Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at...Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.展开更多
Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which...Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous ...While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.展开更多
Approaches to the study of formation keeping for multiple mobile robots are analyzed and a behavior-based robot model is built in this paper. And, a kind of coordination architecture is presented, which is similar to ...Approaches to the study of formation keeping for multiple mobile robots are analyzed and a behavior-based robot model is built in this paper. And, a kind of coordination architecture is presented, which is similar to the infantry squad organization and is used to realize multiple mobile robots to keep formations. Simulations verify the validity of the approach to keep formation, which combines the behavior-based method and formation feedback. The effects of formation feedback on the performance of the system are analyzed.展开更多
Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot o...Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.展开更多
This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Dep...This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering.Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration wasimplemented using the subsumption architecture. In an unknown dynamic indoor environment, the robotachieved real-time obstacle avoidance properties that are cruel for mobile robotics.展开更多
Two limitations of current integrity measurement architectures are pointed out:(1)a reference value is required for every measured entity to verify the system states,as is impractical however;(2)malicious user can for...Two limitations of current integrity measurement architectures are pointed out:(1)a reference value is required for every measured entity to verify the system states,as is impractical however;(2)malicious user can forge proof of inexistent system states.This paper proposes a trustworthy integrity measurement architecture,BBACIMA,through enforcing behavior-based access control for trusted platform module(TPM).BBACIMA introduces a TPM reference monitor(TPMRM)to ensure the trustworthiness of integrity measurement.TPMRM enforces behavior-based access control for the TPM and is isolated from other entities which may be malicious.TPMRM is the only entity manipulating TPM directly and all PCR(platform configuration register)operation requests must pass through the security check of it so that only trusted processes can do measurement and produce the proof of system states.Through these mechanisms malicious user can not enforce attack which is feasible in current measurement architectures.展开更多
The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar po...The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar power, are summarized in the article. The experiences and lessons of implementation of these price policies are analyzed. It is concluded that reasonable price policy is quite effective for promoting re- newable power development. According to the requirement of China's renewable power development, the suggestions for improving renewable power pricing mechanism and price incentive policies are proposed.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since th...At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.展开更多
Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of ...Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.展开更多
The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they h...The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.展开更多
In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.展开更多
AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives ...AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.展开更多
In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs a...In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs and enhance benefits.This study deeply analyzes the characteristics of engineering quantity list pricing,and elaborates on the dilemmas faced by construction enterprises in project cost control,such as lagging concepts,imperfect mechanisms,weak risk management and control,and lack of construction-stage management.Based on this,from the dimensions of strengthening management and control concepts,improving supervision mechanisms,enhancing risk management and control capabilities,and attaching importance to construction-stage cost management,this study proposes project cost management and control strategies that are in line with the actual situation of construction enterprises,aiming to promote construction enterprises to achieve scientific management and optimization of project costs under the engineering quantity list pricing model.展开更多
This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power ...This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.展开更多
The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.G...The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.展开更多
In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In re...In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.展开更多
基金co-supported by the National Natural Science Foundation of China(No.61903350)the Ministry of Education industry-university-research innovation project,China(No.2021ZYA02002)the Beijing Institute of Technology Research Fund Program for Young Scholars,China(No.3010011182130)。
文摘Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.
基金Research on Innovative Method of Drug Rational Use Supervision Decision Based on Big Data of Medical Insurance(Grant No.82273899)。
文摘Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
文摘While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.
文摘Approaches to the study of formation keeping for multiple mobile robots are analyzed and a behavior-based robot model is built in this paper. And, a kind of coordination architecture is presented, which is similar to the infantry squad organization and is used to realize multiple mobile robots to keep formations. Simulations verify the validity of the approach to keep formation, which combines the behavior-based method and formation feedback. The effects of formation feedback on the performance of the system are analyzed.
文摘Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.
文摘This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering.Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration wasimplemented using the subsumption architecture. In an unknown dynamic indoor environment, the robotachieved real-time obstacle avoidance properties that are cruel for mobile robotics.
基金the National High Technology Research and Development Plan of China(2007AA01Z412)the National Key Technology R&D Program of China(2006BAH02A02)the National Natural Science Foundation of China(60603017)
文摘Two limitations of current integrity measurement architectures are pointed out:(1)a reference value is required for every measured entity to verify the system states,as is impractical however;(2)malicious user can forge proof of inexistent system states.This paper proposes a trustworthy integrity measurement architecture,BBACIMA,through enforcing behavior-based access control for trusted platform module(TPM).BBACIMA introduces a TPM reference monitor(TPMRM)to ensure the trustworthiness of integrity measurement.TPMRM enforces behavior-based access control for the TPM and is isolated from other entities which may be malicious.TPMRM is the only entity manipulating TPM directly and all PCR(platform configuration register)operation requests must pass through the security check of it so that only trusted processes can do measurement and produce the proof of system states.Through these mechanisms malicious user can not enforce attack which is feasible in current measurement architectures.
文摘The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar power, are summarized in the article. The experiences and lessons of implementation of these price policies are analyzed. It is concluded that reasonable price policy is quite effective for promoting re- newable power development. According to the requirement of China's renewable power development, the suggestions for improving renewable power pricing mechanism and price incentive policies are proposed.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
文摘At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.
文摘Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT)(RS-2025-16067531:Kwangwon Ahn)Hankuk University of Foreign Studies Research Fund(0f 2025:Sihyun An).
文摘The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.
基金funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
基金supported by the grant of the Russian Science Foundation(RSF Code:23-18-01065).
文摘AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.
文摘In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs and enhance benefits.This study deeply analyzes the characteristics of engineering quantity list pricing,and elaborates on the dilemmas faced by construction enterprises in project cost control,such as lagging concepts,imperfect mechanisms,weak risk management and control,and lack of construction-stage management.Based on this,from the dimensions of strengthening management and control concepts,improving supervision mechanisms,enhancing risk management and control capabilities,and attaching importance to construction-stage cost management,this study proposes project cost management and control strategies that are in line with the actual situation of construction enterprises,aiming to promote construction enterprises to achieve scientific management and optimization of project costs under the engineering quantity list pricing model.
基金financially supported by:National Natural Science Foundation of China(72261002,72141304)Youth Foundation for Humanities and Social Sciences Research of the Ministry of Education(22YJC790190)+1 种基金National Key Research and Development Program of China(2022YFC3303304)Student Research Program of Guizhou University of Finance and Economics(2022ZXS).
文摘This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.
基金supported by the National Natural Science Foundation of China(72141304,72201190).
文摘The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.
基金Hierarchical Identification and Cross-Layer Correlation of Key Core Technologies from the Perspective of Industrial Chain Structure (National Social Science Fund of China, 24BTQ067)Chongqing Education Commission (CEC) Funding:Research on the Co-governance Mechanism of Patent Quality Based on the Dual-Filter Perspective(24SKGH213)Chongqing Graduate Education and Teaching Funding:Research on the Interdisciplinary Law of Intellectual Property and Optimization of Graduate Talent Training Mode(yjg213122)。
文摘In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.
