As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid ...The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid growth in production and consumption.To formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related fields.Multidimensional technical parameters are selected and predicted accurately and reliably in combination with different development scenarios.The findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development scenarios.Under this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in 2060.Thetransportation sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the future.Additionally,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative mu...In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.展开更多
The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand...The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.展开更多
Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimens...Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.展开更多
Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptab...Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.展开更多
This review takes stock of China’s Double Reduction.In the short run,it lowered visible burden and pushed demand from subject tutoring toward on-campus and non-subject services.But with high-stakes selection unchange...This review takes stock of China’s Double Reduction.In the short run,it lowered visible burden and pushed demand from subject tutoring toward on-campus and non-subject services.But with high-stakes selection unchanged,demand reappears as small-group/one-to-one provision,advantaging families with high socioeconomic status and strong schools.Lasting relief will require tighter oversight and admissions reform with targeted,well-funded in-school support.展开更多
Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity...Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.展开更多
This study focuses on the elderly population in Xueyuan Road Street of Haidian District in Beijing.Through KANO questionnaires and the theory of attractive quality,it investigates the demand levels and degrees for dif...This study focuses on the elderly population in Xueyuan Road Street of Haidian District in Beijing.Through KANO questionnaires and the theory of attractive quality,it investigates the demand levels and degrees for different community elderly care services.It introduces the Anderson behavioral model to analyze the influencing factors,categorizes different demographics,and examines the needs of elderly individuals with varying characteristics,proposing suggestions for the improvement of future community elderly care service facilities.展开更多
Based on the demand for complex English talents for the high-quality construction of“Belt and Road,”the study proposes a curriculum restructuring program oriented on“serving professional teaching,career development...Based on the demand for complex English talents for the high-quality construction of“Belt and Road,”the study proposes a curriculum restructuring program oriented on“serving professional teaching,career development,and quality development”in response to the real problems of the current university English curriculum,such as focusing on language but not on application,insufficient vocational relevance,and low degree of integration with the professional field.We propose a curriculum reconstruction plan oriented to“serve professional teaching,career development,and quality development.”We have constructed a three-in-one curriculum goal of“laying a foundation for professionalism,infiltrating humanity,and empowering development,”systematically designed a curriculum content system of“language foundation,industry knowledge,and quality development,”and established an AI-enabled multi-intelligence evaluation system.This will promote the transformation of university English from single-language teaching to a service-oriented curriculum that supports professional development,and cultivate internationalized talents with both workplace language application skills and cross-cultural communication literacy.The study highlights the“vocational”characteristics and“service”functions of college English,and provides an actionable,practical path for the reform of college English curriculum in vocational undergraduate colleges.展开更多
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.Wh...The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.展开更多
With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggr...With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggregated SIDCs have emerged as promising demand response(DR)resources for future power distribution systems.This paper presents an innovative framework for assessing capacity value(CV)by aggregating SIDCs participating in DR programs(SIDC-DR).Initially,we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment.Considering the effects of the data load dynamics,equipment constraints,and user behavior,we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method.Unlike existing studies,the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation.This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process,enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation.Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.展开更多
This paper aims to explore the cognition and demand of nature education.Through the analysis of its connotation,significance,current situation and challenges,corresponding countermeasures and suggestions are put forwa...This paper aims to explore the cognition and demand of nature education.Through the analysis of its connotation,significance,current situation and challenges,corresponding countermeasures and suggestions are put forward.Nature education is a kind of education mode based on the natural environment,which enables learners to integrate with nature through scientific and effective means.This kind of education method has a far-reaching impact on shaping the overall quality of teenagers and cultivating the correct world outlook and values.By means of literature review,case analysis and other means,combined with the development practice of nature education at home and abroad,this study deeply analyzes the cognitive status and demand characteristics of nature education,which provides guidance and basis for the dissemination and development of nature education.展开更多
Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for busine...Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for business English talents.Combining this with the existing problems in China’s current training models,it proposes a reform path for talent training models that are adapted to the construction of the“Belt and Road”Initiative.The aim is to provide theoretical references and practical guidance for enhancing the international competitiveness of business English talents.展开更多
In the context of the energy and climate crises,it is crucial for organizations to utilize advanced methods to reduce energy consumption and energy costs.This study explores the application of deep learning models for...In the context of the energy and climate crises,it is crucial for organizations to utilize advanced methods to reduce energy consumption and energy costs.This study explores the application of deep learning models for predicting energy demands in retail stores,which can enhance market efficiency and contribute to grid stability.We analyze a detailed electricity consumption dataset from a hypermarket in Hungary,focusing on 48-hour forecasts at 15-minute intervals.Our methodology includes the implementation of classical models such as ARIMA and linear regression,as well as state-of-the-art deep learning models like TiDE and foundational models such as Lag-Llama in a“zero shot prediction”as well as a“finetuning”scenario.展开更多
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金supported by the National Natural Science Foundation of China(No.71704178)Beijing Municipal Excellent Talents Foundation(No.2017000020124G133)Major consulting project of the Chinese Academy of Engineering(Nos.2023-JB-08,2022-PP-03).
文摘The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid growth in production and consumption.To formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related fields.Multidimensional technical parameters are selected and predicted accurately and reliably in combination with different development scenarios.The findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development scenarios.Under this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in 2060.Thetransportation sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the future.Additionally,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.,with the project number 52051L240001.
文摘In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.
基金supported by Science and Technology Program of China Southern Power Grid Corporation under grant number 036000KK52222004(GDKJXM20222117)National Key R&D Program of China for International S&T Cooperation Projects(2019YFE0118700).
文摘The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.
基金supported by Mahasarakham University for Piyapatr Busababodhin’s work.Guoqing Chen’s research was supported by Chengdu Jincheng College Green Data Integration Intelligence Research and Innovation Project(No.2025-2027)the High-Quality Development Research Center Project in the Tuojiang River Basin(No.TJGZL2024-07)+1 种基金the Open Fund ofWuhan Gravitation and Solid Earth Tides,National Observation and Research Station(No.WHYWZ202406)the Scientific Research Fund of the Institute of Seismology,CEA,and the National Institute of Natural Hazards,MEM(No.IS202236328).
文摘Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.
文摘Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.
文摘This review takes stock of China’s Double Reduction.In the short run,it lowered visible burden and pushed demand from subject tutoring toward on-campus and non-subject services.But with high-stakes selection unchanged,demand reappears as small-group/one-to-one provision,advantaging families with high socioeconomic status and strong schools.Lasting relief will require tighter oversight and admissions reform with targeted,well-funded in-school support.
文摘Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.
文摘This study focuses on the elderly population in Xueyuan Road Street of Haidian District in Beijing.Through KANO questionnaires and the theory of attractive quality,it investigates the demand levels and degrees for different community elderly care services.It introduces the Anderson behavioral model to analyze the influencing factors,categorizes different demographics,and examines the needs of elderly individuals with varying characteristics,proposing suggestions for the improvement of future community elderly care service facilities.
基金Special Project of Foreign Language Education Reform in Vocational Colleges and Universities in 2023 by the Foreign Language Education Working Committee of China Society for Vocational and Technical Education(WYW2023A05)Teaching Reform Project of Shandong Vocational and Technical University of International Studies(JG202314).
文摘Based on the demand for complex English talents for the high-quality construction of“Belt and Road,”the study proposes a curriculum restructuring program oriented on“serving professional teaching,career development,and quality development”in response to the real problems of the current university English curriculum,such as focusing on language but not on application,insufficient vocational relevance,and low degree of integration with the professional field.We propose a curriculum reconstruction plan oriented to“serve professional teaching,career development,and quality development.”We have constructed a three-in-one curriculum goal of“laying a foundation for professionalism,infiltrating humanity,and empowering development,”systematically designed a curriculum content system of“language foundation,industry knowledge,and quality development,”and established an AI-enabled multi-intelligence evaluation system.This will promote the transformation of university English from single-language teaching to a service-oriented curriculum that supports professional development,and cultivate internationalized talents with both workplace language application skills and cross-cultural communication literacy.The study highlights the“vocational”characteristics and“service”functions of college English,and provides an actionable,practical path for the reform of college English curriculum in vocational undergraduate colleges.
文摘Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
基金supported by National Natural Science Foundation of China(52407126).
文摘The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
基金supported in part by the National Natural Science Foundation of China under Grant 52177082in part by the Beijing Nova Program under Grant 20220484007.
文摘With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggregated SIDCs have emerged as promising demand response(DR)resources for future power distribution systems.This paper presents an innovative framework for assessing capacity value(CV)by aggregating SIDCs participating in DR programs(SIDC-DR).Initially,we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment.Considering the effects of the data load dynamics,equipment constraints,and user behavior,we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method.Unlike existing studies,the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation.This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process,enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation.Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.
基金Research Project of Basic Education in Jiangxi Province(SZUNDZH2021-1136,SZUNDZH2020-1138).
文摘This paper aims to explore the cognition and demand of nature education.Through the analysis of its connotation,significance,current situation and challenges,corresponding countermeasures and suggestions are put forward.Nature education is a kind of education mode based on the natural environment,which enables learners to integrate with nature through scientific and effective means.This kind of education method has a far-reaching impact on shaping the overall quality of teenagers and cultivating the correct world outlook and values.By means of literature review,case analysis and other means,combined with the development practice of nature education at home and abroad,this study deeply analyzes the cognitive status and demand characteristics of nature education,which provides guidance and basis for the dissemination and development of nature education.
文摘Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for business English talents.Combining this with the existing problems in China’s current training models,it proposes a reform path for talent training models that are adapted to the construction of the“Belt and Road”Initiative.The aim is to provide theoretical references and practical guidance for enhancing the international competitiveness of business English talents.
文摘In the context of the energy and climate crises,it is crucial for organizations to utilize advanced methods to reduce energy consumption and energy costs.This study explores the application of deep learning models for predicting energy demands in retail stores,which can enhance market efficiency and contribute to grid stability.We analyze a detailed electricity consumption dataset from a hypermarket in Hungary,focusing on 48-hour forecasts at 15-minute intervals.Our methodology includes the implementation of classical models such as ARIMA and linear regression,as well as state-of-the-art deep learning models like TiDE and foundational models such as Lag-Llama in a“zero shot prediction”as well as a“finetuning”scenario.
