Search Results (563)

Fire hazard events for road tunnel has correspondingly increased with battery electric vehicle (BEV) penetration rate rising. Compared with conventional internal combustion engine vehicles (ICEV), the research on damage degree of road tunnels caused by BEV fires is not mature. To this end, the temperature distribution and residual load-bearing capacity of road tunnel were studied considering the difference temperature rise curve of BEV fire and ICEV fire. By using the indirect thermal–mechanical coupling approach, the temperature field obtained from fire simulations was applied to the structural model. The assessment of mechanical properties after high-temperature exposure was conducted using the deflection limit method and concrete plastic damage theory. The results show that different heating curve conditions have significant differences in the temperature field and damage distribution of the tunnel. Although different fire effects cause different degrees of structural damage to the tunnel lining, the overall bearing capacity of the structure still has a certain surplus. The results provide a basis for the formulation of repair schemes and reinforcement measures for tunnel structures to assess the safety and normal operation of tunnel structures. Full article

When a fire occurs in a tunnel during construction, the smoke cannot be discharged in time and continues to settle near the ground, which threatens the safety of personnel. It is essential to understand smoke layer distribution for safe evacuation. To fill the knowledge gap for the spatio-temporal distribution of the smoke layer, a series of fire experiments are carried out in 1/20 reduced-scale tunnel models. Multiple variables are considered, including longitudinal fire location, heat release rate, aspect ratio of the main tunnel, and the inclined shaft length. Two fire scenarios are defined according to the longitudinal fire location in the main tunnel: near the upstream closed end (scenario 1) and near the downstream closed end (scenario 2). The results show that the structural evolution of the smoke layer inside the main tunnel experiences roughly three stages: single-layer smoke flow stage, transition stage, and two-layer smoke flow stage. In different fire scenarios, the reasonable N value is 10, determined by comparing the smoke layer interface height (h_s) predicted by the N-percentage method with the observed results. Moreover, we find that the FDS simulation method has significant deviation in predicting poor stratification situations. Furthermore, the spatio-temporal distributions of h_s in the main tunnel are predicted based on N = 10. The coupled effects of heat release rate and the longitudinal fire location on the h_s values are analyzed. The t_ar value (time of smoke arrival at the respiratory height) is determined, and its spatial variations are predicted. By comparing the t_ar values at position 2^# (near the inclined shaft) in different fire scenarios, we can provide a reference for the evacuation of personnel. Full article

For 5G tunnel communications, antennas often face critical challenges arising from severe path loss and multipath fading in confined environments, as well as polarization mismatch under dynamic propagation conditions. This paper proposes a 3.5-GHz circularly polarized (CP) endfire antenna array with bidirectional right-hand CP radiation, featuring high gain, low profile, and compact configuration. The array is implemented on a single-layer F4B substrate and integrates eight pairs of electric and magnetic dipoles to synthesize orthogonal linear field components required for CP radiation. By applying the extended method of maximum power transmission efficiency, constraints on the amplitude and phase are introduced to maximize the CP gain in the endfire direction. A 16-element linear array prototype is fabricated and tested for validation. Measurement results show that the proposed array achieves a bidirectional right-hand CP endfire gain exceeding 12.2 dBic, an impedance bandwidth from 3.1 to 3.78 GHz, and a 3 dB axial ratio bandwidth of 19.5%, demonstrating its suitability for 5G tunnel communication applications. Full article

This paper presents a compact four-beam dual-polarized phased array with the high performance front-end module based on system-in-package (SiP) technology. By employing high-temperature co-fired ceramic (HTCC) substrates, the proposed design achieves efficient thermal management and high level of integration within a tile-type architecture. The front-end module based on SiP can simultaneously generate four independent beams with switchable left- and right-hand circular polarizations, providing flexible beam control. To verify the proposed method, a Ku-band 256-element phased array receiver with four beams has been designed and experimentally verified using HTCC and SiP process. Operating in 14–14.5 GHz, the proposed low-profile array demonstrates stable radiation characteristics, beam pointing accuracy and excellent beam consistency across the entire frequency range. The measurement results confirm that the SiP-based phased array maintains efficient thermal management, high polarization purity and robust beam-scanning capability, validating its suitability for mobile satellite communication. Full article

Sealing tunnel portals is widely recognized as a pivotal strategy for mitigating fire hazards in tunnel safety management. Nevertheless, the interplay between fire source locations—both longitudinally and transversely—and its impact on flame behavior and ceiling temperature profiles within enclosed structures has not been fully elucidated. Utilizing a 1:15 reduced-scale rectangular tunnel model, this research investigates how varying the fire source position affects the maximum ceiling temperature under enclosed scenarios. Dimensionless parameters, including the longitudinal dimensionless distance D and transverse dimensionless distance Z′, were derived through dimensional analysis. Observations indicate that as the fire approaches the enclosed end, the flame initially leans toward the boundary, peaking in inclination at D = 0.73, and subsequently exhibits a “wall-attached combustion” pattern due to wall confinement. While lateral displacement of the fire source pushes the high-temperature zone toward the corresponding side wall, the longitudinal temperature rise follows a non-monotonic pattern: declining continuously in in Region I (0 ≤ D ≤ 0.73) and rebounding in Region II (0.73 < D < 1). Based on these findings, a dimensionless prediction model incorporating heat release rate (HRR), transverse offset, and longitudinal fire location was developed. Furthermore, a thermal accumulation factor was introduced to refine the predictive model in Region II. The results offer theoretical insights to support fire protection design and risk assessment in enclosed tunnels. Full article

Direction-of-arrival (DOA) estimation is fundamental in array signal processing, yet classical algorithms suffer from significant performance degradation under low signal-to-noise ratio (SNR) conditions and require computationally intensive eigenvalue decomposition. This study presents a systematic comparative analysis of two backbone networks, a convolutional neural network (CNN) and long short-term memory (LSTM) for DOA estimation, addressing two critical research gaps: the lack of a mechanistic understanding of architecture-dependent performance under varying conditions and insufficient validation using real measured data. Both networks are trained using cross-spectral density matrices (CSDMs) from simulated uniform linear array (ULA) signals. Under baseline conditions (1° classification interval), both CNN and LSTM methods reach an accuracy (ACC) above 98%, in which the error is ±1° for CNN and ±2° for LSTM, only existing in the end-fire direction. Key findings reveal that LSTM maintains above 90% accuracy down to −20 dB SNR, demonstrating superior noise robustness, whereas CNN exhibits better angular resolution. Four performance boundaries are identified: optimal performance is achieved at half-wavelength element spacing; SNR crossover occurs at −20 dB below which accuracy drops sharply; the snapshot threshold of 32 marks the transition from snapshot-deficient to snapshot-sufficient conditions; the array size of 8 is the turning point for the performance variation rate. Comparative analysis against traditional methods demonstrates that deep learning approaches achieve superior resolution ability, batch processing efficiency, and noise robustness. Critically, models trained exclusively on single-target simulated data successfully generalize to multi-target experimental data from the Shallow Water Array Performance (SWAP) program, recovering primary target trajectories without domain adaptation. These results provide concrete engineering guidelines for architecture selection and validate the sim-to-real generalization capability of CSDM-based deep learning approaches in underwater acoustic environments. Full article

Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems. Full article

Under fire conditions, kindergartens typically adopt a fully descending evacuation strategy. However, this approach has certain limitations in roof–courtyard bidirectional evacuation scenarios. Therefore, this study conducted an efficiency analysis of bidirectional evacuation strategies for three-story kindergartens. First, the ascending evacuation velocities of children were collected and used as fundamental input parameters for the simulations. Subsequently, MassMotion software was used to model and compare multiple roof–courtyard bidirectional evacuation strategies. The results indicated that under localized fire scenarios occurring on each floor, the optimal strategies were 3G, 2B, and 1A, respectively. Under overall evacuation conditions, Strategy 3G also achieved the best performance, improving total evacuation efficiency by 8.25% compared with the fully downward strategy and demonstrating strong tail-end clearance capability. This study quantified children’s ascending evacuation velocities and proposed a new bidirectional evacuation strategy tailored for three-story kindergartens, providing methodological guidance and practical insights for safe evacuation design in kindergarten buildings. Full article

This article provides a multifaceted analysis of the feasibility, purposefulness, and legitimacy of the alternative use of industrial hemp (HF) fibres processed into fabrics and mats as multilayer reinforcement in polymer structural composites, potentially replacing glass fibres (GF) in various industries, including the production of recreational vessels (yachts and motorboats) and other floating products (buoys/floats/pontoons, etc.). Based on the results of physical, mechanical, and morphological tests of new polymer structural composites HFRP vs. GFRP and a comparative analysis of their properties, a structural correlation coefficient for HFRP was determined with respect to GFRP [W_K = 1.66 (6), provided that the grammage of reinforcement of the skin/shell of the selected floating object/structure is comparable]. This article presents the possibility of meeting stringent environmental protection requirements for the future safe recycling and/or disposal of products and their post-production waste manufactured from HFRP at the end of their service life. Fire tests of these new materials have shown that it is possible to use them completely (almost 100%) in the near future, mainly through energy recovery. Full article

Ship damage control (DC) is pivotal to platform survivability in the face of battle damage and severe accidents. The DC context features multi-hazard coupling among flooding, fire, and smoke, as well as fast system dynamics and intensive human–machine collaboration, demanding real-time predictive simulation and decision support. Conventional DC simulations fall short in multiphysics fidelity, predictive speed, and integration with onboard sensing and control. A digital twin (DT) framework for predictive shipboard DC is introduced with an explicit capability envelope, observability, and latency requirements, and a cyber-physical mapping to ship systems. Building on this foundation, a three-stage/four-level maturity model charts progression from L1 monitoring, through L2 prediction and L3 human-in-the-loop, override-enabled plan generation, to L4 closed-loop decision control, specifying capability milestones and evaluation metrics. Guided by this model, a four-layer architecture and an end-to-end roadmap are formulated, spanning multi-domain modeling, multi-source sensing and fusion, surrogate-accelerated multiphysics simulation, assisted plan generation with human approval/override, and cyber-physical closed-loop control. The framework aligns interfaces, performance targets, and verification pathways, providing actionable guidance to upgrade shipboard DC toward resilient, efficient, and human-centric operation under multi-hazard coupling. Full article

Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (t_s), concrete compressive strength (f_c), steel yield strength (f_sy), the slenderness ratio (L/D), and the load ratio (μ). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models’ accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f ≈ 1; PI ≈ 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes. Full article

We introduce an ultra-compact, single-neuron-per-class end-to-end readout for binary classification of noisy, image-encoded sensor time series. The approach compares a linear single-unit perceptron (E2E-MLP-1) with a resonate-and-fire (RAF) neuron (E2E-RAF-1), which merges feature selection and decision-making in a single block. Beyond empirical evaluation, we provide a mathematical analysis of the RAF readout: starting from its subthreshold ordinary differential equation, we derive the transfer function $H\left(\mathrm{j}\omega \right)$, characterize the frequency response, and relate the output signal-to-noise ratio (SNR) to ${\left|H\left(\mathrm{j}\omega \right)\right|}^2$ and the noise power spectral density $S_n\left(\omega \right)\propto {\omega }^{\alpha }$ (brown, pink, and blue noise). We present a stable discrete-time implementation compatible with surrogate gradient training and discuss the associated stability constraints. As a case study, we classify walk-in-place (WIP) in a virtual reality (VR) environment, a vision-based motion encoding (72 × 56 grayscale) derived from 3D trajectories, comprising 44,084 samples from 15 participants. On clean data, both single-neuron-per-class models approach ceiling accuracy. At the same time, under colored noise, the RAF readout yields consistent gains (typically +5–8% absolute accuracy at medium/high perturbations), indicative of intrinsic band-selective filtering induced by resonance. With ∼8 k parameters and sub-2 ms inference on commodity graphical processing units (GPUs), the RAF readout provides a mathematically grounded, robust, and efficient alternative for stochastic signal processing across domains, with virtual reality locomotion used here as an illustrative validation. Full article

A compact, single-layer W-band microstrip antenna for forward-looking ADAS radar in the 77–79 GHz band is presented. The 16.5 × 22 mm² PCB element integrates a linear microstrip taper, two shorting vias, and a slot-loaded cavity to stabilize input reactance and broaden the in-band match. Full-wave simulations and launcher-based measurements using WR-12 TRL de-embedding and anechoic-chamber substitution confirm S₁₁ ≤ −10 dB across 77–79 GHz. At 77/79 GHz, the antenna achieves end-fire realized gains of ≈9.9/≈11.2 dBi. The main beam is end-fire (peak near θ ≈ 90°), with −3 dB beamwidths of ≈36° in the θ-cut at φ = 0 (pointing ≈ 61°/56°) and ≈11.6° in the φ-cut at θ = 90°. First sidelobes are about −2.3/−2.5 dB (θ-cut) and −3.1/−3.4 dB (φ-cut). Cross-polarization is ≥18 dB below co-polarization, and the simulated radiation efficiency reaches ≈85% at 77 GHz and ≈80% at 79 GHz. A controlled thermal sweep (25–105 °C) yields < 100 MHz resonance drift while maintaining ≥ 10 dB return loss. Due to its planar architecture and clean feed integration, compact module packaging in short- to medium-range automotive radars. Full article

Geothermal energy provides a stable baseload renewable source that is less affected by weather variability compared with solar and wind power, and is therefore increasingly considered in national energy transition and net-zero strategies. Yet its environmental externalities and associated social benefits are not fully priced in existing electricity markets, raising the question of how much the public is willing to pay for geothermal-based generation. This study applies non-market valuation theory to estimate citizens’ additional annual electricity payment required to replace coal-fired generation with geothermal energy. A contingent valuation method (CVM) survey was conducted through face-to-face interviews, employing a closed-ended single-bounded dichotomous choice format with incentive compatibility. Stratified random sampling yielded 678 valid observations. The estimated mean willingness to pay (WTP) per person per year is USD 56.18 (NTD 1792) under the Probit model and USD 52.16 (NTD 1663) under the Logit model, representing approximately 0.2–0.3% of average annual income and 16–20% of the average annual electricity bill. Aggregated to the population level, total annual WTP amounts to USD 688 million (NTD 21,934 billion; Probit) and USD 638 million (NTD 20,355 billion; Logit). These estimates correspond to support for developing approximately 108–335 MW of geothermal capacity, sufficient to supply around 202,000–624,000 four-person households. The findings indicate substantial public support for geothermal power as part of Taiwan’s renewable energy transition, and provide empirical evidence relevant to regions with comparable geothermal potential. Full article

Amyotrophic lateral sclerosis (ALS) is a rare but fatal neurodegenerative disease. Some occupational exposures are associated with ALS. This study evaluated ALS mortality rates in World Trade Center (WTC)-exposed and non-WTC-exposed rescue/recovery workers. Fire department workers who were 18–70 years old on 11 September 2001 (9/11) were included in the study (N = 33,122). Follow-up began on the later of 9/11 or on their hire date, and ended at the earliest death date or 31 December 2023. Cause of death data were obtained from the National Death Index; ALS (specifically motor neuron disease)-related mortality was the primary outcome. Demographic data were obtained from the fire departments’ databases. We estimated standardized mortality ratios (SMRs) and 95% CIs for ALS-related mortality in WTC-exposed and non-WTC-exposed workers using US population rates as a reference. Multivariable-adjusted Poisson regression models estimated relative rates (RRs) and 95% CIs for ALS-related mortality in the WTC-exposed vs. non-WTC-exposed groups. Between 9/11 and 31 December 2023, five WTC-exposed and sixteen non-WTC-exposed participants died of ALS. ALS mortality rates were lower in WTC-exposed than in non-WTC-exposed rescue/recovery workers (RR = 0.54, 95% CI = 0.49–0.60). ALS-related mortality was not elevated in WTC-exposed (SMR = 0.44, 95% CI = 0.14–1.03) or non-WTC-exposed rescue/recovery workers (SMR = 1.06, 95% CI = 0.60–1.72) compared with the US general population. This initial evaluation of ALS in WTC-exposed workers indicates that the risk of ALS death is not increased in this population. Full article