Search Results (1,178)

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

Survivors of pediatric cancer are at risk for late effects and require risk-adapted long-term follow-up (LTFU) care. Yet less than 50% of survivors attend LTFU care. This study aimed to identify barriers and enablers of engaging with LTFU care as perceived by Canadian survivors of pediatric cancer and healthcare providers (HCPs). Survivors (n = 108) and HCPs (n = 20) completed surveys assessing barriers and enablers to attending LTFU care, summarized using descriptive statistics. Participants were invited to participate in survivor focus groups (n = 22) or HCP semi-structured interviews (n = 7). These were analyzed using reflexive thematic analysis and the Capability, Opportunity, and Motivation for Behaviour Change (COM-B) model, which explores how an individual’s capability, opportunity, and motivation influence a target behaviour. Structural barriers, transitioning from pediatric to adult care, and time constraints were highlighted as barriers that affect survivors’ physical opportunity to engage in LTFU care. Accessibility, financial support, HCPs and family support, and community resources were highlighted as enablers that better survivors’ physical and social opportunity to engage in LTFU care. In conclusion, Canadian survivors of pediatric cancer highlighted barriers that limited their physical opportunity to attend LTFU care, while factors that enhanced their physical and social opportunities facilitated greater engagement with LTFU care. Full article

Background: Human activity is recognised as a major contributor to changes in Earth’s climate, land surface, oceans, ecosystems, and biodiversity. These alterations are largely due to greenhouse gas emissions, deforestation, mass pollution, and land degradation. In light of these environmental challenges, examining the psychological determinants of pro-environmental behaviour has become increasingly important. Study’s Aim: To provide a comprehensive model evaluating the structural relationships among biospheric values, green self-identity, green self-efficacy, and eco-anxiety to investigate the underlying mechanisms relating to the adoption of various pro-environmental behaviours (PEBs). Methods: An online self-report questionnaire was completed by 510 Italian participants (aged 18–55, M = 35.18, SD = 12.58) between November and December 2023. Data analysis was performed using R statistical software, employing Structural Equation Modelling. Results: The results indicate that eco-anxiety, green self-efficacy, and green self-identity are significant positive predictors of PEBs. Furthermore, green self-identity significantly influences eco-anxiety and green self-efficacy, while biospheric values are a major trigger for both green self-efficacy and green self-identity, but not for eco-anxiety. Conclusions: These findings suggest that while eco-anxiety can be an adaptive motivator for PEBs, biospheric values foster a green self-identity and self-efficacy, which in turn drive pro-environmental actions. The study concludes that encouraging biospheric values and strong green self-identity is crucial for promoting sustainable behaviours. Full article

In recent studies, public health has been considered a key stakeholder in climate mitigation and adaptation in cities since they are more exposed to the impact of climate change. Nurses represent a vast majority of public health professionals, playing a key role in health promotion that allows them to influence individuals, families, and communities in adopting healthier behaviours and decarbonized lifestyles. Therefore, the purpose of this study is to map the existing evidence on nursing interventions, which are being led or implemented to reduce the health risks related to climate change in urban areas. The present review follows the JBI methodological framework, including a search on PubMed, MEDLINE complete, CINAHL Complete, Scopus, Web of Science, SciELO (Scientific Electronic Library Online), BASE (Bielefeld Academic Search Engine), and RCAAP. Hand searched references were also considered, including quantitative, qualitative, and mixed-methods studies between January 2014 and October 2024, for a more contemporary perspective. A three-step search strategy and data extraction tool were used by two independent reviewers. Twenty-seven studies in English and Portuguese were eligible for inclusion, all targeting a population of professionals with nursing-related roles: two case studies, one Delphi panel, one descriptive study, one historical research paper, two using a methodological design format, four narrative reviews, one observational study, nine review articles, three scoping reviews, and three systematic reviews. Eight categories of nursing interventions that contribute to decarbonized lifestyles, reducing health risks in relation to climate change, were acknowledged. Nurses play a key role in empowering individuals, families, and communities, promoting climate awareness and literacy, supporting health policy change, advocating for the most vulnerable and engaging in environmental activism, using evidence-based research, and taking advantage of marketing strategies and social media. Full article

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, the cadmium telluride-based vacuum PV glazing has been developed to enhance the thermal performance of BIPV applications. To fully understand the complex thermal behaviour under real-world operational scenarios, this study introduces a one-dimensional transient heat transfer model that can efficiently capture the time-dependent thermal dynamics of this novel glazing system. Based on the numerical solutions using the explicit finite difference method (FDM), the temperature profile of the vacuum PV glazing can be obtained dynamically. Consequently, the heat gain of the semi-transparent vacuum PV glazing can be calculated under time-varying outdoor and indoor conditions. The validated heat transfer model was applied under four different scenarios, viz. summer daytime, summer nighttime, winter daytime, and winter nighttime, to provide a detailed analysis of the dynamic thermal behaviour, including the temperature variation and the energy flow. The dynamic thermal characteristics of the vacuum PV glazing calculated by the transient heat transfer model demonstrate its excellent thermal insulation and solar control capabilities. Moreover, the thermal performance of vacuum PV glazing was compared with a standard double-pane window under various weather conditions of a typical summer day and a typical winter day. The results indicate that the vacuum PV glazing can effectively minimise both heat gain and heat loss. The fluctuation of the inner surface temperature can be controlled within a limited range away from the set point of the indoor room temperature. Therefore, the vacuum PV glazing contributes to stabilising the temperature of the indoor environment despite the fluctuating solar radiation and periodic outdoor temperature. It is suggested that the vacuum PV glazing has the potential to enhance the climate adaptability of BIPV windows under different climate backgrounds. Full article

This paper presents a novel approach to determine dynamic safety and comfort zones in human–robot interactions (HRIs), with a focus on service robots operating in dynamic environments with people. The proposed proxemic model leverages the golden ratio-based comfort zone distribution and ISO safety standards to define adaptive proxemic boundaries for robots around humans. Unlike traditional fixed-threshold approaches, this novel method proposes a gradual and context-sensitive modulation of robot behaviour based on human position, orientation, and relative velocity. The system was implemented on an NVIDIA Jetson Xavier NX platform using a ZED 2i stereo depth camera Stereolabs, New York, USA and tested on two mobile robotic platforms: Go1 Unitree, Hangzhou, China (quadruped) and Scout Mini Agilex, Dongguan, China (wheeled). The initial verification of proposed proxemic model through experimental comfort validation was conducted using two simple interaction scenarios, and subjective feedback was collected from participants using a modified Godspeed Questionnaire Series. The results show that the participants felt comfortable during the experiments with robots. This acceptance of the proposed methodology plays an initial role in supporting further research of the methodology. The proposed solution also facilitates integration into existing navigation frameworks and opens pathways towards socially aware robotic systems. Full article

Nature-based Solutions (NbS) are increasingly adopted in urban settings to restore ecological functions and enhance biodiversity. This study evaluates the effects of NbS interventions on bird, insect, and plant communities in the Cavalum Valley urban green area, Penafiel (northern Portugal). Over a three-year period, systematic field surveys assessed changes in species richness, abundance, and ecological indicators following actions such as riparian restoration, afforestation, habitat diversification, and invasive species removal. Results revealed a marked increase in bird overall abundance from 538 to 941 individuals and in average pollinator population size from 9.25 to 12.20. Plant diversity also improved, with a rise in native and RELAPE-listed species (5.23%). Functional group analyses underscored the importance of vegetative structure in supporting varied foraging and nesting behaviours. These findings highlight the effectiveness of integrated NbS in enhancing biodiversity and ecological resilience in urban landscapes while reinforcing the need for long-term monitoring to guide adaptive management and conservation planning. Future work could evaluate ecological resilience thresholds and community participation in citizen science monitoring. Full article

Innovative approaches in hot forging, such as the use of floating dies, which aim to minimise burr formation by controlling material flow, require precise management of die geometry distortions. These distortions, primarily caused by thermal gradients, must be tightly controlled to prevent malfunctions during production. This study introduces a comprehensive thermal analysis framework that captures the complete forging cycle—from billet transfer and die closure to forging, spray-cooling, and lubrication. Two advanced heat transfer models were developed: a pressure- and lubrication-dependent contact heat transfer model and a spray-cooling model that simulates fluid dispersion over die surfaces. These models were implemented within the finite element software FORGE-NxT to evaluate the thermal behaviour of dies under realistic operating conditions. These two new models, contact and spray-cooling, implemented within a full-cycle thermal simulation and validated with industrial thermal imaging data, represent a novel contribution. The simulation results showed an average temperature deviation of just 5.8%, demonstrating the predictive reliability of this approach. This validated framework enables accurate estimation of thermal fields in the dies, and offers a practical tool for optimising process parameters, reducing burr formation, and extending die life. Moreover, its structure and methodology can be adapted to various hot forging applications where thermal control is critical to ensuring part quality and process efficiency. Full article

Background: Borderline personality disorder (BPD) is a severe psychiatric condition characterised by emotional instability, impulsivity, interpersonal dysfunction, and self-injurious behaviours. Despite growing clinical interest, the neuropsychological mechanisms underlying these symptoms are still not fully understood. This review aims to summarise findings from neuroimaging, psychophysiological, and neurodevelopmental studies in order to clarify the neurobiological and physiological basis of BPD, with a particular focus on emotional dysregulation and implications for the treatment of adolescents. Methods: A narrative review was conducted, integrating results from longitudinal neurodevelopmental studies, functional and structural neuroimaging research (e.g. FMRI and PET), and psychophysiological assessments (e.g., heart rate variability and cortisol reactivity). Studies were selected based on their contribution to understanding the neural correlates of BPD symptom dimensions, particularly emotion dysregulation, impulsivity, interpersonal dysfunction, and self-harm. Results: Findings suggest that early reductions in amygdala volume, as early as age 13 predict later BPD symptoms. Hyperactivity of the amygdala, combined with hypoactivity in the prefrontal cortex, underlies deficits in emotion regulation. Orbitofrontal abnormalities correlate with impulsivity, while disruptions in the default mode network and oxytocin signaling are related to interpersonal dysfunction. Self-injurious behaviour appears to serve a neuropsychological function in regulating emotional pain and trauma-related arousal. This is linked to disruption of the hypothalamic-pituitary-adrenal (HPA) axis and structural brain alterations. The Unified Protocol for Adolescents (UP-A) was more effective to Mentalization-Based Therapy for Adolescents (MBT-A) at reducing emotional dysregulation compared, though challenges in treating identity disturbance and relational difficulties remain. Discussion: The reviewed evidence suggests that BPD has its in early neurodevelopmental vulnerability and is sustained by maladaptive neurophysiological processes. Emotional dysregulation emerges as a central transdiagnostic mechanism. Self-harm may serve as a strategy for regulating emotions in response to trauma-related neural dysregulation. These findings advocate for the integration of neuroscience into psychotherapeutic practice, including the application of neuromodulation techniques and psychophysiological monitoring. Conclusions: A comprehensive understanding of BPD requires a neuropsychologically informed framework. Personalised treatment approaches combining pharmacotherapy, brain-based interventions, and developmentally adapted psychotherapies—particularly DBT, psychodynamic therapy, and trauma-informed care—are essential. Future research should prioritise interdisciplinary, longitudinal studies to further bridge the gap between neurobiological findings and clinical innovation. Full article

The increasing use of Global Positioning System (GPS) tools reshapes nature-based recreational practices. While previous research has examined the role of GPS technologies in outdoor recreation, limited attention has been given to the specific factors driving GPS use in nature-based settings such as natural parks. This case study examines the sociodemographic, behavioural, motivational and experiential factors influencing GPS use among visitors to the Capçaleres del Ter i del Freser Natural Park (Catalonia, Spain). A structured visitor survey (n = 999) was conducted over a one-year period and a hierarchical binary logistic regression model was applied to evaluate the explanatory contribution of four sequential variable blocks. The results showed that the behavioural factors (i.e., physical activity intensity) emerged as the strongest predictor of GPS use. Additionally, the final model demonstrated that visitors who were younger, engaged in higher-intensity physical activities, motivated by health-related goals, undertook longer routes, and reported more positive experiences were significantly more likely to use GPS tools during their visit. These findings highlight the need to adapt communication strategies to diverse visitor profiles and leverage volunteered geographic information (VGI) for improved visitor monitoring, flow management, and adaptive conservation planning. Full article

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article