Search Results (364)

As artificial intelligence and digital human technologies become increasingly integrated into livestream commerce, AI anchors are becoming important marketing agents. Yet prior research has focused primarily on their visual characteristics, leaving dynamic vocal cues largely unexplored. Drawing on social response theory and perceived authenticity research, this study examines the nonlinear association between AI anchor pitch variability and consumer engagement, together with a proposed psychological pathway and boundary condition. Study 1 analyzes 4322 product-presentation segments nested within 330 AI-anchored livestreams and 85 independent accounts on Douyin. Negative binomial models, formal boundary-slope tests, and additional specifications using account and livestream-session fixed effects, a correlated-random-effects decomposition, and viewer-minutes exposure provide robust evidence of an inverted U-shaped association between pitch variability and real-time danmaku engagement. Evidence concerning appearance-realism moderation is conditional and specification-sensitive across alternative pitch operationalizations, exposure definitions, and within-account specifications. Study 2 uses a preregistered multi-stimulus mixed design with four AI anchors, four products, and three between-participants pitch-variability conditions. Correctly scaled planned contrasts show that moderate pitch variability produced greater perceived authenticity and engagement intentions than the average of the two endpoint conditions. A 2-1-1 multilevel analysis yielded an indirect-effect pattern consistent with the proposed role of perceived authenticity. Models allowing treatment effects to vary across the 16 included anchor-product combinations showed a positive average moderate-pitch advantage, although its magnitude varied across stimuli. These findings extend livestream-commerce research from human streamers to AI-mediated communication while indicating that appearance-realism moderation, stimulus-level generalization, and causal mediation require further replication. Full article

Exposure to high-quality visual environments characterized by features such as structural order, biophilic/natural elements, and positive atmosphere is important for walking experience within community life circles (CLCs). However, compared with the static configuration of visual environments within CLCs, dynamic walking-based exposure may highlight unpredictable encounter areas and heterogeneous environmental quality. Neglecting this mismatch may misdirect environmental interventions and limit their health-promoting potential. This study aims to integrate multidimensional visual environment features into interpretable scene clusters to improve comparability and measure configuration–exposure mismatches across CLCs at scale. We examine 1262 CLCs in Harbin, China, identifying visual scene clusters from 67,840 street-view images and extracting exposure frequencies from 981,500 mobility tracks. The results show that (1) eight scene clusters effectively describe the complex visual environments of CLCs; (2) significant small-to-moderate mismatches exist between configuration and exposure; (3) the trend of commute-related walking activity is often consistent with strengthened exposure to high-disorder scenes and weakened exposure to some high-quality scenes with positive atmospheres. This study provides a data-driven framework for identifying mismatches in both the intensity and spatial distribution of visual scene configuration and exposure, supporting refined community environmental governance. Full article

Proximity-based planning frameworks such as the 15 min city and community life circle (CLC) are widely promoted to improve age-friendly access to essential resources, based on the assumption that enlarging spatial scale improves access. This assumption remains largely untested in high-density aging cities, where neighborhood-level deprivation may persist even as aggregate access improves. Using 3737 residential communities in central Tianjin, China, this study jointly evaluates food and physical activity environments across 5, 10, and 15 min walking thresholds. We apply a three-dimensional demand–matching–spatial association framework that combines health-weighted accessibility, location quotient matching, and bivariate spatial autocorrelation. Food access shows a continuous core–periphery gradient, whereas physical activity access is fragmented and facility-dependent. Supply–demand matching shows divergent scale responses across resource domains. Aggregate accessibility increased as the CLC scale expanded. However, 45.88% of communities classified as LL at the 10 min scale remained LL at 15 min, substantially exceeding the overall LL proportion of 18.70% at the 15 min scale. Only 5.42% of communities classified as HL or LH transitioned to HH. This divergence between aggregate accessibility improvement and persistent local disadvantage is defined as ineffective expansion. Eight diagnostic types reveal a clear divide between deprived peripheries and advantage-maintenance conditions in the urban core. These findings suggest that age-friendly CLC planning should move beyond uniform service-area expansion toward type-specific, place-based interventions. Full article

Resource assignment in heterogeneous multi-UAV systems changes both current task outcomes and the task chains that remain feasible across a mission region. Completion, delay, utilization, and role-specific reachability metrics do not retain this spatially resolved option space. This paper proposes the grid-based functional feasible domain (G-FFD), which represents capability- and time-constrained sensing–execution UAV chains subject to an intermediate decision/communication delay. A generalized index aggregates profile-labeled feasible chains and recovers feasible-chain count as the single-profile, unit-weight case. Shared resources and chain overlap describe functional coupling between grids. Controlled simulations show that local task bursts remove alternatives from external grids, with realized loss depending on the candidate-domain size, coupling, accepted demand, and occupied resources. Across 5 × 5, 10 × 10, and 20 × 20 grids, the high-G-FFD region has the largest mean peak external loss, although magnitudes and other regional rankings remain resolution-dependent. In a 20-seed paired stress test, G-FFD-guided selection reduces the cumulative external deficit relative to earliest-finish selection by a mean of $2.519$ retention-minutes (bootstrap 95% confidence interval: $1.073$–$4.089$). The differences in overall completion and post-burst completion of external medium/high-profile tasks remain inconclusive. G-FFD thus complements task outcomes with information about remaining regional alternatives and assignment consequences. Full article

Drowning is a largely preventable global public health problem. Many unintended drownings are linked to the initial responses to cold-water immersion (cold shock), which includes a rapid involuntary cardio-respiratory response that increases the risk of aspirating water within the first two minutes of immersion. Floating rather than struggling to swim can help survival in this circumstance. In light of early research and subsequent changes in anthropometry, we are re-examining whether bone mineral density (BMD) in people from African, Caribbean, and Asian communities influences their float. This study examined predictors of float outcome. A total of 101 participants of African, Caribbean, Asian, or mixed heritage were recruited; 96 attempted a two-minute supine float following instruction and practice, of whom 89 were successful. Body fat percentage strongly correlated with buoyancy and floating outcome, while other predictors, including BMD, showed weak or no association. Passive floaters (no movement required) demonstrated greater buoyancy and less exertion than active floaters (purposeful movement required). These findings suggest that body fat percentage is the primary factor influencing float outcome, and that with appropriate instruction and practice, individuals of African, Caribbean and Asian heritage can “find their float”. The results indicate that floating is a learnable competence and highlight the need for integrated drowning prevention strategies combining existing campaigns with accessible, community-based practices and co-produced approaches to improve engagement and equity. Full article

Traditional approaches for monitoring aquatic organic carbon, such as satellite remote sensing and automated underwater sensors, are often constrained by limited temporal resolution, data gaps under cloudy conditions, maintenance requirements, and cost-effectiveness. To overcome these limitations, we developed and field-demonstrated a ground-based hyperspectral remote sensing system (GHRSS) for continuous, high-frequency monitoring of dissolved organic carbon (DOC) and particulate organic carbon (POC). The system is based on the above-water method and integrates three miniature hyperspectral spectrometers to measure water-surface radiance, sky radiance, and downwelling irradiance for deriving hyperspectral remote sensing reflectance ($R_{rs}$). The spectrometers cover 400–900 nm with a spectral resolution of 1 nm and support a minimum sampling interval of 10 s. The GHRSS also integrates solar power supply, 4G communication, and a microcomputer, enabling autonomous long-term deployment and wireless data transmission. Based on the GHRSS, retrieval models for DOC and POC were developed and validated using 90 paired in situ measurements collected from the Cao’e River. Empirical and machine learning methods were applied to retrieve DOC and POC from the measured $R_{rs}$ data. The empirical models showed limited retrieval performance, whereas partial least squares regression (PLSR) and support vector regression (SVR) substantially improved model accuracy. Among all models, SVR achieved the best performance on the independent test set, with $R^2=0.979$, RMSE = 0.031 mg/L, and MAE = 0.024 mg/L for DOC and $R^2=0.960$, RMSE = 0.152 mg/L, and MAE = 0.066 mg/L for POC. Using the optimal SVR models, minute-scale time series of DOC and POC were reconstructed from the GHRSS observations. The results revealed pronounced sub-daily variability in both parameters, with DOC varying relatively smoothly, whereas POC exhibited stronger short-term fluctuations and more rapid responses to hydrodynamic changes. These findings demonstrate that the GHRSS, combined with machine learning models, provides an effective and practical approach for continuous, high-frequency monitoring of riverine organic carbon dynamics. Full article

Metabolic syndrome is a major public health concern, yet evidence from structured community-based interventions in Latin America remains limited. This study conducted a comparative reanalysis of an 18-week program implemented in Talca, Chile, that combined supervised aerobic exercise, health education, and clinical monitoring. The analysis included archived data from 80 adults with metabolic syndrome who had originally been randomly allocated using SPSS to an intervention group ($n=33$) or a usual-care comparison group ($n=47$). Anthropometric, biochemical, cardiovascular, body-composition, functional-capacity, and estimated cardiorespiratory-fitness outcomes were assessed at baseline and after 18 weeks. At the post-intervention assessment, the intervention group showed greater Six-Minute Walk Test (6MWT) distance, higher 6MWT-based estimates of cardiorespiratory fitness, lower waist circumference, systolic and diastolic blood pressure, fasting glucose, triglycerides, body weight, body fat percentage, and fat mass, and higher high-density lipoprotein (HDL) cholesterol and muscle mass percentage than the comparison group. Within the intervention group, body weight, body fat percentage, and fat mass decreased, whereas muscle mass and 6MWT distance increased. These findings indicate that participation in the integrated program was associated with favorable cardiometabolic, body-composition, and functional outcomes. The results support further prospective evaluation of this community-based model within primary healthcare and public health settings. Full article

High-throughput firewall telemetry increasingly demands real-time interpretation from short, dense observation windows rather than long historical baselines. We operationalize behavioral slicing, a privacy-preserving approach that extracts interpretable micro-behavioral signals—timing irregularities, destination dispersion (entropy), and intensity cues—from sub-minute traffic segments. This study does not claim confirmed intrusion detection. Instead, it evaluates whether short-window firewall telemetry can support privacy-preserving candidate triage under high-throughput conditions and quantifies the limits, feature stability, detector complementarity, and real-time feasibility of such a pipeline. Our case study uses a real operational firewall telemetry slice that was irreversibly anonymized prior to analysis. Using unsupervised outlier detection with explainable summaries, the slice-level pipeline produces investigation-oriented anomaly candidates for triage, without making longitudinal routine or habit claims. Synthetic anomaly injection is used only for relative validation, while behavioral findings are derived from the real telemetry slice. Scenario-level ablations show strong separation for scanning and exfiltration but weak performance for low-variance C2 beaconing, indicating that behavioral slicing should be treated as a triage pre-filter rather than a universal detector. In the context of IoT and edge-network monitoring, firewall telemetry can be treated as a high-rate network sensing stream that reflects device-level communication behavior under operational constraints. Overall, results support behavioral slicing as an operationally feasible pre-filtering and triage method under extreme network load, with clear trade-offs and limits. Full article

Background/Objectives: Active surveillance (AS) is the recommended management strategy for localized, early-stage prostate cancer. Despite promising cancer-specific outcomes, up to one-third of men discontinue AS and undergo radical prostatectomy without evidence of disease progression, often because of stress, anxiety, uncertainty, and unmet supportive care needs. Evidence-based psychosocial interventions tailored to men on AS are lacking. We aimed to: (1) adapt the Stress Management and Resilience Training (SMART) program to address the unique psychosocial and supportive care needs of men with prostate cancer on AS (SMART-AS), and (2) evaluate the feasibility and acceptability of SMART-AS. Methods: Following the Assessment, Decision, Adaptation, Production, Topical Experts, Integration, Training, Testing (ADAPT-ITT) framework, we adapted SMART for men with prostate cancer on AS (SMART-AS) informed by our prior qualitative study and expert input. Next, we conducted a single-arm pilot feasibility study at one large urban academic medical center. Participants attended eight weekly 90-minute telehealth group SMART-AS sessions. Feasibility and acceptability were evaluated one-week post-intervention. Results: Based on our prior qualitative study and expert input, core SMART-AS components included content targeting stress, anxiety, communication, and self-management. In total, 30 participants were enrolled in the pilot feasibility study and completed baseline assessments (mean age = 71 years, standard deviation = 8.3); 26/30 (86.7%) completed six out of eight SMART-AS sessions, and 17/30 (56.7%) completed post-intervention assessments. Nearly all (16/17, 94.1%) reported that they would recommend SMART-AS to others. Most agreed that SMART-AS helped them talk to clinicians (13/17, 76.5%), reduced anxiety (13/17, 76.5%), enhanced coping skills for AS challenges (15/17, 88.2%), and supported self-care (15/17, 88.2%). Nearly two-thirds (11/17, 64.7%) reported SMART-AS helped them continue AS as a management strategy. Conclusions: This pilot study provides preliminary evidence supporting the feasibility and acceptability of SMART-AS among men with prostate cancer on AS. Participants reported perceived improvements in anxiety, coping, communication with clinicians, and self-management. Findings support further evaluation in a randomized controlled trial. Full article

The reliability of electric vehicle fast-charging infrastructure is becoming increasingly critical as deployment accelerates and the number of unavailable charging points grows. This work presents an unsupervised anomaly detection framework aimed at supporting predictive maintenance in DC fast-charging stations. The approach uses real minute-resolution operational data from a real charging station, including active, reactive and apparent power, power factor, phase power measurements and charger-side power measurements. Three complementary anomaly detection models were designed to capture different abnormal operating conditions: deviations in consumption patterns, efficiency losses between charger and grid analyser measurements, and phase imbalance in three-phase operation. Local Outlier Factor and Isolation Forest algorithms were integrated into an automated monitoring pipeline. Since labelled fault data were not available, validation was based on controlled injection of synthetic anomalies into real test signals, including physically coherent power disturbances, sensor or communication inconsistencies, progressive efficiency degradation and phase imbalance events. The results show that the framework is effective for detecting anomaly families that produce clear or sustained deviations, while more subtle temporal behaviours remain more challenging. Overall, the proposed framework provides a practical condition monitoring and early-warning approach that can support predictive maintenance decisions in DC charging infrastructure, while further temporal modelling is required for explicit degradation forecasting. Full article

Background: Functional decline emerges early in Alzheimer’s disease (AD) and may support clinical staging. However, criterion-referenced thresholds for interpreting the six-minute walk distance (6MWD) across AD stages are lacking. This study aims to derive sex-specific 6MWD cut-off values to differentiate mild cognitive impairment (MCI) from moderate AD dementia. Methods: In this cross-sectional study, 233 community-dwelling adults (128 women) were consecutively recruited from a neurology department and classified using IWG-2 criteria (MCI: MMSE ≥ 26; moderate AD dementia: MMSE 10–19). All participants completed a standardized 6 min walk test (6MWT) following American Thoracic Society guidelines. Receiver operating characteristic (ROC) analyses were performed overall and by sex, optimal thresholds were selected using Youden’s index, and diagnostic indices such as area under the curve (AUC), sensitivity, specificity, 95% confidence intervals (CI), likelihood ratios (LR) and odds ratio (OR) were computed. Results: Overall, participants with moderate AD dementia exhibited substantially lower 6MWD values than those with MCI (313.7 ± 46.5 m vs. 461.2 ± 60.1 m, p < 0.001). The optimal overall threshold was 390 m, yielding an AUC of 0.97, sensitivity of 98.0%, and specificity of 79.4%. Sex-specific thresholds were 389 m in men (AUC = 0.96, sensitivity = 96.4%, specificity = 79.3%) and 367 m in women (AUC = 0.98, sensitivity = 97.8%, specificity = 87.7%). Conclusions: The 6MWD demonstrates strong discriminatory ability between MCI and moderate AD dementia in this sample. Sex-specific thresholds may support functional staging and monitoring but require internal and external validation before clinical implementation. Full article

The 15-minute life circle has become a critical planning paradigm for developing age-friendly cities; however, its implementation in small and medium-sized cities (SMSCs) is often constrained by topographical heterogeneity and the oversimplified use of facility density as a proxy for service efficiency. This study challenges the conventional assumption that a higher facility density automatically leads to better service outcomes and proposes a dynamic “space–demand–policy” analytical framework to identify the mechanisms underlying spatial mismatch. Using Zhifu District, Yantai, a rapidly aging urban area with complex topography, as a case study, we integrated kernel density estimation (KDE), slope-adjusted network analysis, a modified Gaussian-based two-step floating catchment area (2SFCA) method, and standard deviational ellipse (SDE) analysis. Our results reveal three key findings. First, a “high-density, low-efficiency” paradox is prevalent: older urban cores contain clusters of facilities but have supply–demand ratios below 0.5 because of limited service diversity and slope-induced reductions in accessibility, with the effective service radius decreasing to 750 m. Second, newly developed areas achieve service coverage rates below 50% when terrain constraints are considered, highlighting the limitations of static planning radii. Third, a 90% overlap between the directional distributions of residential areas and elderly care facilities, as indicated by their SDE major axes, supports the spatial feasibility of community-embedded aging-in-place models; however, physical proximity alone does not guarantee effective service delivery. By adapting generic spatial algorithms to create a terrain-sensitive planning tool, this study provides a transferable framework for evidence-based and targeted elderly care planning in SMSCs facing similar demographic and geographic constraints. The proposed framework contributes to the global sustainability agenda and advances SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) by promoting more equitable resource allocation and supporting the development of age-friendly, walkable, and sustainable communities in topographically complex urban areas. Full article

The complete community paradigm, emphasising walkable, self-sufficient neighbourhoods, has gained significant global traction, yet the role of sports and physical activity spaces within such frameworks remains undertheorised, particularly in dense Chinese urban environments. This paper introduces the Chrono-Adaptive Sports Space Index (CASI), a preliminary evaluative framework whose diagnostic utility is demonstrated through three contrasting case studies. CASI integrates temporal usage dynamics, demographic composition, smart-city sensor data, and multi-generational programming flexibility to assess sports environments within China’s 15-Minute Complete Community (完整社区, Wánzhěng Shèqū) policy framework. Unlike existing frameworks that assess sports spaces through static metrics, CASI proposes that sports environments must be evaluated across four temporal dimensions: diurnal rhythms, weekly cycles, seasonal transitions, and demographic lifecycle shifts. The four sub-indices are weighted equally (25 points each) as a deliberate first-generation design choice reflecting the absence of pre-existing empirical benchmarks; this assumption requires empirical validation in future research. Through exploratory multi-case analysis of communities in Shanghai (Jing’an District), Chengdu (Tianfu New Area), and Beijing (Chaoyang District), this study provides preliminary evidence suggesting that temporally inert sports spaces may be associated with utilisation losses of 38–52% during off-peak periods and the systematic exclusion of elderly and child populations; these figures should be treated as indicative estimates pending broader empirical validation. A Chrono-Adaptive Design Protocol (CADP) is proposed as a conceptual, practice-informed protocol awaiting prospective empirical evaluation. This research is better characterised as a framework development and initial exploratory application study rather than a validation of a mature instrument. Its primary contribution is a new theoretical and diagnostic lens for complete community planning, together with an agenda for the empirical work needed to develop CASI into a broadly applicable assessment tool. The study’s limitations and a structured five-priority future research agenda are presented in a dedicated section following the Conclusions. Full article

Interoperability remains a central issue in multi-performance building simulation, where heterogeneous domain-specific tools must be combined despite differences in modeling formalisms, numerical solvers, and execution schemes. Existing approaches, including data exchange standards and component-based frameworks such as the Functional Mock-up Interface (FMI), address specific levels of interoperability but often require model-level access, component wrapping, Functional Mock-up Unit (FMU) packaging, or framework-specific integration. This paper examines service-level interoperability, where domain-specific simulation tools are exposed as autonomous web services coordinated through an external orchestration mechanism. A structured, JSON-based Pivot DataSet (PDS) organizes data exchange between services, while coupling strategies are implemented at the orchestration level to manage interactions without accessing internal model structures. The approach is evaluated using a classroom case study from the Agence Nationale de la Recherche (ANR) COSIMPHI research project, focusing on communication overhead, synchronization constraints, and coupling behavior in distributed co-simulation. Under the investigated weak-coupling conditions, the waveform relaxation method (WRM) reduces synchronization iterations by 144× over one day and by approximately 3319× over one month compared with minute-by-minute sequential chaining. These results, obtained under weak thermal–acoustic coupling conditions, highlight the relevance of service-level interoperability and orchestration-level coupling for distributed building-performance simulation workflows involving independently developed domain tools. Their generalization to stronger coupling regimes, however, remains a direction for future work. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article