Search Results (233)

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

The superiority of Variable Resistance Training (VRT) over traditional Constant Resistance Training (CRT) for enhancing lower-limb performance is debated, with previous meta-analyses limited by aggregating disparate exercises. This systematic review and meta-analysis, the first to focus exclusively on the squat, compared the acute and long-term effects of VRT versus CRT on maximal strength and explosive power. Following PRISMA guidelines, 20 studies were analyzed (literature search up to 15 June 2025), with Hedges’ g used for effect size (ES) calculation. Results demonstrated VRT’s superiority for both acute (ES = 0.34) and long-term adaptations. Acutely, effects peaked with an 8–12 min recovery (ES = 0.43). Long-term, VRT produced greater gains in maximal strength (ES = 0.31) and explosive power (ES = 0.17). Subgroup analyses on maximal strength revealed that elastic bands were highly effective (ES = 0.67), particularly in trained individuals (ES = 0.35), males (ES = 0.41), within cycles < 8 weeks (ES = 0.44), and at frequencies of ≤2 sessions/week (ES = 0.45). For explosive power, chains were most effective (ES = 0.37), significantly improving jumping performance but not sprinting. In conclusion, VRT is a more effective modality for squat training; optimal programs should utilize elastic bands for strength and chains for power, with strength-focused blocks being short-term (<8 weeks) and lower-frequency (≤2 sessions/week) for trained individuals. Full article

Generally, most inversion approaches model the seabed as a stack of range-independent homogeneous layers with unknown geoacoustic parameters and layer numbers. In our previous study, we established a layered geoacoustic seabed model based on sub-bottom profiler data to characterize low-frequency (100–500 Hz) airgun signal propagation at short ranges (0–20 km). However, when applying the same model to simulate high-frequency (500–1000 Hz) explosive sound signal propagation, it failed to adequately reproduce the observed significant transmission loss phenomenon. Through systematic analysis of transmission loss (including water column sound speed profiles, seabed topography, and sediment properties), this study proposes a range-dependent layered geoacoustic model using the Range-dependent Acoustic Model–Parabolic Equation (RAM-PE). Stepwise inversion implementation has successfully explained the observed experimental phenomena. To generalize the proposed model, this study further introduces a trans-dimensional inversion framework that automatically resolves sediment property interfaces along propagation paths. The method effectively combines prior information with trans-dimensional inversion techniques, providing improved characterization of range-dependent seabed environments. Full article

Understanding structural regime shifts in crypto asset markets is vital for early detection of systemic risk. This study applies the Generalized Sup Augmented Dickey–Fuller (GSADF) test to daily high-frequency price data of five major crypto assets—BTC, ETH, SOL, AAVE, and BCH—from 2023 to 2025. The results reveal asset-specific structural breaks: BTC and BCH aligned with macroeconomic shocks, while DeFi tokens (e.g., AAVE, SOL) exhibited fragmented, project-driven shifts. The S&P 500 index, in contrast, showed no persistent regime shifts, indicating greater structural stability. To examine inter-asset linkages, we construct co-occurrence matrices based on GSADF breakpoints. These reveal strong co-explosivity between BTC and other assets, and unexpectedly weak synchronization between ETH and AAVE, underscoring the sectoral idiosyncrasies of DeFi tokens. While the GSADF test remains central to our analysis, we also employ a Markov Switching Model (MSM) as a secondary tool to capture short-term volatility clustering. Together, these methods provide a layered view of long- and short-term market dynamics. This study highlights crypto markets’ structural heterogeneity and proposes scalable computational frameworks for real-time monitoring of explosive behavior. Full article

The geometric symmetry of the pipeline constitutes a critical determinant in regulating the energy propagation dynamics during the explosion process. In the present study, a transparent plexiglass pipe experimental system incorporating a range of angles (30° to 150°) was meticulously constructed. Leveraging high-frequency pressure sensors in conjunction with high-speed camera technology, this investigation examines the influence of the pipe angle, which disrupts geometric symmetry, on the coupling explosion of gas and coal dust. The experimental findings illustrate that an increase in the pipeline turning angle significantly enhances the velocity of the explosion flame front (with the maximum velocity escalating from 97.92 m/s to 361.28 m/s) and concurrently reduces the total propagation time (from 71 ms to 56.5 ms). Moreover, there is a notable reduction in the duration of the explosion flame, decreasing from 240.5 ms to 64.17 ms at the coal dust deposition point. The peak overpressure of the shock wave exhibits a significant increase with the augmentation of the turning angle (rising from 7.07 kPa at 30° to 88.40 kPa at 150°). Furthermore, the overpressure in the fore section of the turning is amplified, attributable to the superimposition of reflected waves and turbulent effects. This study elucidates critical mechanisms including turbulence-enhanced combustion, secondary dust generation from coal dust, and energy dissipation resulting from abrupt alterations in pipeline geometry, thereby offering a theoretical framework for the prevention and effective emergency management of coal mine explosion disasters. Full article

Lightning represents a critical danger to facilities such as oil tank farms, with the potential to cause major explosive incidents. To address this risk, Indonesia’s oil and gas industry has adopted the NFPA 780 Standard for lightning protection systems. However, tank explosions and refinery disruptions caused by lightning strikes continue to occur annually, highlighting the need to reassess the standard’s self-protection criteria, particularly in Indonesia’s tropical climate. The NFPA 780 standard was primarily developed based on lightning characteristics in subtropical regions. This study evaluates its effectiveness in tropical environments, where lightning parameters such as peak currents, frequencies, and ground flash densities differ significantly. By analyzing specific incidents of tank explosions in Indonesia, the research reveals that compliance with the NFPA 780 standard alone may not be adequate to protect critical infrastructure. To address these challenges, this study proposes a novel approach to lightning protection by designing solutions tailored to the unique characteristics of tropical climates. By incorporating local lightning parameters, the proposed measures aim to enhance safety and resilience in oil and gas facilities. This research provides a framework for adapting international standards to regional needs, improving the effectiveness of lightning protection in tropical environments. Full article

Badminton is an intermittent sport with a diverse exercise profile that stresses both aerobic and anaerobic energy systems. The aim of this study was to compare the internal and external load profiles of multi-feeding (MF) drills and simulated match play (SMP) in elite junior badminton players, and to explore potential sex-based differences. Forty-two players (24 males (age 17.4 ± 2.6 years, training experience 9.9 ± 1.8 years) and 18 females (age 16.9 ± 2.9 years, training experience 9.4 ± 2.1 years)) completed MF and SM sessions while external load (e.g., relative distance, explosive distance, relative jumps) and internal load (heart rate [HR], session rating of perceived exertion [sRPE]) variables were recorded using inertial measurement units and HR monitors. Two-way ANOVA revealed that MF induced significantly greater external (p < 0.05) and internal (p < 0.001) loads compared to SM, with large effect sizes. Male players showed markedly higher jump frequency (1.60 n/min vs. 0.80 n/min) and maximum speed (19.80 km/h vs. 15.80 km/h), although HR and sRPE values were similar between sexes (p > 0.05), suggesting that female athletes may experience greater relative physiological load. These findings highlight the importance of using MF drills to target specific conditioning goals and reinforce the need for individualized training strategies considering sex differences. Full article

Water transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident risks by integrating complex network theory and link prediction methods. First, 371 maritime accident investigation reports were analyzed to identify the underlying risk factors associated with such incidents. A risk evolution network model was then constructed, within which the importance of each risk factor node was evaluated. Subsequently, several node similarity indices based on node importance were proposed. The performance of these indices was compared, and the optimal indicator was selected. This indicator was then integrated into the risk evolution network model to assess the interdependence between risk factors and accident types, ultimately identifying the most probable evolution paths from various risk factors to specific accident outcomes. The results show that the risk evolution path shows obvious characteristics: “lookout negligence” is highly correlated with collision accidents; “improper route selection” plays a critical role in the risk evolution of grounding and stranding incidents; “improper on-duty” is closely linked to sinking accidents; and “illegal operation” show a strong association with fire and explosion events. Additionally, the average risk evolution paths for collisions, groundings, and sinking accidents are relatively short, suggesting higher frequencies of occurrence for these accident types. This research provides crucial insights for managing water transportation systems and offers practical guidance for accident prevention and mitigation. Full article

Wireless power transfer (WPT) systems are critical for enabling safe and efficient charging of inspection drones in flammable oilfield environments, yet existing solutions struggle with multi-target compatibility and reactive power losses. This study proposes a novel frequency-regulated LCC-S topology that achieves both constant current (CC) and constant voltage (CV) charging modes for heterogeneous drones using a single hardware configuration. By dynamically adjusting the operating frequency, the system minimizes the input impedance angle (θ < 10°) while maintaining load-independent CC and CV outputs, thereby reducing reactive power by 92% and ensuring spark-free operation in explosive atmospheres. Experimental validation with two distinct oilfield inspection drones demonstrates seamless mode transitions, zero-phase-angle (ZPA) resonance, and peak efficiencies of 92.57% and 91.12%, respectively. The universal design eliminates the need for complex alignment mechanisms, offering a scalable solution for multi-drone fleets in energy, agriculture, and disaster response applications. Full article

Deepfake technology uses artificial intelligence to create highly realistic but fake audio, video, or images, often making it difficult to distinguish from real content. Due to its potential use for misinformation, fraud, and identity theft, deepfake technology has gained a bad reputation in the digital world. Recently, many works have reported on the detection of deepfake videos/images. However, few studies have concentrated on developing robust deepfake voice detection systems. Among most existing studies in this field, a deepfake voice detection system commonly requires a large amount of training data and a robust backbone to detect real and logistic attack audio. For acoustic feature extractions, Mel-frequency Filter Bank (MFB)-based approaches are more suitable for extracting speech signals than applying the raw spectrum as input. Recurrent Neural Networks (RNNs) have been successfully applied to Natural Language Processing (NLP), but these backbones suffer from gradient vanishing or explosion while processing long-term sequences. In addition, the cross-dataset evaluation of most deepfake voice recognition systems has weak performance, leading to a system robustness issue. To address these issues, we propose an acoustic feature-fusion method to combine Mel-spectrum and pitch representation based on cross-attention mechanisms. Then, we combine a Transformer encoder with a convolutional neural network block to extract global and local features as a front end. Finally, we connect the back end with one linear layer for classification. We summarized several deepfake voice detectors’ performances on the silence-segment processed ASVspoof 2019 dataset. Our proposed method can achieve an Equal Error Rate (EER) of 26.41%, while most of the existing methods result in EER higher than 30%. We also tested our proposed method on the ASVspoof 2021 dataset, and found that it can achieve an EER as low as 28.52%, while the EER values for existing methods are all higher than 28.9%. Full article

A cyclonic ocean eddy (COE) exhibited an extraordinarily prolonged response to sequential typhoons Hinnamnor (1 September 2022) and Muifa (11 September 2022), reaching its peak strength 20 days post-typhoon (1 October 2022), almost double the typical 7–14-day latency for mesoscale eddies. In this study, we use a functional analysis apparatus, namely the multiscale window transform (MWT) and the MWT-based theory of canonical transfer and multiscale energetics analysis, to investigate the dynamics underlying this phenomenon. The original fields, which are obtained from HYCOM reanalysis data, are initially decomposed into three parts in three different scale windows, respectively, with the eddy-scale window (or COE window) lying in between. By examining the evolution of eddy kinetic energy (EKE), the response can be divided into two stages. From the energetic diagnosis, the COE’s response is not only visible at the surface but was even strengthened through interactions between the subsurface and surface, with vertical transport playing a crucial role. This response can be categorized into two stages: The energetics of the long-delayed response is in the first stage due to the storage of the eddy-scale available potential energy (EAPE) from the high-frequency scale window, where the typhoon injects energy through an inverse canonical transfer. The resulting EAPE is transported downward to the sub-surface. In the second stage, the subsurface EKE is carried upward to the surface via pressure work, leading to an explosive growth of the COE. These findings illuminate the significance of subsurface–surface interactions in modulating long-delayed eddy responses. Full article

In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape. Full article

Strong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. Existing structural designs for shock attenuation typically rely on shock response spectra methods, which require multiple finite element calculations to determine the optimal geometric parameters, leading to relatively low efficiency. In this work, we propose a spectral response prediction method for spacecraft structures using the Deep Operator Network (DeepONet). This method preserves the physical relationships between input variables, modularizes geometric and positional input data, and outputs the spectral response. We integrate this neural model to analyze the impact of spacecraft structural parameters on shock resistance performance, revealing that circumferential reinforcement has the most significant influence on shock resistance. Then, we conduct a detailed analysis of the DeepONet model, noting that models with a higher number of neurons per layer train more quickly but are prone to overfitting. Additionally, we find that focusing on specific frequency bands for spectral response prediction yields more accurate results. Full article

SiC_f/SiC ceramic matrix composite (CMC), a hard and brittle material, faces significant challenges in efficient and high-quality processing of small-sized shapes. To address these challenges, the nanosecond laser was used to process micro-holes in the SiC_f/SiC CMC using a two-step drilling method, including laser pre-drilling in air and laser final-drilling with a water jet. The results of the single-parameter variation and optimized orthogonal experiments reveal that the optimal parameters for laser pre-drilling in air to process micro-holes are as follows: 1000 processing cycles, 0.7 mJ single-pulse energy, −4 mm defocus, 15 kHz pulse-repetition frequency, and 85% overlap rate. With these settings, a micro-hole with an entrance diameter of 343 μm and a taper angle of 1.19° can be processed in 100 s, demonstrating high processing efficiency. However, the entrance region exhibits spattering slags with oxidation, while the sidewall is covered by the recast layer with a wrinkled morphology and attached oxides. These effects are primarily attributed to the presence of oxygen, which enhances processing efficiency but promotes oxidation. For the laser final-drilling with a water jet, the balanced parameters for micro-hole processing are as follows: 2000 processing cycles, 0.6 mJ single-pulse energy, −4 mm defocus, 10 kHz pulse-repetition frequency, 85% overlap rate, and a 4.03 m/s water jet velocity. Using these parameters, the pre-drilled micro-hole can be finally processed in 96 s, yielding an entrance diameter of 423 μm and a taper angle of 0.36°. Due to the effective elimination of spattering slags and oxides by the water jet, the final micro-hole exhibits a clean sidewall with microgrooves, indicating high-quality micro-hole processing. The sidewall morphology could be ascribed to the different physical properties of SiC fiber and matrix, with steam explosion and cavitation erosion. This two-step laser drilling may provide new insights into the high-quality and efficient processing of SiC_f/SiC CMC with small-sized holes. Full article

Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causal–noncausal invertible–noninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMA–GARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewness—common in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news ($\alpha$) while overstating volatility persistence ($\beta$). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics. Full article