Search Results (783)

Efficient soil fertility monitoring is essential for sustainable agriculture, food security, and environmental management across Africa, yet conventional laboratory methods remain prohibitively costly and slow for continental-scale applications. Soil spectroscopy is considered as a rapid, non-destructive alternative with transformative potential. This review provides a systematic synthesis of spectroscopic applications across Africa, encompassing laboratory, field, airborne, and satellite-based platforms, while examining major data sources including the Africa Soil Information Service (AfSIS) and GEO-CRADLE spectral libraries. We critically evaluate the evolution of modeling approaches, revealing that Partial Least Squares Regression (PLSR) dominates, but a shift toward advanced frameworks like hybrid physically based models, ensemble learning and deep neural networks is essential. Critically, we identify a pronounced imbalance wherein laboratory spectroscopy prevails while imaging and satellite-based approaches remain comparatively underutilized, despite their unparalleled potential for scaling point measurements to continental extents. The review consolidates findings on key soil properties, demonstrating consistent successes for primary constituents with direct spectral responses (i.e., organic carbon), while revealing relative uncertainty for properties inferred indirectly via covariance (e.g., available phosphorus, potassium). Despite significant local and regional progress, the absence of a standardized pan-African spectral library and the intractable transferability problem remain formidable barriers. Future research must pivot decisively toward imaging spectroscopy and satellite platforms, mitigating PLSR dominance through systematic adoption of ensemble methods, transfer learning, and model harmonization frameworks to fully operationalize these technologies in support of Africa’s sustainable development goals. Full article

Podiatry residents may benefit from addiction medicine rotations due to substantial overlap between podiatric needs and substance use disorders (SUDs), particularly in the hospital setting. In a semi-structured format, we describe the cases of two podiatry residents, perhaps the first ever documented, who rotated with an inpatient addiction medicine consult team at an urban, academic hospital. These two residents joined the addiction consult team in 2023 and 2024 and rated their confidence in twelve learning objectives via a five-point Likert scale before and after the rotation (2 weeks long). They also rated their attitude toward the value of addiction services. Post-rotation feedback from the module and ad hoc e-mail correspondence are included. Residents 1 and 2 joined the team and engaged well in orientating with the team, eventually providing near-independent addiction medicine consultations for primary inpatient teams. Pre/post data showed large increases in confidence in learning objectives (mean scores 2.1 to 4.9 for Resident 1, and mean scores 1.5 to 4.0 for Resident 2). They both reported positive experiences, and months later reflected on both pragmatic (e.g., available resources) and attitude-related (e.g., understanding the importance of substance use context for patients) educational gains. In summary, residents from surgical specialties like podiatry may benefit from inpatient addiction medicine exposure. It is unclear whether their rotation spots could be better utilized by those in other specialties, but SUDs are prevalent in a multitude of settings, arguing for broad dissemination of SUD treatment education. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article

Atmospheric corrosion of carbon and low-alloy steels causes direct economic losses that are estimated at around 3.4% of the global GDP, and its accurate multi-year prediction is essential for protective coating selection, service-life estimation, and infrastructure maintenance scheduling. In this study, machine learning (ML) algorithms, including gradient boosting regressor (GBR), eXtreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and ridge regression, were trained on a 600-sample physics-grounded dataset to predict the cumulative atmospheric corrosion loss (µm) of low-alloy steels over 1–10 years of exposure. The dataset was constructed using the exact ISO 9223:2012 dose–response function (DRF) for a first-year corrosion rate and the ISO 9224:2012 power-law multi-year kinetic model (C(t) = C₁·t0.5), spanning ISO 9223 corrosivity categories C2–CX across 11 environmental and material input features. All models were evaluated on the original (untransformed) corrosion scale under an 80/20 train/test split and five-fold cross-validation. Gradient boosting achieved the best overall performance with test set R² = 0.968, CV-R² = 0.969, RMSE = 10.58 µm, MAE = 5.99 µm, and MAPE = 12.6%. XGBoost was a close second (R² = 0.958, CV-R² = 0.960). RF achieved an R² of 0.944. SHAP (SHapley Additive exPlanations) analysis identified SO₂ deposition rate, exposure time, relative humidity, Cl⁻ deposition rate, and temperature as the five most influential predictors. The dominance of the SO₂ deposition rate (mean |SHAP| = 26.37 µm) and the high second-place ranking of exposure time (13.67 µm) are fully consistent with the ISO 9223:2012 dose–response function and ISO 9224:2012 power-law kinetics, respectively, while among the material features, Cu and Cr contents showed the strongest negative SHAP contributions, confirming their corrosion-inhibiting roles in weathering steels. These results establish a physics-consistent, interpretable ML benchmark exceeding R² = 0.90 for multi-year cumulative corrosion loss prediction and provide a quantitative tool for alloy screening, coating selection in aggressive atmospheric environments, and service-life planning. Full article

There is a mental health crisis among young people in Canada, suggesting a need for evidence-based, community-engaged strategies to strengthen the youth mental health workforce. This study explores the learning and professional development outcomes of participation in the National Youth Council (NYC) of Kids Help Phone (KHP), Canada’s only “national 24/7, free, confidential, and multilingual e-mental health service, blending technology with the empathy of clinical experts”. We surveyed and conducted focus groups with current and former NYC members to identify professional development outcomes associated with council participation. The results suggest that involvement in the NYC fostered professional skill-building, increased interest in mental health and youth-facing careers, improved civic engagement, and created a sense of empowerment and belonging. Barriers to full participation in youth councils included imposter syndrome, limited regional access to in-person activities, and limited representation from certain geographic areas (e.g., the Territories). These findings highlight the potential of youth advisory councils to support youth professional development, while emphasizing the importance of integrating structured mentorship and equity-focused practices into youth engagement models. NYCs may therefore serve as promising venues for strengthening the future youth mental health workforce. Full article

The development of effective Intrusion Detection Systems (IDS) for Internet of Things (IoT) environments is constrained by the absence of realistic, large-scale datasets, particularly for the Message Queuing Telemetry Transport (MQTT) protocol, which is prevalent in industrial IoT. Existing datasets are frequently limited in scope, imbalanced, or do not capture MQTT-specific attack patterns, thereby impeding the training of accurate machine learning models. To address this gap, the extensible Message Queuing Telemetry Transport (eMQTT) Traffic Generator is introduced as a modular platform capable of simulating both legitimate MQTT communication and targeted denial-of-service (DoS) attacks. The framework features a scalable and reproducible architecture that incorporates protocol-aware attack modeling, automated traffic labeling, and direct export of datasets suitable for machine learning applications. The system produces standardized, configurable, repeatable, and publicly accessible datasets, thereby facilitating reproducible research and scalable experimentation. Experimental validation demonstrates that the simulated traffic aligns with established DoS behavior models. Two high-volume datasets were generated: one representing normal MQTT traffic and another emulating CONNECT-flooding attacks. Machine learning classifiers trained on these datasets exhibited strong performance, with gradient boosting models achieving over 95% accuracy in distinguishing benign from malicious traffic. This work offers a practical solution to the scarcity of datasets in IoT security research. By providing a controlled, extensible, and reproducible traffic-generation platform alongside validated datasets, eMQTT enables systematic experimentation, supports the advancement of IDS solutions, and enhances MQTT security for critical IoT infrastructures. Full article

Road traffic accidents at intersections pose a persistent challenge in Riyadh, Saudi Arabia, contributing significantly to public health burdens and economic losses. Traditional statistical approaches often fail to capture the complex, non-linear interactions among geometric design, traffic parameters, and accident severity. This study develops a multi-methodological machine learning framework to predict intersection accident severity using the Equivalent Property Damage Only (EPDO) metric. Historical data (2017–2023) from Riyadh Municipality for 150 high-risk intersections were analyzed, incorporating predictors such as service road distance (SRD), U-turn distance (UTD), median width (MW), peak hour volume (PHV), heavy vehicle percentage (HV%), and injury/frequency counts. Six algorithms, i.e., Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Linear Regression, and Artificial Neural Network, were compared using a 70/30 train–test split and k-fold cross-validation in this study. The Gradient Boosting model achieved superior performance (R² = 0.89 with MSE = 63.43 and RMSE = 7.96) and was selected for final deployment. SHAP feature importance analysis revealed minor injuries (MIs), serious injuries (SRIs), and fatalities (FAs) as the most important dominant predictors, with geometric factors (UTD, MW) and traffic composition (HV%) providing actionable infrastructure insights. The model ranked intersections and identified the “Jeddah Road with Taif Road” (predicted EPDO = 137.22) as the highest-risk location. Evidence-based recommendations include enforcing the minimum 300 m U-turn buffers with staggering service road exits ≥150 m and restricting heavy vehicles during peak hours. The scalable framework developed in this study supports the data-driven prioritization of safety interventions and aligns with sustainable urban mobility goals and offers transferability to other metropolitan contexts worldwide. Full article

Background: Artificial intelligence (AI) holds transformative potential for mental health services. However, existing reviews have predominantly focused on algorithmic accuracy, with limited attention to how these technologies are implemented and integrated into real-world service delivery. This scoping review addresses this gap by examining the contexts in which AI technologies—including large language models (LLMs) and machine learning—are implemented, as well as the factors influencing their sustainable adoption within real-world mental health service systems. Methods: Following the established methodological framework, a systematic search (2015–2026) was conducted in PubMed and Scopus. Two independent reviewers screened an initial pool of 829 records using Zotero and Rayyan to minimize selection bias. Following title, abstract, and full-text screening based on predefined eligibility criteria, 26 studies focusing on real-world AI applications (e.g., clinical settings, community services, and case management) were included in the final synthesis. Results: The findings indicate a rapid acceleration in research, with 50% of included studies (n = 13) published since 2024. AI-driven decision support systems were the most prevalent (50%, n = 13), followed by predictive machine learning models (27%) and generative AI applications (15%). Most tools were designed for clinician use (77%) and implemented in hospital-based settings (46%). Although 46% of studies reported real-world implementation, more than half remained at the pilot stage. Notably, research emphasis has shifted from technical efficacy toward feasibility, and implementation contexts (n = 17). Conclusion: AI in mental health is transitioning from laboratory validation to real-world integration. However, the current landscape remains heavily centered on clinician workflows and screening functions, with limited expansion into community-based recovery and long-term prevention. To move beyond the pilot stage, future initiatives should prioritize seamless workflow integration and the application of structured ethical and implementation frameworks that support clinician–patient relationships. This review provides an evidentiary basis for advancing sustainable, AI-enhanced mental health service delivery. Full article

This study introduces a four-pillar student-activity framework (Studying and Learning, Research and Projects, Personal and Career Development, and Campus and Community Life) to analyze AI’s ethical challenges in higher education. Drawing on peer-reviewed sources from 2022 to 2025, we identify recurring risks across pillars: academic integrity, privacy/data protection, bias/fairness/equity, student agency/(de)skilling, and governance gaps. We distill three cross-pillar principles: disclosure plus process evidence (e.g., prompt/version logs), privacy-by-design, and proportionality and equity/fairness scaffolds (institutional access, bias audits, and multilingual support). These translate into actionable strategies for assessment redesign, research supervision, career services, and campus operations. The framework unifies fragmented discourse, supports institutional decision making, and reveals gaps for longitudinal and causal research. It demonstrates that responsible AI use emerges when processes are visible, data practices are proportionate, and access is equitable, amplifying human learning without eroding trust or integrity. Full article

The increasing deployment of robotic systems in complex and high-stakes environments, such as advanced manufacturing, healthcare, space exploration, and service robotics, requires robust strategies to ensure operational reliability, safety, and predictive maintenance. Real-time prognostics and health management, supported by recent advances in artificial intelligence, has emerged as a powerful approach for monitoring system health, detecting faults, and predicting failures before they occur. Unlike earlier review studies that mainly summarize traditional machine learning applications, the novelty of this paper lies in presenting a comprehensive taxonomy and critical synthesis of state-of-the-art AI-driven PHM techniques designed specifically for robotic systems. We evaluate a wide range of approaches, beginning with conventional machine learning models and extending to recent deep learning advancements, including transformers, vision transformers, and self-supervised learning frameworks. Furthermore, a novel contribution of this study is the rigorous benchmarking of their real-time feasibility, computational complexity, scalability, and performance trade-offs in practical robotic applications. In addition, this review introduces widely used benchmark datasets and highlights representative industrial case studies that demonstrate the practical effectiveness of AI-enabled PHM systems. The study also discusses important research gaps, including challenges related to model interpretability addressed through eXplainable AI, data privacy supported by federated learning, and the integration of cloud and edge computing within cloud robotics frameworks. Through a comprehensive gap matrix and quantitative comparative evaluations, this review provides insights to support the development of resilient, interpretable, and intelligent PHM systems for next-generation robotic applications. Full article

This exploratory study analyzes the relationship between cooperative oral reading and intercultural competence within the field of teacher education (i.e., training of pre-service FL teachers in primary education) at the University of Cádiz (Spain), aiming to move beyond traditional, Eurocentric conceptions of interculturality, by aligning the framework with the United Nations Sustainable Development Goals (SDGs), particularly SDGs 4, 5, 10, and 16. A mixed-methods design is adopted, combining quantitative and qualitative approaches through cooperative oral reading activities based on selected literary texts in English, French, and German addressing diversity, identity, inclusion, among others. Data are collected via recording forms administered to language assistants and two focus groups involving students and language assistants. The quantitative indicators of the study suggest that cooperative oral reading may contribute to foreign language learning, strengthen engagement between students and assistants, promote collaborative dialogue, and provide opportunities to challenge stereotypes, while interaction with native speakers (i.e., assistants) deepens understandings of cultural diversity and identity. Overall, the research proposes that cooperative oral reading is an illustrative pedagogical strategy for fostering inclusive intercultural competence and that linking classroom practices to the SDGs can contribute not only to language development but also to broader goals of equity, inclusion, and social justice. Full article

Cryptocurrency trading platforms operate in highly volatile, technology-intensive, and risk-sensitive environments, yet empirical evaluations of their performance from an information systems perspective remain limited. Prior studies applying the DeLone and McLean Information Systems Success Model (ISSM) have largely focused on traditional e-commerce and e-learning contexts, leaving its applicability to cryptocurrency exchanges underexplored. This study addresses this gap by examining how system quality, information quality, and service quality influence system use, user satisfaction, and net benefits in cryptocurrency trading platforms. This study employs a quantitative research design using survey data collected from 389 active Binance users in Indonesia through purposive sampling. The proposed ISSM-based research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi Group Analysis (MGA) to assess the relationships among system quality, information quality, service quality, system use, user satisfaction, and perceived net benefits. The findings indicate that four of the nine hypothesized relationships are statistically supported. System quality emerges as the most influential determinant of both system use and user satisfaction, highlighting the importance of platform reliability, performance, and usability. Information quality also demonstrates a significant effect, whereas service quality exhibits a limited direct influence on user outcomes. Overall, system use and performance-related factors play a more critical role in driving perceived net benefits than service-related attributes. This study extends the DeLone and McLean ISSM to the context of cryptocurrency trading platforms and demonstrates its relevance in high-risk, blockchain-based financial environments. The results offer theoretical insights by refining the relative importance of ISSM constructs in fintech settings and provide practical guidance for developers and platform architects to prioritize system robustness, efficiency, and usability to enhance user satisfaction and engagement. Full article

The rapid expansion of mobile healthcare (mHealth) applications has transformed health-related e-commerce, creating new challenges for understanding and responding to user needs. This study proposes a data-driven framework to systematically identify and prioritize unmet user requirements from negative reviews of Chinese mHealth applications. Using a dataset of 31,124 user reviews collected between 2019 and 2025, the framework integrates sentiment analysis, topic modeling, and machine learning regression to uncover six key areas of user concern and examine their temporal evolution. Among several predictive models linking user concerns to app ratings, the k-nearest neighbors (KNN) model demonstrated superior performance. Subsequent SHAP-based interpretability analysis reveals that account authentication, system accessibility, and application stability have the most significant impact on user ratings, highlighting the critical roles of trust and technical reliability in health e-commerce. This research not only provides actionable insights for platform governance but also contributes a generalizable methodology for leveraging user-generated content to inform evidence-based management and policy decisions in mobile digital services. Full article

Rapid advancements in artificial intelligence (AI) technologies, coupled with UNESCO’s Education 2030 vision, necessitate a re-evaluation of teachers’ technological and pedagogical competencies aligned with sustainability goals. This study investigates the impact of pre-service teachers developing digital materials within the framework of the Sustainable Development Goals (SDGs) using AI and AI-supported Web 2.0 tools (e.g., ChatGPT, DeepSeek, Alayna, Padlet, Canva, Kahoot) on their Artificial Intelligence Technological Pedagogical Content Knowledge (AI-TPACK) levels. Employing an explanatory sequential mixed-methods design, the research was conducted with 31 pre-service teachers over a 10-week applied training period. Data were collected via the AI-TPACK Scale and semi-structured interviews. Quantitative findings revealed that the applied training significantly enhanced the pre-service teachers’ Pedagogical Knowledge (PK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), and overall AI-TPACK levels. However, no statistically significant difference was observed in the Content Knowledge (CK) dimension. Qualitative data demonstrated that AI-supported tools made the learning environment more engaging and efficient, concretized abstract sustainability concepts, and bolstered the pre-service teachers’ digital self-confidence. Consequently, this study establishes that integrating AI tools into SDG education is an effective strategy for cultivating pre-service teachers’ technopedagogical competencies, empowering them to perceive technology as a facilitator of professional development rather than an instructional barrier. Full article