The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rol...The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rolled method are chosen as the theoretical foundations of the program, and then benefit model is improved to accord with the actuality of urban traffic in China. Consequently, program flows, module functions and data structures are designed, and particularly an original data structure of road ...展开更多
In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Inf...In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.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
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
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
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
China’s employment landscape looks sparse right now as many private enterprises that absorb much of the country’s workforce are succumbing to economic stagnation. While the Western world is caught in a tailspin, num...China’s employment landscape looks sparse right now as many private enterprises that absorb much of the country’s workforce are succumbing to economic stagnation. While the Western world is caught in a tailspin, numerous private enterprises, and exporters in particular, have no place to hide. Their orders are declining to a trickle, and the supply chains of many also have broken down because of a lack of cash. In such a time of gloom, how can they weather the storm through innovation and industrial upgrading? Beijing Review reporter Hu Yue interviewed several economists and entrepreneurs about this issue at the 2008 Dialogue Between Chinese Private Enterprises and Global Fortune 500 held last December in Wenzhou, Zhejiang Province.展开更多
Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Al...Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.展开更多
With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a f...With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.展开更多
With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between indepe...With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between independent studies is still low.Meanwhile,there have been increasing efforts on functional annotations of the entire human genome,such as the Encyclopedia of DNA Elements(ENCODE)project and other similar projects.It has been shown that incorporating these functional annotations to prioritize genome wide association signals may help identify true association signals.However,to our knowledge,the extent of the improvement when functional annotation data are considered has not been studied in the literature.In this article,we propose a statistical framework to estimate the improvement in replication rate with annotation data,and apply it to Crohn’s disease and DNase I hypersensitive sites.The results show that with cell line specific functional annotations,the expected replication rate is improved,but only at modest level.展开更多
The aim of the present contribution has been to present a methodological framework to gauge/assess the perceptions and identify the policy priorities of local-decision-makers for the management of the coastal zone und...The aim of the present contribution has been to present a methodological framework to gauge/assess the perceptions and identify the policy priorities of local-decision-makers for the management of the coastal zone under a changing climate,on the basis of structured‘interviews’of the local decision makers.The framework was applied in two different coastal areas in Greece:a)Elefsina,an urban-industrial area west of Athens with a long industrial history(and the 2023 European Capital of Culture);and b)the Aegean island of Santorini/Thera,a major international tourist destination due to the rare aesthetics of its volcanic landscape.The framework implementation showed that a)policy prioritization is characterized by an(understandably)overarching objective to address immediate environmental and socio-economic challenges in short time tables due also to constraints in appropriate human and financial resources and the reliance on higher governance(regional/national)levels;b)policy axis and action prioritizations are controlled by the local environmental setting and development model;c)interestingly for coastal municipalities policy actions associated with the study/protection of coastal ecosystems ranked very low albeit for different stated reasons;and d)climate change impacts and adaptation have not been prioritized highly in both coastal municipalities,in contrast to the large impacts and needs for adaptation projected for these areas and the evolving policy and legislation frameworks.It appears that higher efforts should be made in terms of the assessment of climate change impacts,and the dissemination of the assessment results and the relevance of the evolving policy and legislation regimes to the local policy makers.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
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 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
Considering the crucial role of wetland conservation in China for the sustainability of biodiversity,it is imperative to identify key habitat functional areas(KHFAs),which are suitable for sustaining waterbirds and en...Considering the crucial role of wetland conservation in China for the sustainability of biodiversity,it is imperative to identify key habitat functional areas(KHFAs),which are suitable for sustaining waterbirds and ensuring landscape connectivity,to optimize wetland management.This study identifies the past changes,present status,and future patterns of wetland KHFAs in China by using the Zonation model with comprehensive data inputs,including wetland distribution,key bird distribution areas(such as Ramsar sites and Important Bird Areas),and flagship waterbird species.Results show that the current wetland KHFAs in China is 41,613.5 km^(2),mainly in the Sanjiang Plain(SJP),Songnen Plain(SNP),middle and lower reaches of the Yangtze River,and the QinghaiXizang Plateau(QXP)regions.The area of wetland KHFAs has been declining since 1990,especially in 2000,mainly due to anthropogenic impacts such as urbanization and agricultural expansion.The future projections suggest a continued decline in the area of wetland KHFAs,although the trend is expected to be slowed.The conservation gap analysis indicates that prioritizing wetland reserves in KHFAs areas,such as the SJP,SNP,and QXP,can significantly enhance the protection of wetland flagship species and their habitats.The results of this study establish conservation priorities that align with national goals of a 55% wetland protection rate and the global biodiversity framework in protected areas and biodiversity,indicating that the spatial conservation optimization approach is an effective method for identifying wetland KHFAs.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
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
Soil erosion in the Hare watershed led to significant land degradation,water pollution,and reduced agricultural productivity.Despite its effects,very few researchers have used combined morphometric and RUSLE model tec...Soil erosion in the Hare watershed led to significant land degradation,water pollution,and reduced agricultural productivity.Despite its effects,very few researchers have used combined morphometric and RUSLE model techniques to quantify soil erosion and thereby prioritize impacted areas.This work used an automated GIS-based tool(SWPT)to prioritize crucial areas based on topohydrological and morphometric factors and predict soil loss in sub-watersheds using the RUSLE model.Land use/cover data were obtained from Landsat imagery,while slope and morphometric information were extracted from digital elevation data with a resolution of 12.5 m.Soil erodibility was determined using Ethiopian soil maps,and rainfall erosivity was computed using meteorological data.An average annual soil loss of 49 t ha-1 yr-1 was observed in the Hare watershed.Sub-watershed 11 was found to be the most affected,with an average annual soil loss of 85.12 t ha-1 yr-1and a compound parameter value(CPV)of 0.059.Subwatershed 17 has the least amount of soil loss,with 3.67t ha-1 yr-1 and a CPV of 1.32.The study emphasizes the usefulness of integrating RUSLE and morphometric analysis for soil and water conservation planning,suggesting a variety of modeling tools in data-sparse locations to quantify and prioritize erosion-prone areas.展开更多
Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunat...Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction(FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation,we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods,the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime(3.514 s).展开更多
In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapi...In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.展开更多
文摘The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rolled method are chosen as the theoretical foundations of the program, and then benefit model is improved to accord with the actuality of urban traffic in China. Consequently, program flows, module functions and data structures are designed, and particularly an original data structure of road ...
文摘In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.
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 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.
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.
文摘China’s employment landscape looks sparse right now as many private enterprises that absorb much of the country’s workforce are succumbing to economic stagnation. While the Western world is caught in a tailspin, numerous private enterprises, and exporters in particular, have no place to hide. Their orders are declining to a trickle, and the supply chains of many also have broken down because of a lack of cash. In such a time of gloom, how can they weather the storm through innovation and industrial upgrading? Beijing Review reporter Hu Yue interviewed several economists and entrepreneurs about this issue at the 2008 Dialogue Between Chinese Private Enterprises and Global Fortune 500 held last December in Wenzhou, Zhejiang Province.
文摘Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.
文摘With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.
基金supported in part by the National Institutes of Health(R01 GM59507 and U01 HG005718)the VA Cooperative Studies Program of the Department of Veterans Affairs,Office of Research and Development
文摘With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between independent studies is still low.Meanwhile,there have been increasing efforts on functional annotations of the entire human genome,such as the Encyclopedia of DNA Elements(ENCODE)project and other similar projects.It has been shown that incorporating these functional annotations to prioritize genome wide association signals may help identify true association signals.However,to our knowledge,the extent of the improvement when functional annotation data are considered has not been studied in the literature.In this article,we propose a statistical framework to estimate the improvement in replication rate with annotation data,and apply it to Crohn’s disease and DNase I hypersensitive sites.The results show that with cell line specific functional annotations,the expected replication rate is improved,but only at modest level.
基金supported by the Hellenic Foundation for Research and Innovation(H.F.R.I.)under the“2nd Call for H.F.R.I.Research Projects to support Post-Doctoral Researchers”(Project Number:211).
文摘The aim of the present contribution has been to present a methodological framework to gauge/assess the perceptions and identify the policy priorities of local-decision-makers for the management of the coastal zone under a changing climate,on the basis of structured‘interviews’of the local decision makers.The framework was applied in two different coastal areas in Greece:a)Elefsina,an urban-industrial area west of Athens with a long industrial history(and the 2023 European Capital of Culture);and b)the Aegean island of Santorini/Thera,a major international tourist destination due to the rare aesthetics of its volcanic landscape.The framework implementation showed that a)policy prioritization is characterized by an(understandably)overarching objective to address immediate environmental and socio-economic challenges in short time tables due also to constraints in appropriate human and financial resources and the reliance on higher governance(regional/national)levels;b)policy axis and action prioritizations are controlled by the local environmental setting and development model;c)interestingly for coastal municipalities policy actions associated with the study/protection of coastal ecosystems ranked very low albeit for different stated reasons;and d)climate change impacts and adaptation have not been prioritized highly in both coastal municipalities,in contrast to the large impacts and needs for adaptation projected for these areas and the evolving policy and legislation frameworks.It appears that higher efforts should be made in terms of the assessment of climate change impacts,and the dissemination of the assessment results and the relevance of the evolving policy and legislation regimes to the local policy makers.
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).
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.
基金jointly funded by the National Key R&D Program of China(Grants No.2022YFF1300904,2023YFF0807201-1)the National Natural Science Foundation of China(Grants No.42301430,U2243230)the“Young support talents program”from Science and Technology Association of Jilin Province(2024-2026)to Dr.Hengxing Xiang(QT202417)。
文摘Considering the crucial role of wetland conservation in China for the sustainability of biodiversity,it is imperative to identify key habitat functional areas(KHFAs),which are suitable for sustaining waterbirds and ensuring landscape connectivity,to optimize wetland management.This study identifies the past changes,present status,and future patterns of wetland KHFAs in China by using the Zonation model with comprehensive data inputs,including wetland distribution,key bird distribution areas(such as Ramsar sites and Important Bird Areas),and flagship waterbird species.Results show that the current wetland KHFAs in China is 41,613.5 km^(2),mainly in the Sanjiang Plain(SJP),Songnen Plain(SNP),middle and lower reaches of the Yangtze River,and the QinghaiXizang Plateau(QXP)regions.The area of wetland KHFAs has been declining since 1990,especially in 2000,mainly due to anthropogenic impacts such as urbanization and agricultural expansion.The future projections suggest a continued decline in the area of wetland KHFAs,although the trend is expected to be slowed.The conservation gap analysis indicates that prioritizing wetland reserves in KHFAs areas,such as the SJP,SNP,and QXP,can significantly enhance the protection of wetland flagship species and their habitats.The results of this study establish conservation priorities that align with national goals of a 55% wetland protection rate and the global biodiversity framework in protected areas and biodiversity,indicating that the spatial conservation optimization approach is an effective method for identifying wetland KHFAs.
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.
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.
文摘Soil erosion in the Hare watershed led to significant land degradation,water pollution,and reduced agricultural productivity.Despite its effects,very few researchers have used combined morphometric and RUSLE model techniques to quantify soil erosion and thereby prioritize impacted areas.This work used an automated GIS-based tool(SWPT)to prioritize crucial areas based on topohydrological and morphometric factors and predict soil loss in sub-watersheds using the RUSLE model.Land use/cover data were obtained from Landsat imagery,while slope and morphometric information were extracted from digital elevation data with a resolution of 12.5 m.Soil erodibility was determined using Ethiopian soil maps,and rainfall erosivity was computed using meteorological data.An average annual soil loss of 49 t ha-1 yr-1 was observed in the Hare watershed.Sub-watershed 11 was found to be the most affected,with an average annual soil loss of 85.12 t ha-1 yr-1and a compound parameter value(CPV)of 0.059.Subwatershed 17 has the least amount of soil loss,with 3.67t ha-1 yr-1 and a CPV of 1.32.The study emphasizes the usefulness of integrating RUSLE and morphometric analysis for soil and water conservation planning,suggesting a variety of modeling tools in data-sparse locations to quantify and prioritize erosion-prone areas.
基金sponsored by the Innovation Foundation for National Natural Science Foundation of China(No.11872312)。
文摘Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction(FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation,we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods,the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime(3.514 s).
基金supported by the Basic Scientific Research Business Fund Project of Higher Education Institutions in Heilongjiang Province(145409601)the First Batch of Experimental Teaching and Teaching Laboratory Construction Research Projects in Heilongjiang Province(SJGZ20240038).
文摘In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.
