In wireless networks,the prioritized transmission scheme is essential for accommodating different priority classes of users sharing a common channel.In this paper,we propose a prioritized random access scheme based on...In wireless networks,the prioritized transmission scheme is essential for accommodating different priority classes of users sharing a common channel.In this paper,we propose a prioritized random access scheme based on compute-and-forward,referred to as expanding window sign-compute diversity slotted ALOHA(EW-SCDSA).We improve the expanding window technique and apply it to a high-throughput random access scheme,i.e.,the signcompute diversity slotted ALOHA(SCDSA)scheme,to implement prioritized random access.We analyze the probability of user resolution in each priority class utilizing a bipartite graph and derive the corresponding lower bounds,the effectiveness of which is validated through simulation experiments.Simulation results demonstrate that the EW-SCDSA scheme can provide heterogeneous reliability performance for various user priority classes and significantly outperforms the existing advanced prioritized random access scheme.展开更多
The emergence of the SARS-CoV-2 virus resulted in a health and economic crisis worldwide. Although everyone is susceptible to COVID-19, the elderly have compromised immune systems and often suffer from chronic underly...The emergence of the SARS-CoV-2 virus resulted in a health and economic crisis worldwide. Although everyone is susceptible to COVID-19, the elderly have compromised immune systems and often suffer from chronic underlying diseases, which makes them more vulnerable. This study aims to assess variation in COVID-19 vaccine distribution patterns across different age groups in European countries and to understand the extent to which European countries have prioritized vulnerable age groups (age > 70) in their vaccination programs. The study utilized open data from the European Center for Disease Prevention and Control (ECDC) and employed an observational, retrospective study design to examine the distribution of the COVID-19 vaccine among various age groups in several European countries from September 2021 to September 2023. Results reveal that vaccination rates increase with age, peaking at the 25 - 49 age group (1.34 × 10−4), after which there was a decline in vaccination rate. Analysis of variance (ANOVA) was used to investigate the equality of vaccination rates across the 29 countries in Europe, which resulted in a p-value of 70) during the study period as no country achieved the 70% coverage aimed by WHO. Continuous efforts must be made to ensure larger coverage of COVID-19 vaccination among this vulnerable population in order to protect them from severe outcomes in this region.展开更多
In view of the environment competencies,selecting the optimal green supplier is one of the crucial issues for enterprises,and multi-criteria decision-making(MCDM)methodologies can more easily solve this green supplier...In view of the environment competencies,selecting the optimal green supplier is one of the crucial issues for enterprises,and multi-criteria decision-making(MCDM)methodologies can more easily solve this green supplier selection(GSS)problem.In addition,prioritized aggregation(PA)operator can focus on the prioritization relationship over the criteria,Choquet integral(CI)operator can fully take account of the importance of criteria and the interactions among them,and Bonferroni mean(BM)operator can capture the interrelationships of criteria.However,most existing researches cannot simultaneously consider the interactions,interrelationships and prioritizations over the criteria,which are involved in the GSS process.Moreover,the interval type-2 fuzzy set(IT2FS)is a more effective tool to represent the fuzziness.Therefore,based on the advantages of PA,CI,BM and IT2FS,in this paper,the interval type-2 fuzzy prioritized Choquet normalized weighted BM operators with fuzzy measure and generalized prioritized measure are proposed,and some properties are discussed.Then,a novel MCDM approach for GSS based upon the presented operators is developed,and detailed decision steps are given.Finally,the applicability and practicability of the proposed methodology are demonstrated by its application in the shared-bike GSS and by comparisons with other methods.The advantages of the proposed method are that it can consider interactions,interrelationships and prioritizations over the criteria simultaneously.展开更多
Pancreatic cancer （PC） occurs when malignant cells develop in the part of the pancreas, a glandular organ behind the stomach. For 2015, there are about 40,560 people dead of pancreatic cancer （20,710 men and 19,850...Pancreatic cancer （PC） occurs when malignant cells develop in the part of the pancreas, a glandular organ behind the stomach. For 2015, there are about 40,560 people dead of pancreatic cancer （20,710 men and 19,850 women） in the US （Siegel et al., 2015）. Though PC accounts for about 3% of all cancers in the US, it can cause about 7% of cancer deaths. This is mainly because that the early stages of this cancer do not usually produce symptoms, and thus the cancer is almost always fatal when it is diagnosed.展开更多
In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle...In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.展开更多
Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different ...Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.展开更多
Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to su...Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to survive them independently.There are many improvements in MIoTs,but still,there are critical issues that might affect the Quality of Service(QoS)of a network.Congestion handling is one of the critical factors that directly affect the QoS of the network.The congestion in MIoT can cause more energy consumption,delay,and important data loss.If a patient has an emergency,then the life-critical signals must transmit with minimum latency.During emergencies,the MIoTs have to monitor the patients continuously and transmit data(e.g.,ECG,BP,heart rate,etc.)with minimum delay.Therefore,there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability.The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data.In this paper,Emergency Prioritized and Congestion Handling Protocol for Medical IoTs(EPCP_MIoT)is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes.Whereas the emergency data transmissions are prioritized and transmit at SIFS time.The proposed technique is implemented and compared with the previous technique,the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput,end to end delay,energy consumption,and packet loss ratio.展开更多
Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount impo...Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.展开更多
The object-based scalable coding in MPEG-4 is investigated, and a prioritized transmission scheme of MPEG-4 audio-visual objects （AVOs） over the DiffServ network with the QoS guarantee is proposed. MPEG-4 AVOs are e...The object-based scalable coding in MPEG-4 is investigated, and a prioritized transmission scheme of MPEG-4 audio-visual objects （AVOs） over the DiffServ network with the QoS guarantee is proposed. MPEG-4 AVOs are extracted and classified into different groups according to their priority values and scalable layers （visual importance）. These priority values are mapped to the 1P DiffServ per hop behaviors （PHB）. This scheme can selectively discard packets with low importance, in order to avoid the network congestion. Simulation results show that the quality of received video can gracefully adapt to network state, as compared with the ‘best-effort＇ manner. Also, by allowing the content provider to define prioritization of each audio-visual object, the adaptive transmission of object-based scalable video can be customized based on the content.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)op...On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)operator with respect to interval neutrosophic numbers(INNs).Firstly,the definition,operational laws,characteristics,expectation and comparative method of INNs are introduced.Then,the INPOWA operator is developed,and some properties of the operator are analyzed.Furthermore,based on the INPOWA operator and the comparative formula of the INNs,an approach to decision making with INNs is established.Finally,an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
Addressing the widespread issues of internal fragmentation within protected areas and the neglect of surrounding critical habitat networks,this study aims to develop an assessment framework for the precise identificat...Addressing the widespread issues of internal fragmentation within protected areas and the neglect of surrounding critical habitat networks,this study aims to develop an assessment framework for the precise identification and remediation of regional conservation gaps.To this end,we introduce the Framework for Conservation Priority Identification(FCPI).The framework integrates Morphological Spatial Pattern Analysis(MSPA),the Remote Sensing Ecological Index(RSEI),Circuit Theory,and the Minimum Cumulative Resistance(MCR)model to formulate a multidimensional conservation priority index.This index facilitates the identification of critical ecological network components and enables the dynamic prioritization of conservation efforts.A case study of Fuzhou City from 2014 to 2020 reveals that despite an overall improvement in regional environmental quality,the functionality of core ecological sources has markedly declined.Between 2014 and 2020,the number of ecological sources grew by 76.9%,yet their total area shrank by 13.9%.Concurrently,the number of ecological corridors rose from 27 to 53,extending their total length by 380.23 km,which indicates an intensifying trend of habitat fragmentation.Furthermore,a significant number of crucial ecological network nodes,particularly within Minhou County,lie explicitly outside the existing protected area system.This confirms the presence of conservation gaps and unveils the spatiotemporal dynamics of shifting conservation priorities.The research validates that the proposed FCPI can effectively diagnose the dynamic deficiencies within conservation systems.It offers scientific decisionsupport for local governments,facilitating a transition from isolated conservation efforts towards systematic and comprehensive ecological network governance.展开更多
Purpose-The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The proper...Purpose-The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.Further,based on these operators,a decision-making approach is presented under the Pythagorean fuzzy set environment.An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Design/methodology/approach-In real-world situations,Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data.The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship.The idea of a priority degree is introduced.The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels.Consequently,the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.Findings-The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.Further,based on these operators,a decision-making approach is presented under the Pythagorean fuzzy set environment.An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Originality/value-The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels.Consequently,the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.展开更多
Uncovering causal genes for human inherited diseases,as the primary step toward understanding the pathogenesis of these diseases,requires a combined analysis of genetic and genomic data.Although bioinformatics methods...Uncovering causal genes for human inherited diseases,as the primary step toward understanding the pathogenesis of these diseases,requires a combined analysis of genetic and genomic data.Although bioinformatics methods have been designed to prioritize candidate genes resulting fromgenetic linkage analysis or association studies,the coverage of both diseases and genes in existing methods is quite limited,thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome level.To overcome this limitation,we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic data.We derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 genes.Based on a pair of phenotype and gene similarities,we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease.A weighted version of the Fisher’s method with dependent correction is adopted to integrate 27 scores obtained in this way,and a final q-value is calibrated for prioritizing candidate genes.A series of validation experiments are conducted to demonstrate the superior performance of this approach.We further show the effectiveness of this method in exome sequencing studies of autism and epileptic encephalopathies.An online service and the standalone software of pgWalk can be found at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgwalk.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing ...In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.展开更多
As the world transitions to a more environment-friendly and sustainable global economy,innovations in green technology are playing a crucial role.Recognizing this,China,along with many other nations,is adapting its pa...As the world transitions to a more environment-friendly and sustainable global economy,innovations in green technology are playing a crucial role.Recognizing this,China,along with many other nations,is adapting its pat⁃ent system to better support green innovations,establishing what is known as a green patent regime.While extensive empirical research highlights the significance of China's green patent regime in achieving environmental sustainabil⁃ity,there is a noticeable lack of normative studies on enhancing its efficacy.This work aims to fill this gap by conduct⁃ing a normative study using doctrinal analysis and proposing that transparency and accessibility are key factors in evaluating the effectiveness of a green patent regime.Through a doctrinal analysis of China's green patent legislation and regulations,this work assesses the legal rights conferred by this regime and identifies how these rights are con⁃strained by substantive and procedural norms.The findings reveal significant limitations to the transparency and acces⁃sibility of China's green patent regime and propose improvements.These recommendations offer insights for policymak⁃ers in China and other countries.The doctrinal analysis conducted in this research could stimulate further theoretical discussion in the field of sustainable development and intellectual property law.Moreover,it might enlighten more hy⁃potheses for future empirical studies.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62301008China Postdoctoral Science Foundation under Grant No.2022M720272。
文摘In wireless networks,the prioritized transmission scheme is essential for accommodating different priority classes of users sharing a common channel.In this paper,we propose a prioritized random access scheme based on compute-and-forward,referred to as expanding window sign-compute diversity slotted ALOHA(EW-SCDSA).We improve the expanding window technique and apply it to a high-throughput random access scheme,i.e.,the signcompute diversity slotted ALOHA(SCDSA)scheme,to implement prioritized random access.We analyze the probability of user resolution in each priority class utilizing a bipartite graph and derive the corresponding lower bounds,the effectiveness of which is validated through simulation experiments.Simulation results demonstrate that the EW-SCDSA scheme can provide heterogeneous reliability performance for various user priority classes and significantly outperforms the existing advanced prioritized random access scheme.
文摘The emergence of the SARS-CoV-2 virus resulted in a health and economic crisis worldwide. Although everyone is susceptible to COVID-19, the elderly have compromised immune systems and often suffer from chronic underlying diseases, which makes them more vulnerable. This study aims to assess variation in COVID-19 vaccine distribution patterns across different age groups in European countries and to understand the extent to which European countries have prioritized vulnerable age groups (age > 70) in their vaccination programs. The study utilized open data from the European Center for Disease Prevention and Control (ECDC) and employed an observational, retrospective study design to examine the distribution of the COVID-19 vaccine among various age groups in several European countries from September 2021 to September 2023. Results reveal that vaccination rates increase with age, peaking at the 25 - 49 age group (1.34 × 10−4), after which there was a decline in vaccination rate. Analysis of variance (ANOVA) was used to investigate the equality of vaccination rates across the 29 countries in Europe, which resulted in a p-value of 70) during the study period as no country achieved the 70% coverage aimed by WHO. Continuous efforts must be made to ensure larger coverage of COVID-19 vaccination among this vulnerable population in order to protect them from severe outcomes in this region.
基金supported by the National Natural Science Foundation of China(71771140)Project of Cultural Masters and“the Four Kinds of a Batch”Talents,the Special Funds of Taishan Scholars Project of Shandong Province(ts201511045)the Major Bidding Projects of National Social Science Fund of China(19ZDA080)。
文摘In view of the environment competencies,selecting the optimal green supplier is one of the crucial issues for enterprises,and multi-criteria decision-making(MCDM)methodologies can more easily solve this green supplier selection(GSS)problem.In addition,prioritized aggregation(PA)operator can focus on the prioritization relationship over the criteria,Choquet integral(CI)operator can fully take account of the importance of criteria and the interactions among them,and Bonferroni mean(BM)operator can capture the interrelationships of criteria.However,most existing researches cannot simultaneously consider the interactions,interrelationships and prioritizations over the criteria,which are involved in the GSS process.Moreover,the interval type-2 fuzzy set(IT2FS)is a more effective tool to represent the fuzziness.Therefore,based on the advantages of PA,CI,BM and IT2FS,in this paper,the interval type-2 fuzzy prioritized Choquet normalized weighted BM operators with fuzzy measure and generalized prioritized measure are proposed,and some properties are discussed.Then,a novel MCDM approach for GSS based upon the presented operators is developed,and detailed decision steps are given.Finally,the applicability and practicability of the proposed methodology are demonstrated by its application in the shared-bike GSS and by comparisons with other methods.The advantages of the proposed method are that it can consider interactions,interrelationships and prioritizations over the criteria simultaneously.
文摘Pancreatic cancer （PC） occurs when malignant cells develop in the part of the pancreas, a glandular organ behind the stomach. For 2015, there are about 40,560 people dead of pancreatic cancer （20,710 men and 19,850 women） in the US （Siegel et al., 2015）. Though PC accounts for about 3% of all cancers in the US, it can cause about 7% of cancer deaths. This is mainly because that the early stages of this cancer do not usually produce symptoms, and thus the cancer is almost always fatal when it is diagnosed.
文摘In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.
文摘Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.
基金the Deanship of Scientific Research(DSR),at KingAbdulaziz University,Jeddah,under grant no.G:292-612-1440.
文摘Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to survive them independently.There are many improvements in MIoTs,but still,there are critical issues that might affect the Quality of Service(QoS)of a network.Congestion handling is one of the critical factors that directly affect the QoS of the network.The congestion in MIoT can cause more energy consumption,delay,and important data loss.If a patient has an emergency,then the life-critical signals must transmit with minimum latency.During emergencies,the MIoTs have to monitor the patients continuously and transmit data(e.g.,ECG,BP,heart rate,etc.)with minimum delay.Therefore,there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability.The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data.In this paper,Emergency Prioritized and Congestion Handling Protocol for Medical IoTs(EPCP_MIoT)is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes.Whereas the emergency data transmissions are prioritized and transmit at SIFS time.The proposed technique is implemented and compared with the previous technique,the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput,end to end delay,energy consumption,and packet loss ratio.
文摘Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.
文摘The object-based scalable coding in MPEG-4 is investigated, and a prioritized transmission scheme of MPEG-4 audio-visual objects （AVOs） over the DiffServ network with the QoS guarantee is proposed. MPEG-4 AVOs are extracted and classified into different groups according to their priority values and scalable layers （visual importance）. These priority values are mapped to the 1P DiffServ per hop behaviors （PHB）. This scheme can selectively discard packets with low importance, in order to avoid the network congestion. Simulation results show that the quality of received video can gracefully adapt to network state, as compared with the ‘best-effort＇ manner. Also, by allowing the content provider to define prioritization of each audio-visual object, the adaptive transmission of object-based scalable video can be customized based on the content.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金supported by the National Natural Science Foundation of China under Grant Nos.71471172and 71271124the Humanities and Social Sciences Research Project of Ministry of Education of China under Grant No.13YJC630104+2 种基金Shandong Provincial Social Science Planning Project under Grant No.13BGLJ10the National Soft Science Research Project under Grant No.2014GXQ4D192the Natural Science Foundation of Shandong Province under Grant No.ZR2014JL046
文摘On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)operator with respect to interval neutrosophic numbers(INNs).Firstly,the definition,operational laws,characteristics,expectation and comparative method of INNs are introduced.Then,the INPOWA operator is developed,and some properties of the operator are analyzed.Furthermore,based on the INPOWA operator and the comparative formula of the INNs,an approach to decision making with INNs is established.Finally,an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金supported by the Natural Science Foundation of Fujian Province(2023J01434)the Science and Technology Innovation Special Fund Project of Fujian Agriculture and Forestry University(KFb22028XA)。
文摘Addressing the widespread issues of internal fragmentation within protected areas and the neglect of surrounding critical habitat networks,this study aims to develop an assessment framework for the precise identification and remediation of regional conservation gaps.To this end,we introduce the Framework for Conservation Priority Identification(FCPI).The framework integrates Morphological Spatial Pattern Analysis(MSPA),the Remote Sensing Ecological Index(RSEI),Circuit Theory,and the Minimum Cumulative Resistance(MCR)model to formulate a multidimensional conservation priority index.This index facilitates the identification of critical ecological network components and enables the dynamic prioritization of conservation efforts.A case study of Fuzhou City from 2014 to 2020 reveals that despite an overall improvement in regional environmental quality,the functionality of core ecological sources has markedly declined.Between 2014 and 2020,the number of ecological sources grew by 76.9%,yet their total area shrank by 13.9%.Concurrently,the number of ecological corridors rose from 27 to 53,extending their total length by 380.23 km,which indicates an intensifying trend of habitat fragmentation.Furthermore,a significant number of crucial ecological network nodes,particularly within Minhou County,lie explicitly outside the existing protected area system.This confirms the presence of conservation gaps and unveils the spatiotemporal dynamics of shifting conservation priorities.The research validates that the proposed FCPI can effectively diagnose the dynamic deficiencies within conservation systems.It offers scientific decisionsupport for local governments,facilitating a transition from isolated conservation efforts towards systematic and comprehensive ecological network governance.
文摘Purpose-The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.Further,based on these operators,a decision-making approach is presented under the Pythagorean fuzzy set environment.An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Design/methodology/approach-In real-world situations,Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data.The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship.The idea of a priority degree is introduced.The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels.Consequently,the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.Findings-The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.Further,based on these operators,a decision-making approach is presented under the Pythagorean fuzzy set environment.An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Originality/value-The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels.Consequently,the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.The properties of the existing method are routinely compared to those of other current approaches,emphasizing the superiority of the presented work over currently used methods.Furthermore,the impact of priority degrees on the aggregate outcome is thoroughly examined.
基金This work was supported by the National Basic Research Program of China(2012CB316504)the National High Technology Research and Development Program of China(2012AA020401)the National Natural Science Foundation of China(61175002).
文摘Uncovering causal genes for human inherited diseases,as the primary step toward understanding the pathogenesis of these diseases,requires a combined analysis of genetic and genomic data.Although bioinformatics methods have been designed to prioritize candidate genes resulting fromgenetic linkage analysis or association studies,the coverage of both diseases and genes in existing methods is quite limited,thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome level.To overcome this limitation,we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic data.We derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 genes.Based on a pair of phenotype and gene similarities,we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease.A weighted version of the Fisher’s method with dependent correction is adopted to integrate 27 scores obtained in this way,and a final q-value is calibrated for prioritizing candidate genes.A series of validation experiments are conducted to demonstrate the superior performance of this approach.We further show the effectiveness of this method in exome sequencing studies of autism and epileptic encephalopathies.An online service and the standalone software of pgWalk can be found at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgwalk.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
文摘In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.
基金Funded by the National Key R&D Program of China (Grant No:2022YFC3303000)。
文摘As the world transitions to a more environment-friendly and sustainable global economy,innovations in green technology are playing a crucial role.Recognizing this,China,along with many other nations,is adapting its pat⁃ent system to better support green innovations,establishing what is known as a green patent regime.While extensive empirical research highlights the significance of China's green patent regime in achieving environmental sustainabil⁃ity,there is a noticeable lack of normative studies on enhancing its efficacy.This work aims to fill this gap by conduct⁃ing a normative study using doctrinal analysis and proposing that transparency and accessibility are key factors in evaluating the effectiveness of a green patent regime.Through a doctrinal analysis of China's green patent legislation and regulations,this work assesses the legal rights conferred by this regime and identifies how these rights are con⁃strained by substantive and procedural norms.The findings reveal significant limitations to the transparency and acces⁃sibility of China's green patent regime and propose improvements.These recommendations offer insights for policymak⁃ers in China and other countries.The doctrinal analysis conducted in this research could stimulate further theoretical discussion in the field of sustainable development and intellectual property law.Moreover,it might enlighten more hy⁃potheses for future empirical studies.
