Search Results (133)

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

Supersonic cabins are characterized by high heat flux and high occupant density, which can adversely affect passenger comfort, health, and energy efficiency. This study proposed a multi-objective optimization framework for determining supply air parameters in a supersonic aircraft cabin, evaluating the performances of different optimization methods. The optimization focused on three design objectives: thermal comfort (PMV), air freshness (air age), and the temperature differential between the supply and exhaust air. Two fast calculation methods—Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANN)—were compared alongside two optimization algorithms: Multi-Objective Genetic Algorithm (MOGA) and Pareto search. The results indicate that the POD method has a smaller relative root mean square error compared to the ANN method. The relative root mean square error of the ANN method in predicting PMV is 2.7 times higher than the POD method and 3.9 times higher in air age prediction. The Pareto search algorithm outperformed MOGA in computational efficiency, generating 3.3 times more Pareto-optimal solutions in less time. The entropy weight method was used to assign weight for both optimization algorithms, revealing that neither algorithm achieved universally optimal performance across all objectives. Therefore, selecting the best solution requires aligning optimization outcomes with specific design priorities. Full article

This study proposes a machine learning-based framework for classifying occupational stress levels among nurses using physiological time-series data collected from wearable sensors. The dataset comprises multimodal signals including electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and tri-axial accelerometer measurements (X, Y, Z), which are labeled into three categorical stress levels: low (0), medium (1), and high (2). To enhance the usability of the raw data, a resampling process was performed to aggregate the measurements into one-minute intervals, followed by the application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate severe class imbalance. Subsequently, a comparative classification analysis was conducted using four supervised learning algorithms: Random Forest, XGBoost, k-Nearest Neighbors (k-NN), and LightGBM. Model performances were evaluated based on accuracy, weighted F1-score, and confusion matrices to ensure robustness across imbalanced class distributions. Additionally, temporal pattern analyses by the day of the week and the hour of the day revealed significant trends in stress variation, underscoring the influence of circadian and organizational factors. Among the models tested, ensemble-based methods, particularly Random Forest and XGBoost with optimized hyperparameters, demonstrated a superior predictive performance. These findings highlight the feasibility of integrating real-time, sensor-driven stress monitoring systems into healthcare environments to support proactive workforce management and improve care quality. Full article

Background: Maintaining long-term exercise adherence in occupational settings remains a challenge, particularly in remote or unsupervised environments. This study aimed to investigate the effect of individualized feedback on exercise adherence, body composition, and physical function during a remote home-based training intervention utilizing the video-based exercise system “SUKUBARA^®”. Methods: In total, 66 care facility workers were randomly categorized into either a feedback (FB) group or a non-feedback (NF) group. Both groups performed a combined exercise program comprising low-load resistance training (slow squats) and balance exercises (one-leg standing time of closed eye) for approximately 15 min, thrice weekly over 12 weeks. The FB group received individualized feedback sheets visualizing total video play time (TT), exercise frequency, and interruptions, alongside reminder emails. The primary outcome was TT. Secondary outcomes included body composition measures (body weight, fat-free mass, and body fat mass rate) and one-leg standing time of opened eye. Results: The FB group demonstrated significantly greater TT, approximately 1.5 times that of the NF group, indicating enhanced exercise adherence. Moreover, significant improvements in fat-free mass and body fat mass rate were observed in the FB group. A significant correlation was identified between changes in TT and body composition parameters, suggesting TT as a valid proxy for exercise engagement. Conclusions: Individualized feedback within a remote monitoring home exercise program effectively improved exercise adherence and body composition among care workers. The “SUKUBARA^®” system shows promise as a tool to support exercise continuity in occupational health and long-term care settings. Full article

Background: Load carriage is an activity of daily living, can be an occupational risk, and is a popular method of resistance training. Type of carry could be related to vascular and blood pressure (BP) responses and may be related to cardiovascular risk. As physical activity is recommended by clinicians and exercise physiologists, understanding the vascular responses in relation to type of carry is important to consider in terms of risk. The purpose of this study was to compare the vascular health and BP responses to the farmer’s handle (load at the side of the body) and zercher (load in front of the body) carry and to examine sex differences in the responses. Methods: A total of 38 adults (22 females and 16 males) completed farmer’s handle and zercher load carriage with pre- and post-vascular measurements using pulse wave analysis. Results: We found that load carriage with weight in front of the body (zercher) elicits a larger increase in arterial stiffness (AIx@75), and load carriage with the load on the side of the body (farmer’s handle) elicits a larger increase in peripheral systolic BP. There were no sex differences in the responses to carry. Females exercise (3.1 vs. 4.8) and weight train (1.5 vs. 2.6) fewer times per week and had a lower resting systolic BP (121.5 ± 10.3 vs. 131.3 ± 8.3 mmHg) (p < 0.05). Both types of load carriage increased peripheral systolic BP, central systolic BP, and vascular stiffness (p < 0.05). Conclusions: Location of load carriage is important to consider based on potential cardiac risk of patients. Full article

Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation $SD$ decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article

The use of LUZs in urban environments is a critical factor for ensuring efficient vehicle mobility in cities. Poor utilisation of these zones can generate negative externalities, such as double parking or illegal occupation of pedestrian crossings or garage doors. The purpose of the study is to provide city governance with a methodology based on the OEE model to evaluate the efficiency of individual zones or sets of zones and to inform decisions that improve their use without disrupting the coexistence with other city users. To validate the methodology, all deliveries made in selected areas of the city of Zaragoza over the course of one month were studied. The results of the study reveal a considerable loss of efficiency and some recommendations are proposed achieve a better use: only 51.44% of deliveries used the LUZs correctly, and the total OEE ratio was just 0.37. This low level of efficiency is due to the incorrect use by delivery drivers, who often use LUZs as parking spaces, and the illegal occupation of the zones by unauthorised private vehicles. Full article

Background: Adolescents with a chronological age of less than 15 years or a gynecological age of less than 2 years may have a higher probability of complications because they are more likely to conceive within 1 to 2 years of menarche and, therefore, are still growing and maturing. This could impair their ability to adapt to the physiological demands of pregnancy. Objective: To evaluate the relationship between chronological age and gynecological age with low birth weight and small for gestational age among adolescent mothers in Mexico City. Methods: A retrospective cohort design of adolescent mother–child dyads was conducted. The study followed 1242 adolescents under 19 years of age and their children, collecting data on physical, socioeconomic, and clinical characteristics, including hemoglobin levels. Low birth weight was assessed using the Intergrowth-21st project standards and categorized as above or below 2500 g. The mothers were grouped by chronological age (<15 years and ≥15 years) and gynecological age (<3 years and ≥3 years). Adjusted odds ratios were calculated using binary logistic regression models. The outcome variables were low birth weight and small for gestational age. The independent variables included gynecological age, chronological age, age at menarche, hemoglobin concentration, and gestational weight gain, among others. All independent variables were converted to dummy variables for analysis. Calculations were adjusted for the following variables: marital status, maternal education, occupation, educational lag, family structure, socioeconomic level, pre-pregnancy body mass index, and initiation of prenatal care. Results: The average age of the participants was 15.7 ± 1 years. The frequency of small for gestational age and low birth weight was 20% and 15.3%, respectively. Factors associated with small for gestational age included gynecological age < 3 years [aOR = 2.462, CI 95%; 1.081–5.605 (p = 0.032)], hemoglobin < 11.5 g/dL [aOR = 2.164, CI 95%; 1.08–605 (p = 0.019)], insufficient gestational weight gain [aOR = 1.858, CI 95%; 1.059–3.260 (p = 0.031)], preterm birth [aOR = 1.689, CI 95%; 1.133–2.519 p = 0.01], and living more than 50 km from the care center [aOR = 2.256, CI 95%; 1.263–4.031 (p = 0.006)]. An early age of menarche [aOR = 0.367, CI 95%; 0.182–0.744 (p = 0.005)] showed a protective effect against small for gestational age. Factors associated with low birth weight included gynecological age < 3 years [aOR = 3.799, CI 95%; 1.458–9.725 (p = 0.006)], maternal age < 15 years [aOR = 5.740, CI 95%; 1.343–26.369 (p = 0.019)], preterm birth [aOR = 54.401, CI 95%; 33.887–87.335, p = 0.001], living more than 50 km from the care center [aOR = 1.930, CI 95%; 1.053–3.536 (p = 0.033)], and early age of menarche [aOR = 0.382, CI 95%; 0.173–0.841 (p = 0.017), which demonstrated a protective effect, respectively. Conclusions: The study concludes that biological immaturity, particularly early gynecological age, significantly contributes to adverse birth outcomes during adolescent pregnancies. Interestingly, early menarche appeared to have a protective effect, whereas chronological age was not a significant predictor of small for gestational age. Chronological age has an even greater impact: women younger than 15 years are 5.7 times more likely to have low birth weight infants. However, chronological age did not increase the likelihood of having an SGA newborn. Full article

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. Full article

In response to the growing demand for sustainable and performance-driven building design, this study proposes an integrated digital methodology that combines Building Information Modeling (BIM), parametric scripting, and multi-criteria decision-making (MCDM) to optimize external wall assemblies. The approach leverages Autodesk Revit and Dynamo to automate the parametrization of insulation thickness while ensuring compliance with regulatory thresholds for thermal transmittance and surface mass. Acoustic performance is estimated using ECHO software, and a Weighted Sum Model (WSM) is applied to evaluate and rank configurations based on four criteria: economic cost, Global Warming Potential (GWP), embodied energy, and acoustic insulation. A case study involving 24 wall assemblies—generated from eight base stratigraphies and three insulation materials—demonstrates the method’s ability to balance environmental impact, occupant comfort, and construction feasibility. The results indicate that natural and bio-based materials, such as rammed earth and cork, offer the best overall performance, while conventional systems remain competitive in terms of cost. The proposed workflow reduces design time, increases transparency, and supports informed decision-making during early design stages. This research contributes to the digitalization of sustainability assessment in architecture by promoting integrative, replicable, and regulation-aligned practices for low-impact building envelopes. Full article

With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. Full article

Cruise construction involves a lengthy logistical cycle, complex processes, and large volumes of diverse materials, inevitably generating reverse flows. To mitigate risks such as stock congestion, production disruption, and occupational hazards, this study proposes a novel reverse logistics network optimization model that integrates cost, efficiency, and Health, Safety, Environment (HSE) risk factors. Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. A three-phase Levy mutation discrete crow search algorithm (DCSA) was developed, introducing the Levy flight strategy to replace random search and enhance the discretization and solution diversity. The comparative analysis shows that DCSA performs as well as NSGA-II, while outperforming DGWO, demonstrating both stability and efficiency. Comparative analysis with a cost-only scenario revealed that although short-term economic gains may be achieved under cost minimization, such approaches often overlook risks with potential long-term impacts. This highlights the necessity of integrating safety concerns into reverse logistics planning, and confirms the model’s robustness and practical value, thus supporting decision makers in aligning reverse logistics planning in shipyards with sustainability and operational efficiency goals. Full article

Firefighters experience high noise levels from various sources, such as sirens, alarms, pumps, and emergency vehicles. Unlike industrial workers who experience continuous noise exposure, firefighters are subject to intermittent high-intensity noise, increasing their risk of noise-induced hearing loss (NIHL). Despite global concerns regarding firefighters’ auditory health, research on Korean firefighters remains limited. This study aimed to assess personal noise exposure among Korean firefighters across three primary job roles—fire suppression, rescue, and emergency medical services (EMS)—and to predict worst-case noise exposure scenarios. This study included 115 firefighters from three fire stations (one urban, two suburban). We measured personal noise exposure using dosimeters attached near the ear following the Korean Ministry of Employment and Labor (MOEL) and International Organization for Standardization (ISO) criteria. Measurements included threshold levels of 80 dBA, exchange rates of 5 dB (MOEL) and 3 dB (ISO), and a peak noise criterion of 140 dBC. We categorized firefighters’ activities into routine tasks (shift handovers, equipment checks, training) and emergency responses (fire suppression, rescues, EMS calls). We performed statistical analyses to compare noise levels across job roles, vehicle types, and specific tasks. The worst-case exposure scenarios were estimated using 10th percentile recorded noise levels. The average 8 h time-weighted noise exposure levels varied significantly by job role. Rescue personnel exhibited the highest mean noise exposure (MOEL: 71.4 dBA, ISO: 81.2 dBA; p < 0.05), whereas fire suppression (MOEL: 66.5 dBA, ISO: 74.2 dBA) and EMS personnel (MOEL: 68.6 dBA, ISO: 73.0 dBA) showed no significant difference. Peak noise levels exceeding 140 dBC were most frequently observed in rescue operations (33.3%), followed by fire suppression (30.2%) and EMS (27.2%). Among vehicles, noise exposure was the highest for rescue truck occupants. Additionally, EMS personnel inside ambulances had significantly higher noise levels than drivers (p < 0.05). Certain tasks, including shift handovers, equipment checks, and firefighter training, recorded noise levels exceeding 100 dBA. Worst-case scenario predictions indicated that some work conditions could lead to 8 h average exposures surpassing MOEL (91.4 dBA) and ISO (98.7 dBA) limits. In this study, Korean firefighters exhibited relatively low average noise levels. However, when analyzing specific tasks, exposure was sufficiently high enough to cause hearing loss. Despite NIHL risks, firefighters rarely used hearing protection, particularly during routine tasks. This emphasizes the urgent need for hearing conservation programs, including mandatory hearing protection during high-noise activities, noise exposure education, and the adoption of communication-friendly protective devices. Future research should explore long-term auditory health outcomes and assess the effectiveness of noise control measures. Full article

Background: An effective risk assessment and management methodology is essential to minimize/mitigate health risks associated with firefighting activities. The use of a self-contained breathing apparatus (SCBA) is mandatory during structure fires to protect firefighters from hazardous fire effluents, yet the protectiveness of the SCBA system has rarely been evaluated. Objective: This study characterizes, for the first time, the levels of 18 polycyclic aromatic hydrocarbons (PAHs) inside the SCBA facemask, during 7 structure-firefighting exercises and estimates associated respiratory risks. Methods: Cotton disk samples were collected via passive air sampling and analyzed using liquid chromatography with fluorescence and UV–Vis detection. Results: Levels of total PAHs (∑PAHs: 9.17–29.6 ng/m³) and ∑PAHs_carcinogenic (0.41–5.73 ng/m³) were below the occupational limits defined by governmental agencies. The low-molecular-weight PAHs were predominant (79.5–91.4%), and the (possible/known) carcinogenic naphthalene (0.26–2.00 ng/m³), anthracene (0.088–0.31 ng/m³), chrysene (0.046–0.39 ng/m³), benzo(b+j)fluoranthene (0.18–0.40 ng/m³), and benzo(a)pyrene (0.041–0.18 ng/m³) were detected in all samples. The respiratory health risk analysis demonstrated negligible risks associated with the inhalation of PAHs. A health principal component analysis could identify firefighters at increased respiratory risk. Conclusions: The effectiveness of SCBA was demonstrated, reinforcing the need to ensure its correct use during all the phases of structure fires, including during overhaul. Full article