Search Results (342)

Flooding poses a major hazard to rapidly urbanising cities in Southeast Asia, and risks are projected to intensify under climate change. Accurate risk assessment, however, is hindered by scarcity of hydrological and topographic data. Focusing on the Lower Prek Thnot River Basin, a peri-urban catchment of Phnom Penh, Cambodia, the study applied the Rainfall–Runoff–Inundation model and systematically augmented inputs: hourly satellite rainfall data, field-surveyed river cross-sections and representation of hydraulic infrastructure such as weirs and pumping. Validation used Sentinel-1 SAR-derived flood-extent maps for the October 2020 event. Scenario comparison shows that rainfall input and channel geometry act synergistically: omitting either degrades performance and spatial realism. The best configuration (Sim. 5) Accuracy = 0.891, Hit Ratio = 0.546 and True Ratio = 0.701 against Sentinel-1, and reproduced inundation upstream of weirs while reducing overestimation in urban districts through pumping emulation. At the study’s 500 m grid, updating land use from 2002 to 2020 had only a minor effect relative to rainfall, geometry and infrastructure. The results demonstrate that targeted data augmentation—combining satellite products, field surveys and operational infrastructure—can deliver robust inundation maps under data scarcity, supporting hazard mapping and resilience-oriented flood management in rapidly urbanising basins. Full article

Intelligent in-vehicle voice systems face critical challenges in robustness, security, and semantic flexibility under complex acoustic conditions. To address these issues holistically, this paper proposes a novel multimodal and secure voice-control framework. The system integrates a hybrid dual-channel wake-up mechanism, combining a commercial English engine (Picovoice) with a custom lightweight ResNet-Lite model for Chinese, to achieve robust cross-lingual activation. For reliable identity authentication, an optimized ECAPA-TDNN model is introduced, enhanced with spectral augmentation, sliding window feature fusion, and an adaptive threshold mechanism. Furthermore, a two-tier fuzzy command matching algorithm operating at character and pinyin levels is designed to significantly improve tolerance to speech variations and ASR errors. Comprehensive experiments on a test set encompassing various Chinese dialects, English accents, and noise environments demonstrate that the proposed system achieves high performance across all components: the wake-up mechanism maintains commercial-grade reliability for English and provides a functional baseline for Chinese; the improved ECAPA-TDNN attains low equal error rates of 2.37% (quiet), 5.59% (background music), and 3.12% (high-speed noise), outperforming standard baselines and showing strong noise robustness against the state of the art; and the fuzzy matcher boosts command recognition accuracy to over 95.67% in quiet environments and above 92.7% under noise, substantially outperforming hard matching by approximately 30%. End-to-end tests confirm an overall interaction success rate of 93.7%. This work offers a practical, integrated solution for developing secure, robust, and flexible voice interfaces in intelligent vehicles. Full article

This study explores the role of social trust, a critical informal institution, in mitigating corporate brownwashing—the strategic concealment of positive environmental performance. Drawing on a panel of 15,081 firm-year observations from Chinese A-share listed firms between 2010 and 2022, we operationalize brownwashing as a strategy where firms demonstrate substantive environmental compliance (i.e., no environmental penalties) while simultaneously practicing symbolic verbal conservatism (below-median environmental disclosure). Our empirical analysis reveals that higher regional social trust significantly curbs the propensity for firms to engage in brownwashing. This effect is not only statistically significant but also economically meaningful: a one-standard-deviation increase in social trust is associated with a 1.85 percentage point decrease in the likelihood of brownwashing. This effect operates through two key channels: enhancing stakeholder monitoring and reinforcing internal governance for environmental accountability. The impact of trust is significantly amplified under specific conditions: its role is more pronounced where formal sustainability regulations are weaker, highlighting trust as a crucial informal pillar of the sustainability governance architecture, and its inhibitory effect is strengthened when firms face higher reputational risks tied to their environmental performance. This study makes several contributions: it provides broad, cross-industry evidence on a key challenge in sustainability reporting; offers empirical support for the “trust fidelity” theory in the context of environmental disclosure; and develops a ‘channel-amplifier’ framework that advances our understanding of the complex institutional interplay required to foster corporate environmental transparency. Full article

We study thrust production in a single-fluid magnetohydrodynamic (MHD) thruster with Hall-type coaxial geometry and show how velocity–field alignment and magnetic topology set the operating regime. Starting from the momentum equation with anisotropic conductivity, the axial Lorentz force density reduces to $f_z={\sigma }_{\theta z}{E}_z{B}_r(\chi -1)$, with the motional-field ratio $\chi \equiv \left(u{B}_r\right)/E_z$. Hence, net accelerating force ($f_z>0$) is achieved if and only if the motional electric field $E_m=u{B}_r$ exceeds the applied axial bias $E_z$ ($\chi >1$), providing a compact, testable design rule. We separate alignment diagnostics (cross-helicity $h_c=\mathit{u}·\mathit{B}$) from the thrust criterion ($\chi$) and generate equation-only axial profiles for $\chi \left(z\right)$, $j_{\theta }\left(z\right)$, and $f_z\left(z\right)$ for representative parameters. In a baseline case $(E_z=150\mathrm{V}{\mathrm{m}}^{-1},{\sigma }_{\theta z}=50\mathrm{S}{\mathrm{m}}^{-1},u_0=12\mathrm{km}{\mathrm{s}}^{-1},B_{r0}=0.02\mathrm{T},L=0.10\mathrm{m})$, the $\chi >1$ band spans $\approx 21.2\%$ of the channel; a lagged correlation peaks at $\Delta z^{★}\approx 8.82\mathrm{mm}$$(C_{HU}=0.979)$, and ${\int }_0^L{f}_z\mathrm{d}z$ is slightly negative—indicating that enlarging the $\chi >1$ region or raising ${\sigma }_{\theta z}$ are effective levers. We propose a reproducible validation pathway (finite-volume MHD simulations and laboratory measurements: PIV, Hall probes, and thrust stand) to map $f_z$ versus $\chi$ and verify the response length. The framework yields concrete design strategies—$B_r\left(z\right)$ shaping where u is high, conductivity control, and modest $E_z$ tuning—supporting applications from station-keeping to deep-space cruise. Full article

This study examines the impact of environmental, social, and governance (ESG) performance on corporate performance in China’s manufacturing sector, incorporating trade credit financing as a mediator and environmental information disclosure quality as a moderator. Using unbalanced panel data from Chinese A-share listed manufacturing firms between 2011 and 2023 and employing two-way fixed effects models, we provide robust empirical evidence that superior ESG performance directly enhances corporate performance by reducing information asymmetry, strengthening corporate reputation, and lowering capital costs. Furthermore, we identify a key mediating mechanism: strong ESG practices improve access to trade credit financing—an efficient non-bank funding alternative—which alleviates financing constraints, optimizes resource allocation, and amplifies operational and financial outcomes. In a notable departure from conventional expectations, we find that high-quality information disclosure negatively moderates these relationships. Excessive disclosure induces signal overload and adverse selection, raising financing costs and external scrutiny that ultimately diminish the marginal benefits of ESG investments. Cross-sectional analyses reveal that these effects are particularly pronounced in non-state-owned enterprises, non-heavy-polluting industries, and firms located in eastern regions, highlighting the contextual boundaries of ESG efficacy. Our contributions are twofold: we theoretically advance the ESG-finance literature by unveiling trade credit as a transmission channel and revealing the unintended consequences of disclosure overload, and we offer practical guidance for firms seeking to optimize ESG disclosure strategies and for policymakers aiming to design targeted sustainable transition policies. Full article

To address the limitations of traditional acoustic experimental equipment, such as large volume, discrete modules, and complex operation, this paper proposes and implements a set of digital portable acoustic teaching systems. The hardware component is based on an FPGA, enabling a highly integrated design for signal source excitation and multi-channel synchronous acquisition. It supports the output of various signals, including pulses, sine waves, chirps, and arbitrary waveforms. The software component is developed based on the Qt framework, offering cross-platform compatibility and excellent graphical interaction capabilities. It supports signal configuration, data acquisition, real-time processing, result visualization, and historical playback, establishing a closed-loop experimental workflow of signal excitation–synchronous acquisition–real-time processing–data storage–result visualization. The system supports both local USB connection and remote TCP operation modes, accommodating scenarios such as real-time classroom experiments and cross-regional collaborative teaching. The verification results of three typical experiments, namely, multi-media sound velocity measurement, TDOA hydrophone positioning, and remote acoustic detection, demonstrate that the system performs well in terms of measurement accuracy, positioning stability, and the feasibility of remote detection. This study demonstrates the technical advantages and engineering adaptability of a digital teaching platform in acoustic experimental education. It provides a scalable system solution for cross-regional hybrid teaching models and practice-oriented education under the framework of emerging engineering disciplines. Future work will focus on expanding experimental scenarios, enhancing system intelligence, and improving multi-user collaboration capabilities, aiming to develop a more comprehensive and efficient platform to support acoustic teaching. Full article

Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of the operational safety risk, this study proposes a comprehensive risk assessment framework that integrates risk probability and risk loss. The former is quantified using the Consequence Reverse Diffusion Method (CRDM), which systematically identifies and categorizes key factors of primary dike failure modes into four domains: hydrological characteristics, channel morphology, engineering structures, and operational management. The latter is assessed by integrating socioeconomic impacts, including population exposure, infrastructure investment, and industrial and agricultural production. A structured assessment framework is established through systematic indicator selection, justified weight assignment, and standardized scoring criteria. Application of the framework to Yangtze-to-Huaihe Water Diversion Project (Henan Reach) reveals that the risk probability across four segments falls within the (1, 3) range, indicating a generally low to moderate risk profile, while channel morphology shows greater spatial variability than hydrological, structural, and management indicators, driven by local differences in crossing structure density, sinuosity, and regime coefficients. Meanwhile, the segments along the Qingshui River face higher risk losses owing to their upstream location and large-scale water supply capacity, resulting in a relatively higher comprehensive risk level. Full article

This paper proposes a novel design method for multifunctional modular metasurfaces based on cascaded multilayer modules. Strong electromagnetic coupling between cascaded modules and balanced interface impedance achieved through optimized resonator configurations enable broadband operation. By pairwise cascading of the three modules to maximize utilization and achieve maximum channel count, the system realizes comprehensive electromagnetic wavefront manipulation across 4 broadband frequency ranges, demonstrating diverse functionalities including orbital angular momentum beam generation, polarization conversion, beam splitting, and radar cross-section reduction with 7 operational channels: two reciprocal co-polarized transmission channels at 14–20.7 GHz, individual reflection channels in +z and −z spaces at 32–38 GHz, two reciprocal cross-polarized transmission channels at 11.9–13.2 GHz, and a reflection channel in −z space at 20–28 GHz, spanning both transmission and reflection modes. The proposed cascading method is accomplished through direct attachment assembly, avoiding air coupling while enabling rapid installation and fast functional switching, providing flexibility for multifunctional electromagnetic wave control applications. Full article

River bridge engineering alters the hydraulic characteristics of rivers, impacting fluvial morphological stability. To investigate issues concerning flood conveyance capacity within the river reach hosting a new bridge and the safe operation of existing bridges, comparative physical model tests employing both mobile-bed and fixed-bed configurations were conducted. A 1:60 scale model was used to test flood peak discharges corresponding to 30-year and 100-year return periods and investigate pier spacings of 30 m and 40 m. These tests evaluated the relative advantages and limitations of each model type in simulating flow patterns, sediment transport, and riverbed evolution. Specifically, mobile-bed models more effectively capture the interaction between water flow and sediment dynamics, while fixed-bed experiments enable more precise measurement of hydraulic parameters. Pier spacing is recognized as one of the most critical factors influencing river flow regimes. Larger pier spacing (40 m) was found to reduce upstream backwater and local scour depth compared to smaller spacing (30 m), particularly under the 30-year flood scenario. Consequently, this study investigated the effects of pier spacing on flow patterns, obtained flood conveyance characteristics under various flood frequencies, and analyzed the underlying mechanisms governing flow fields, velocity variations, and local scour around piers. The research outcomes not only elucidate multiscale coupling mechanisms between water flow and sediment but also quantify the relationship between the extent of pier-induced flow disturbance and subsequent channel morphological adjustments. This quantification provides a dynamic criterion for risk mitigation of river-crossing structures and establishes a hydrodynamic foundation for studying flood hazards in complex river reaches. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

As the core refrigeration equipment in cooling systems, the operational state of chiller units is crucial for ship support, equipment cooling, and mission stability. However, because of their sensitivity and the complexity of operating environments, obtaining large volumes of complete, fault-labeled data is difficult in practical engineering appli-cations. This limitation makes it challenging for traditional data-driven approaches to deliver accurate fault diagnoses. Furthermore, data collected from different devices or under varying operating conditions often differ significantly in both feature dimensions and distributions, i.e., data heterogeneity, which further complicates model transfer. To address these challenges, this study proposes a deep transfer learning–based fault di-agnosis method designed to leverage abundant knowledge from the source domain while adaptively learning features of the target domain. Given the persistent difficulties in collecting sufficient high-quality labeled fault data, traditional data-driven models continue to face restricted diagnostic performance on target equipment. At the same time, data heterogeneity across devices or operating conditions intensifies the challenge of cross-domain knowledge transfer. To overcome these issues, this study develops a heterogeneous transfer learning method that integrates a dual-channel autoencoder, domain adversarial training, and pseudo-label self-training. This combination enables precise small-sample knowledge transfer from the source to the target domain. Specifi-cally, the dual-channel autoencoder is first applied to align heterogeneous feature di-mensions. Then, a Gradient Reversal Layer (GRL) and a domain discriminator are in-troduced to extract domain-invariant features. In parallel, high-confidence pseu-do-labeled samples from the target domain are incorporated into joint training to im-prove generalization and robustness. Experimental results confirm that the method achieves high fault diagnosis accuracy in typical industrial application scenarios, ena-bling effective identification of common faults in various types of chiller units under conventional operating conditions, the proposed method achieves higher accuracy and F1-scores in multi-class fault diagnosis tasks compared with both traditional approaches and existing transfer learning methods. These findings provide a novel perspective for advancing the intelligent operation and maintenance of chiller units. Full article

Objective evaluation of pilot fatigue is crucial for enhancing aviation safety. Although electroencephalography (EEG) is regarded as an effective tool for recognizing pilot fatigue, the direct application of deep learning models to raw EEG signals faces significant challenges due to issues such as massive data volume, excessively long training time, and model overfitting. Moreover, existing feature-based methods often suffer from data redundancy due to the lack of effective feature and channel selections, which compromises the model’s recognition efficiency and accuracy. To address these issues, this paper proposes a framework, named ASFT-Transformer, for fast and accurate detection of pilot fatigue. This framework first extracts time-domain and frequency-domain features from the four EEG frequency bands. Subsequently, it introduces a feature and channel selection strategy based on one-way analysis of variance and support vector machine (ANOVA-SVM) to identify the most fatigue-relevant features and pivotal EEG channels. Finally, the FT-Transformer (Feature Tokenizer + Transformer) model is employed for classification based on the selected features, transforming the fatigue recognition problem into a tabular data classification task. EEG data is collected from 32 pilots before and after actual simulator training to validate the proposed method. The results show that ASFT-Transformer achieved average accuracies of 97.24% and 87.72% based on cross-clip data partitioning and cross-subject data partitioning, which were significantly superior to several mainstream machine learning and deep learning models. Under the two types of cross-validation, the proposed feature and channel selection strategy not only improved the average accuracy by 2.45% and 8.07%, respectively, but also drastically reduced the average training time from above 1 h to under 10 min. This study offers civil aviation authorities and airline operators a tool to manage pilot fatigue objectively and effectively, thereby contributing to flight safety. Full article

To address the challenge that MOSFET temperature in motor controllers is influenced by multiple factors, exhibits strong temporal dependence, and involves complex feature interactions, this study proposes a temperature prediction model that integrates Temporal Convolutional Networks (TCNs) and the Informer architecture in parallel, enhanced with a cross-attention mechanism. The model leverages TCNs to capture local temporal patterns, while the Informer extracts long-range dependencies, and cross-attention strengthens feature interactions across channels to improve predictive accuracy. A dataset was constructed based on measured MOSFET temperatures under various operating conditions, with input features including voltage, load current, switching frequency, and multiple ambient temperatures. Experimental evaluation shows that the proposed method achieves a mean absolute error of 0.2521 °C, a root mean square error of 0.3641 °C, and an R² of 0.9638 on the test set, outperforming benchmark models such as Times-Net, Informer, and LSTM. These results demonstrate the effectiveness of the proposed approach in reducing prediction errors and enhancing generalization, providing a reliable tool for real-time thermal monitoring of motor controllers. Full article

Flange bolts are vital fasteners in civil, mechanical, and aerospace structures, where preload stability directly affects overall safety. Conventional methods for bolt loosening detection often suffer from missed detections, weak feature representation, and insufficient cross-scale fusion under complex backgrounds. This paper presents an integrated detection and angle estimation framework using a lightweight deep learning detection network. A MobileViT backbone is employed to balance local texture with global context. In the spatial pyramid pooling stage, large separable convolutional kernels are combined with a channel and spatial attention mechanism to highlight discriminative features while suppressing noise. Together with content-aware upsampling and bidirectional multi-scale feature fusion, the network achieves high accuracy in detecting small and low-contrast targets while maintaining real-time performance. For angle estimation, the framework adopts an efficient training-free pipeline consisting of oriented FAST and rotated BRIEF feature detection, approximate nearest neighbor matching, and robust sample consensus fitting. This approach reliably removes false correspondences and extracts stable rotation components, maintaining success rates between 85% and 93% with an average error close to one degree, even under reflection, blur, or moderate viewpoint changes. Experimental validation demonstrates strong stability in detection and angular estimation under varying illumination and texture conditions, with a favorable balance between computational efficiency and practical applicability. This study provides a practical, intelligent, and deployable solution for bolt loosening detection, supporting the safe operation of large-scale equipment and infrastructure. Full article

In this paper, a target detection network algorithm based on a multi-branch convolution and attention improvement Cross-Stage Partial-Fusion Bottleneck with Two Convolutions (C2F) module is proposed for the difficult task of detecting small targets in unmanned aerial vehicles. A C2F module method consisting of fusing partial convolutional (PConv) layers was designed to improve the speed and efficiency of extracting features, and a method consisting of combining multi-scale feature fusion with a channel space attention mechanism was applied in the neck network. An FA-Block module was designed to improve feature fusion and attention to small targets’ features; this design increases the size of the miniscule target layer, allowing richer feature information about the small targets to be retained. Finally, the lightweight up-sampling operator Content-Aware ReAssembly of Features was used to replace the original up-sampling method to expand the network’s sensory field. Experimental tests were conducted on a self-complied mountain pedestrian dataset and the public VisDrone dataset. Compared with the base algorithm, the improved algorithm improved the mAP50, mAP50-95, P-value, and R-value by 2.8%, 3.5%, 2.3%, and 0.2%, respectively, on the Mountain Pedestrian dataset and the mAP50, mAP50-95, P-value, and R-value by 9.2%, 6.4%, 7.7%, and 7.6%, respectively, on the VisDrone dataset. Full article