeneous radio access, fluctuating traffic density, stringent latency and reliability requirements, and persistent security threats. Artificial intelligence (AI) and machine learning (ML) are therefore being investigated as adaptive mechanisms for functions that conventional rule-based or optimisation approaches handle poorly under uncertainty. This critical narrative review evaluates how learning-enabled methods are being used in VANET and vehicle-to-everything (V2X) networking, with emphasis on routing and clustering, congestion and traffic prediction, radio-resource allocation, proactive caching, vehicular edge computing, federated learning, intrusion and misbehaviour detection, and emerging graph-based and multi-agent approaches. Literature published from 1 January 2000 to 1 August 2026 was considered, with conceptually important foundational evidence retained when necessary within this boundary. The evidence indicates that reinforcement learning and deep reinforcement learning are particularly well aligned with sequential resource, routing and offloading decisions, while supervised and ensemble methods remain useful for prediction and attack classification. Federated learning can reduce raw-data centralisation, and graph neural networks offer a promising representation of topology-dependent decisions. Nevertheless, most reported gains remain simulation-dependent and are difficult to compare because mobility, radio, attack, workload and baseline assumptions vary widely. Data drift, non-independent vehicular data, unstable client participation, adversarial manipulation, communication and computation overhead, limited interpretability, and weak cross-scenario validation constrain deployment claims. The review therefore argues that the central research problem is no longer whether ML can improve a selected VANET metric in a controlled experiment, but whether learning-enabled network functions remain safe, efficient and transferable under realistic mobility, mixed radio technologies, adversarial conditions and resource constraints. Progress requires common evaluation protocols, cross-domain validation, uncertainty-aware and safe learning, communication-efficient distributed intelligence, and closer integration of networking, security and edge-computing objectives.