Search Results (13)

Urban mobility in Bangkok is constrained by congestion, modal fragmentation, and gaps in First and Last Mile (FLM) access. This study develops a GIS-based framework that combines maximal-coverage location allocation with post-optimization accessibility diagnostics to inform intermodal hub siting. The network model compares one-, three-, and five-hub configurations using a 20 min coverage standard, and we conduct sensitivity tests at 15 and 25 min to assess robustness. Cumulative isochrones and qualitative overlays on BTS, MRT, SRT, Airport Rail Link, and principal water routes are used to interpret spatial balance, peripheral reach, and multimodal alignment. In the one-hub scenario, the model selects Pathum Wan as the optimal central node. Transitioning to a small multi-hub network improves geographic balance and reduces reliance on the urban core. The three-hub arrangement strengthens north–south accessibility but leaves the west bank comparatively underserved. The five-hub configuration is the most spatially balanced and network-consistent option, bridging the west bank and reinforcing rail interchange corridors while aligning proposed hubs with existing high-capacity lines and waterway anchors. Methodologically, the contribution is a transparent workflow that pairs coverage-based optimization with isochrone interpretation; substantively, the findings support decentralized, polycentric hub development as a practical pathway to enhance FLM connectivity within Bangkok’s current network structure. Key limitations include reliance on resident population weights that exclude floating or temporary populations, use of typical network conditions for travel times, a finite pre-screened candidate set, and the absence of explicit route choice and land-use intensity in the present phase. Full article

Aim: This study analyzed the concurrent validity of the Optojump infrared photocell system for estimating lower limb peak power by comparing it with the 15 s Wingate anaerobic test (WAnT) and examining relationships with sprint performance indicators. Methods: Twelve physically active university students (ten males, two females; age: 23.39 ± 1.47 years; body mass: 73.08 ± 9.19 kg; height: 173.67 ± 6.97 cm; BMI: 24.17 ± 1.48 kg·m⁻²) completed a cross-sectional validation protocol. Participants performed WAnT on a calibrated Monark ergometer (7.5% body weight for males, 5.5% for females), 30 s continuous jump tests using the Optojump system (Microgate, Italy), and 30 m sprint assessments with 10 m and 20 m split times. Peak power was expressed in absolute (W), relative (W·kg⁻¹), and allometric (W·kg^−0.67) terms. Results: Thirty-second continuous jump testing produced systematically higher peak power values across all metrics (p < 0.001). Mean differences indicated large effect sizes: relative power (Cohen’s d = 0.99; 18.263 ± 4.243 vs. 10.99 ± 1.58 W·kg⁻¹), absolute power (d = 0.86; 1381.71 ± 393.44 vs. 807.28 ± 175.45 W), and allometric power (d = 0.79). Strong correlations emerged between protocols, with absolute power showing the strongest association (r = 0.842, p < 0.001). Linear regression analysis revealed that 30 s continuous jump-derived measurements explained 71% of the variance in Wingate outcomes (R² = 0.710, p < 0.001). Sprint performance showed equivalent predictive capacity for both tests (Wingate: R² = 0.66; 30 s continuous jump: R² = 0.67). Conclusions: The Optojump infrared photocell system provides a valid and practical alternative to laboratory-based ergometry for assessing lower limb anaerobic power. While it systematically overestimates absolute values compared with the Wingate anaerobic test, its strong concurrent validity (r > 0.80), large effect sizes, and equivalent predictive ability for sprint performance (R² = 0.66–0.71) confirm its reliability as a field-based assessment tool. These findings underscore the importance of sport-specific, weight-bearing assessment technologies in modern sports biomechanics, providing coaches, practitioners, and clinicians with a feasible method for monitoring performance, talent identification, and training optimization. The results further suggest that Optojump-based protocols can bridge the gap between laboratory precision and ecological validity, supporting both athletic performance enhancement and injury prevention strategies. Full article

The Wanning Bridge is a critical component of Beijing’s Central Axis World Heritage site and the only Yuan Dynasty heritage bridge in Beijing still in service. Investigating its structural response under complex traffic conditions is therefore essential for ensuring the longevity of this ancient structure and the safety of the urban transport system. However, the application of traditional research methods, such as direct sampling, is often constrained by the cultural relic characteristics of heritage bridges. This study first conducted a macroscopic on-site survey to document its current appearance and global geometry. Subsequently, more accurate geometric and material parameters of the bridge were acquired through non-destructive testing techniques including 3D laser scanning, ground-penetrating radar, and ultrasonic testing. Subsequently, using a combined approach of experimental and numerical simulation, this study reveals key structural responses and damage conditions of the bridge through static, dynamic, and metro-induced vibration tests. Dynamic tests show a maximum deformation of 0.26 mm and a natural frequency of 10.547 Hz, indicating shear strain accumulation as the primary damage driver. Subway-induced vibrations are well within the safety limits for stone relics, and the structure’s current load-bearing capacity complies with Class-II highway standards. Full article

Accurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particulate matter have been largely neglected. This study bridges this gap by evaluating the performance of five boosting ML models—AdaBoost, XGBoost, LightGBM, GentleBoost, and LogitBoost—under dynamic environmental conditions. The models were compared with theoretical models (Log-Distance and Okumura-Hata) and existing studies that employed the same dataset based on metrics such as RMSE, MAE, and R². Furthermore, a detailed performance vs. complexity analysis was conducted using metrics such as training time, inference latency, model size, and energy consumption. Notably, barometric pressure emerged as the most influential environmental factor affecting path loss across all models. Bayesian Optimization was applied to fine-tune hyperparameters to improve model accuracy. Results showed that LightGBM outperformed other models with the lowest RMSE of 0.5166 and the highest R² of 0.7151. LightGBM also offered the best trade-off between accuracy and computational efficiency. The findings show that boosting algorithms, particularly LightGBM, are highly effective for path loss prediction in LoRaWANs. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

The Wan’an Bridge, the longest wooden lounge bridge in China with a history of more than 900 years, was devastated by a catastrophic fire in 2022. This tragic event underscores the susceptibility of historical wooden structures to fire damage. In this article, the bridge’s intricate structure and the development of the fire incident are introduced in detail. To gain a deeper insight into the patterns of fire propagation across the bridge and assess the reliability of fire simulations in predicting fire spread in historical wooden structures, we utilized the Fire Dynamics Simulator (FDS), with a sophisticated pyrolysis model and thermal response parameters specifically tailored to ancient fir wood. The modeling results reveal that the FDS simulation reflects the actual fire spread process well. Both the investigation and simulation findings indicate that once the flame reaches above the bridge deck, it enters a rapid three-dimensional propagation phase that is exceptionally challenging to control. Furthermore, the modeling results suggest that the application of intumescent fire-retardant coatings can significantly delay fire spread, reduce heat release rates, and suppress smoke production, thereby making them an effective fire prevention measure for historical wooden buildings. Full article

Due to limited infrastructure and remote locations, rural areas often need help providing reliable and high-quality network connectivity. We propose an innovative approach that leverages Software-Defined Wide Area Network (SD-WAN) architecture to enhance reliability in such challenging rural scenarios. Our study focuses on cases in which network resources are limited to network solutions such as Long-Term Evolution (LTE) and a Low-Earth-Orbit satellite connection. The SD-WAN implementation compares three tunnel selection algorithms that leverage real-time network performance monitoring: Deterministic, Random, and Deep Q-learning. The results offer valuable insights into the practical implementation of SD-WAN for rural connectivity scenarios, showing its potential to bridge the digital divide in underserved areas. Full article

High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system’s core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals. Full article

In this paper, the authors present the results of a set of measurements carried out to analyze the transmission capabilities of the LoRaWAN technology for underwater to above water transmission in saline water. A theoretical analysis was used to model the link budget of the radio channel in the considered operative conditions and to estimate the electrical permittivity of salt water. Preliminary measurements were performed in the laboratory at different salinity levels to confirm the application boundaries of the technology, then field tests were conducted in the Venice lagoon. While these test are not focused on demonstrating the usability of LoRaWAN to collect data underwater, the achieved results demonstrate that LoRaWAN transmitters can be used in all those conditions when they are expected to be partially or totally submerged below a thin layer of marine water, in accordance with the prediction of the proposed theoretical model. This achievement paves the way for the deployment of superficial marine sensor networks in the Internet of Underwater Things (IoUT) context, as for the monitoring of bridges, harbor structures, water parameters and water sport athletes and for the realization of high-water or fill-level alarm systems. Full article

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in the obtained dynamic deflection with the low signal-to-noise ratio. To solve this problem, this paper proposes an innovative high-frequency de-noising method combining the wavelet synchro-squeezing transform (WSST) method with the extreme point symmetric mode decomposition (ESMD) method. First, the ESMD method is applied to decompose the observed dynamic deflection signal into a series of intrinsic mode functions (IMFs), and the frequency boundary of the original signal autocorrelation is filtered by the mutual information entropy (MIE) for each IMF pair. Second, the high-frequency IMF components are fused into a high-frequency sub-signal. WSST is performed to remove the influence of noise to reconstruct a new sub-signal. Finally, the de-noised bridge dynamic deflection is reconstructed by the new sub-signal, the remaining IMF components, and the residual curve R. For the simulated signal with 5 dB noise, the signal-to-noise ratio (SNR) after noise reduction is increased to 11.13 dB, and the root-mean-square error (RMSE) is reduced to 0.30 mm. For the on-site experiment for the Wanning Bridge, the noise rejection ratio (NRR) is 5.48 dB, and ratio of the variance root (RVR) is 0.05 mm. The results indicate that the proposed ESMD-WSST method can retain more valid information and has a better noise reduction ability than the ESMD, WSST, and EMD-WSST methods. Full article

Present-day technologies used in SHM (Structural Health Monitoring) systems in many implementations are based on wireless sensor networks (WSN). In the context of the continuous development of these systems, the costs of the elements that form the monitoring system are decreasing. In this situation, the challenge is to select the optimal number of sensors and the network architecture, depending on the wireless system’s other parameters and requirements. It is a challenging task for WSN to provide scalability to cover a large area, fault tolerance, transmission reliability, and energy efficiency when no events are detected. In this article, fundamental issues concerning wireless communication in structural health monitoring systems (SHM) in the context of non-destructive testing sensors (NDT) were presented. Wireless technology developments in several crucial areas were also presented, and these include engineering facilities such as aviation and wind turbine systems as well as bridges and associated engineering facilities. Full article

The aim of the present study was to examine the variation in core stability and symmetry of youth female volleyball players by age, and its relationship with anthropometric characteristics, the 30 s Wingate anaerobic test (WAnT), and the 30 s Bosco test. Female volleyball players (n = 24, age 13.9 ± 1.9 years, mean ± standard deviation) performed a series of anthropometric, core stability tests (isometric muscle endurance of torso flexors, extensors, and right and left lateral bridge), WAnT (peak power, mean power, Pmean, and fatigue index, FI) and Bosco test (Pmean). Flexors-to-extensors ratio and right-to-left lateral bridge ratio were also calculated. Participants were grouped into younger (n = 12, 12.3 ± 1.2 years) or older than 14 years (n = 12, 15.4 ± 1.0 years), and into normal (flexors-to-extensors ratio < 1; n = 17) or abnormal flexors-to-extensors ratio (≥1; n = 7). The older age group was heavier (+11.3 kg, mean difference; 95% CI, 2.0, 20.6) and with higher body mass index (+2.8 kg m⁻²; 95% CI, 0.4, 5.1) than the younger age group. The group with abnormal flexors/extensors had larger flexors muscle endurance (+77.4 s; 95% CI, 41.8, 113.0) and higher flexors/extensors ratio (+0.85; 95% CI, 0.61, 1.10) than the normal group. Body fat percentage (BF) correlated moderately-to-largely with flexors (r = −0.44, p = 0.033), extensors (r = −0.51, p = 0.011), and left lateral bridge (r = −0.45, p = 0.027); WAnT Pmean moderately-to-largely with right (r = 0.46, p = 0.027) and left lateral bridge (r = 0.55, p = 0.006); FI moderately-to-largely with right (r = −0.45, p = 0.031) and left lateral bridge (r = −0.67, p < 0.001), and right/left ratio (r = 0.42, p = 0.046); Bosco Pmean correlated moderately-to-largely with right (r = 0.48, p = 0.020) and left lateral bridge (r = 0.67, p = 0.001). A stepwise regression analysis indicated FI and BF as the most frequent predictors of core stability. The findings of the present study suggested that increased core stability was related to decreased BF and increased anaerobic capacity. A potential misbalance between torso flexors and extensors might be attributed to bidirectional variations (either high or low scores) of flexors muscle endurance rather than decreased extensors muscle endurance. Full article

LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data. Full article