Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam cons...Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.展开更多
LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity....LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity.To address this issue,we propose a handover strategy of LEO satellite user terminal based on multi-attribute and multi-point(MAMP)cooperation.Firstly,the satellite-user-time matrix is established by using the satellite constellation coverage and handover model.Then,combined with the visual time and signal quality,the user access matrix and satellite load matrix are extracted to determine the weight equation of the handover strategy with the channel reservation.According to the system modeling simulation,the algorithm improves the handover success rate by 2.5%,the lasted call access success rate by 3.2%,the load balancing degree by 20%,and the robustness by two orders of magnitude.展开更多
The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are ofte...The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.展开更多
To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementar...To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.展开更多
Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properti...Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properties of the diffusion matrix.Through the adjustment of the sintering process,we effectively prepared magnets with varied densities that serve as the matrix for grain boundary diffusion with TbH,diffusion.The mobility characteristics of the Nd-rich phase during the densification stage are leveraged to ensure a more extensive distribution of heavy rare earth elements within the magnets.According to the experimental results,the increase in coercivity of low-density magnets after diffusion is significantly greater than that of relatively high-density magnets.The coercivity values measured are 805.32 kA/m for low-density magnets and 470.3 kA/m for high-density magnets.Additionally,grain boundary diffusion notably enhances the density of initial low-density magnets,addressing the issue of low density during the sintering stage.Before the diffusion treatment,the Nd-rich phases primarily concentrate at the triangular grain boundaries,resulting in an increased number of cavity defects in the magnets.These cavity defects contain atoms in a higher energy state,making them more prone to transition.Consequently,the diffusion activation energy at the void defects is lower than the intracrystalline diffusion activation energy,accelerating atom diffusion.The presence of larger cavities also provides more space for atom migration,thereby promoting the diffusion process.After the diffusion treatment,the proportion of bulk Nd-rich phases significantly decreases,and they infiltrate between the grains to fill the cavity defects,forming continuous fine grain boundaries.Based on these observations,the study aims to explore how to utilize this information to develop an efficient technique for grain boundary diffusion.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the w...In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.展开更多
Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditiona...Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditional villages can provide objective basis for its overall protection and development.Based on the field research,drawing on the theory of cultural landscape,southwest Hubei is taken as the research object,and the index system of cultural landscape type division of traditional villages is constructed from three levels of culture,geography and village carrier.Adopting the multi-attribute weighted k-modes clustering algorithm,92 traditional villages in southwest Hubei are divided into three major types,which are the western Tujia cultural characteristic area,the southern Tujia-Miao cultural penetration area,and the northern multi-ethnic cultural mixed area,and the characteristics of each area are summarized.The regional characteristics of traditional villages in southwest Hubei at the cultural landscape level are analysed from a macro point of view,which provides a reference for more objective cognition of the distribution law of traditional villages in southwest Hubei,and carrying out the contiguous protection of traditional villages.展开更多
Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challen...Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challenging tasks.The goal of subgraph matching is to find all subgraphs in the data graph that are isomorphic to the query graph.Traditional methods mostly rely on search strategies with high computational complexity and are hard to apply to large-scale real datasets.With the advent of graph neural networks(GNNs),researchers have turned to GNNs to address subgraph matching problems.However,the multi-attributed features on nodes and edges are overlooked during the learning of graphs,which causes inaccurate results in real-world scenarios.To tackle this problem,we propose a novel model called subgraph matching on multi-attributed graph network(SGMAN).SGMAN first utilizes improved line graphs to capture node and edge features.Then,SGMAN integrates GNN and contrastive learning(CL)to derive graph representation embeddings and calculate the matching matrix to represent the matching results.We conduct experiments on public datasets,and the results affirm the superior performance of our model.展开更多
Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the...Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.展开更多
This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund produc...This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund products.Customers click on their preferred fund products when visiting a fund recommendation web-page.The system collects customer click sequences to continually estimate and update their utility function.The system generates product lists using the ε-greedy algorithm,where each product on the list has the probability of 1-ε of being selected as an exploitation strategy,and the probability of ε is chosen as the exploration strategy.We perform a series of numerical tests to evaluate the estimation performance with different values of ε.展开更多
As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined fact...As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.展开更多
CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development o...CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development of related materials have attracted considerable research attention.Carbon-based materials,characterized by tunable pore structures,abundant active sites,high specific surface area,and excellent chemical stability,demonstrate significant potential for applications in CO_(2) capture and utilization.This review systematically analyzes the adsorption behaviors and performance variations of typical carbon materials,including activated carbon,porous carbon,graphene,and carbon nanotubes during CO_(2) capture processes.Concerning CO_(2) utilization,emphasis is placed on recent advances in the catalytic applications of carbon-based materials in key reactions such as methanation,reverse water-gas shift,dry reforming of methane,and alcohol synthesis.Moreover,the benefits and drawbacks of carbon materials in terms of CO_(2) adsorption capacity,catalytic activity,and stability are thoroughly evaluated,and their potential applications in integrated CO_(2) capture and utilization technologies are discussed.Finally,key strategies for enhancing the performance of carbonaceous materials through structural modulation and surface modification are elucidated.This review aims to provide theoretical guidance for the future development and large-scale implementation of carbon-based materials in CCU technologies.展开更多
Citrus is the world's most produced fruit.With the rapid growth of citrus cultivation and processing industries globally,the volume of by-products,including dropped fruits,defective fruits,and waste generated duri...Citrus is the world's most produced fruit.With the rapid growth of citrus cultivation and processing industries globally,the volume of by-products,including dropped fruits,defective fruits,and waste generated during processing,has surged.Consequently,resource wastage and environmental pollution due to the low utilization rate of these by-products have become increasingly prominent issues.Currently,citrus by-products are directly utilized as seasonings,tea,and traditional Chinese medicine,or for the extraction of pectin,flavonoids,carotenoids,limonoids,essential oils,synephrine,and other functional ingredients.They are also processed into ethanol,citric acid,feed,and organic fertilizer through biomass fermentation.Despite these applications,the overall utilization rate of citrus by-products remains low.Additionally,there is a lack of key technologies and core equipment,and the production of high value-added functional products is limited.The future direction for citrus by-product utilization lies in green,low-carbon,high-efficiency,and high-value comprehensive recycling.To address the serious environmental pollution and recycling challenges posed by citrus rotting,it is proposed for the first time to develop new products and mold prevention strategies throughout the entire citrus supply chain-"Planting-field management-harvesting-transportation-storage"-to achieve a circular economy approach.This strategy aims to"Take from citrus and give back to citrus"effectively preventing and reducing citrus rotting.Furthermore,it can mitigate the significant economic losses caused by fruit decay and provide insights into the high-quality development of comprehensive citrus by-product utilization.展开更多
BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Resear...BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.展开更多
Among the “three data rights,” the data utilization right has been persistently overlooked, and is similar to a neglected “middle child” in the context of the data rights family. However, it is precisely during th...Among the “three data rights,” the data utilization right has been persistently overlooked, and is similar to a neglected “middle child” in the context of the data rights family. However, it is precisely during the stages of processing and utilization that data undergoes its transformations and where its economic value is ultimately created. A series of recent policy documents on treating data as a factor of production have emphasized that the building of a scientific data property rights system requires a fair and efficient mechanism for benefit distribution, which provides reasonable preference for creators of data value and use value in terms of the income generated by data elements. Constrained by the inertial thinking of property right logic, the data utilization right is often regarded as a “transitional fulcrum” wherein the holders of data resources have to authorize the operators of data products to realize data value thereby. In the future structural design and implementation of the coordination mechanism for the property right system against the backdrop of the data factor-oriented reform, the establishment of data processing and utilization as an independent right will require the implementation of two core initiatives: first, attaching importance to the independent protection of the benefit distribution;second, implementing risk regulation for data security through optimization of governance. These two initiatives will serve as the key for optimizing the data factor governance system and accelerating the release of data value.展开更多
Carbon dioxide(CO_(2))is the main greenhouse gas(GHG)released by human activities.The substitution of fossil resources by biomass as a bio-renewable resource,has significant potential to reduce GHG emissions.The appro...Carbon dioxide(CO_(2))is the main greenhouse gas(GHG)released by human activities.The substitution of fossil resources by biomass as a bio-renewable resource,has significant potential to reduce GHG emissions.The approach to biomass,as the only true full-scale alternative to fossil resources,is progressing rapidly.Converting biomass into furanic compounds,as versatile platform chemicals for synthesizing a wide range of bio-based products is the cornerstone of sustainable technologies.The extensive body of this review combines the biomass valorization to furanic compounds by CO_(2)utilization and furanic compounds conversion by CO_(2)fixation.These processes can be strategically applied through both‘thermochemical’and‘electrochemical’pathways,by utilizing CO_(2)from the atmosphere or industrial emission point and returning it to the natural carbon cycle.In the thermochemical pathway CO_(2)acts as a carbon source(carboxylation and polymerization)or active reaction assistant in the biomass conversion(CO_(2)-assisted conversion),without altering its oxidation state,facilitating the synthesis of valuable products and polymers.Conversely,in the electrochemical pathway,CO_(2)can be used as a carbon source(electrocarboxylation)to give the corresponding carboxylic acid,or it can undergo reduction,yielding methanol,carbon monoxide(CO),formic acid,and analogous compounds,while on the other side,furanic compounds undergo oxidation yielding high-value-added chemicals.Finally,potential future research directions are suggested to promote CO_(2)utilization and fixation in the valorization of biomass-derived furanic compounds,and challenges facing further research are highlighted.展开更多
An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attr...An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.展开更多
In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternati...In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.展开更多
文摘Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.
基金supported by the Innovation Funding of ICT,CAS under Grant(No.E261020)Jiangsu Key Research and Development Program of China(No.BE2021013-2)Zhejiang Key Research and Development Program(No.2021C01040).
文摘LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity.To address this issue,we propose a handover strategy of LEO satellite user terminal based on multi-attribute and multi-point(MAMP)cooperation.Firstly,the satellite-user-time matrix is established by using the satellite constellation coverage and handover model.Then,combined with the visual time and signal quality,the user access matrix and satellite load matrix are extracted to determine the weight equation of the handover strategy with the channel reservation.According to the system modeling simulation,the algorithm improves the handover success rate by 2.5%,the lasted call access success rate by 3.2%,the load balancing degree by 20%,and the robustness by two orders of magnitude.
基金supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant No.2024S077)+1 种基金International Sci-tech Cooperation Projects under the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H019)the Ningbo Key R&D Program(Grant No.2024Z287).
文摘The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.
基金supported by Science and Technology Foundation of State Grid Corporation of China (No.520940120036)the Key Project of the National Twelfth-Five Year Research Programme of China (No.2013BAA01B04)
文摘To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
基金Project supported by the National Natural Science Foundation of China(52361033)National Key Research and Development Program(2022YFB3505400)+3 种基金Ministry of Industry and Information Technology Heavy Rare Earth Special Use of Sintered NdFeB Project(TC220H06J)Academic and Technical Leaders in Major Disciplines in Jiangxi Province(2022BCJ23007)Jiangxi Province Science and Technology Cooperation Key Project(20212BDH80007)Jiangxi Graduate Student Innovation Special Fund Project(YC2023-B213)。
文摘Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properties of the diffusion matrix.Through the adjustment of the sintering process,we effectively prepared magnets with varied densities that serve as the matrix for grain boundary diffusion with TbH,diffusion.The mobility characteristics of the Nd-rich phase during the densification stage are leveraged to ensure a more extensive distribution of heavy rare earth elements within the magnets.According to the experimental results,the increase in coercivity of low-density magnets after diffusion is significantly greater than that of relatively high-density magnets.The coercivity values measured are 805.32 kA/m for low-density magnets and 470.3 kA/m for high-density magnets.Additionally,grain boundary diffusion notably enhances the density of initial low-density magnets,addressing the issue of low density during the sintering stage.Before the diffusion treatment,the Nd-rich phases primarily concentrate at the triangular grain boundaries,resulting in an increased number of cavity defects in the magnets.These cavity defects contain atoms in a higher energy state,making them more prone to transition.Consequently,the diffusion activation energy at the void defects is lower than the intracrystalline diffusion activation energy,accelerating atom diffusion.The presence of larger cavities also provides more space for atom migration,thereby promoting the diffusion process.After the diffusion treatment,the proportion of bulk Nd-rich phases significantly decreases,and they infiltrate between the grains to fill the cavity defects,forming continuous fine grain boundaries.Based on these observations,the study aims to explore how to utilize this information to develop an efficient technique for grain boundary diffusion.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金This research is jointly funded by the National Key Research and Development Program of China(No.2017 YFC0307401)the National Natural Science Foundation of China(No.41230318)+1 种基金the Fundamental Research Funds for the Central Universities(No.201964017)and the National Science and Technology Major Project of China(No.2016ZX05024-001-002).
文摘In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.
基金Philosophy and Social Sciences Research Project of Hubei Provincial Department of Education(22D057).
文摘Cultural landscape zoning research of traditional villages is the basic premise for carrying out overall protection and regional development.Through the clustering algorithm,cultural area zoning research of traditional villages can provide objective basis for its overall protection and development.Based on the field research,drawing on the theory of cultural landscape,southwest Hubei is taken as the research object,and the index system of cultural landscape type division of traditional villages is constructed from three levels of culture,geography and village carrier.Adopting the multi-attribute weighted k-modes clustering algorithm,92 traditional villages in southwest Hubei are divided into three major types,which are the western Tujia cultural characteristic area,the southern Tujia-Miao cultural penetration area,and the northern multi-ethnic cultural mixed area,and the characteristics of each area are summarized.The regional characteristics of traditional villages in southwest Hubei at the cultural landscape level are analysed from a macro point of view,which provides a reference for more objective cognition of the distribution law of traditional villages in southwest Hubei,and carrying out the contiguous protection of traditional villages.
文摘Graphs have been widely used in fields ranging from chemical informatics to social network analysis.Graph-related problems become increasingly significant,with subgraph matching standing out as one of the most challenging tasks.The goal of subgraph matching is to find all subgraphs in the data graph that are isomorphic to the query graph.Traditional methods mostly rely on search strategies with high computational complexity and are hard to apply to large-scale real datasets.With the advent of graph neural networks(GNNs),researchers have turned to GNNs to address subgraph matching problems.However,the multi-attributed features on nodes and edges are overlooked during the learning of graphs,which causes inaccurate results in real-world scenarios.To tackle this problem,we propose a novel model called subgraph matching on multi-attributed graph network(SGMAN).SGMAN first utilizes improved line graphs to capture node and edge features.Then,SGMAN integrates GNN and contrastive learning(CL)to derive graph representation embeddings and calculate the matching matrix to represent the matching results.We conduct experiments on public datasets,and the results affirm the superior performance of our model.
文摘Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.
基金This research was supported by National Key R&D Program of China under No.2022YFA1004000National Natural Science Foundation of China under No.11991023 and 12371324.
文摘This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund products.Customers click on their preferred fund products when visiting a fund recommendation web-page.The system collects customer click sequences to continually estimate and update their utility function.The system generates product lists using the ε-greedy algorithm,where each product on the list has the probability of 1-ε of being selected as an exploitation strategy,and the probability of ε is chosen as the exploration strategy.We perform a series of numerical tests to evaluate the estimation performance with different values of ε.
基金financially supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant Nos.2023S007 and 2023S165)the Key Research and Development Program of Zhejiang(Grant No.2023C03183).
文摘As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.
基金Supported by National Key R&D Program of China(2025YFE0109700)the National Natural Science Foundation of China(52106150)。
文摘CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development of related materials have attracted considerable research attention.Carbon-based materials,characterized by tunable pore structures,abundant active sites,high specific surface area,and excellent chemical stability,demonstrate significant potential for applications in CO_(2) capture and utilization.This review systematically analyzes the adsorption behaviors and performance variations of typical carbon materials,including activated carbon,porous carbon,graphene,and carbon nanotubes during CO_(2) capture processes.Concerning CO_(2) utilization,emphasis is placed on recent advances in the catalytic applications of carbon-based materials in key reactions such as methanation,reverse water-gas shift,dry reforming of methane,and alcohol synthesis.Moreover,the benefits and drawbacks of carbon materials in terms of CO_(2) adsorption capacity,catalytic activity,and stability are thoroughly evaluated,and their potential applications in integrated CO_(2) capture and utilization technologies are discussed.Finally,key strategies for enhancing the performance of carbonaceous materials through structural modulation and surface modification are elucidated.This review aims to provide theoretical guidance for the future development and large-scale implementation of carbon-based materials in CCU technologies.
基金supported by the National Natural Science Foundation of China(82104340)。
文摘Citrus is the world's most produced fruit.With the rapid growth of citrus cultivation and processing industries globally,the volume of by-products,including dropped fruits,defective fruits,and waste generated during processing,has surged.Consequently,resource wastage and environmental pollution due to the low utilization rate of these by-products have become increasingly prominent issues.Currently,citrus by-products are directly utilized as seasonings,tea,and traditional Chinese medicine,or for the extraction of pectin,flavonoids,carotenoids,limonoids,essential oils,synephrine,and other functional ingredients.They are also processed into ethanol,citric acid,feed,and organic fertilizer through biomass fermentation.Despite these applications,the overall utilization rate of citrus by-products remains low.Additionally,there is a lack of key technologies and core equipment,and the production of high value-added functional products is limited.The future direction for citrus by-product utilization lies in green,low-carbon,high-efficiency,and high-value comprehensive recycling.To address the serious environmental pollution and recycling challenges posed by citrus rotting,it is proposed for the first time to develop new products and mold prevention strategies throughout the entire citrus supply chain-"Planting-field management-harvesting-transportation-storage"-to achieve a circular economy approach.This strategy aims to"Take from citrus and give back to citrus"effectively preventing and reducing citrus rotting.Furthermore,it can mitigate the significant economic losses caused by fruit decay and provide insights into the high-quality development of comprehensive citrus by-product utilization.
文摘BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.
文摘Among the “three data rights,” the data utilization right has been persistently overlooked, and is similar to a neglected “middle child” in the context of the data rights family. However, it is precisely during the stages of processing and utilization that data undergoes its transformations and where its economic value is ultimately created. A series of recent policy documents on treating data as a factor of production have emphasized that the building of a scientific data property rights system requires a fair and efficient mechanism for benefit distribution, which provides reasonable preference for creators of data value and use value in terms of the income generated by data elements. Constrained by the inertial thinking of property right logic, the data utilization right is often regarded as a “transitional fulcrum” wherein the holders of data resources have to authorize the operators of data products to realize data value thereby. In the future structural design and implementation of the coordination mechanism for the property right system against the backdrop of the data factor-oriented reform, the establishment of data processing and utilization as an independent right will require the implementation of two core initiatives: first, attaching importance to the independent protection of the benefit distribution;second, implementing risk regulation for data security through optimization of governance. These two initiatives will serve as the key for optimizing the data factor governance system and accelerating the release of data value.
基金the National Key R&D Program of China(No.2021YFC2101604)National Natural Science Foundation of China(Nos.U23A20123,22278339)+1 种基金Fujian Provincial Key Science and Technology Program of China(No.2022YZ037013)Xiamen University for the financial support.
文摘Carbon dioxide(CO_(2))is the main greenhouse gas(GHG)released by human activities.The substitution of fossil resources by biomass as a bio-renewable resource,has significant potential to reduce GHG emissions.The approach to biomass,as the only true full-scale alternative to fossil resources,is progressing rapidly.Converting biomass into furanic compounds,as versatile platform chemicals for synthesizing a wide range of bio-based products is the cornerstone of sustainable technologies.The extensive body of this review combines the biomass valorization to furanic compounds by CO_(2)utilization and furanic compounds conversion by CO_(2)fixation.These processes can be strategically applied through both‘thermochemical’and‘electrochemical’pathways,by utilizing CO_(2)from the atmosphere or industrial emission point and returning it to the natural carbon cycle.In the thermochemical pathway CO_(2)acts as a carbon source(carboxylation and polymerization)or active reaction assistant in the biomass conversion(CO_(2)-assisted conversion),without altering its oxidation state,facilitating the synthesis of valuable products and polymers.Conversely,in the electrochemical pathway,CO_(2)can be used as a carbon source(electrocarboxylation)to give the corresponding carboxylic acid,or it can undergo reduction,yielding methanol,carbon monoxide(CO),formic acid,and analogous compounds,while on the other side,furanic compounds undergo oxidation yielding high-value-added chemicals.Finally,potential future research directions are suggested to promote CO_(2)utilization and fixation in the valorization of biomass-derived furanic compounds,and challenges facing further research are highlighted.
文摘An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.
文摘In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.
