Search Results (8,576)

Daily exchange-rate returns show weak average persistence, yet their multiscale organization may inform future movement magnitude. This study evaluates whether time-varying multifractal states improve one-trading-day-ahead forecasts of USD/KRW absolute returns. The raw dataset contains 6520 Federal Reserve H.10 observations from 1999 to 2024. After initialization of the 252-day rolling MFDFA window, 6268 feature-aligned observations remain for model estimation and evaluation. Seven scaling features augment Extra Trees, and forecasts are routed through pre-test terciles of singularity-spectrum width. Shuffled, phase-randomized, and IAAFT surrogates, matched-feature ablations, temporal-shift placebos, specification variants, and expanding-window thresholds assess robustness. In the locked 2020–2024 test of 1249 observations, the routed model yields an RMSE of 0.367 and an MAE of 0.271, improving on market-only Extra Trees by 1.627% and 2.245%, respectively. Temporal displacement produces upper-tail p-values of 0.020 for RMSE and 0.010 for MAE. The model’s R² is 0.045 and its forecast–realization correlation is 0.218. Validation-calibrated 95% and 99% bands reduce interval scores by 2.160% and 5.730%, although coverage remains below nominal. External-currency gains are mixed and do not survive multiplicity adjustment. The results indicate modest, time-aligned, USD/KRW-specific predictive information; they do not establish universal transferability, causal effects, directional prediction, or trading profitability. Full article

Internet of Things (IoT) traffic anomaly detection is essential for limiting device compromise and large-scale attacks. Existing detectors may miss attack families absent from model development, while heterogeneous benign traffic makes it difficult to maintain a low false-positive rate (FPR). To address these two practical limitations, we propose the Mode-Calibrated Dual-Evidence Detector (MCDE). Its supervised branch estimates the probability that a sample is malicious from labeled benign and known-attack traffic, while its benign-deviation branch measures distance from multiple learned benign traffic modes, providing a complementary route for unseen attacks. MCDE maps the heterogeneous probability and distance scores to comparable empirical benign-tail evidence, normalizes each branch by its allocated share of the target FPR, and fuses them into an anomaly score. A disjoint held-out benign set determines the decision threshold. Equivalently, the fusion compares budget-adjusted benign-tail surprisal, linking the decision rule to empirical self-information. We further establish the conditions under which the budgeted fusion controls the nominal overall FPR. Family-hold-out experiments on IoT-23 and N-BaIoT validate MCDE. At a 1% target benign FPR, MCDE improves IoT-23 unseen recall over histogram-based gradient boosting from 85.77% to 90.85% and harmonic known–unseen recall from 91.82% to 95.07%, while maintaining a 0.96% benign-test FPR. It also achieves 99.87% unseen recall on N-BaIoT. Full article

Architectural colour in public libraries significantly influences indoor atmospheric quality and occupant emotional well-being, yet palette selection in design practice remains predominantly reliant on subjective intuition, precedents, and vendor catalogues. This study develops a computational framework that links multimodal image colour extraction, circular clustering, human perceptual evaluation, and multi-criteria decision weighting into a structured analysis pipeline. Using a corpus of 500 curated library interior images across diverse geographic regions, a multimodal large language model extracted dominant architectural colour records and visual weights, which were deterministically converted to HSV space and clustered using weighted circular k-means. A semantic differential experiment involving fifty design students across five bipolar scales provided empirical perceptual ratings, evaluated through repeated nested ten-fold cross-validation across five machine learning algorithms. The analysis identified five recurrent colour paradigms, demonstrating that spatial hue distributions form distinct chromatic clusters across modern library architecture. Perceptual models revealed that average colour features reliably predict perceived warmth and quietness, whereas modernity and naturalness depend more heavily on non-chromatic spatial cues. By integrating these predictive models with analytic hierarchy process and Delphi expert weights, the framework establishes a transparent decision-support protocol for early schematic design. This allows architects and clients to quantitatively compare alternative colour schemes against functional zoning requirements, providing an objective, accountable foundation for evidence-based interior design. Full article

This paper presents a measurement-driven study of end-to-end latency in an indoor private standalone 5G network deployed at TU Wien IFT TEC-Lab. The testbed combines pico radio units, edge computing resources, and a local 5G core to form a campus-scale private network architecture designed for low-latency communication. To characterize network performance, TCP throughput and UDP one-way latency measurements were collected in a single-user, constant-bit-rate downlink setting at seven predefined indoor locations using iPerf-based tests at six traffic levels (1–500 Mbps), with five repetitions per condition. Based on these measurements, a compact parametric model was calibrated for this deployment to describe latency as a function of achieved bandwidth and location-dependent effects. The results show that latency remained low and relatively stable at low and medium traffic levels, generally staying below 20 ms between 1 and 200 Mbps, but increased more strongly as the operating point approached the practical throughput limit of the setup. The fitted model captured the overall latency trend with an in-sample MAE of 2.52 ms, an RMSE of 3.13 ms, and an $R^2$ of 0.842, while retaining comparable predictive performance under a trial-based test split ($R^2=0.835$) and leave-one-location-out validation ($R^2=0.818$). The compact model also achieved lower out-of-sample errors than the minimal M/M/1-type and polynomial-regression baselines under both validation schemes. Overall, the findings indicate that traffic load was the main driver of latency growth in the studied environment, while spatial effects remained measurable but secondary. The resulting formulation should be understood as an interpretable empirical model of end-to-end latency for one indoor private standalone 5G deployment under single-user, constant-bit-rate downlink conditions, rather than as a general latency model for private 5G networks. Within that scope, the model provides an interpretable description of the measured latency behavior and a basis for preliminary capacity assessment in this deployment. Its applicability to another private 5G network has not been established and would require a new measurement campaign, complete parameter re-estimation, and independent validation. Full article

The National Survey of Children’s Health (NSCH) is widely used to estimate adverse childhood experiences (ACEs) in pediatric populations, but it only captures household adversity and economic hardship, omitting direct measures of emotional, physical, and sexual abuse present in the original ACE Study. We quantified this measurement gap by projecting unmeasured abuse domains onto the pooled 2023–2024 NSCH (n = 104,838) using parameter-bridging models estimated from the Behavioral Risk Factor Surveillance System (BRFSS; adults aged 18–49 years, n = 27,499) and the Youth Risk Behavior Surveillance System (YRBSS; adolescents, n = 11,016), both of which directly measure these abuse domains. Raw NSCH data classify 93.9% of infants under 12 months and 61.4% of adolescents aged 14–17 years as free of any ACEs; incorporating projected abuse domains reduces these figures to approximately 53–57% and 36–46%, respectively, depending on donor source. Conversely, the proportion with four or more ACEs—0.2% and 3.8% in the raw NSCH data for these two age groups—rises to approximately 2% and 7–14% once projected abuse is incorporated. These projected values represent model-based structural risk conditional on a child’s observed household environment, not direct measures of accumulated exposure or observed prevalence. Out-of-sample calibration testing indicates that projections from each donor source carry substantial asymmetric uncertainty: BRFSS-derived estimates over-predict and YRBSS-derived estimates under-predict emotional and sexual abuse relative to each source’s own directly observed rates. Despite this uncertainty, the direction and scale of the gap are consistent across donor sources and sensitivity analyses. NSCH-based ACE indices that omit direct abuse likely substantially understate true childhood adversity, with implications for screening, resource allocation, and cross-population comparisons relying on this widely used pediatric surveillance tool. Full article

Background and Objectives: Pulsed radiofrequency (PRF) of the cervical medial branches is a non-destructive neuromodulatory option for chronic cervical facet joint pain, yet evidence regarding the durability of clinical response and factors associated with sustained benefit remains limited. This study aimed to characterize the 6-month trajectory of pain and disability after fluoroscopy-guided cervical medial branch PRF and to explore baseline factors associated with meaningful pain relief (MPR). Materials and Methods: In this retrospective single-center cohort study, 75 patients with chronic cervical facet joint pain who achieved ≥50% temporary pain relief following a diagnostic medial branch block underwent fluoroscopy-guided cervical medial branch PRF, followed by injection of local anesthetic and corticosteroid at the treated levels. Numeric Rating Scale (NRS) and Neck Disability Index (NDI) scores were evaluated at baseline and at 1, 3, and 6 months. MPR was defined as ≥50% reduction in NRS from baseline. Global Perceived Effect (GPE) and changes in analgesic medication use were assessed as complementary clinical outcomes. An exploratory multivariable logistic regression analysis was performed to examine factors independently associated with MPR at 6 months. Results: Median NRS scores decreased from 7.0 at baseline to 4.0, 4.0, and 5.0 at 1, 3, and 6 months, respectively, while median NDI scores decreased from 30.0 to 14.0, 16.0, and 20.0 (both overall p < 0.001). Despite sustained improvement relative to baseline, MPR progressively declined from 57.3% at 1 month to 49.3% at 3 months and 36.0% at 6 months (overall p < 0.001). Favorable GPE and reduced analgesic medication use showed parallel declines, supporting attenuation of overall clinical benefit over time. In the exploratory multivariable model, fibromyalgia was independently associated with 75% lower odds of 6-month MPR (OR = 0.25, 95% CI: 0.088–0.709; p = 0.009). Age, sex, and baseline NRS were not significantly associated with 6-month MPR. No serious acute periprocedural adverse events were documented during the 2 h observation period. Conclusions: Fluoroscopy-guided cervical medial branch PRF, combined with post-procedural corticosteroid and local anesthetic administration, was associated with clinically meaningful improvements in both pain and neck-related disability, with the greatest benefit during the first 3 months and partial attenuation by 6 months. The progressive decline across pain response, patient-reported global improvement, and analgesic-use outcomes highlights the time-dependent nature of the benefit associated with the combined intervention. Fibromyalgia was independently associated with substantially lower odds of 6-month MPR in this exploratory analysis; this association should be interpreted cautiously and requires prospective validation before it can inform patient selection, although it may be relevant when counseling patients regarding the durability of response. Full article

Spectral remote sensing struggles in hyper-arid environments because desert substrates share overlapping optical signatures. We evaluate AlphaEarth foundation-model embeddings—64-dimensional annual representations fused from Sentinel-1, Sentinel-2, Landsat, and LiDAR—for land-cover classification and change detection across Saudi Arabia (2017–2024). From an equal-allocation draw of ESA WorldCover 2021 strata, 25,241 labelled samples entered cross-validation; seven classes reached the requested 3000 and the two rarest returned their full national extent at the sampling scale. A Random Forest reproduces the WorldCover labels at a spatially blocked cross-validated overall accuracy of 0.815 ± 0.007, averaged over twenty independent assignments of the spatial blocks to folds, compared with 0.859 ± 0.005 under standard random cross-validation; this figure measures agreement with WorldCover rather than accuracy against independent ground truth, and is not directly comparable with WorldCover’s own globally validated accuracy; the 4.4 percentage-point gap quantifies spatial leakage and is reported transparently. Design weighting following Olofsson et al. corrects the distortion introduced by equal per-class allocation for 2021; because the reference labels are not independent of the training labels, the resulting fractions are reported as model-predicted national composition rather than accuracy-adjusted area estimates. UMAP visualisation of the embedding manifold reveals five sub-types within the single WorldCover bare/sparse vegetation class, consistent with geomorphologically distinct desert substrates. An indicative cross-feature benchmark produced an OA 14.1 percentage points higher for the foundation-model representation than for the strongest Sentinel-2 baseline under the same fold partition. Because the conditions were evaluated on non-identical samples, this difference cannot be attributed solely to feature representation. An independent probability sample of 374 points, interpreted on very-high-resolution imagery in two rounds by two analysts and reconciled to 97.2% agreement, gives a design-weighted overall accuracy of 0.868 for the 2021 map. The same interpretation confirms only 49.5% of the WorldCover class assignments at those points, with tree cover, grassland and herbaceous wetland largely reassigned to cropland, bare/sparse vegetation and shrubland; WorldCover and the Random Forest map agree with the independent reference at the same design-weighted rate of 0.868. The classifier therefore reproduces its label source closely, including where that source departs from independent interpretation, which shows directly that agreement with WorldCover and accuracy against land cover are distinct quantities in this landscape. Together, these results establish a methodologically transparent workflow for foundation model-based land-cover monitoring in data-scarce arid environments, with independent validation limited to the 2021 map and no independent annual reference data available across the complete 2017–2024 period. A separate 49-point cropland two-date sub-test found interpreted field-state change in nine of 25 flagged points (36%) versus two of 24 unflagged points (8%; Fisher’s exact p = 0.037); within this class the screening flag had precision = 0.360, recall = 0.818 and F1 = 0.500, but these class-specific metrics do not constitute national validation of the change layer. Full article

As robots take on a growing role in creative work, an important question is often overlooked: how do we preserve people’s sense of psychological ownership over what they create together with a machine? This study integrates a large-scale scientometric analysis of 817 publications (2015–July 2026) with a PRISMA 2020–guided systematic review of 52 studies on Ownership-Preserving Autonomy in creative Human–Robot Interaction (HRI). Bibliometric mapping (a keyword co-occurrence network built with NetworkX and Louvain community detection, together with publication-trend, citation, author, country, and keyword temporal-intensity statistics computed directly from the corpus) shows a compound annual growth rate of approximately 26.5% over the complete calendar years 2018–2025, as well as a Core HRI and Autonomy thematic cluster that dominates a much smaller Psychological and Ownership cluster, a pattern that is at least partly attributable to the intentionally broad search strategy. The systematic review shows that only 3.8% of the 52 studies (n = 2) report a concrete algorithmic implementation coupled with robotic integration and empirical evaluation, while 96.2% do not meet this specific implementation criterion. Considered alongside converging industrial and HCI evidence on authorship erosion in AI-assisted design, as well as a previously reported ∼20% ownership decline under passive AI use in a single non-embodied creative-writing study, these SLR findings point to a pronounced theory–practice gap: rich conceptual discussion of ownership, agency, and Diminished Creative Agency contrasts sharply with the scarcity of technical implementations, validated evaluation frameworks, and real-world deployments. To help bridge this gap, the paper offers an exploratory positioning of Ownership–Autonomy Adaptive Collaboration (OAAC), a preliminary four-layer conceptual reference model; a set of falsifiable research hypotheses; and a structured research agenda on dynamic scaffolding, negotiated autonomy, and multidimensional evaluation intended to guide future ownership-preserving, human-centric co-creativity research in Industry 5.0. Full article

Safe walking and cycling are part of sustainable mobility, but aggregate collision totals do not show whether speed policies reach vulnerable road users (VRUs). Wales reduced the default speed limit on restricted roads from 30 to 20 mph in September 2023. To avoid mechanical reclassification after the reform, roads posted at either 20 or 30 mph were pooled. A 2022Q1–2025Q4 panel compared 22 Welsh and 289 English local authorities using Poisson and ordinary least squares (OLS) fixed-effects models, supplemented by policy-level and spatial analyses. Combined pedestrian and cyclist casualties declined by 19.4% in the Poisson model and 26.0% when the OLS coefficient was scaled to the Welsh pre-reform mean. Poisson reductions were 22.2% for pedestrians, 25.7% for child VRUs and 14.8% for those killed or seriously injured; corresponding OLS outcomes survived false discovery rate control. The police-force placebo yielded a two-sided p value of 0.086. School-context estimates did not survive multiplicity adjustment, while current road status provided descriptive rather than segment-level causal evidence. Mean speed declined by 3.56 mph at 42 selected sites. The findings indicate lower recorded casualties among groups central to active travel. They do not establish lower risk per journey or isolate all Wales-specific changes, but they support transparent monitoring of whether sustainable transport policies reach vulnerable groups. Full article

Decisions made during the pre-construction phase play a decisive role in the success of healthcare building projects. Although pre-construction critical success factors (CSFs) have received considerable research attention, few studies have transformed CSF-based expert ratings into a quantitative and interpretable performance index. This paper develops and applies a two-stage Mamdani Fuzzy Inference System (FIS) to assess pre-construction performance in healthcare building projects. Data were collected using an online survey completed by construction professionals experienced in healthcare projects. After excluding incomplete responses, careless responses, and multivariate outliers, 201 valid responses were retained. A total of 54 CSFs grouped into eight pre-construction domains were assessed on a five-point Likert scale. The Relative Importance Index (RII) values were calculated and utilized as fuzzy rule weights. In Stage A, CSF-level ratings were converted into eight group-level performance scores, and in Stage B, these scores were integrated to form a single Pre-Construction Healthcare Project Performance Index (PCHPPI). The model was developed in MATLAB R2025b and used five triangular membership functions. The results showed that the scores at group level varied from 63.9% for Technology and Digital Integration to 66.7% for Procurement Planning. The overall PCHPPI was 62.7%, classified as High. Sensitivity analysis was performed using alternate weightings, membership function settings, defuzzification techniques and ±5% input variations. The PCHPPI was classified as High in all examined situations, indicating that it is relatively robust to modest variations in model assumptions and input conditions. This indicates that healthcare pre-construction performance is generally satisfactory but remains closer to the lower boundary of the High classification, suggesting room for improvement. The PCHPPI and Performance Octagon provide a structured basis for performance assessment, diagnosis, and potential benchmarking across pre-construction domains, providing a practical assessment instrument for healthcare construction organizations and project teams. The proposed framework provides a structured decision-support approach for project readiness evaluation during the early planning of healthcare building projects. Full article

Traditional household-based decentralized agro-processing is an important source of air pollution in rural China, yet the pollution–carbon co-benefits of centralization remain poorly quantified. In this paper, a LEAP-based scenario framework (2018–2035) is developed for Yibin’s Camphora longepaniculata essential oil processing industry under four scenarios: baseline, policy (27% centralization share), enhanced scenario 1 (ENH1; 50% centralization share), and enhanced scenario 2 (ENH2; 50% centralization share with a 2% efficiency retrofit). The framework simulates energy demand and emissions of CO₂, PM₁₀, SO₂, NOx, and VOCs based on specified emission factors and technological assumptions. The results indicate that centralization reduces unit energy consumption by 51.20% and CO₂ intensity by 63.12%. The 27% penetration rate (current policy) lowers emission levels but cannot alter the growth trajectory during output expansion, whereas the 50% penetration rate achieves net CO₂ and energy reductions of 31.56% and 25.60%, respectively, relative to the baseline. All pollutants exhibit synergy elasticity coefficients (ε) above unity (PM₁₀ > SO₂ > VOCs > NOx); these coefficients are governed by the emission factor structure rather than the penetration rate. Thus, scaling centralization drives absolute reductions, whereas co-benefit enhancement requires process or end-of-pipe upgrades. All of the reported results are scenario-based model outputs, not field measurements, and biogenic CO₂ is included only for cross-scenario comparability, not as a climate impact claim. Full article

Dairy cold-chain network planning requires coordinated decisions under demand variability, product perishability, and environmental constraints. To address these interrelated challenges, this study formulates an order-driven multi-objective mixed-integer nonlinear programming (MINLP) model for the tactical planning of a three-echelon dairy cold-chain network. The model coordinates distribution-center selection, inventory, transportation allocation, vehicle configuration, and refrigeration decisions to minimize total cost, transportation-related carbon emissions, and the quantity- and importance-weighted average freshness-loss rate. Demand variability is represented through service-level-based safe demand, whereas product freshness is evaluated using Weibull-based shelf-life reliability and inventory–transportation exposure. Transportation congestion is further incorporated to capture its effects on travel time, refrigeration emissions, and freshness deterioration. NSGA-II is employed to generate Pareto solutions, with entropy-weighted TOPSIS used for compromise-solution selection and MOEA/D serving as the benchmark algorithm. Numerical results indicate that NSGA-II achieves favorable convergence performance and comparable solution diversity relative to MOEA/D, while small-scale mixed-integer approximation tests support the quality of the obtained solutions. Multi-scale experiments demonstrate stable computational performance as network size increases. Sensitivity and scenario analyses further reveal distinct effects of service levels, shelf-life characteristics, and road capacity on economic, environmental, and freshness performance. The proposed framework provides tactical decision support for coordinating demand-responsive supply, low-carbon operations, and freshness preservation in dairy cold-chain networks. Full article

Measles virus infection couples nonlinear processes across biological scales, including within-host viral amplification, immune-cell depletion, delayed adaptive control, persistent viral RNA, heterogeneous host severity and vaccination-dependent population spread. A multiscale mathematical framework is developed by coupling a seven-variable within-host ordinary differential equation model to a stochastic cellular automaton. The within-host system extends a four-variable measles immunodynamics core by including IFN-$\gamma$-dominant and IL-17-associated immune responses, persistent viral RNA and neutralizing antibodies. Six host archetypes are represented as structured parameter perturbations of this common dynamical core. The principal novelty is an explicit cross-scale coupling operator that separates genuinely ODE-derived host descriptors from hybrid epidemiological mapping rules and independently specified population-level contact and susceptibility assumptions, allowing within-host heterogeneity to propagate transparently into a spatial stochastic epidemic model. An explicit ODE-to-cellular-automaton map translates within-host trajectories into infectious timing, daily infectivity profiles and an illustrative ODE-informed severity-to-death transition mapping used internally by the cellular automaton. The mortality map depends on viral burden, infectious duration, IFN-$\gamma$ deficit, cumulative infectivity and an immune-deficit–infectivity interaction term. Population simulations show a nonlinear reduction in attack rate with increasing vaccination coverage, reduced modeled death burden under targeted high-risk in silico perturbations and additional suppression under reactive vaccination campaigns. A direct local cellular-automaton secondary-infection estimate is reported instead of interpreting cumulative infectivity burden as a reproduction number. A targeted contact-structure sensitivity further shows that matching the expected local direct-secondary-infection potential does not imply equivalent population-level attack rates, emphasizing that the quantitative CA outcomes are geometry specific. Sobol sensitivity analysis with convergence up to $N_{\mathrm{base}}=4096$ identifies core viral and immune parameters as dominant drivers of within-host and multiscale outputs. The framework provides an explicit dynamical-systems approach for coupling differential-equation immunodynamics to spatial stochastic population models in mathematical biology. Full article

Landslide susceptibility mapping identifies terrain prone to slope failure under comparable environmental and triggering conditions. Grid-based machine-learning outputs can be spatially fragmented and difficult to translate into slope-scale mitigation. We developed a two-stage grid-to-slope-unit framework for rainfall-conditioned susceptibility assessment in Pingyuan County, Guangdong Province, China. Elevation, slope angle, slope aspect, normalized difference vegetation index, and rainfall were derived from an ALOS-PALSAR digital elevation model, Sentinel-2 imagery, and rainfall-station observations. A Random Forest model was trained at grid scale using 28 pre-2024 landslides and 28 pseudo-absence samples. Grid-scale scores and the five conditioning factors were then summarized within 4786 slope units and used in a second Random Forest model. An inventory of 359 landslides mapped after the June 2024 rainfall event was used for event-conditioned validation. High and very-high susceptibility classes occupied 21.10% of the modeled area and contained 299 validation landslides, yielding a capture rate of 83.3%. The very-high class contained 64.62% of validation landslides within 9.10% of the area. The framework produced geomorphically coherent terrain units for regional screening and field investigation, while detailed stability assessment remains necessary for individual sites. Full article

Obesity is among the most prevalent health conditions worldwide. Incorporating variables beyond metabolic factors, such as body image (BI), may improve understanding of its underlying mechanisms. Body image dissatisfaction (BID) and body size misperception (BSM) have been associated with psychological outcomes; however, further research is needed. This study aimed to characterize BI in Mexican adults with obesity and examine its relationships with BSM and BID. A cross-sectional study was conducted at the Unidad de Investigación en Epidemiología Clínica in 2025. The Stunkard Figure Rating Scale, BSQ-34, BAS-2, and Rosenberg Self-Esteem Scale assessed BSM, body concern (BC), body appreciation (BA), and self-esteem (SE), respectively. Pearson correlations and two PROCESS serial mediation models (6) were applied, with BMI as the dependent variable and age and sex as covariates. Model 1 specified BSM as the independent variable and BC, BA, BID and SE as sequential mediators; Model 2 specified BID as the independent variable and included BSM as the final mediator. BSM and BID were associated with BMI (B = 1.46, p < 0.01 and B = 1.43, p < 0.01). No significant indirect effect was observed. These findings suggest distinct associations of psychological components of body image with obesity. Full article