With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyz...With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.展开更多
Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling ap...Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.展开更多
This study investigates in-station pressure drop mechanisms in a shale gas gathering system,providing a quantitative basis for flow system optimization.Computational fluid dynamics(CFD)simulations,based on field-measu...This study investigates in-station pressure drop mechanisms in a shale gas gathering system,providing a quantitative basis for flow system optimization.Computational fluid dynamics(CFD)simulations,based on field-measured parameters related to a representative case(a shale gas platform located in Sichuan,China)are conducted to analyze the flow characteristics of specific fittings and manifolds,and to quantify fitting resistance coefficients and manifold inlet interference.The resulting coefficients are integrated into a full-station gathering network model in PipeSim,which,combined with production data,enables evaluation of pressure losses and identification of equivalent pipeline blockages.The results indicate that the resistance coefficients,valid only for fittings under the studied field-specific geometries,are 0.21 for 90◦elbows in the fully open position,0.16 for gate valve passages in the fully open position,and 2.3 for globe valve passages.Manifold interference decreases with lower high-pressure inlet values,whereas inlets farther from the high-pressure side experience stronger disturbances.Interestingly,significant discrepancies between simulated and measured pressure drops reveal partial blockages,corresponding to effective diameter reductions of 65 mm,38 mm,44 mm,38 mm,and 28 mm for Wells 1#,3#,5#,and 6#,respectively.展开更多
Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and env...Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.展开更多
Purpose:To evaluate the effects of healthcare failure mode and effect analysis(FMEA)on the prevention of deep venous thrombosis(DVT)in elderly patients undergoing femoral fracture surgery.Methods:Eighty elderly patien...Purpose:To evaluate the effects of healthcare failure mode and effect analysis(FMEA)on the prevention of deep venous thrombosis(DVT)in elderly patients undergoing femoral fracture surgery.Methods:Eighty elderly patients undergoing femoral fracture surgery who did not apply FMEA in Suzhou BenQ Medical Center from June 1,2022 to May 31,2023 were selected as the control group.According to the equal group experiment method,80 elderly patients who were managed using FMEA from June 1,2023 to May 31,2024 were selected as the FMEA group.The control group received traditional nursing management,while the FMEA group applied FMEA to analyze failure causes,calculate Risk Priority Numbers(RPNs),identify failure modes with higher RPNs,analyze the influencing factors,develop improvement measures,and optimize processes.The RPN values and the incidence of DVT,as well as nursing satisfaction scores,were compared in the two groups.Results:Compared with the control group,the total RPN values of the FEMA group decreased significantly,with a reduction rate of 87.0%.Besides,the incidence of DVT was 1.3%in the FMEA group,lower than 10.0%in the control group(8/80).What’s more,the patients in the FMEA group were more satisfied with the nursing service compared with the patients in the control group.Conclusion:The application of the FMEA in elderly patients undergoing femoral fracture surgery has demonstrated its potential to prevent the incidence of DVT,lower RPN values,and improve nursing satisfaction.展开更多
Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through s...Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.展开更多
Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing...Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.展开更多
With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling an...With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.展开更多
Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be so...Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.展开更多
Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the...Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.展开更多
This study explores off-grid power generation business models in the Lao People's Democratic Republic(Lao PDR),with the objective of identifying viable pathways to expand energy access in rural and underserved reg...This study explores off-grid power generation business models in the Lao People's Democratic Republic(Lao PDR),with the objective of identifying viable pathways to expand energy access in rural and underserved regions.The research aims to analyze and evaluate various business models in terms of their technical,economic,and social viability within the unique geographic and policy context of Lao PDR.There are two level of the research objectives:High Level Objectives(HLO)and Concreted Research Objectives(CRO).For HLO is that an appropriated off-grid power generation business model for Laos supports the Lao PDR Government’s commitment to promote an inclusive green growth development agenda that ensures lowered GHG emissions and increased energy efficiency.The Lao PDR National Determined Contribution(NDC)to the United Nations Framework Convention on Climate Change(UNFCCC)notes the country’s ambitious plans to lower energy consumption and reduce GHG emissions.While the CRO are focused on learning strategies,regulation and practical lessons from other countries the ASEAN region on the off-grid development and business model.To analyze and investigate the environmental strategy of business model under external and internal context and related and considered factors.And finally,this is to conclude and recommend the off-grid power generation business model as the research conclusion,which will become a support mechanism for the companies to operate consistently over many years into the future according to ambitious goal for supplying modern and save energy for rural families by 2030.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC...Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.展开更多
In this paper,we develop a multi-scalar auxiliary variables(MSAV)scheme for the Cahn-Hilliard Magnetohydrodynamics system by introducing two scalar auxiliary variables(SAV).This scheme is linear,fully decoupled and un...In this paper,we develop a multi-scalar auxiliary variables(MSAV)scheme for the Cahn-Hilliard Magnetohydrodynamics system by introducing two scalar auxiliary variables(SAV).This scheme is linear,fully decoupled and unconditionally stable in energy.Subsequently,we provide a detailed implementation procedure for full decoupling.Thus,at each time step,only a series of linear differential equations with constant coefficients need to be solved.To validate the effectiveness of our approach,we conduct an error analysis for this first-order scheme.Finally,some numerical experiments are provided to verify the energy dissipation of the system and the convergence of the proposed approach.展开更多
Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(...Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(HCC)from the perspective of China’s healthcare system.Methods A decision tree+partitioned survival model was constructed for early diagnosis of HCC based on literature data.Taking quality-adjusted life year(QALY)as the main health outcome measure for incremental cost-effectiveness ratio(ICER)analysis,the sensitivity analysis by Monte Carlo simulation was constructed to generate corresponding tornado diagram,incremental cost-effectiveness scatter plot,and cost-effectiveness acceptability curve.Results and Conclusion The basic analysis results showed that the ICER value of Gd-BOPTA diagnostic scheme compared with Gd-DTPA diagnostic scheme was 17302.46 yuan/QALY,which is less than 1 times of China’s gross domestic product(GDP)per capita.The sensitivity analysis results showed that the cost of delayed treatment and timely treatment had a significant impact on the results.When the willingness to pay(WTP)was 1 time of GDP per capita,the probability of cost-effectiveness advantage of Gd-BOPTA diagnostic scheme was 65.30%.When the WTP value is set at 1 times of GDP per capita,Gd-BOPTA MRI has cost-effectiveness advantages for the early diagnosis of HCC.展开更多
To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric v...To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric vibrator was constructed by reasonably arranging multiple groups of piezoelectric ceramic(PZT)sheets based on the most typical rectangular plate piezoelectric motors.Suitable working modes were selected,and the excitation method of these operating modes was also analyzed.Besides,interactions between selected operating modes were also investigated.The finite element software,ANSYS,was adopted to optimize the structural parameters of the vibrator through modal analysis to match the resonance frequencies of specific modes.After that,whether the selected operating modes can be successfully motivated was verified by harmonic response analysis.Finally,the vibration characteristics of piezoelectric vibrators under conventional vibration modes and multiple modes were acquired by transient analysis,respectively.Simulation results reveal that under dual-frequency excitation scheme 1,response displacements of the driving point are relatively larger.This strategy not only facilitates the excitation of B4 mode but also enables control over the ratio of horizontal to vertical displacements of the driving point.Additionally,incorporating B4 mode expands the frequency adjustment range of piezoelectric vibrators.展开更多
Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of ...Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of wind turbines have driven the development of simplified models,whose applicability remains controversial.In this paper,a wide-band oscillation analysis method based on the average-value model(AVM)is proposed for wind farms(WFs).A novel linearization analysis framework is developed,leveraging the continuous-time characteristics of the AVM and MATLAB/Simulink’s built-in linearization tools.This significantly reduces modeling complexity and computational costs while maintaining model fidelity.Additionally,an object-based initial value estimation method of state variables is introduced,which,when combined with steady-state point-solving tools,greatly reduces the computational effort required for equilibrium point solving in batch linearization analysis.The proposed method is validated in both doubly fed induction generator(DFIG)-based and permanent magnet synchronous generator(PMSG)-based WFs.Furthermore,a comprehensive analysis is conducted for the first time to examine the impact of the machine-side system on the system stability of the nonfully controlled PMSG-based WF.展开更多
In the modern higher education music curriculum system,the teacher-student interaction mode is a key factor affecting the effectiveness of piano teaching.However,the current teacher-student interaction mode in piano t...In the modern higher education music curriculum system,the teacher-student interaction mode is a key factor affecting the effectiveness of piano teaching.However,the current teacher-student interaction mode in piano teaching still has limitations,such as one-way transmission and a lack of personalized feedback.Based on constructivist learning theory and social interaction theory,combined with information technology,this paper explores the optimization strategy of interaction mode in the piano teaching process of normal universities.This study adopts classroom observation and interview methods to analyze the impact of different interaction modes on students’piano learning effectiveness,learning engagement,and autonomous learning ability.The research results show that the constructivist interactive teaching mode supported by information technology can significantly enhance students’interest in learning and playing skills,optimize the classroom teaching atmosphere,and promote the improvement of their comprehensive literacy.展开更多
The dynamic,heterogeneous nature of Edge computing in the Internet of Things(Edge-IoT)and Industrial IoT(IIoT)networks brings unique and evolving cybersecurity challenges.This study maps cyber threats in Edge-IoT/IIoT...The dynamic,heterogeneous nature of Edge computing in the Internet of Things(Edge-IoT)and Industrial IoT(IIoT)networks brings unique and evolving cybersecurity challenges.This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics,Techniques,and Common Knowledge(ATT&CK)framework by MITRE and introduces a lightweight,data-driven scoring model that enables rapid identification and prioritization of attacks.Inspired by the Factor Analysis of Information Risk model,our proposed scoring model integrates four key metrics:Common Vulnerability Scoring System(CVSS)-based severity scoring,Cyber Kill Chain–based difficulty estimation,Deep Neural Networks-driven detection scoring,and frequency analysis based on dataset prevalence.By aggregating these indicators,the model generates comprehensive risk profiles,facilitating actionable prioritization of threats.Robustness and stability of the scoring model are validated through non-parametric correlation analysis using Spearman’s and Kendall’s rank correlation coefficients,demonstrating consistent performance across diverse scenarios.The approach culminates in a prioritized attack ranking that provides actionable guidance for risk mitigation and resource allocation in Edge-IoT/IIoT security operations.By leveraging real-world data to align MITRE ATT&CK techniques with CVSS metrics,the framework offers a standardized and practically applicable solution for consistent threat assessment in operational settings.The proposed lightweight scoring model delivers rapid and reliable results under dynamic cyber conditions,facilitating timely identification of attack scenarios and prioritization of response strategies.Our systematic integration of established taxonomies with data-driven indicators strengthens practical risk management and supports strategic planning in next-generation IoT deployments.Ultimately,this work advances adaptive threat modeling for Edge/IIoT ecosystems and establishes a robust foundation for evidence-based prioritization in emerging cyber-physical infrastructures.展开更多
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China,grant number 21YJA790009National Natural Science Foundation of China,grant number 72140001.
文摘With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.
基金funded by the Ministry of Higher Education through Universiti Putra Malaysia(UPM)under Grant FRGS/1/2023/STG07/UPM/02/4.
文摘Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.
基金the National Natural Science Foundation of China under Grant 52441411,52325402 and 52274057Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project under Grant 2024ZD1004302-04the National Key R&D Program of China under Grant 2023YFB4104200.
文摘This study investigates in-station pressure drop mechanisms in a shale gas gathering system,providing a quantitative basis for flow system optimization.Computational fluid dynamics(CFD)simulations,based on field-measured parameters related to a representative case(a shale gas platform located in Sichuan,China)are conducted to analyze the flow characteristics of specific fittings and manifolds,and to quantify fitting resistance coefficients and manifold inlet interference.The resulting coefficients are integrated into a full-station gathering network model in PipeSim,which,combined with production data,enables evaluation of pressure losses and identification of equivalent pipeline blockages.The results indicate that the resistance coefficients,valid only for fittings under the studied field-specific geometries,are 0.21 for 90◦elbows in the fully open position,0.16 for gate valve passages in the fully open position,and 2.3 for globe valve passages.Manifold interference decreases with lower high-pressure inlet values,whereas inlets farther from the high-pressure side experience stronger disturbances.Interestingly,significant discrepancies between simulated and measured pressure drops reveal partial blockages,corresponding to effective diameter reductions of 65 mm,38 mm,44 mm,38 mm,and 28 mm for Wells 1#,3#,5#,and 6#,respectively.
基金supported in part by the National Natural Science Foundation of China(Key Program)under Grant No.62031021。
文摘Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
文摘Purpose:To evaluate the effects of healthcare failure mode and effect analysis(FMEA)on the prevention of deep venous thrombosis(DVT)in elderly patients undergoing femoral fracture surgery.Methods:Eighty elderly patients undergoing femoral fracture surgery who did not apply FMEA in Suzhou BenQ Medical Center from June 1,2022 to May 31,2023 were selected as the control group.According to the equal group experiment method,80 elderly patients who were managed using FMEA from June 1,2023 to May 31,2024 were selected as the FMEA group.The control group received traditional nursing management,while the FMEA group applied FMEA to analyze failure causes,calculate Risk Priority Numbers(RPNs),identify failure modes with higher RPNs,analyze the influencing factors,develop improvement measures,and optimize processes.The RPN values and the incidence of DVT,as well as nursing satisfaction scores,were compared in the two groups.Results:Compared with the control group,the total RPN values of the FEMA group decreased significantly,with a reduction rate of 87.0%.Besides,the incidence of DVT was 1.3%in the FMEA group,lower than 10.0%in the control group(8/80).What’s more,the patients in the FMEA group were more satisfied with the nursing service compared with the patients in the control group.Conclusion:The application of the FMEA in elderly patients undergoing femoral fracture surgery has demonstrated its potential to prevent the incidence of DVT,lower RPN values,and improve nursing satisfaction.
基金The National Key R&D Program Projects(Grant No.2022YFC2803601)the Natural Science Foundation of Shandong Province(Grant No.ZR2021YQ29)+1 种基金the Natural Science Foundation of Heilongjiang Province(Grant No.YQ2024E036)the Taishan Scholars Project(Grant No.tsqn202312317).
文摘Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant No.DGSSR-2024-02-01011.
文摘Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.
基金supported by the Chinese Civil Aircraft Project(No.MJ-2017-S49).
文摘With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.
基金Heilongjiang Provincial Natural Science Foundation of China (LH2021F009)。
文摘Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.
基金the financial support from the Fujian Science Foundation for Outstanding Youth(2023J06039)the National Natural Science Foundation of China(Grant No.41977259,U2005205,41972268)the Independent Research Project of Technology Innovation Center for Monitoring and Restoration Engineering of Ecological Fragile Zone in Southeast China(KY-090000-04-2022-019)。
文摘Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.
文摘This study explores off-grid power generation business models in the Lao People's Democratic Republic(Lao PDR),with the objective of identifying viable pathways to expand energy access in rural and underserved regions.The research aims to analyze and evaluate various business models in terms of their technical,economic,and social viability within the unique geographic and policy context of Lao PDR.There are two level of the research objectives:High Level Objectives(HLO)and Concreted Research Objectives(CRO).For HLO is that an appropriated off-grid power generation business model for Laos supports the Lao PDR Government’s commitment to promote an inclusive green growth development agenda that ensures lowered GHG emissions and increased energy efficiency.The Lao PDR National Determined Contribution(NDC)to the United Nations Framework Convention on Climate Change(UNFCCC)notes the country’s ambitious plans to lower energy consumption and reduce GHG emissions.While the CRO are focused on learning strategies,regulation and practical lessons from other countries the ASEAN region on the off-grid development and business model.To analyze and investigate the environmental strategy of business model under external and internal context and related and considered factors.And finally,this is to conclude and recommend the off-grid power generation business model as the research conclusion,which will become a support mechanism for the companies to operate consistently over many years into the future according to ambitious goal for supplying modern and save energy for rural families by 2030.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金funding from the National Natural Science Foundation of China (Grant No.42277175)the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056)the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).
文摘Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.
基金Research Project Supported by Shanxi Scholarship Council of China(2021-029)International Cooperation Base and Platform Project of Shanxi Province(202104041101019)Basic Research Plan of Shanxi Province(202203021211129)。
文摘In this paper,we develop a multi-scalar auxiliary variables(MSAV)scheme for the Cahn-Hilliard Magnetohydrodynamics system by introducing two scalar auxiliary variables(SAV).This scheme is linear,fully decoupled and unconditionally stable in energy.Subsequently,we provide a detailed implementation procedure for full decoupling.Thus,at each time step,only a series of linear differential equations with constant coefficients need to be solved.To validate the effectiveness of our approach,we conduct an error analysis for this first-order scheme.Finally,some numerical experiments are provided to verify the energy dissipation of the system and the convergence of the proposed approach.
文摘Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(HCC)from the perspective of China’s healthcare system.Methods A decision tree+partitioned survival model was constructed for early diagnosis of HCC based on literature data.Taking quality-adjusted life year(QALY)as the main health outcome measure for incremental cost-effectiveness ratio(ICER)analysis,the sensitivity analysis by Monte Carlo simulation was constructed to generate corresponding tornado diagram,incremental cost-effectiveness scatter plot,and cost-effectiveness acceptability curve.Results and Conclusion The basic analysis results showed that the ICER value of Gd-BOPTA diagnostic scheme compared with Gd-DTPA diagnostic scheme was 17302.46 yuan/QALY,which is less than 1 times of China’s gross domestic product(GDP)per capita.The sensitivity analysis results showed that the cost of delayed treatment and timely treatment had a significant impact on the results.When the willingness to pay(WTP)was 1 time of GDP per capita,the probability of cost-effectiveness advantage of Gd-BOPTA diagnostic scheme was 65.30%.When the WTP value is set at 1 times of GDP per capita,Gd-BOPTA MRI has cost-effectiveness advantages for the early diagnosis of HCC.
基金funded by National Natural Science Foundation of China,grant number 52205292.
文摘To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric vibrator was constructed by reasonably arranging multiple groups of piezoelectric ceramic(PZT)sheets based on the most typical rectangular plate piezoelectric motors.Suitable working modes were selected,and the excitation method of these operating modes was also analyzed.Besides,interactions between selected operating modes were also investigated.The finite element software,ANSYS,was adopted to optimize the structural parameters of the vibrator through modal analysis to match the resonance frequencies of specific modes.After that,whether the selected operating modes can be successfully motivated was verified by harmonic response analysis.Finally,the vibration characteristics of piezoelectric vibrators under conventional vibration modes and multiple modes were acquired by transient analysis,respectively.Simulation results reveal that under dual-frequency excitation scheme 1,response displacements of the driving point are relatively larger.This strategy not only facilitates the excitation of B4 mode but also enables control over the ratio of horizontal to vertical displacements of the driving point.Additionally,incorporating B4 mode expands the frequency adjustment range of piezoelectric vibrators.
基金supported by the National Natural Science Foundation of China under Grant 52277072.
文摘Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of wind turbines have driven the development of simplified models,whose applicability remains controversial.In this paper,a wide-band oscillation analysis method based on the average-value model(AVM)is proposed for wind farms(WFs).A novel linearization analysis framework is developed,leveraging the continuous-time characteristics of the AVM and MATLAB/Simulink’s built-in linearization tools.This significantly reduces modeling complexity and computational costs while maintaining model fidelity.Additionally,an object-based initial value estimation method of state variables is introduced,which,when combined with steady-state point-solving tools,greatly reduces the computational effort required for equilibrium point solving in batch linearization analysis.The proposed method is validated in both doubly fed induction generator(DFIG)-based and permanent magnet synchronous generator(PMSG)-based WFs.Furthermore,a comprehensive analysis is conducted for the first time to examine the impact of the machine-side system on the system stability of the nonfully controlled PMSG-based WF.
文摘In the modern higher education music curriculum system,the teacher-student interaction mode is a key factor affecting the effectiveness of piano teaching.However,the current teacher-student interaction mode in piano teaching still has limitations,such as one-way transmission and a lack of personalized feedback.Based on constructivist learning theory and social interaction theory,combined with information technology,this paper explores the optimization strategy of interaction mode in the piano teaching process of normal universities.This study adopts classroom observation and interview methods to analyze the impact of different interaction modes on students’piano learning effectiveness,learning engagement,and autonomous learning ability.The research results show that the constructivist interactive teaching mode supported by information technology can significantly enhance students’interest in learning and playing skills,optimize the classroom teaching atmosphere,and promote the improvement of their comprehensive literacy.
基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2025-RISE-01-018-05)supported by Quad Miners Corp。
文摘The dynamic,heterogeneous nature of Edge computing in the Internet of Things(Edge-IoT)and Industrial IoT(IIoT)networks brings unique and evolving cybersecurity challenges.This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics,Techniques,and Common Knowledge(ATT&CK)framework by MITRE and introduces a lightweight,data-driven scoring model that enables rapid identification and prioritization of attacks.Inspired by the Factor Analysis of Information Risk model,our proposed scoring model integrates four key metrics:Common Vulnerability Scoring System(CVSS)-based severity scoring,Cyber Kill Chain–based difficulty estimation,Deep Neural Networks-driven detection scoring,and frequency analysis based on dataset prevalence.By aggregating these indicators,the model generates comprehensive risk profiles,facilitating actionable prioritization of threats.Robustness and stability of the scoring model are validated through non-parametric correlation analysis using Spearman’s and Kendall’s rank correlation coefficients,demonstrating consistent performance across diverse scenarios.The approach culminates in a prioritized attack ranking that provides actionable guidance for risk mitigation and resource allocation in Edge-IoT/IIoT security operations.By leveraging real-world data to align MITRE ATT&CK techniques with CVSS metrics,the framework offers a standardized and practically applicable solution for consistent threat assessment in operational settings.The proposed lightweight scoring model delivers rapid and reliable results under dynamic cyber conditions,facilitating timely identification of attack scenarios and prioritization of response strategies.Our systematic integration of established taxonomies with data-driven indicators strengthens practical risk management and supports strategic planning in next-generation IoT deployments.Ultimately,this work advances adaptive threat modeling for Edge/IIoT ecosystems and establishes a robust foundation for evidence-based prioritization in emerging cyber-physical infrastructures.
