Search Results (625)

Urban farming microinterventions are small, place-based cultivation projects that operate under severe spatial and resource constraints yet can generate social learning and locally embedded resilience. The present paper examines how design decisions shape the effectiveness of such interventions through three design-led case studies: Blooming Structure (2018, Warsaw), a temporary hydroponic “laboratory” installation; Micro-cultivation (2018, Warsaw), a shopfront vertical demonstration farm; and Micro-cultivation 2 (2019), modular “cultivation furniture” for interiors and exhibition deployment. The analysis combines project documentation with practice-based observations and applies five interpretive dimensions: spatial fit, technical feasibility, communicative legibility, replicability, and social programming. Findings highlight that successful microinterventions align legible cultivation infrastructure with high visibility, accessibility and participatory formats that support skills transfer and copying-based scaling. Rather than offering universal claims about urban agriculture outcomes, the paper provides a reference set of design principles that may inform similar micro-scale interventions in other contexts, subject to local constraints. Limitations include the small sample size and the concentration on projects from Poland. Practically, the findings can support designers, municipalities, and civic organisations in structuring microinterventions as replicable, low-threshold prototypes and in aligning technical systems with maintenance capacity and public engagement. Full article

In industrial machine learning, predictive performance alone is insufficient to ensure reliable deployment, as model behaviour may vary across different regions of the input space under limited data and evolving process conditions. This work investigates whether such variation can be systematically analysed through post-hoc methods. A model-agnostic framework is proposed in which interpretability metrics, including residuals and feature attributions, are embedded into a low-dimensional space and approximated using a continuous surrogate model. This representation enables the analysis of model behaviour as a structured landscape, rather than as isolated pointwise explanations. The approach is applied to ceramic heating element production, where two distinct regimes are identified. One corresponds to a stable region with consistent and accurate predictions, while the other reflects a transitional regime associated with increased ambiguity and sensitivity to feature interactions. These regimes are shown to align with known process conditions and temporal variation. The results demonstrate that model behaviour can be organised into coherent regions that are not observable through aggregate performance metrics alone. This provides a structured basis for post-hoc analysis, supporting targeted interpretation and further investigation of model reliability in industrial settings. Full article

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article

Background/Objectives: Nurse transition programmes are widely implemented to support newly hired nurses and promote workforce retention. Despite the growing number of published reviews, conceptual inconsistency and methodological heterogeneity limit the interpretability and cumulative value of the evidence. This umbrella review aimed to synthesise and critically examine review-level evidence on nurse transition programmes, clarifying programme typologies, contexts, methodological approaches, reported outcomes, and thematic patterns. Methods: An umbrella review was conducted in accordance with PRISMA 2020 guidance. Systematic searches were performed in CINAHL, PubMed, Scopus, Web of Science, and Google Scholar, supplemented by citation tracking. Results: Fourteen reviews published between 2010 and 2025 were included: 12 reviews of primary studies and two reviews of secondary evidence (one umbrella review and one meta-review). Programme models and outcome measures were highly heterogeneous, and primary study overlap was slight (CCA = 2.55), indicating that reviews in the corpus drew on largely non-overlapping sets of primary studies. Transition programmes for new nurses commonly use one-on-one preceptorships with supernumerary practice, simulation-based learning, and active methods like case studies and reflective journaling to build competence and confidence. Their duration varies from a few days to 12 months, aligning with the progressive learning curve of new graduates. Professional outcomes, particularly competence and confidence, were consistently reported, whereas organisational outcomes, such as retention, showed mixed, methodologically constrained evidence. Patient-level outcomes were rarely examined. Thematic analysis revealed a shift over time from individual professional readiness towards implementation and organisational considerations. Conclusions: Given this conceptual plurality, there is an urgent need to standardise key indicators for evaluating the effectiveness of nurse transition programmes across healthcare settings globally. Full article

This study presents an integrated bibliometric analysis (BA) and systematic literature review (SLR) of construction safety research (CSR) to examine its evolution and emerging technological directions. It aims to move beyond descriptive mapping by linking long-term research trends with recent technological advancements to provide a structured understanding of how construction safety is transitioning toward data-driven and resilient systems. Utilising the PRISMA-guided approach, 1979 publications were analysed, revealing an average annual growth rate of 18%, driven by increasing safety concerns and the rapid implementation of digital technologies. The findings demonstrate that conventional safety research, centred on hazard identification, safety culture, and management commitment, is gradually being complemented by advanced technologies such as artificial intelligence (AI), machine learning (ML), extended reality (XR), and digital twins. These technologies enable predictive risk assessment, real-time monitoring, and immersive training, supporting a shift from reactive to proactive safety management. Despite these advancements, critical gaps remain, including limited real-world validation of AI-based systems, insufficient integration of technologies into cohesive frameworks, and underexplored socio-cultural factors influencing adoption. These challenges were addressed by proposing a research-to-practice framework for integrating emerging technologies into construction safety management. The framework incorporates technological, organisational, and human factors to enhance adaptability, risk management, and overall construction project resilience. Additionally, the research contributes to the body of knowledge by providing a comprehensive and analytically grounded framework that bridges the gap between research and practical implementation, while also identifying future research directions to support the development of intelligent, resilient, and adaptive construction safety systems. Full article

Autonomous driving systems require the integration of heterogeneous sensor data, distributed V2X communication, and safety-critical decision-making into coherent and interpretable world models. This review provides a systematic analysis of knowledge graph (KG)-based approaches in autonomous driving between 2015 and 2025, following a PRISMA-aligned methodology. The literature is organised along a perception → representation → reasoning → decision taxonomy, covering traffic ontologies, V2X knowledge integration, dynamic KG updates, real-time reasoning architectures, and benchmark datasets. A clear shift from static representational ontologies toward predictive and, in a smaller subset, closed-loop validated neuro-symbolic architectures. Knowledge graphs emerge as semantic integration layers that improve contextual reasoning, explainability, and rule compliance in safety-critical environments. Key challenges include scalable real-time reasoning, standardised evaluation frameworks, and safety-aligned integration of learning-based components. Full article

Cognitive load theory-informed curriculum design in health sciences education refers to the purposeful organisation of teaching strategies and learning materials based on the principles of Cognitive Load Theory (CLT), a framework developed by John Sweller in the late 1980s. CLT is grounded in cognitive psychology and recognises that the working memory has a limited capacity for processing new information. It identifies three types of cognitive load: intrinsic load, which refers to the inherent complexity of the material being learned; extraneous load, which results from ineffective instructional design or irrelevant information; and germane load, which reflects the mental effort directed toward understanding, integrating, and organising information into long-term memory. In health sciences education, students frequently engage with tasks that require the simultaneous processing of multiple interacting elements, placing high demands on working memory at specific points in time. This includes foundational biomedical sciences such as anatomy, physiology, and pathophysiology extending to applied clinical skills, diagnostic reasoning under uncertainty, health service management within complex systems, and ethically grounded decision-making. Without thoughtful instructional design, learners may be overwhelmed by excessive information and cognitive demands, which can hinder understanding, retention, and performance. Applying CLT-informed strategies, educators can reduce unnecessary cognitive burden, sequence learning activities to align with learners’ cognitive capacity, and promote deeper learning. This approach supports more effective knowledge acquisition and transfer and is particularly valuable in content dense academic environments such as medicine, nursing, allied health education, public health and health service management education. Therefore, integrating CLT-informed principles into curriculum design can help optimise learning experiences and support the development of competent health professionals. Full article

Background: Governments increasingly seek whole-system, public-health approaches to prevent serious youth violence. However, there is limited empirical evidence on how such approaches are implemented and sustained in complex, post-conflict settings characterised by coercive control, political instability, and fragmented system ownership. Aim: This study examines the Executive Programme on Paramilitarism and Organised Crime (EPPOC) in Northern Ireland as a system-level implementation architecture for addressing serious youth violence, with a focus on how coordinated action was enabled, constrained, and adapted over time. Methods: We conducted an embedded qualitative case study of EPPOC using systematic analysis of programme documentation, independent evaluations, oversight reports, and population-level data spanning nine years of delivery. Implementation science frameworks (ERIC, Proctor’s implementation outcomes, and CFIR) were applied retrospectively as analytic lenses to examine implementation strategies, outcomes, and contextual determinants. Results: EPPOC demonstrated strong implementation outcomes in acceptability and adoption across statutory and community sectors, supported by cross-government governance, trauma-informed workforce development, and shared learning systems. Penetration and sustainability were more variable and constrained by political instability, short-term funding cycles, uneven departmental ownership, and coercive community conditions. Conclusions: The findings suggest that the most transferable element of EPPOC is not individual interventions but the implementation architecture that enabled coordinated, trauma-responsive action across government in a highly complex environment. This architecture represents a potentially replicable design pattern for jurisdictions seeking to address serious youth violence where traditional programme models struggle to operate. Full article

This article analyses how capacity building programmes interact with structural constraints in mission-oriented climate policy, focusing on the Italian pilot Let’sGOv (GOverning the Transition through Pilot Actions) within the EU Mission “100 Climate-Neutral and Smart Cities by 2030”. Using an iterative, reflexive methodology (document analysis, direct observation, and qualitative analysis of questionnaires, workshop outputs, and online training feedback), it examines how municipal actors experience and reinterpret capacity building across three coupled dimensions: internal organisational capacity, external stakeholder relations, and multilevel governance interfaces. The empirical setting is a network of nine Italian Mission Cities (Bergamo, Bologna, Florence, Milan, Padua, Parma, Prato, Rome, Turin) supported by technical partners. The bench-learning pathway combined barrier diagnosis, an intensive in-person workshop, and a codesigned online curriculum structured around three thematic clusters (engagement, data, climate finance). Findings indicate that persistent barriers—departmental silos, resource and time scarcity, rigid human resources and procurement routines, asymmetric data access, and regulatory instability—are not removed by capacity building; rather, they are progressively articulated, specified, and reframed into actionable organisational and policy demands. Bench-learning strengthens diagnostic and relational capacities and enables modest institutional innovations (templates, protocols, internal task forces, shared policy briefs), while “hard” governance infrastructures largely remain unchanged. The paper argues that networked capacity building contributes to the emergence of nascent, project-dependent multilevel interfaces only when it supports collective negotiation with national actors and translates local experimentation into durable multilevel interfaces, mitigating risks of projectification and downward responsibility shifting. Full article

The scope of actual pro-environmental initiatives, programs, interventions, and campaigns is limited. Therefore, spillover effects from these activities to other domains of economy, the private sphere, and society are crucial to achieve a transformation of society towards sustainability. Starting from the known literature and using Google Scholar as a platform for searching additional studies, this explorative, traditional narrative review analyses behavioural spillover effects, where either one behaviour influences the likelihood of another behaviour, or an intervention shows an impact on an environmentally significant behaviour, which it did not primarily address. In the scientific literature, spillover is classified by direction (environmentally positive versus negative), involved behaviours (similar or cross-behavioural), timing (short or long term), context (e.g., work to private life), and social scope (personal, interpersonal, intra- and inter-organisational, intergroup, or international). Positive spillover can result from cognitive dissonance reduction, consistent self-perception, pro-environmental values, norms, self-identity, action-based learning, and habit formation. Negative spillover emerges through rebound effects, moral licensing, and psychological reactance. Stronger spillover is observed between similar behaviours, while cross-domain spillover is generally weaker. According to previous research, a facilitated participatory approach with strong pro-environmental orientation appears recommendable for practitioners to foster the value change required for effective and sustained positive spillover. Full article

Human serum albumin (HSA) binding critically influences drug distribution and pharmacokinetics. In this study, HSA affinity chromatography was integrated with machine-learning-based quantitative structure–retention relationship (QSRR) modeling to elucidate structural determinants of albumin binding in a library of 115 fluoroquinolone (FQs) derivatives. Experimentally determined logk_HSA values were obtained using biomimetic chromatography, and these were then used as modelling endpoints. Following descriptor reduction via Least Absolute Shrinkage and Selection Operator (LASSO) and systematic benchmarking of 42 regression algorithms, support vector regression (SVR) and nu-support vector regression (ν-SVR) with radial basis function kernels demonstrated superior predictive performance. A parsimonious 12-descriptor ν-SVR model achieved strong calibration and validation metrics (R² = 0.916, Q²_test = 0.823, concordance correlation coefficient (CCC) = 0.899) and satisfied Organisation for Economic Co-operation and Development (OECD) criteria, including applicability domain assessment. Shapley Additive exPlanations (SHAP)-based interpretation revealed that albumin binding is governed by a balance between hydrophobic surface area and distributed electronic properties, whereas excessive localized polarity and quaternary ammonium functionalities reduce affinity. This experimentally anchored and interpretable modeling framework provides mechanistic insight into HSA binding in fluoroquinolones and offers a robust tool for rational pharmacokinetic optimization. Furthermore, in order to make the model easily accessible to users, we have packaged it in the form of an online application. Full article

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment. Full article

The growing prevalence of home education necessitates exploration of parental involvement outside traditional schooling environments. This paper conceptualises parental involvement within home education decision-making. Core elements of decision making, including Choices, Contexts, Challenges and Changes, are integrated with Bronfenbrenner’s bioecological systems theory to create the 4Cs model of parental decision-making in home education. The 4Cs model is developed from integrating findings from the literature with previous empirical work on how parents make and explain decisions in home education. The present paper uses this model to organise and explain parental decision-making in a structured way. Building on critiques of school-centric parental involvement models, the 4Cs model steps away from assumptions that position parents as passive participants in schools’ agendas to instead illustrate parents’ active collaboration and involvement in their children’s education. The paper goes on to use the 4Cs model to help reframe Epstein’s typology of parental involvement to bridge home education research and broader scholarship on parental involvement. It provides a structured lens to analyse the decision-making processes that underpin why families choose home education and how it is enacted in practice. Central to this framework is the concept of parental agency, which is decoupled from school-based imperatives and positioned as the driving force in constructing tailored learning environments. This theorisation offers a critical lens for examining how parents navigate educational trade-offs, socioecological constraints, and adaptive strategies. We reframe parental involvement as deliberative, context-responsive praxis, creating potential for the 4Cs framework to act as a transferable model for analysing agency-driven parental engagement across diverse educational settings. Full article

This paper presents a holistic development model for South African schools that aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as defined by the United Nation’s Sustainable Development Goal 4: Quality Education, by 2030. It addresses critical gaps in private secondary schools, including unclear performance objectives, inadequate monitoring, and limited data-driven decision-making. To meet these needs, the study proposes a new performance management model based on Kaplan and Norton’s balanced scorecard framework, combining four perspectives: Students, Academic excellence, Learning and growth, and Resources. Using a positivist approach, the model was validated by confirmatory factor analysis of 244 respondents across 12 private schools in Durban. The Comparative Fit Index, Normed Fit Index, and Tucker–Lewis Index confirmed its structural validity, while the Root Mean Square of Error Approximation indicated excellent absolute fit. Several intercorrelations emerged within the Learning and growth perspective, particularly regarding staff respect for students and their value to students. Implementation revealed an overall satisfactory performance rating of 3.85 on a 5-point scale. The Student perspective scored lowest (3.39), highlighting inadequate student preparation as a key issue, with learners’ pre-class reading of material scoring just 2.81. These findings underscore the model’s utility in identifying areas for improvement, particularly in student engagement, academic excellence, and organisational culture within the Learning and Growth dimension. Full article

Distributed denial-of-service (DDoS) attacks, a type of Denial-of-Service (DoS) attack in which the targeted server, service or network is overloaded with malicious traffic originating from various different sources with the aim of making such targets inaccessible for legitimate users, continue to pose a pertinent threat to the availability and integrity of organisational digital assets. While many studies have shown that machine learning models can provide high predictive accuracy in detecting such attacks, they often fail to evaluate the practicality of deploying such models to production. This study aims to address this gap by evaluating a considerable amount of pipelines based on five popular supervised classifiers for detecting DDoS attacks using the CICDDoS2019 dataset. The study employs a comprehensive methodology that combines both manual feature removal with automated encoding, scaling and feature selection integrated within pipelines. A total of 210 pipelines formed of five classifiers, three features selectors, two hyperparameter tuners and seven train–test splits were initially evaluated. Pipeline performance was assessed using both conventional and computational performance metrics. To identify the champion pipeline, a two-step approach was employed: composite scoring for shortlisting and statistical testing using Friedman and post hoc Nemenyi tests. The champion pipeline was shown to be Decision Tree coupled with Recursive Feature Elimination (with 20 features selected) and Grid Search hyperparameter tuning with a 90-10 train–test split. It achieved the most optimal balance of predictive capabilities and computational overheads, achieving an MCC of $0.993\pm 0.024$, training time of $0.194\pm 0.001$ s, inference time of $0.000998\pm 0.00008$ s, CPU time of $0.194\pm 0.008$ s and average memory usage of 15,167 ± 322 kilobytes across training and inference. The findings highlight the importance of a holistic and more nuanced approach when selecting a champion pipeline that is not only effective but also feasible for deployment in resource-constrained environments. Full article