Search Results (2,567)

Large-scale urban traffic signal optimization presents substantial challenges arising from high-dimensional decision spaces, expensive microscopic simulations, and competing mobility and environmental objectives. This study formulates large-scale traffic signal timing as a high-dimensional bi-objective black-box optimization problem, in which average waiting time and total CO₂ emissions are minimized simultaneously, thereby ensuring a symmetric and preference-neutral treatment of the two conflicting objectives without imposing predefined preference weights. To address the computational challenges, a Pareto–Hypervolume Guided Trust-Region Bayesian Optimization (PHV-TuRBO) method is proposed. The framework integrates Pareto-guided center selection, exploiting front sparsity and surrogate uncertainty, with hypervolume-driven trust-region adaptation to balance exploration and exploitation under limited simulation budgets. Experiments on 100-dimensional DTLZ1 and DTLZ2 benchmarks demonstrate superior convergence and Pareto-front approximation compared with NSGA-II, ParEGO, qEHVI-MOBO, and MORBO. Further validation on a large-scale Beijing traffic network involving 456 continuous decision variables and a budget of 5000 simulator evaluations demonstrates the effectiveness of PHV-TuRBO in identifying diverse efficiency–emission trade-offs. Representative Pareto-optimal solutions achieve substantial improvements in waiting time, travel time, queue length, stop frequency, mean speed, and CO₂ emissions relative to the original signal plan. These results establish PHV-TuRBO as a sample-efficient approach to high-dimensional, multi-objective traffic signal optimization for sustainable urban mobility. Full article

Background/Objectives: Preserving independence in later life is a public health priority in Türkiye’s ageing population, yet socioeconomic inequalities across functional domains and regions remain insufficiently characterized. We examined educational and income gradients across five domains of functional vulnerability among older adults and assessed regional variation. Methods: The nationally representative 2023 Türkiye Older Persons Profile Survey included 11,657 adults aged ≥ 65 years from 8754 households. Outcomes comprised dependency in activities of daily living (ADL), limitations in instrumental activities of daily living (IADL), health–mobility burden, sensory/communication burden and reported cognitive difficulty. Survey-weighted generalized estimating equation models examined education and equivalized household income simultaneously, accounting for household clustering and adjusting for sociodemographic covariates and region. All outcomes were expressed on a 0–1 scale. Log-transformed income was modelled using restricted cubic splines. Regional interactions were assessed with Holm correction. Results: Lower education and income were associated with greater vulnerability across all five domains after mutual adjustment. Compared with higher education, no completed schooling showed the largest differences for IADL limitation (adjusted mean difference [AMD] 0.216, 95% CI 0.187–0.245) and reported cognitive difficulty (AMD 0.166, 95% CI 0.146–0.186). The largest income contrast was observed for IADL limitation (AMD 0.126, 95% CI 0.102–0.150; weighted 10th versus 90th percentile). After Holm correction, regional heterogeneity remained for education and income gradients in IADL limitation and for the education gradient in health–mobility burden. Conclusions: Socioeconomic differences varied across functional domains and selected gradients differed by region. IADL limitation showed the largest absolute education- and income-related differences under the scoring approach used. The cross-sectional design precludes causal interpretation. Full article

The rapid growth of digital crime through cyber intrusions, ransomware, social engineering, and cloud exploitation has substantially increased the volume, heterogeneity, and complexity of digital evidence. Modern investigations span mobile devices, operating systems, volatile memory, networks, cloud infrastructures, and emerging environments such as the Internet of Things (IoT) and blockchain, challenging the scalability of conventional forensic workflows. Artificial Intelligence (AI) and Machine Learning (ML) offer promising capabilities to automate evidence analysis, identify complex patterns, and accelerate investigative processes. This systematic review provides a comprehensive and critical synthesis of AI/ML applications across major digital forensic domains, including file, memory, network, cloud, IoT, blockchain, mobile, social media, multimedia, biometric, vehicular, and drone forensics, as well as cross-cutting investigative tasks. A structured literature search and eligibility assessment identified 76 studies for qualitative synthesis. The review synthesizes the literature with attention to dataset availability, validation practices, reproducibility, explainability, adversarial robustness, and forensic admissibility. Beyond descriptive synthesis, this study introduces a six-dimensional task maturity framework based on observability, labelability, stationarity, time-to-insight, explainability, and adversarial robustness. An evidence audit links individual ratings to supporting references and qualifications. The framework assigns literature-informed ordinal scores using explicit dimensional criteria. Supporting references, rationales, and scope limitations are documented for all 96 task-dimension ratings. Sensitivity analysis examines the same profiles under alternative weights, thresholds, and individual rating changes. The reference mapping does not independently establish the validity of each numerical level. Contextual and interpretive judgment remains constrained by the need for human situational and legal reasoning. The review further identifies persistent gaps in benchmark datasets, real-world validation, explainability, adversarial testing, and legal defensibility. These findings motivate a research agenda centered on standardized evaluation protocols, robust and explainable forensic AI, improved benchmark resources, and accountable human–AI collaboration. Overall, this study provides a structured assessment of the current capabilities and limitations of AI/ML in digital forensics and identifies priorities for developing reliable, transparent, and legally defensible intelligent forensic systems. Full article

Understanding the social antecedents of eco-friendly vehicle purchase intentions is essential for advancing the transition toward sustainable urban mobility. From a socio-technical systems perspective, vehicle adoption depends not only on technologies and infrastructure but also on the social contexts in which users make decisions. Drawing on behavioral spillover theory and the theory of normative social behavior (TNSB), this study examines whether philanthropic engagement, namely charitable donation and volunteering, is associated with eco-friendly vehicle purchase intentions, and whether descriptive social norms (measured as perceived compliance with everyday civic rules) moderate these associations. Using weighted binary logistic regression with design-based standard errors and data from the 2024 Seoul Survey (n = 36,280), we found that charitable donation was positively related to purchase intention, whereas volunteering showed no significant overall association in the main-effects model. Descriptive norms moderated both associations: the association of donation was positive and significant at moderate-to-high norm levels, whereas the association of volunteering changed from negative at low norm levels to positive at high norm levels. The findings were robust to alternative specifications, although the norm-conditioned association of donation was concentrated in hybrid vehicle intention. These cross-sectional associations are consistent with cross-domain behavioral spillover and suggest that descriptive norms may constitute an important boundary condition. For urban mobility systems, the results suggest hypotheses, to be tested experimentally and longitudinally, that adoption-oriented policies may benefit from coordination with civic engagement initiatives and from attention to the normative context of everyday social life. Full article

Mass evacuation during a natural hazard can concentrate displaced populations in a limited number of destinations, altering mobility and contact patterns in ways that may increase infectious disease transmission. Existing evacuation models largely emphasize logistical considerations, while epidemic models typically treat population distribution as fixed, leaving limited integration between evacuation decisions and the epidemic risk those decisions create. This study proposes an epidemic-aware evacuation allocation framework to assign evacuees to shelters during co-occurring hurricanes and pandemics. A mixed-integer linear program is proposed that utilizes system-level reproduction numbers estimated using a spatio-temporal endemic-epidemic (EE) model under candidate evacuation scenarios. Regional transmission dynamics are estimated using the EE model with mobility-derived time-varying geographic coupling weights. For each candidate evacuation plan, post-evacuation population and mobility are updated, regional reproduction numbers are recalculated, and an effective, population-weighted system-level reproduction number, proposed as a heuristic epidemic risk index, is enforced via constraints. The framework is evaluated using a five-region synthetic study and a 30-region Hurricane Sally case study based on GPS mobility and COVID-19 data. Results show that alternative routing decisions can produce different post-evacuation epidemic risks even when the same populations are evacuated to the same total destination capacity. The proposed framework consistently maintained system-level epidemic risk index below a user specified threshold while achieving lower post-evacuation risk than the logistics-only allocation benchmark. Full article

Phosphorus (P) and potassium (K) are essential nutrients for plant growth, but most soil P and K exist in insoluble minerals, restricting plant uptake and driving higher chemical fertilizer use. Phosphate-solubilizing fungi (PSF) are recognized as effective bioinoculants due to their high ability to concurrently mobilize poorly soluble P and K via biomechanical and biochemical pathways. The mechanisms of P and K release by PSF include the secretion of low molecular weight organic acids, acidification, chelation, enzymatic mineralization, and mycelium network erosion. However, the growth and metabolism of PSF are usually influenced by various environmental factors, such as soil pH, temperature, mineral type, and fungal source. These factors determine the efficiency of P and K release and the contribution of PSF to plant nutrient uptake and crop yield. This article reviews the potential practical and biotechnological strategies to enhance PSF performance and highlights priorities for future research on its sustainable application. Full article

Distributed denial-of-service (DDoS) attacks pose a serious threat to the availability and reliability of unmanned aerial vehicle (UAV)-assisted Space–Air–Ground Integrated Networks (SAGIN). In such networks, UAVs can serve as aerial access points, mobile relays, and lightweight edge nodes to provide flexible connectivity between ground users and satellite networks. However, UAV mobility, time-varying network topology, heterogeneous communication links, and constrained airborne resources lead to highly dynamic traffic patterns and cross-layer attack surfaces, making conventional DDoS detection methods difficult to directly apply. To address these challenges, this paper proposes DDoSPre-LLM, a knowledge-graph-based DDoS detection framework for UAV-assisted SAGIN. First, MulFea-LLM employs large language models to extract multidimensional attack entities and semantic relations from heterogeneous security data and constructs a SAGIN-oriented attack knowledge graph containing attack, traffic, network-resource, and UAV-related entities. Then, a graph-attention-based feature weighting mechanism dynamically evaluates the contributions of traffic volume, request frequency, access-point connections, and bandwidth utilization to different DDoS attacks. The proposed method is evaluated in a SAGIN simulation environment integrating moving UAV access/relay nodes, satellite links, and terrestrial networks. Simulation results demonstrate improved detection accuracy, F1-score, and QoS compliance, with competitive recall, AUC-ROC, and throughput preservation, compared with representative baseline methods. Full article

Osteoarthritis is a prevalent chronic joint disease in humans, companion animals such as dogs and cats, and economic animals like horses and cattle. It primarily manifests as degeneration of articular cartilage, synovitis, and subchondral bone sclerosis, often leading to impaired mobility, reduced quality of life, and decreased production performance in affected animals. Chondroitin sulfate, a key component of the articular cartilage matrix, plays a role in alleviating and treating osteoarthritis. However, the molecular weight of chondroitin sulfate affects its permeability and bioavailability, exhibiting different effects on osteoarthritis. In this study, the monosodium iodoacetate-induced osteoarthritis model was established to evaluate the effects of three different-molecular-weight chondroitin sulfate on osteoarthritis through behavioral scoring, knee joint swelling degree, pain index, and knee joint histopathology, as well as the detection of inflammatory factors and COL2A1. Results demonstrated that all three types of chondroitin sulfate could alleviate joint swelling and pain, reduce the levels of IL-1β, IL-6, and TNF-α in cartilage tissue; inhibit the expression of MMP-13, MMP-3, and MMP-9; and promote the expression of COL2A1, SOX9 proteins, and Bcl-2/Bax proteins. Different-molecular-weight chondroitin sulfates showed significant ameliorating effects on the monosodium iodoacetate-induced osteoarthritis model, with protective efficacy increasing as the molecular weight decreased. The effect of low-molecular-weight chondroitin sulfate 2 was similar in magnitude to that of the positive drug. Full article

The study employs an integrated Entropy-TOPSIS model to evaluate and rank the digital access readiness of the households of the 12 statistical regions of Türkiye, between 2019 and 2024. A Global Decision Matrix (72 × 5) was created to have all the composite scores on the same scale, and Entropy weights and TOPSIS ideal solutions were calculated once from the unified matrix, to achieve longitudinal comparability. The results show that Western Anatolia and Istanbul have always been among the top regions in the nation, and in 2020, 2022 and 2023, Western Anatolia overtook Istanbul in the ranking. The lowest scores are consistently observed in Northeastern Anatolia, Central Eastern Anatolia and Southeastern Anatolia in all years. The low discrimination power of the mobile phone penetration and internet access is shown by the entropy weights, which show that these have a near homogeneous distribution, resulting in no significant regional differences. The majority of the score variance is attributable to the usage of desktop computers, laptops and tablets, and this reaffirms the fact that the digital divide in Türkiye has gone beyond basic internet connectivity and is now also a matter of ownership of digital devices at home. The SD tracks plotted longitudinally shows a non-linear, cyclical pattern. However, the pandemic did not cause uniform growth but was expected to magnify the regional inequality in the first few years, followed by temporary convergence in 2021–2022. These gains were then undone by high inflation, leading to higher inequality in 2023 with a delay in hardware spending by eastern households. Ranking robustness is confirmed by the results of sensitivity analyses such as equal-weight simulation, leave-one-out criterion removal, SAW and VIKOR comparison, as well as Variance Inflation Factors checks. Regional inequality, measured by the standard deviation of composite scores, ranged from $\sigma =0.2095$ in 2019 to a peak of $\sigma =0.2495$ in 2023, with the highest-scoring region ($CC=0.924$, Istanbul, 2021) scoring nearly 90 times higher than the lowest ($CC=0.010$, Central Eastern Anatolia, 2023). The study provides the first hardware-access ranking, which is comparable across the statistical regions in Türkiye and which has been carried out over a long period of time, and reveals that the regional policy must shift from expanding broadband to providing targeted incentives for acquiring hardware in structurally disadvantaged eastern regions. Full article

Driving simulation needs labeled 3D scenes, but mobile mapping system (MMS) light detection and ranging (LiDAR) is often delivered unclassified, without point-wise labels for training. We present a training-free pipeline that transfers the semantics of a vector high-definition (HD) map, the Korean precision road map, onto MMS LiDAR and outputs a labeled 0.5 m voxel scene, and we analyze what limits its accuracy. Each voxel takes the label of the nearest map feature whose category-specific radius and elevation window it satisfies, with elevations measured from a ground model built from the map’s 3D vertices. The pipeline needs no training data but requires a 3D vector map. On an expressway interchange, the ground model reached a median error of 6.6 cm, on par with inverse-distance weighting (IDW), triangulated irregular network (TIN), and local-plane interpolation. A blind, stratified random sample of 695 cells and a blind review of all 91 output objects showed that 98.0% [95.8, 99.5] of the output voxels are static solid structure and 73% [63, 81] of the objects are the mapped facility; the cell-level macro-F1 over six categories was 0.44 [0.39, 0.52] (guardrail 0.73, tunnel 0.94, sign 0.14). Errors were concentrated: four tunnel-mounted signals labeled the tunnel ceiling, and the radii of signs and posts captured roadside vegetation. The results show where map-based labels are reliable for simulation and which map content needs treatment. Full article

Background: Weight-bearing ankle dorsiflexion may be relevant to postural control and mobility, but its relationship with fall history in older adults remains insufficiently examined. This study compared weight-bearing ankle dorsiflexion between older adults with and without a history of falls and examined associations with balance, functional mobility, concern about falling, and faller status. Materials and Methods: This cross-sectional study included 90 community-dwelling older adults (mean age 72.6 ± 5.4 years; 62.2% women), comprising 45 fallers and 45 non-fallers classified by self-reported fall history during the previous 12 months. Weight-bearing ankle dorsiflexion was assessed using the weight-bearing lunge test (WBLT); balance, mobility, and concern about falling were assessed using Frailty and Injuries: Cooperative Studies of Intervention Techniques (FICSIT-4), Timed Up and Go (TUG), and the Greek Falls Efficacy Scale-International (FES-I). Results: Fallers had lower bilateral mean WBLT values than non-fallers (7.1 ± 2.0 vs. 9.4 ± 2.1 cm, p < 0.001). Greater bilateral mean WBLT distance was associated with higher FICSIT-4 scores (r = 0.47), shorter TUG times (r = −0.52), and lower FES-I scores (r = −0.44; all p < 0.001). Bilateral mean WBLT distance was associated with lower odds of faller status after adjustment for age, sex, and body mass index (OR = 0.56, 95% CI 0.42–0.73, p < 0.001), remaining significant after additional adjustment for medication count (OR = 0.54, 95% CI 0.40–0.72, p < 0.001). In an exploratory within-sample ROC analysis without independent validation, WBLT bilateral mean distance discriminated previous faller status (AUC = 0.79, 95% CI 0.69–0.88), but did not establish prediction of future falls. Conclusions: Older adults with a history of falls demonstrated reduced weight-bearing ankle dorsiflexion. The WBLT may provide complementary ankle-specific information associated with faller status and fall-related functional characteristics. Longitudinal studies are needed to determine its prognostic value for future falls. Full article

This study specifies a multi-hop vehicular clustering algorithm combining a stability factor (SF), parent persistence under a hysteresis margin, trust-gated parent eligibility and road-side-unit (RSU)-assisted maintenance. Five stateful implementations are compared across four scenarios with 20 paired seeds per condition. Counting singleton heads, SF’s CH tenure is longest on the baseline and high-inflow highways (restricted mean to 120 s: about 52 s versus 21–37 s), but not on the low-inflow highway or the urban arterial. This head-level advantage does not extend to cluster affiliation. SF’s urban direct-parent persistence (70.19 s) exceeds MCSC and Center-based but not Weighted-MR (90.95 s). SF has higher root-change rates in ten of sixteen contrasts and shorter fixed-root membership persistence in thirteen. In a controlled study, composite SF reduces root changes relative to mobility-only scoring by 9–11 per 1000 vehicle-seconds at h = 0.08, versus 133–313 from the margin. Degree-only contrasts confound scoring with effective retention. Nominal trust filtering reduces the dropped-attempt fraction from 15.62% to 0.37%, but erroneous penalties still reduce honest-parent eligibility. Full article

Assessing feather condition in broiler breeders is essential for monitoring poultry welfare and identifying feather damage and loss associated with management, health, natural mating, and behavioral challenges. Feather condition can be associated with fertility failures, as featherless areas can suffer scratches from roosters during mating, reducing reproductive success. Manual feather scoring (FS) is labor-intensive, subjective, and difficult to scale in broiler breeder production. This study proposed a stacking ensemble framework for automated multiclass FS classification using RGB images. A total of 67,231 images were categorized into six FS classes (FS_0–FS_5) and used to train three independently trained models: MobileNetV2, EfficientNetB0, and ResNet50. Their class probability outputs were integrated using a Logistic Regression (LR) meta-learner. The proposed stacking ensemble achieved 99.83% accuracy, 99.83% macro precision, 99.86% macro recall, 99.84% macro F1-score, an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9999, and a quadratic weighted kappa (QWK) of 0.9997. The QWK and ordinal error analysis indicated near-perfect chance-corrected agreement, with no severe cross-category misclassifications. Bird-grouped five-fold cross-validation showed stable performance within the experimental domain evaluated. Gradient-Weighted Class Activation Mapping (Grad-CAM) provided qualitative insight into model attention while also indicating some background activation. The proposed framework shows strong performance on a finer six-class FS task and demonstrates the potential of stacking ensemble learning for objective poultry welfare assessment. Broader cross-farm and cross-device validation remains necessary before practical deployment. Full article

Mobile mapping system (MMS) panoramas routinely capture the survey vehicle, which occludes a substantial portion of the road surface beneath the camera. Reconstructing this large missing region is challenging because of complex road textures, cast shadows, and illumination variations. Although GAN-based inpainting typically combines multiple loss terms to improve reconstruction quality, fixed loss weights cannot adapt to the evolving optimization requirements during training. This study proposes a quality-driven loss-balancing framework in which a Proximal Policy Optimization (PPO) controller dynamically adjusts auxiliary-loss weights. At each control step, the controller observes interval-level generator losses, the current loss weights, and training progress, and receives a reward derived from paired improvements in SSIM and LPIPS on a disjoint controller-feedback set. Because ground-truth training data are unavailable, training pairs are constructed by projecting the occluded region into a perspective viewport and retrieving the corresponding background from nearby panoramas using GPS and heading information. The framework is evaluated against Fixed-Weight training, SoftAdapt, and GradNorm on two real-world datasets, MMS-Urban and MMS-Suburban, using Pix2Pix and Big-LaMa backbones. Experimental results show that PPO-guided weight adaptation achieves the lowest FID across all four backbone–dataset configurations. Relative to Fixed-Weight training, it reduces LPIPS, MAE, and FID by 14.80%, 15.64%, and 20.65% on average, respectively, while improving SSIM by 4.08%, with less than 1% additional training time. Further comparisons with predefined monotonic scheduling and progress-only PPO demonstrate the backbone-dependent contribution of state-informed adaptive loss balancing, with clearer reconstruction improvements for Pix2Pix and limited additional benefits for Big-LaMa. Full article

Regional sustainability increasingly depends on interactions among regional digitalization, green mobility, and tourism development, yet the temporal structure of these relationships remains insufficiently understood. Using a balanced panel of 31 provincial-level regions in mainland China from 2012 to 2023, this study constructs entropy-weighted composite indices of regional digitalization, green mobility, and tourism competitiveness. These indices are analyzed using a panel vector autoregression model, Granger-causality tests, impulse-response functions, and forecast error variance decomposition. The results identify a digitally centered and asymmetric pattern of lagged predictive relationships from regional digitalization to green mobility and tourism competitiveness, from green mobility to tourism competitiveness, and from tourism competitiveness back to regional digitalization. These relationships differ markedly in their temporal profiles. Specifically, responses to digitalization peak near one-fifth of a standard deviation and persist. Digitalization shocks account for an increasing share of the forecast error variance of green mobility and tourism competitiveness over time, whereas the response of tourism competitiveness to green mobility emerges quickly but attenuates over time. By integrating the three systems within a joint dynamic framework, this study extends research based on separate pairwise relationships and shows that they co-evolve at different speeds. The findings support long-term digital investment and closer coordination between tourism demand and green-mobility provision in regional sustainability strategies. Full article