Search Results (101)

Artificial intelligence (AI) is reshaping the architecture, engineering and construction (AEC) sector. However, AEC education remains rooted within traditional disciplinary boundaries and a technology-centric training model, creating a widening mismatch between graduates’ capabilities and the cognitive, collaborative, and interdisciplinary demands of AI-enabled practice. Situated within the Chinese higher education context, this study bridges this gap through a multi-stakeholder survey (n = 352) noting perspectives from academia, industry and research. One-way ANOVA and Tukey’s HSD test were used to examine differences across stakeholder groups and disciplines, while a Bayesian Network was developed to model competency pathways, simulate intervention scenarios and identify key leverage points for curriculum reform. The analysis reveals that meaningful AI integration requires a reconstruction of competency, rather than the mere addition of standalone technical or software courses; it calls for fundamental changes in professional formation, curricula and pedagogy. Four core competencies emerged from the data: professional expertise, systems thinking, interdisciplinary collaboration and AI-enabled problem-solving. Bayesian Network simulations further indicated that curriculum expansion alone improved AI knowledge acquisition by 39.6%, but yielded only modest gains in practical skills (13.9%) and application competencies (4.5%). By contrast, integrated interventions that combined teacher development, university–industry collaboration and project-based practice produced substantial improvements in AI application competencies (37.9%), employment adaptability (29.3%) and industry satisfaction (14.1%). These divergent findings highlight the necessity of coordinated educational interventions to reconcile stakeholder expectations and foster AI-oriented competency development. Based on this evidence, the study proposes a competency-oriented framework and a phased curriculum transformation pathway, providing an empirical foundation for AI-driven curriculum reform and competency reconstruction in AEC education. Full article

Complex flight maneuver recognition (FMR) underpins intelligent flight training, including training assessment, pilot skill profiling, and flight safety monitoring. Existing FMR methods typically require large labeled datasets, generalize poorly across aircraft, and provide limited decision transparency. We propose TableManeuver, an explainable LLM-based FMR method that reformulates multivariate flight parameter time series as table-understanding inputs. The method updates no base LLM parameters and uses a small labeled training set only as a retrieval library; it is therefore not a zero-shot setting. TableManeuver first converts flight parameter sequences into tabular text that preserves temporal indices and channel semantics, reducing the mismatch between numerical time series and the textual semantic space of LLMs. It then combines domain knowledge, neighborhood sample references, and task decomposition prompts in a retrieval-augmented reasoning architecture that guides explicit step-by-step inference. We evaluate the method on a flight dataset collected from human pilots on a high-fidelity flight simulation platform. Without base LLM parameter updates, TableManeuver achieves 96.2% precision, 96.8% recall, and a 96.5% F1 score, exceeding the strongest supervised baseline by 3.5 percentage points in F1. In cross-aircraft evaluation, the F1 score decreases by only 1.4 percentage points, which is substantially smaller than the degradation observed for deep learning baselines. Retrieval-only baselines that transfer neighbor labels without LLM inference perform markedly worse, indicating that the performance gains are not explained by neighbor label transfer alone. TableManeuver combines recognition accuracy, cross-aircraft robustness, and readable step-by-step reasoning evidence, offering a practical route for applying LLMs to aviation time series analysis. Full article

This study employs a qualitative narrative inquiry to explore how support systems and industry engagement influence youth employability and entrepreneurial aspirations within South Africa’s TVET sector. The central research question examines how young people’s personal stories reveal the impact of systemic barriers and support mechanisms on their identity construction, resilience, and agency. Using in-depth interviews and focus groups with 24 purposively sampled youth across Gauteng’s TVET colleges, the analysis combines thematic and narrative approaches to uncover recurring patterns and narrative structures. Participants ranged in age from 18 to 30, with a balanced gender distribution, representing various socio-economic backgrounds and career interests, selected through purposive sampling to ensure data richness and saturation. The analysis integrated thematic and narrative approaches, with a detailed coding process, theme development, and measures to mitigate bias through member checking and peer debriefing. Findings indicate that experiential learning, mentorship, family encouragement, and industry connections serve as critical support mechanisms that shape youth perceptions of opportunity and self-efficacy. Conversely, systemic barriers such as resource limitations and skills mismatches are frequently narrated as obstacles constraining pathways to employment and entrepreneurship. The study concludes that integrating youth narratives into policy development can foster more inclusive and effective strategies for youth empowerment. These insights contribute to the literature on youth employment by highlighting the active role of storytelling in identity negotiation and social critique within socio-economic contexts. Full article

Career decision-making remains a critical challenge for computer science students, particularly within rapidly evolving labor markets such as Saudi Arabia. Traditional guidance approaches often rely on subjective advice or limited academic indicators, which frequently result in mismatched career choices and reduced employability. To address this gap, we propose a hybrid career recommendation system that integrates academic performance, technical and soft skills, and personal preferences. The proposed system combines artificial neural network (ANN) prediction, content-based career-profile matching, and collaborative peer similarity through weighted score fusion to generate ranked career path recommendations. The content-based component employs a similarity function (cosine similarity) to measure the alignment between the student’s attributes and each career profile. The collaborative component applies a k-Nearest Neighbors model to capture interaction-like signals from peer profiles. The ANN learns complex nonlinear relationships between student features and career outcomes, enabling the system to generate predictive probabilities for each career path by applying the TensorFlow framework for multi-class classification of student data. Final recommendations are produced through a weighted fusion of models. An experimental evaluation of 400 student records using stratified macro-level evaluation metrics demonstrates robust performance, achieving an accuracy of 93.75%, precision of 0.94, recall of 0.94, and F1-score of 0.94. The system provides Top-k-ranked career suggestions, along with interpretable feature importance, enhancing transparency for academic advisors and students. The results highlight the effectiveness of hybrid recommenders in mitigating cold-start and sparsity problems while capturing multidimensional student attributes. This study contributes a reproducible methodology and implementation pipeline that can be extended to real institutional contexts. Future work will include expansion to more university disciplines and integration with academic consulting platforms to support data-driven career planning. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

This study explores how artificial intelligence (AI)-driven labour market analytics can support the alignment of university curricula with emerging skill demands and inform the development of targeted micro-credentials. A mixed-methods approach was applied, combining AI-assisted analysis of approximately 30,000 online job advertisements in Latvia with ESCO-based skill mapping, curriculum analysis, and expert interviews. The study develops and validates a three-layer analytical framework integrating labour market demand, professional standards, and programme learning outcomes. Three occupations—Personnel Specialist, Finance Manager, and Organisation Manager—were analysed at Riga Technical University as proof-of-concept cases. The findings demonstrate that strict one-to-one ESCO matching overestimates curriculum gaps because labour market and educational actors often describe competencies at different levels of abstraction. Composite matching significantly improves alignment estimates by identifying functionally equivalent competencies embedded across curricula. Nevertheless, the analysis reveals a persistent under-representation of digital competencies across all programmes, confirmed by industry experts. Interviews further identify a “pedagogical transfer gap”, where formally acquired competencies are insufficiently applied in practice, and highlight employer support for high-quality micro-credentials focused on technical upskilling. The study contributes an AI-assisted curriculum-monitoring framework that combines large-scale skill extraction, semantic alignment, and stakeholder validation, offering universities a practical tool for evidence-based curriculum renewal and lifelong learning development. Full article

This study proposes a deep learning-based precipitation retrieval framework to improve precipitation retrieval using Fengyun-4B (FY-4B) multi-channel infrared brightness temperatures and cloud-top temperature (CTT). Four deep learning models are evaluated, ranging from lightweight architectures (DS-UNet, SmaAt-UNet) to more computationally intensive ones (U-Net, Attention U-Net). Unlike previous FY-4B/deep learning precipitation retrieval studies that mainly emphasize satellite-derived cloud-top features, this study explicitly evaluates the contribution of underlying-surface information within a unified retrieval framework. To quantitatively assess the impacts of underlying-surface information on retrieval performance, a digital elevation model (DEM), topographic factors (slope and surface roughness), and land use/land cover (LULC) are integrated into the framework. The impacts of surface information on precipitation retrieval were further examined through validation against the Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM (IMERG) product and rain-gauge observations. Results show that precipitation detection can be improved by incorporating underlying-surface information. Model architecture strongly influenced the results. Lightweight models (DS-UNet and SmaAt-UNet) significantly enhanced retrieval performance by providing clearer spatial constraints because of their greater sensitivities to discrete LULC boundaries. Deeper models (U-Net and Attention U-Net) more effectively encoded continuous topographic gradients because they benefited more from terrain-related variables. Precipitation retrieval skill may not necessarily be improved by a simple combination of DEM and LULC, probably due to thermodynamic forcing and spatial scale mismatches. By comparison, incorporating multiple factors can effectively alleviate these conflicts and improve precipitation detection. This study highlights that precipitation monitoring over complex regions can be enhanced through the proper integration of underlying-surface information. Considering both network architecture and operational objectives is therefore important for developing optimal integration strategies, which can support regional hydrological modeling and flood early-warning systems by providing more reliable input datasets. Full article

This study investigated whether weekly training sessions replicate the running demands of match play in sub-elite rugby union athletes, examining the principle of training specificity across positional roles. A total of 30 male players from a sub-elite rugby union team participated, contributing 306 training and match data points. External load variables were collected using 18 Hz GNSS devices and analyzed across ten time-motion KPIs, including distance per minute, maximal speed, distance distribution across speed zones, and acceleration–deceleration profiles. Linear mixed models assessed differences between training and match play within forwards, backs, and scrum-halves. Results revealed significant mismatches between training and match running demands, with positional roles exhibiting distinct profiles. Forwards performed substantially lower running volumes and intensities in training, particularly in moderate-speed zones. Backs showed similar total volumes but markedly reduced high-intensity actions, including maximal speed, high-speed running, and severe decelerations. Scrum-halves displayed the greatest alignment, with no major discrepancies across conditions. These findings indicate that game-based training alone does not sufficiently reproduce match play running intensity for any position. Integrating targeted closed-skill blocks and supplementary high-intensity conditioning within predominantly game-based frameworks may help balance representativeness, repetition, and physical overload, supporting more effective preparation for the running demands of competition. Full article

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies. Full article

Background: Effective sick-day management, including ketosis home management aimed at preventing diabetic ketoacidosis (DKA), is essential for families living with a child/adolescent with type 1 diabetes (T1D). Methods: Adolescents living with T1D and caregivers of younger children living with T1D were invited to participate in an interview consisting of five parts: (I) demographic data, (II) subjective self-ratings on competence in ketosis home management, (III) objective assessment of competence in ketosis home management using a standardized clinical case scenario consisting of 10 management steps, in which participants were asked to describe the actions they would take to prevent DKA, and (IV) practical demonstrations to objectively assess skills in (IVa) urine dipstick self-testing and (IVb) insulin administration, (V) household availability of (Va) urine dipsticks and (Vb) insulin cartridges. Results: (I) We enrolled 61 adolescents and 79 caregivers. (II) Competence in ketosis home management was subjectively self-rated as good to very good. (III) Adolescents reported 4 (median; Q25/Q75 3/5) and caregivers 5 (4/5) of 10 management steps. Never self-testing ketone levels was reported by 33% of adolescents and 11% of caregivers. (IVa) At least one handling error occurred in 100% of adolescents’ and in 98% of caregivers’ practical demonstrations of urine dipstick self-testing and in (IVb) 98% of adolescents’ and 98% of caregivers’ insulin administrations. (Va) Altogether urine dipsticks were available in 43% of households, whereas (Vb) insulin cartridges were available in 78% of households. Conclusions: Our results demonstrate a mismatch between challenges in ketosis home management and high subjective self-ratings. Full article

Due to acute resource constraints and environmental turbulence, many small and medium-sized construction enterprises (SMEs) prioritize short-term survival over corporate social responsibility (CSR) initiatives. Grounded in social exchange theory (SET), this study investigates how CSR implementation drives financial performance (FP) via the mediating role of non-financial performance (NP), aiming to deconstruct the “psychological black box” of this transformation. Drawing on a sequential mixed-methods design involving PLS-SEM analysis of 380 responses and 10 semi-structured interviews, the results confirm that CSR practices, particularly ethical practices and community engagement, can be effectively translated into improved NP, which acts as a vital strategic conduit for enhancing FP. However, skills development and training showed limited immediate impact due to a systemic “digital mismatch” and significant time-lag effects. Theoretically, this research refines SET by identifying a hierarchical transition where socio-emotional assets serve as compensatory resources in volatile and resource-constrained environments. Practically, the findings offer a strategic roadmap for SMEs to mitigate technological and systemic barriers, providing novel pathways for fostering CSR to achieve sustainable growth. Full article

Mixed Reality (MR) teleoperation offers an intuitive interface for Human-Robot Collaboration (HRC), yet it often faces the “Embodiment Gap”—a physical and kinematic mismatch between human operators and robotic platforms. Existing MR systems primarily rely on a “direct mapping” approach, where user movements are transferred directly to the robot. This forces operators to manually adapt to robotic constraints, such as singularities and joint limits, making task performance heavily dependent on individual skill. This study proposes Mixed reality Adaptive Spatial and Kinematic support (MASK), an adaptive shared-control framework designed to bridge the “Gulf of Execution” and “Gulf of Evaluation” by separating target selection from reachability and kinematic feasibility. The MASK system integrates three core modules: (1) Target Object Identification (TOI) based on body motion features to identify the intended manipulation target; (2) a Base Relocation Module (BRI) utilizing Inverse Reachability Maps to optimize the robot’s spatial configuration; and (3) a Kinematic Correction Module (KCM) that autonomously resolves kinematic constraints through pose blending and null-space optimization. Initial experimental results suggest that MASK reduces the operator’s cognitive and physical load by shifting the burden of kinematic resolution from the human to the system. This approach enables high-precision manipulation through an intuitive interface, potentially reducing the performance gap between different levels of operator proficiency. Full article

Translating large-scale motion datasets into robust, deployable humanoid controllers is a critical challenge in engineering informatics, primarily due to the scarcity of high-quality annotations, the risk of mode collapse in conditional generation, and the strict constraints of onboard computing hardware. This paper presents a deployable two-stage learning system that maps clip-level motion datasets to a single-policy multi-skill controller and its deployable counterpart. We adopt coarse one-hot skill labels that can be assigned automatically at the clip level with negligible manual effort, enabling scalable dataset construction. To prevent conditional discriminators from ignoring skill conditions, we inject mismatched (transition, label) pairs and introduce a condition-aware loss that explicitly penalizes incorrect transition–label associations, improving controllability and mitigating mode collapse. For real-world deployment, we further propose a two-stage training strategy: a privileged teacher policy is first trained in simulation and then distilled into a student policy that relies on stacked historical proprioceptive observations, ensuring robustness against sensing noise and latency without relying on external state estimation. Extensive evaluations in simulation and on real hardware demonstrate improved skill coverage, transition coverage, realism, and training efficiency across heterogeneous embodiments. With the onboard computer of a Unitree G1 robot, the distilled policy runs at 100 Hz with 15–25 ms latency, confirming the system’s engineering feasibility. Full article