Search Results (198)

The rapid development of deep learning methods has significantly improved the effectiveness of object detection in Earth Observation (EO) imagery. However, standard metrics such as Mean Average Precision (mAP) do not fully reflect their utility in operational analyses. This paper proposes a multi-stage methodology for evaluating vehicle detection models, combining classical evaluation with functional analysis encompassing object counting, density estimation, and occupancy index. The research was conducted on high-resolution imagery (WorldView, Pleiades) and the xView dataset, evaluating five YOLO variants alongside transformer-based and two-stage detectors under three training strategies, including fine-tuning. The results show that models achieving high mAP values (up to 0.952) can simultaneously produce significant errors in object count estimation. Models trained exclusively on xView exhibit a substantial performance drop (mAP@0.50 ≈ 0.45) under domain shift conditions. The best results were obtained using a fusion-based approach combining YOLOv9 and YOLOv12, which reduced the mean relative error to 0.14 and the counting error to 13 objects, maintaining a low density error (0.0023). Functional validation across 20 parking areas confirmed the stability of the proposed approach. The findings confirm that functional analysis constitutes a critical complement to classical evaluation in remote sensing applications. Full article

Background and Objectives: Gambling disorder (GD) is a behavioral addiction associated with distress, comorbidity, and functional impairment. This exploratory cross-sectional study examined associations between DSM-5-TR symptom burden, personality dimensions, cognitive emotion regulation, quality of life, and sociodemographic variables in a Romanian clinical sample. Materials and Methods: The sample included 122 adults with psychiatrist-confirmed pathological gambling/GD recruited from “Prof. Dr. Alexandru Obregia” Clinical Hospital of Psychiatry, Bucharest. Personality was assessed with the Personality Clinical Form (PCF; 109 valid profiles), cognitive emotion regulation with the Cognitive Emotion Regulation Questionnaire, and quality of life with the Quality of Life Inventory. Symptom burden was measured using a nine-item binary DSM-5 symptom burden index. Results: The symptom burden index showed a pronounced ceiling effect: median = 9.00 (IQR = 9.00–9.00; range = 4–9), with 91.0% classified as severe and 77.9% meeting all nine criteria. In PCF analyses, symptom burden was positively associated, after Benjamini–Hochberg correction, with broad personality pathology, including maladaptive personality dimensions, personality-functioning indicators, and personality-disorder feature scales; the strongest association involved borderline features. Catastrophizing and Blaming Others were positively associated with severity, whereas Positive Reappraisal, Putting into Perspective, and Positive Refocusing were negatively associated. Quality of life was very low overall and associated with personality and coping variables, but not directly with symptom burden. Criterion-count rank distributions differed by marital status and perceived social support; occupational status showed an omnibus distributional difference, but no pairwise contrast survived correction. Conclusions: GD was characterized by severe symptom burden and restricted score variability. Findings support multidimensional assessment of personality functioning, emotion regulation, quality of life, and social–contextual vulnerability. Full article

Background/Objectives: Rehospitalization after traumatic spinal cord injury (SCI) is common, but binary or count summaries may obscure heterogeneity in timing, recurrence, frequency, and duration. We aimed to identify clinically interpretable rehospitalization burden profiles in the SCIMS 2021ARPublic dataset and examine descriptive associations with clinical correlates and participation outcomes. Methods: We analyzed Form I, Form II, and Record Status public-use files. Among 29,310 individuals with at least one non-lost follow-up interview, 28,745 with at least one non-missing rehospitalization indicator entered latent class analysis. Four prespecified indicators captured early, recurrent, frequent, and prolonged rehospitalization. Candidate two- through six-class models were compared using AIC, BIC, entropy, class size, posterior probabilities, and interpretability. Pairwise adjusted logistic models examined candidate clinical correlates in 10,407 participants with complete 2016+ follow-up data. Adjusted linear models examined CHART participation domains in 20,766–20,949 participants. Results: A four-profile solution was retained: low rehospitalization burden (59.8%), early/prolonged rehospitalization (18.9%), frequent/prolonged rehospitalization (7.7%), and high recurrent/frequent/prolonged burden (13.6%). UTI and pressure ulcer history showed the most consistent associations with burdened profiles. Severe pain and frequent sleep problems were associated with selected heavier-burden profiles, while depressive symptoms showed smaller and less precise associations. Sensitivity analyses supported structural stability while highlighting observation-time bias and classification uncertainty inherent to wave-based public-use data. Compared with the low-burden profile, burden profiles showed lower CHART scores, especially for mobility and occupation. Conclusions: Rehospitalization after traumatic SCI is heterogeneous. These utilization burden profiles summarize distinct observed patterns but require prospective validation before use in risk stratification or follow-up planning. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

Background: Type II workplace violence by patients, relatives, or visitors is an occupational health and patient-safety concern in primary care. Cranston Ridge Medical Clinic (CRMC), a single urban family medicine and walk-in primary care clinic in Calgary, Alberta, plans to implement a universal, risk-informed workplace-safety bundle that is based on observable behaviour, situational risk, and documented safety concerns rather than demographic profiling. Methods: This article describes a single-site internal quality improvement and workplace-safety evaluation protocol. The comparison is CRMC usual practice during the pre-implementation baseline period; there is no concurrent external control group. The planned evaluation will use aggregate, de-identified operational data from a 12-month pre-implementation baseline, a four-week implementation period, and 12 months of post-implementation monitoring. All clinic staff will receive workplace-safety training as part of routine implementation. No staff, patients, or visitors will be recruited as research participants, and the evaluation will not use individual-level staff survey, interview, or focus-group data. Patient/visitor information will be used only as aggregate operational monitoring data when needed to assess safety, access, patient flow, and complaints. Intervention and analysis: The bundle includes worksite analysis, staff training, a brief arrival safety screen, a response algorithm, standardized reporting, monthly safety huddles, and post-incident support. The primary metric will be the Type II workplace-violence incident rate per 1000 clinic visits. Planned analyses include run charts, pre–post rate ratios, and Poisson or negative binomial segmented regression if monthly counts are sufficient. Implementation learning will be summarized from routine training records, safety-huddle summaries, post-incident debrief themes, and other aggregate de-identified operational indicators. Expected contribution: The protocol contributes a transparent, equity-sensitive, and operationally feasible model for balancing staff safety with patient access in primary care. Full article

Background/Objectives: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. Understanding its epidemiological characteristics is essential for guiding public health strategies. In this study, we characterized the epidemiological profiles that may contribute to the risk of ALL in children in southern Brazil. Methods: Clinical and epidemiological data from 71 children (1–15 years old) admitted and newly diagnosed with ALL at four hospitals in Curitiba, Paraná, Brazil, were retrieved and analyzed. Results: Among the 71 children with ALL, the majority were male (n = 43, 60.6%), with an age range of 1–3 years (n = 26, 36.6%), self-identified as White (n = 47, 66.2%), and were born in Paraná state (n = 61, 85.9%). Nearly half had a family history of cancer (n = 33, 46.5%), primarily among grandparents (n = 36, 61%). Parental environmental exposures included smoking (n = 30, 42.3%) and occupational exposure to chemicals or radiation (n = 17, 23.9%). At diagnosis, most patients (n = 43, 60.5%) had a bone marrow blast count > 70%, and 27 patients (38%) had a peripheral blood blast count > 70%. B-cell ALL was the predominant subtype (n = 61, 85.9%). In B-cell ALL cases, the most frequent molecular subtype was high hyperdiploidy (n = 17, 23.9%). White blood cell counts differed significantly between the B-cell ALL and T-cell ALL groups (p = 0.029). Conclusions: Our findings provide insights into ALL epidemiology in southern Brazil and highlight regional differences across the country. Full article

Confined space operations are characterized by environmental complexity and latent hazards, where failures in human risk perception represent a primary precursor to industrial accidents, posing a significant challenge to sustainable occupational health and safety (OHS) management. This study investigates the mechanism by which individual traits (risk propensity) influence risk perception performance through cognitive processes (inattentional blindness). Utilizing psychological scales and eye-tracking technology, we quantitatively analyzed the visual search behaviors of participants with varying risk propensities across typical confined space hazard scenarios. The results indicate that individuals with high risk propensity tend to adopt a “random-exploratory superficial scanning strategy,” characterized by significantly delayed Time to First Fixation (TFF) and lower Fixation Count (FC) within critical hazard areas compared to the low-risk propensity group. Statistical analysis reveals that inattentional blindness exerts a full mediating effect between risk propensity and risk perception performance, accounting for 72.56% of perception failures. This research confirms that an imbalance in attentional resource allocation leads to higher cognitive omission of salient hazards among high-risk propensity individuals. These findings provide a theoretical foundation for cognitive reliability assessment and the design of sustainable safety training programs in high-risk industries, ultimately contributing to the social sustainability and well-being of industrial workforces. Full article

Accurate and stable indoor occupancy information is essential for occupant-based intelligent ventilation control. Under a single-camera setting, existing indoor occupancy detection methods commonly suffer from missed detections caused by occlusion and blind zones, false detections caused by people outside the room, and cumulative entry–exit errors that are difficult to correct. These problems lead to false fluctuations in detected occupancy, affect control performance, and may further reduce indoor comfort or cause unnecessary energy use. To address the practical situation in which indoor spaces are commonly equipped with a single security camera, this study proposes an indoor occupancy detection method by fusing field-of-view information and entry–exit events with a single camera. The study covers method development, multi-scenario validation, parameter analysis, and a ventilation control application. The proposed method uses YOLOv8x and DeepSORT as front-end models and performs post-processing on their outputs to extract field-of-view occupancy information, entry–exit events, and blind-zone events. An occupancy confirmation and correction module is then constructed. The blind-zone event mechanism reduces the influence of missed entry–exit events and camera blind zones on occupancy judgment. The correction module integrates frame-by-frame ID counts, historical outputs, and multiple event signals to verify and suppress false occupancy changes caused by false detections, missed detections, and blind zones, thereby producing more stable indoor occupancy results. Experimental results show that the proposed method outperforms the baseline methods based on front-end object detection and tracking in terms of score, RMSE, and F1 score in three typical scenarios: an office, a home, and a classroom. In the office scenario, the proposed method achieved a score of 99.36%, an RMSE of 0.081, and an F1 score of 0.781. The detection stability was also improved in the home and classroom scenarios. In the high-density and strongly occluded classroom scenario, the absolute detection performance of the fusion-based detection method was limited by the front-end models, indicating that the method still has certain applicability boundaries in complex high-density scenes. Parameter sensitivity analysis shows that key parameters, including the entry–exit area depth, confidence threshold, and time threshold, affect the detection results of the fusion-based detection method. Under the test conditions of this study, the method performs well when the entry–exit area depth is approximately 1.5d, the YOLOv8x confidence threshold is 40%, and the time threshold is 5 × FPS. These results can provide a reference for initial parameter setting and on-site calibration in similar scenarios. Using the office scenario as a case study, the method was further applied to occupant-based ventilation control. The average CO₂ concentration during occupied periods under the proposed method was 622.43 ppm, which was closest to the result under ground-truth occupancy control, with a deviation of only 0.9 ppm. This indicates that the method can help improve indoor air quality. Compared with conventional schedule-based control, occupant-based ventilation control driven by the proposed fusion method reduced cumulative fan energy consumption by approximately 65.2%, showing good energy-saving potential at the ventilation-control level. In summary, the proposed method can effectively improve the accuracy and stability of indoor occupancy detection under a single-camera setting and provide more reliable input for occupant-based ventilation control. The framework is modular, and the front-end object detection and tracking models can be replaced according to actual deployment needs. However, the validation in this study is still mainly based on scenarios where existing security cameras can cover the main activity areas and all entry–exit passages. The applicability of the method under more complex camera arrangements, lighting variations, and automatic region configuration requires further investigation. Full article

This paper proposes the Branch-Priority Exploration (BPE) framework for autonomous coverage in confined industrial corridor environments. BPE integrates three components: (1) a symmetry-aware LiDAR branch detector; (2) a hierarchical BFS/DFS mode-switching policy; and (3) a barrier-based branch memory. Frontier-based methods often struggle in industrial corridors where branches split off from the main corridor. The symmetric layout of such environments, featuring T-shaped junctions and L-shaped turns, creates recurring geometric patterns that conventional frontier scoring fails to exploit. When the robot reaches a junction, nearby frontier candidates often receive similar scores, causing repeated target switching as the local map changes. Meanwhile, frontier cells inside a branch tend to score lower than those along the main corridor; so, the robot often bypasses the branch and continues forward, which leads to additional backtracking later. Even when the robot eventually returns, residual frontier cells near the entrance may attract the planner repeatedly, causing redundant re-entry into already-covered branches. To address these issues, a branch-priority exploration framework is developed. A symmetry-aware branch detection module uses LiDAR range measurements from multiple directions to identify T-shaped junctions and lateral openings, applying identical geometric criteria to lateral openings on either side of the robot. This allows branch entry to be triggered by explicit geometric evidence, rather than frontier score comparisons that tend to be unreliable near intersections. When a branch is detected, the robot transitions from BFS mode to DFS mode for systematic branch coverage. Entry and post-return locks prevent mode reversal before the robot commits to the new heading. Once a branch is completed, a permanent virtual barrier is placed at its entrance; so, the planner no longer routes the robot back into that branch. The framework is formalized as a constrained coverage problem on occupancy grids, and monotonic coverage progress and finite branch completion under barrier memory are established theoretically. A fully reproducible ROS implementation on a wheeled platform with differential drive is validated. Experiments span several corridor environments of increasing topological complexity. Compared to a nearest-frontier baseline, the proposed method substantially reduces both exploration time and goal cancellations while achieving complete coverage across all trials. The cancellation count matches the number of T-branches per environment, with near-zero variance across repeated runs. Full article

Occupational noise-induced hearing loss (NIHL) is a common occupational disorder, yet non-invasive molecular indicators of chronic occupational noise exposure remain insufficiently characterized. Although the cochlear mechanisms behind NIHL have been extensively studied in experimental models, peripheral blood transcriptomic alterations in affected human populations are less well defined. In this exploratory study, we aimed to describe peripheral blood gene expression patterns associated with occupational NIHL and to generate candidate molecular signals for future validation. Peripheral blood RNA sequencing (RNA-seq) was performed in 11 male individuals with occupational bilateral sensorineural hearing loss and four noise-unexposed healthy male controls. Transcript abundance was quantified using a standardized RNA-seq workflow, and formal differential expression analysis was conducted on gene-level count data derived from Salmon quantification using DESeq2 with Benjamini–Hochberg correction. Through our analysis, we identified a limited set of differentially expressed genes, including upregulated interferon-associated transcripts, such as RSAD2, IFIT1, IFI44L, and CMPK2, host-defense-related genes, including DEFA1, DEFA3, and DEFA4, and immune-regulatory transcripts such as HLA-DRB1 and GPR15, together with downregulated non-coding RNAs including SNORD3A and SNORD3C. These findings suggest that occupational NIHL may be accompanied by detectable peripheral blood transcriptomic alterations, predominantly involving immune- and host-defense-related pathways. Given the limited cohort size and exploratory design, these genes represent preliminary candidates for validation in larger independent cohorts. Full article

Lightweight Unmanned Aerial Vehicles (UAVs) have limited space, low payload capacity, and constrained power supply capabilities. Therefore, their payloads are constrained by size, weight, and power (SWaP). Thus, designing edge-side signal processing architectures for the payloads of UAVs faces severe challenges. Traditional ASIC design based on manual optimization struggles to meet the demands of low latency and low resource occupancy in edge-side applications. To address this challenge, this paper proposes a signal processing hardware accelerator architecture design framework with algorithm-hardware co-design. The framework employs a cross-level dataflow graph representation to formally capture task characteristics. Reconfigurable dataflow templates and reusable operator IP components are systematically constructed based on this representation. Through multi-objective design space exploration, the framework achieves Pareto-optimal mapping from algorithmic specifications to hardware implementations. Finally, automatic generation of top-level hardware descriptions enables rapid FPGA-based prototyping and functional validation. Taking synthetic aperture radar (SAR) imaging as a study example, compared with non-reconfigurable architectures, this scheme reduces the equivalent gate count by 51.4% without increasing processing latency. Compared with a conventional reconfigurable dataflow architecture, the design improves energy efficiency from 12.8 MS/J to 16.0 MS/J, representing a 25.4% enhancement, while also scaling the supported data processing size by a factor of 4×. It provides a high-performance and scalable hardware acceleration solution for lightweight edge-side computing platforms. Full article

Bioaerosol emissions from biological treatment processes like composting, livestock operations, and wastewater plants pose notable occupational and environmental health risks. Biofiltration is a common mitigation measure for gaseous pollutants, but its effectiveness in controlling bioaerosols is less studied. This review synthesizes current evidence on biofiltration for the removal of bioaerosols. Findings indicate that biofiltration can significantly reduce emissions from waste-related biological processes, although results vary widely and depend heavily on design and operational factors. In composting, agricultural, and wastewater treatment contexts, fungal bioaerosols are consistently removed with high efficiency, often over 90%. Conversely, bacterial removal shows greater variability, from negligible to above 90%, influenced primarily by airflow rate, bed depth, and media stability. Systems with residence times of tens of seconds and bed depths of at least 1 m tend to reliably reduce bacterial counts, whereas undersized, high-flow systems experience marked efficiency losses. The choice of packing material is also crucial; mature, stable media maintain performance, whereas nutrient-rich or unstable substrates can lead to fungal emissions, turning the biofilter into a secondary source. Data on endotoxin removal are limited and remain insufficient for firm design recommendations. Overall, biofiltration’s effectiveness depends on complex interactions among physical retention, biological stability, and design. These insights emphasize the need for future research to focus on standardized, performance-based design criteria supported by consistent reporting and full-scale validation. Full article

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

This study investigates the effects of vehicle air-conditioning parameters on cabin thermal environment and occupant comfort. Computational fluid dynamics and discrete particle simulations involving different inlet-vent angles, inlet relative humidity (RH) levels, and occupant counts were conducted to analyze airflow, temperature, and RH. Thermal comfort was assessed using predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), equivalent homogeneous temperature, and mean age of air (MAA). As a result, the uniform airflow at a 30° inlet angle provided the best global thermal comfort based on PMV (0.49) and PPD (10.02), whereas a 0° inlet angle improved local comfort around the chest area. Maintaining an inlet RH of 40–50% enhanced overall thermal comfort. Increasing the occupant counts raised the average cabin temperature to 301.76 K (Case 9), while also affecting local airflow patterns and MAA distributions; the addition of rear-seat occupants increased the local temperature around the driver’s left hand. These findings provide practical guidance for vehicle heating, ventilation, and air-conditioning system design, indicating that ventilation strategies should consider global comfort indices, localized airflow, thermal patterns, and particle removal performance. Overall, this parametric study highlights the association between vehicle cabin conditions and thermal comfort, providing baseline data for digital twin–based adaptive ventilation systems. Full article

Occupational sedentary behavior presents a public health risk, yet current interventions often rely on subjective self-reports or context-blind prompts. This study validates a privacy-preserving, edge-computing time-of-flight (ToF) sensor that detects postural states and quantifies therapeutic exercise gestures in real time. The dual-sensor architecture distinguishes between sitting, standing, and absence, while capturing rapid sit-to-stand repetitions suitable for active-break interventions. In this paper, a laboratory study (N = 7) evaluated the system against ground truth comprising activPAL3 accelerometry and video analysis. Across 378 postural events, the sensor achieved high temporal fidelity (mean absolute error < 1.6 s) and 100% sensitivity in counting exercise repetitions. The system differentiated workstation occupancy from physical absence. These findings demonstrate that ToF sensing matches the accuracy of video analysis without privacy concerns while offering the contextual awareness required for just-in-time, adaptive workplace interventions. Full article