Search Results (1,686)

This study investigates the spread patterns of tunnel fires and examines issues related to emergency response. It focuses on the temperature characteristics, spread patterns, conditions leading to multi-source fires, and the efficacy of water mist suppression methods in heavy-haul railway tunnel fires. The research employs theoretical derivations and numerical simulations to achieve its objectives. It was discovered that, during a fire in a heavy-haul railway tunnel, the temperature inside the tunnel can exceed 500 °C. Furthermore, depending on the nature of the goods transported by the train and under specific wind speed conditions, the fire source has the potential to spread to other carriages, resulting in a multi-source fire. Using the numerical simulation software Pyrosim 2022, various wind speed conditions were simulated. The results revealed that at lower wind speeds, the smoke demonstrates a reverse flow phenomenon. Concurrently, when the adjacent carriage on the leeward side of the fire is ignited, the high-temperature reverse flow smoke, along with the thermal radiation from the flames, ignites combustible materials in the adjacent carriage on the windward side of the burning carriage. Through theoretical derivation and numerical simulation, the critical wind speed for the working conditions was determined to be 2.14 m/s. It was found that while a higher wind speed can lead to a decrease in temperature, it also increases the flame deflection angle. When the wind speed exceeds 2.4 m/s, although the temperature significantly drops in a short period, the proximity of combustible materials on the leeward side of the carriage becomes a concern. At this wind speed, the flame deflection angle causes heat radiation on the leeward side, specifically between 0.5 m and 3 m, to ignite the combustible materials on the carriage surface, resulting in fire spread and multiple fire incidents. The relationship between wind speed and the angle of deflection from the fire source was determined using relevant physics principles. Additionally, the relationship between wind speed and the trajectory of water mist spraying was established. It was proposed to optimize the position of the water mist based on its deviation, and the results indicated that under critical wind speed conditions, when the water mist spraying is offset approximately 5 m towards the upwind side of the fire source, it can act more directly on the surface of the fire source. Numerical simulation results show a significant reduction in the maximum temperature and effective control of fire spread. Under critical wind speed conditions, the localized average temperature of the fire decreased by approximately 140 °C when spraying was applied, compared to the conditions without spraying, and the peak temperature decreased by about 190 °C. This modification scheme can effectively suppress the threat of fire to personnel evacuation under simulated working conditions, reflecting effective control over fires. Additionally, it provides theoretical support for the study of fire patterns in tunnels and emergency response measures. Full article

(1) Background: Cultural heritage plays a vital role in shaping collective identity and supporting tourism, yet it faces increasing threats from natural and human-induced disasters. As a response, digital technologies—especially drone-based monitoring systems—are being explored for disaster prevention. This study examines whether a Genetic Algorithm can effectively optimize the placement of drone stations for the economic protection of cultural heritage. (2) Method: A simulation was conducted in a 2500 km² virtual space divided into 25 km² grid units, each assigned a random land price. Drone stations have an operational radius of 40 km. GA optimization uses a fitness function based on the ratio of cultural artifacts covered to installation cost. To prevent premature convergence, multi-point crossover and roulette wheel selection are employed. Key GA parameters were fine-tuned through repeated simulations. (3) Results: The optimal parameter set—population size of 300, mutation rate of 0.2, mutation strength of ±5 km, and crossover ratio of 0.3—balances exploration and convergence. The results show convergence toward low-cost, high-coverage locations without premature stagnation. Visualization clearly illustrates the optimization process. (4) Conclusions: GA proves effective for economically optimizing drone station placement. Though virtual, this method offers practical implications for real-world cultural heritage protection strategies. Full article

Soil erosion poses a significant threat to natural resources and agricultural productivity in arid regions. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to simulate rainfall erosivity and soil erosion risk in the Wadi Allith basin, Saudi Arabia, using rainfall data from 2016 to 2018. The results demonstrated that the basin experienced a predominant slight level of erosion risk, with around 5 tons/ha annually. This study revealed that a very slight erosion risk was predominant in 2016 (97% of the basin area), 2017 (96%), and 2018 (95%), while less than 1% of the study area was exposed to severe erosion risks across all three years. An increasing trend in erosion severity was observed between 2016 and 2018, correlating with rising average annual rainfall amounts of 120 mm, 145 mm, and 155 mm. This underscores the importance of understanding how climatic factors influence soil stability, particularly in arid regions where water scarcity is typically a limiting factor. The successful application of Geographic Information Systems (GISs) and remote sensing tools integrating the various components of the RUSLE model showcases the effectiveness of these technologies in environmental monitoring and risk assessment. These tools facilitate a comprehensive analysis of the factors contributing to soil erosion, enabling researchers and policymakers to visualize erosion risk across the basin and prioritize areas for intervention. This study highlights the importance of ongoing soil erosion monitoring in arid environments such as the Wadi Allith basin, Saudi Arabia. Full article

In recent years, frequent vehicle–bridge pier collision accidents have posed a serious threat to people’s economic and life security. In order to avert the impairment of reinforced concrete bridge piers (RCBPs) under the impact of vehicles, three kinds of Mg–Al alloy AlSi10Mg anti-collision structures designed by selective laser melting (SLM) printing were tested by the numerical simulation method in this study: an ultra-high performance concrete (UHPC) anti-collision structure, a bio-inspired honeycomb column thin-walled structure (BHTS) buffer interlayer, and a UHPC–BHTS composite structure were used to reduce the damage degree of RCBPs caused by vehicle impact. In accordance with the prototype configuration of the pier, a scaled model with a scale ratio of 1:10 was fabricated. Three anti-collision structures were installed on the reinforced concrete (RC) column specimens for the steel ball impact test. The impact simulation under low-energy and high-energy input was carried out successively, and the protective effect of the three anti-collision devices on the RC column was comprehensively evaluated. The outcomes demonstrate that the BHTS buffer interlayer and the UHPC–BHTS composite structure are capable of converting the shear failure of RC columns into bending failure, thereby exerting an efficacious role in safeguarding RC columns. The damage was evaluated under all impact conditions of BHTS and UHPC–BHTS composite structures, and the RC column only suffered slight damage, while the RC column without protective measures and the RC column with the UHPC anti-collision structure alone showed serious damage and collapse behavior. This approach can offer a valuable reference for anti-collision design within analogous projects. Full article

Frequency regulation (FR) constitutes a fundamental aspect of power system stability, particularly in the context of the growing integration of intermittent renewable energy sources (RES) and electric vehicles (EVs). The load frequency control (LFC) mechanism, essential for achieving FR, is increasingly reliant on communication infrastructures that are inherently vulnerable to cyber threats. Cyberattacks targeting these communication links can severely compromise coordination among smart grid components, resulting in erroneous control actions that jeopardize the security and stability of the power system. In light of these concerns, this study proposes a cyber-physical LFC framework incorporating a fuzzy linear active disturbance rejection controller (F-LADRC), wherein the controller parameters are systematically optimized using the quasi-opposition-based reptile search algorithm (QORSA). Furthermore, the proposed approach integrates a comprehensive cyberattack detection and prevention scheme, employing Haar wavelet transforms for anomaly detection and long short-term memory (LSTM) networks for predictive mitigation. The effectiveness of the proposed methodology is validated through simulations conducted on a restructured power system integrating RES and EVs, as well as a modified IEEE 39-bus test system. The simulation outcomes substantiate the capability of the proposed framework to deliver robust and resilient frequency regulation, maintaining system frequency and tie-line power fluctuations within nominal operational thresholds, even under adverse cyberattack scenarios. Full article

The expansion of Zigbee-enabled IoT networks has generated significant security issues, especially around threat detection and secure key management. Using RBF and blockchain technology, this study shows a smart hybrid framework to find threats early and distribute keys safely on IoT networks enabled by Zigbee. This methodology incorporates Radial Basis Function (RBF) networks for prompt threat detection and a blockchain-based trust framework for decentralized and tamper-proof key distribution. It guarantees safe network access, comprehensive authentication, and effective key updates, reducing risks associated with IoT-related DoS attacks and Man in the Middle Attacks. The Trust-Based Security Provider (TBSP) enhances security by administering critical credentials across diverse networks. Comprehensive simulations and performance assessments illustrate the effectiveness of the framework in increasing threat detection precision, minimizing key distribution delay, and bolstering overall network security. The findings confirm its efficacy in safeguarding IoT settings from new risks while ensuring scalability and resource efficiency. We proposed an RBF-based threat detection framework for network keys using the ZBDS2023 dataset and the J48 decision tree algorithm. In conclusion, we demonstrate the security and efficiency of our proposed work. Full article

Icing poses a serious threat to flight safety, and ice accretion simulations are essential for addressing aircraft icing problems. In ice accretion prediction, systematic research covering all icing conditions based on actual flight phases is lacking, and the performance of anti-icing systems has not been investigated. In this study, maximum ice thickness prediction models for airfoils considering all flight phases were developed, and the performance of hot air anti-icing systems was analyzed. A hot air anti-icing system model was established, and the anti-icing effectiveness of the system under severe icing conditions was evaluated via conjugate heat transfer (CHT) calculations. The calculation results showed that during climbing above 10,000 ft under glaze ice conditions, the maximum ice thickness reached 13.47 mm at −6 °C, with a median volumetric diameter (MVD) of 20 μm. Under rime ice conditions, the maximum thickness exhibited linear relationships with the icing parameters, remaining below 5 mm. The calculation results revealed nonlinear relationships between maximum ice thickness on the airfoil leading edge and the icing conditions. Ice thickness models were established via polynomial regression. The maximum ice thickness data were classified, and 15 regression models were obtained. The relative errors between the predicted and calculated values remained below 3%, demonstrating high predictive accuracy. These models were employed to estimate the effectiveness of piccolo tube hot air anti-icing systems under the most severe icing conditions. The results indicated that 100% anti-icing efficiency was achieved at high ambient temperatures (above −10 °C). During takeoff, holding, and climbing phases with a high speed of 154.3 m/s, the system may face challenges in maintaining anti-icing protection, resulting in runback ice with a maximum thickness exceeding 5 mm. Full article

The growing deployment of the Internet of Things (IoT) across various sectors introduces significant security and privacy challenges. Although numerous individual solutions exist, comprehensive frameworks that effectively combine advanced technologies to address evolving threats are lacking. This paper presents the Integrated Adaptive Security Framework for IoT (IASF-IoT), which integrates artificial intelligence, blockchain technology, and quantum-resistant cryptography into a unified solution tailored for IoT environments. Central to the framework is an adaptive AI-driven security orchestration mechanism, complemented by blockchain-based identity management, lightweight quantum-resistant protocols, and Digital Twins to predict and proactively mitigate threats. A theoretical performance model and large-scale simulation involving 1000 heterogeneous IoT devices were used to evaluate the framework. Results showed that IASF-IoT achieved detection accuracy between 85% and 99%, with simulated energy consumption remaining below 1.5 mAh per day and response times averaging around 2 s. These findings suggest that the framework offers strong potential for scalable, low-overhead security in resource-constrained IoT environments. Full article

The ongoing threat of viral pandemics such as COVID-19 highlights the urgent need for novel antiviral therapeutics targeting conserved viral proteins. In this study, peptides of 10–30 kDa derived from the marine diatom Phaeodactylum tricornutum were identified as potential inhibitors of SARS-CoV-2 main protease (Mpro), a key enzyme in viral replication. Peptides less than 60 amino acids in length were retrieved from the UniProt database and aligned with reference antiviral sequences using the Biopython pairwise2 algorithm. Six candidates were selected for structural modeling using AlphaFold2 and Swiss-Model, followed by molecular docking using ClusPro2. LigPlot+ was used to assess molecular interactions, while NetMHCpan 4.1 and AVPpred evaluated immunogenicity and antiviral potential, respectively. Molecular dynamics simulations over 100 ns were conducted using OpenMM. These peptides demonstrated stable binding interactions with key catalytic residues of Mpro. Specifically, peptide A0A8J9SA87 interacted with Cys145 and Glu166, while peptide A0A8J9SDW0 exhibited interactions with His41 and Phe140, both of which are known to be essential for Mpro inhibition. Although peptide A0A8J9X3P8 also interacted with catalytic residues, it exhibited greater structural fluctuations during molecular dynamics simulations and achieved lower AVPpred scores, suggesting lower overall antiviral potential. Therefore, A0A8J9SA87 and A0A8J9SDW0 were identified as the most promising candidates. Molecular dynamics simulations further supported the high structural stability of these peptide-Mpro complexes over a 100 ns timescale, reinforcing their potential as effective inhibitors. These findings support P. tricornutum as a valuable source of antiviral peptides and demonstrate the feasibility of in silico pipelines for identifying therapeutic candidates against SARS-CoV-2. Full article

Campylobacteriosis has been described as an ever-changing disease and health issue that is rather dangerous for different population groups all over the globe. The World Health Organization (WHO) reports that 33 million years of healthy living are lost annually, and nearly one in ten persons have foodborne illnesses, including Campylobacteriosis. This explains why there is a need to develop new policies and strategies in the management of diseases at the intergovernmental level. Within this framework, an advanced stochastic fractional delayed model for Campylobacteriosis includes new stochastic, memory, and time delay factors. This model adopts a numerical computational technique called the Grunwald–Letnikov-based Nonstandard Finite Difference (GL-NSFD) scheme, which yields an exponential fitted solution that is non-negative and uniformly bounded, which are essential characteristics when working with compartmental models in epidemic research. Two equilibrium states are identified: the first is an infectious Campylobacteriosis-free state, and the second is a Campylobacteriosis-present state. When stability analysis with the help of the basic reproduction number $R_0$ is performed, the stability of both equilibrium points depends on the $R_0$ value. This is in concordance with the actual epidemiological data and the research conducted by the WHO in recent years, with a focus on the tendency to increase the rate of infections and the necessity to intervene in time. The model goes further to analyze how a delay in response affects the band of Campylobacteriosis spread, and also agrees that a delay in response is a significant factor. The first simulations of the current state of the system suggest that certain conditions can be achieved, and the eradication of the disease is possible if specific precautions are taken. The outcomes also indicate that enhancing the levels of compliance with the WHO-endorsed SOPs by a significant margin can lower infection rates significantly, which can serve as a roadmap to respond to this public health threat. Unlike most analytical papers, this research contributes actual findings and provides useful recommendations for disease management approaches and policies. Full article

Over the past two decades, extensive coastal development in China has led to numerous small-scale enclosed coastal water bodies. Due to complex shoreline geometries, these areas suffer from disturbed hydrodynamic conditions, weak water exchange, which quickly leads to sediment accumulation, and difficulty maintaining ecological water levels, posing serious environmental threats. Enhancing seawater exchange capacity and achieving coordinated optimization of exchange efficiency and ecological water level are critical prerequisites for the environmental restoration of eutrophic enclosed coastal areas. This study takes the Ligao Block in Tianjin as a case study and proposes a real-time sluice gate regulation scheme. By incorporating hydrodynamic conditions, engineering layout, and present characteristics of the benthic substrate environment, the number, width, location, and operation modes of sluice gates are optimized to maximize water exchange efficiency while maintaining natural flow patterns. The result of the numerical simulation of hydrodynamic exchange and intelligent optimization analysis reveals that the optimal sluice gate operation strategy should be tailored to regional tidal flow characteristics and substrate conditions. Through intelligent scheduling of exchange sluice gates, systematic gate parameter optimization, and active control of gate opening, this approach achieves intelligent seawater exchange, optimized flow dynamics, active exchange, and sustained ecological water levels in enclosed coastal water bodies. Full article

Wildfires pose significant threats to ecosystems, human lives, and property worldwide. Understanding the behavior of fire spread on sloped terrain is essential for developing effective firefighting strategies and improving fire prediction models. Previous research has successfully demonstrated the accuracy of numerical tools in comparison to laboratory experiments. This study focuses on the influence of terrain slope and wind speed on wildland fire behavior using Computational Fluid Dynamics (CFD) simulations. In the first phase, the numerical model was validated for a 5 m high single Douglas Fir tree under various mesh sizes, yielding heat release and mass loss rates in close agreement with experimental data. The second phase extends the model to simulate a plantation of 66 Douglas Fir trees under varying slopes and wind conditions. The results indicate that a downward slope of 30° reduces the peak heat release rate, while an upward slope of 30° increases it, with wind speed amplifying these effects. Based on these data, a new reduced-order model is proposed to quantify the influence of slope angle on the heat release rate (HRR) in wildland fires. These findings are critical for enhancing predictive fire models and mitigating wildfire risks in complex terrains. Full article

In recent years, the frequent collapse of bridges has underscored the severe threats posed by ship collisions and seismic forces to bridge pile foundations, particularly under scour conditions. Scour significantly increases bending moments, weakens foundation stability, and exacerbates damage under ship impacts and seismic loading. This review systematically examines the dynamic responses of bridge pile foundations subjected to multi-hazard scenarios, focusing on how scour-induced degradation exacerbates the impacts of ship collisions and seismic events. The synthesis covers experimental studies, numerical simulations, and theoretical approaches, providing a comprehensive evaluation of methodologies and findings. Advanced bibliometric tools, such as CiteSpace and VOSviewer, are employed to identify research trends, hotspots, and collaborations in this domain. Additionally, the review highlights the integration of intelligent technologies for mitigating ship collision risks and improving bridge safety management in scour-prone environments. By consolidating existing knowledge, this paper can serve as a critical reference for understanding the compounded effects of scour and other hazards on bridge pile foundations, offering guidance for future research and engineering practices. Full article

Voltage source converter-based high-voltage direct current (VSC-HVDC) systems integrated into weak AC grids may exhibit oscillation-induced instability, posing significant threats to power system security. With increasing structural complexity and diverse control strategies, the stability characteristics of VSC-HVDC system require further investigation. This paper focuses on the stability of a receiving-end VSC-HVDC system consisting of a DC voltage-controlled VSC parallel-connected to a power-controlled VSC, under various operating conditions. First, small-signal models of each subsystem were developed and a linearized full-system model was constructed based on port relationships. Then, eigenvalue and participation factor analyses were utilized to evaluate the influence of control strategy, asymmetrical grid strength, power flow direction, and tie line on the system’s small-signal stability. A feasible short-circuit ratio (SCR) region was established based on joint power–topology joint, forming a stable operating space for the system. Finally, the correctness of the theoretical analysis was validated via MATLAB/Simulink time-domain simulations. Results indicate that, in comparison to the power control strategy, the DC voltage control strategy was more sensitive to variations in the AC system and demands a strong grid, and this disparity was predominantly caused by the DC voltage control. Furthermore, the feasible region of the short-circuit ratio (SCR) diminished with the increase in the length of the tie-line and alterations in power flow direction under the mutual-support power mode, leading to a gradual reduction in the system’s stability margin. Full article

As China’s largest peninsula, the Shandong Peninsula faces recurrent threats from both tropical and extratropical cyclone-induced storm surges. Understanding the distinct mechanisms governing these surge types is critical for developing targeted coastal hazard mitigation strategies. This investigation employs the FVCOM-SWAVE coupled wave–current model to conduct numerical simulations and comparative analyses of two 2022 surge events, Typhoon Muifa (tropical) and the “221003” extratropical surge. The results demonstrate that hydrodynamic responses exhibit strong dependence on surge-generating meteorological regimes. Tropical surge dynamics correlate closely with typhoon track geometry, intensity gradients, and asymmetric wind field structures, manifesting rightward-biased energy intensification relative to storm motion. Conversely, extratropical surge variations align with evolving wind-pressure configurations during cold air advection, driven by synoptic-scale atmospheric reorganization. The hydrodynamic environmental response in the sea areas surrounding Jiaodong and Laizhou Bay is particularly pronounced, influenced by the intensity of wind stress on the sea surface, as well as the bathymetry and coastal geometry. Full article