The Sun et al's training program for clinical research nurses(CRNs)in the World Journal of Clinical Cases is a comprehensive and scientific approach.It includes structured frameworks for CRN training,aiming to imp...The Sun et al's training program for clinical research nurses(CRNs)in the World Journal of Clinical Cases is a comprehensive and scientific approach.It includes structured frameworks for CRN training,aiming to improve CRN competency.This program emphasizes practical abilities,updates training content,and improves evaluation methods.The cultivation of CRN talents focuses on enhancing the training system,establishing a multifaceted evaluation framework,and continuously refining the training programs.Regular feedback and evalua-tion are essential to improve CRNs'competency in practical settings.展开更多
For mankind’s survival and development,water,energy,and food(WEF)are essential material guarantees.In China,however,the spatial distribution of WEF is seriously unbalanced and mismatched.Here,a collaborative governan...For mankind’s survival and development,water,energy,and food(WEF)are essential material guarantees.In China,however,the spatial distribution of WEF is seriously unbalanced and mismatched.Here,a collaborative governance mechanism that aims at nexus security needs to be urgently established.In this paper,the Yellow River Basin in China with a representative WEF system,was selected as a case.Firstly,a comprehensive framework for WEF coupling coordination was constructed,and the relationship and mechanism between them were analyzed theoretically.Then,we investigated the spatiotemporal characteristics and driving mechanisms of the coupling coordination degree(CCD)with a composite evaluation method,coupling coordination degree model,spatial statistical analysis,and multiscale geographic weighted regression.Finally,policy implications were discussed to promote the coordinated development of the WEF system.The results showed that:1)WEF subsystems showed a significant imbalance of spatial pattern and diversity in temporal changes;2)the CCD for the WEF system varied little and remained at moderate coordination.Areas with moderate coordination have increased,while areas with superior coordination and mild disorder have decreased.In addition,the spatial clustering phenomenon of the CCD was significant and showed obvious characteristics of polarization;and 3)the action of each factor is self-differentiated and regionally variable.For different factors,GDP per capita was of particular importance,which contributed most to the regional development’s coupling coordination.For different regions,GDP per capita,average yearly precipitation,population density,and urbanization rate exhibited differences in geographical gradients in an east-west direction.The conclusion can provide references for regional resource allocation and sustainable development by enhancing WEF system utilization efficiency.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.展开更多
Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mobility aspects.In addition,much of the previous mobile usability conception was built on desktop co...Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mobility aspects.In addition,much of the previous mobile usability conception was built on desktop computing measurements,such as desktop and web application checklists,or scarcely addressed the mobile user interface.Moreover,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners.Therefore,conducting usability evaluation using conventional usability measurement would result in irrelevant results.In addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development team.The lack of expertise could lead to unreliable usability evaluations.This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work.The framework is dedicated to smartphone apps,which integrate evaluator skills and design concerns.However,there is no evidence of its usefulness in practice.Therefore,the usefulness of the framework measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review.The expert review involved eleven industrial developers and was complemented by a semi-structured interview.The method is replicated in comparison with a framework from another study.The findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.展开更多
Global warming and rapid economic development have led to increased levels of disaster risk in China.Previous attempts at assessing drought risk were highly subjective in terms of assessment methods and selection of t...Global warming and rapid economic development have led to increased levels of disaster risk in China.Previous attempts at assessing drought risk were highly subjective in terms of assessment methods and selection of the assessment indicators and which resulted in appreciable uncertainty in the results of these risk assessments.Based on the assumption that areas with historically high drought losses are more likely to suffer future high drought losses,we develop a new drought risk assessment model that includes historical drought loss data.With this model,we map the regional differentiation of Chinese drought risk.Regions with high(extreme high)drought risk account for 4.3%of China’s area.Five significant high-risk areas have been identified:Northeast China,North China,the east part of Northwest China,the east part of Southwest China and a small part in the west of Northwest China.Areas with high and extreme high drought risk are dominant in the Heilongjiang Province,accounting for 32%of the total area,followed by the Ningxia Hui Autonomous Region,with 26%of total area.The contribution of each influencing factor has been quantified,which indicates that high-exposure and high-vulnerability account for the high-risk of drought.We recommend that measures like strengthening the protection of cultivated land and reducing dependence on the primary industry should be taken to mitigate to drought-induced losses.展开更多
As natural ecosystems provide the material basis and fundamental support for regional sustainable devel-opment,the sustainability of natural ecosystems is an important prerequisite and a viable approach for the achiev...As natural ecosystems provide the material basis and fundamental support for regional sustainable devel-opment,the sustainability of natural ecosystems is an important prerequisite and a viable approach for the achievement of regional sustainable development.It is also the final criteria to assess whether sustainable development paradigm is successful.Along with the increasing impacts of human activities on natural ecosystems,the evaluation of regional ecological sustainability has become one of the key issues for research on macro ecology and sustainable development.Based on different unit of indicators,this study firstly groups the evaluation frameworks of regional ecological sus-tainability into three major types:comprehensive index evaluation with dimensionless unit,monetary valuation,and biophysical quantity measurement.We then discuss and compare these types in terms of basic principles,scope of ap-plications,advantages and shortcomings.Finally,drawn on the discussion about characteristics of ecological sustain-ability,we outline the current trend and future directions of regional ecological sustainability evaluation,for instance,transition from sustainable development evaluation to sustainability science,integration of goal-oriented and problem-solving approaches,combination of spatial pattern analysis and ecological sustainability evaluation,and en-hancement of ecological sustainability evaluation at landscape scale.展开更多
‘Empowerment’is the result of pursuing special capabilities under a specific value orientation.The changes in related object capabilities triggered by scientific and technical information activities in the new envir...‘Empowerment’is the result of pursuing special capabilities under a specific value orientation.The changes in related object capabilities triggered by scientific and technical information activities in the new environment are important to the national scientific and technical(S&T)information governance.Based on the empowerment theories and evaluation practices,this study attempts to construct an empowerment evaluation framework for national S&T information governance and takes the participatory technology assessment and Altmetrics methods as examples to demonstrate its advantages:1)The capability changes and development potential are regarded as important basis for evaluation;2)The multi-person participation and multi-indicator comprehensive evaluation method is conducive to the democratic and objective nature of science and technology information governance policy formulation.展开更多
In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottlene...In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.展开更多
Federated Learning(FL)is a type of distributed deep learning framework in which multiple devices train a local model using local data,and the gradients of the local model are then sent to a central server that aggrega...Federated Learning(FL)is a type of distributed deep learning framework in which multiple devices train a local model using local data,and the gradients of the local model are then sent to a central server that aggregates them to create a global model.This type of framework is ideal where data privacy is of utmost importance because the data never leave the local device.However,a major concern in FL is ensuring the data quality of local training data.Since there is no control over the local training data,ensuring that the local model is trained on clean data becomes challenging.A model trained on poor-quality data can have a significant impact on its accuracy.In this paper,we propose a decentralized approach using blockchain to ensure local model data quality.We use miners to validate each local model by checking its accuracy against a secret testing dataset.This is done using a smart contract that the miners invoke during the mining process.The local model is aggregated with the global model only if it passes a preset accuracy threshold.We test our proposed method on two datasets:the Brain Tumor Classification dataset from Kaggle,comprised of 7000 MRI images divided into two classes(Tumor/No Tumor),and the Medical MNIST dataset,which includes 58,954 images classified into six different classes:AbdomenCT,BreastMRI,ChestCT,Chest X-ray,Hand X-ray,and HeadCT.Our results show that our method outperforms the original FL approach in all experiments.展开更多
Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Sma...Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.展开更多
Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simpl...Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired.展开更多
This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world sof...This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.展开更多
Metal-organic frameworks(MOFs)are among the most promising materials for lithium-ion batteries(LIBs)owing to their high surface area,periodic porosity,adjustable pore size,and controllable chemical composition.For ins...Metal-organic frameworks(MOFs)are among the most promising materials for lithium-ion batteries(LIBs)owing to their high surface area,periodic porosity,adjustable pore size,and controllable chemical composition.For instance,their unique porous structures promote electrolyte penetration,ions transport,and make them ideal for battery separators.Regulating the chemical composition of MOF can introduce more active sites for electrochemical reactions.Therefore,MOFs and their related composites have been extensively and thoroughly explored for LIBs.However,the reported reviews solely include the applications of MOFs in the electrode materials of LIBs and rarely involve other aspects.A systematic review of the application of MOFs in LIBs is essential for understanding the mechanism of MOFs and better designing related MOFs battery materials.This review systematically evaluates the latest developments in pristine MOFs and MOF composites for LIB applications,including MOFs as the main materials(anode,cathode,separators,and electrolytes)to auxiliary materials(coating layers and additives for electrodes).Furthermore,the synthesis,modification methods,challenges,and prospects for the application of MOFs in LIBs are discussed.展开更多
Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore mic...Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore microenvironments.Since the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy storage.Among them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive interest.In this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and aptasensors.Notably,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary applications.Additionally,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic fram...Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.展开更多
First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism...First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.展开更多
Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calcu...Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.展开更多
文摘The Sun et al's training program for clinical research nurses(CRNs)in the World Journal of Clinical Cases is a comprehensive and scientific approach.It includes structured frameworks for CRN training,aiming to improve CRN competency.This program emphasizes practical abilities,updates training content,and improves evaluation methods.The cultivation of CRN talents focuses on enhancing the training system,establishing a multifaceted evaluation framework,and continuously refining the training programs.Regular feedback and evalua-tion are essential to improve CRNs'competency in practical settings.
基金Under the auspices of Graduate Innovation Program of China University of Mining and Technology (No.2022WLKXJ095)National Natural Science Foundation of China (No.71874192)Youth Project of Fundamental Research Funds for the Central Universities (No.2021QN1076)。
文摘For mankind’s survival and development,water,energy,and food(WEF)are essential material guarantees.In China,however,the spatial distribution of WEF is seriously unbalanced and mismatched.Here,a collaborative governance mechanism that aims at nexus security needs to be urgently established.In this paper,the Yellow River Basin in China with a representative WEF system,was selected as a case.Firstly,a comprehensive framework for WEF coupling coordination was constructed,and the relationship and mechanism between them were analyzed theoretically.Then,we investigated the spatiotemporal characteristics and driving mechanisms of the coupling coordination degree(CCD)with a composite evaluation method,coupling coordination degree model,spatial statistical analysis,and multiscale geographic weighted regression.Finally,policy implications were discussed to promote the coordinated development of the WEF system.The results showed that:1)WEF subsystems showed a significant imbalance of spatial pattern and diversity in temporal changes;2)the CCD for the WEF system varied little and remained at moderate coordination.Areas with moderate coordination have increased,while areas with superior coordination and mild disorder have decreased.In addition,the spatial clustering phenomenon of the CCD was significant and showed obvious characteristics of polarization;and 3)the action of each factor is self-differentiated and regionally variable.For different factors,GDP per capita was of particular importance,which contributed most to the regional development’s coupling coordination.For different regions,GDP per capita,average yearly precipitation,population density,and urbanization rate exhibited differences in geographical gradients in an east-west direction.The conclusion can provide references for regional resource allocation and sustainable development by enhancing WEF system utilization efficiency.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
基金partially funded by the Research University Grant Scheme(RUGS),Universiti Putra Malaysia(UPM).
文摘Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mobility aspects.In addition,much of the previous mobile usability conception was built on desktop computing measurements,such as desktop and web application checklists,or scarcely addressed the mobile user interface.Moreover,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners.Therefore,conducting usability evaluation using conventional usability measurement would result in irrelevant results.In addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development team.The lack of expertise could lead to unreliable usability evaluations.This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work.The framework is dedicated to smartphone apps,which integrate evaluator skills and design concerns.However,there is no evidence of its usefulness in practice.Therefore,the usefulness of the framework measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review.The expert review involved eleven industrial developers and was complemented by a semi-structured interview.The method is replicated in comparison with a framework from another study.The findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.
基金the China National Key R&D Program(Grant No.2019YFA0606900)the National Science Founda-tion of China(Grant No.41771536)the National Science Founda-tion for Distinguished Young Scholars of China(Grant No.51425903)。
文摘Global warming and rapid economic development have led to increased levels of disaster risk in China.Previous attempts at assessing drought risk were highly subjective in terms of assessment methods and selection of the assessment indicators and which resulted in appreciable uncertainty in the results of these risk assessments.Based on the assumption that areas with historically high drought losses are more likely to suffer future high drought losses,we develop a new drought risk assessment model that includes historical drought loss data.With this model,we map the regional differentiation of Chinese drought risk.Regions with high(extreme high)drought risk account for 4.3%of China’s area.Five significant high-risk areas have been identified:Northeast China,North China,the east part of Northwest China,the east part of Southwest China and a small part in the west of Northwest China.Areas with high and extreme high drought risk are dominant in the Heilongjiang Province,accounting for 32%of the total area,followed by the Ningxia Hui Autonomous Region,with 26%of total area.The contribution of each influencing factor has been quantified,which indicates that high-exposure and high-vulnerability account for the high-risk of drought.We recommend that measures like strengthening the protection of cultivated land and reducing dependence on the primary industry should be taken to mitigate to drought-induced losses.
基金Under the auspices of National Natural Science Foundation of China (No.40635028,40801066)State Key Laboratory of Earth Surface Processes and Resource Ecology of China (No.2008-KF-04)
文摘As natural ecosystems provide the material basis and fundamental support for regional sustainable devel-opment,the sustainability of natural ecosystems is an important prerequisite and a viable approach for the achievement of regional sustainable development.It is also the final criteria to assess whether sustainable development paradigm is successful.Along with the increasing impacts of human activities on natural ecosystems,the evaluation of regional ecological sustainability has become one of the key issues for research on macro ecology and sustainable development.Based on different unit of indicators,this study firstly groups the evaluation frameworks of regional ecological sus-tainability into three major types:comprehensive index evaluation with dimensionless unit,monetary valuation,and biophysical quantity measurement.We then discuss and compare these types in terms of basic principles,scope of ap-plications,advantages and shortcomings.Finally,drawn on the discussion about characteristics of ecological sustain-ability,we outline the current trend and future directions of regional ecological sustainability evaluation,for instance,transition from sustainable development evaluation to sustainability science,integration of goal-oriented and problem-solving approaches,combination of spatial pattern analysis and ecological sustainability evaluation,and en-hancement of ecological sustainability evaluation at landscape scale.
基金Supported by the National Social Science Fund of China(No.16BTQ058)
文摘‘Empowerment’is the result of pursuing special capabilities under a specific value orientation.The changes in related object capabilities triggered by scientific and technical information activities in the new environment are important to the national scientific and technical(S&T)information governance.Based on the empowerment theories and evaluation practices,this study attempts to construct an empowerment evaluation framework for national S&T information governance and takes the participatory technology assessment and Altmetrics methods as examples to demonstrate its advantages:1)The capability changes and development potential are regarded as important basis for evaluation;2)The multi-person participation and multi-indicator comprehensive evaluation method is conducive to the democratic and objective nature of science and technology information governance policy formulation.
基金supported in part by Program for Changjiang Scholars and Innovative Research Team in University No.IRT1078The Key Program of NSFC-Guangdong Union Foundation No.U1135002The Fundamental Research Funds for the Central Universities No.JY0900120301
文摘In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.
文摘Federated Learning(FL)is a type of distributed deep learning framework in which multiple devices train a local model using local data,and the gradients of the local model are then sent to a central server that aggregates them to create a global model.This type of framework is ideal where data privacy is of utmost importance because the data never leave the local device.However,a major concern in FL is ensuring the data quality of local training data.Since there is no control over the local training data,ensuring that the local model is trained on clean data becomes challenging.A model trained on poor-quality data can have a significant impact on its accuracy.In this paper,we propose a decentralized approach using blockchain to ensure local model data quality.We use miners to validate each local model by checking its accuracy against a secret testing dataset.This is done using a smart contract that the miners invoke during the mining process.The local model is aggregated with the global model only if it passes a preset accuracy threshold.We test our proposed method on two datasets:the Brain Tumor Classification dataset from Kaggle,comprised of 7000 MRI images divided into two classes(Tumor/No Tumor),and the Medical MNIST dataset,which includes 58,954 images classified into six different classes:AbdomenCT,BreastMRI,ChestCT,Chest X-ray,Hand X-ray,and HeadCT.Our results show that our method outperforms the original FL approach in all experiments.
文摘Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.
文摘Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired.
基金This work is the result of commissioned research project supported by the Affiliated Institute of ETRI(2022-086)received by Junho AhnThis research was supported by the National Research Foundation of Korea(NRF)Basic Science Research Program funded by the Ministry of Education(No.2020R1A6A1A03040583)this work was supported by Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea government(MOTIE)(P0008691,HRD Program for Industrial Innovation).
文摘This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.
基金supported by the National Natural Science Foundation of China(22179006)。
文摘Metal-organic frameworks(MOFs)are among the most promising materials for lithium-ion batteries(LIBs)owing to their high surface area,periodic porosity,adjustable pore size,and controllable chemical composition.For instance,their unique porous structures promote electrolyte penetration,ions transport,and make them ideal for battery separators.Regulating the chemical composition of MOF can introduce more active sites for electrochemical reactions.Therefore,MOFs and their related composites have been extensively and thoroughly explored for LIBs.However,the reported reviews solely include the applications of MOFs in the electrode materials of LIBs and rarely involve other aspects.A systematic review of the application of MOFs in LIBs is essential for understanding the mechanism of MOFs and better designing related MOFs battery materials.This review systematically evaluates the latest developments in pristine MOFs and MOF composites for LIB applications,including MOFs as the main materials(anode,cathode,separators,and electrolytes)to auxiliary materials(coating layers and additives for electrodes).Furthermore,the synthesis,modification methods,challenges,and prospects for the application of MOFs in LIBs are discussed.
基金This research was supported by Natural Science Foundation of Jiangsu Province(BK20220405)National Natural Science Foundation of China(21834004,22276100,22304086)+5 种基金Key Laboratory for Organic Electronics&Information Displays,NJUPT(GZR2022010010,GZR2023010045)Nanjing Science and Technology Innovation Project for Chinese Scholars Studying Abroad(NJKCZYZZ2022-01)Research Fund for Jiangsu Distinguished Professor(RK030STP22001)Natural Science Research Start-up Foundation of Recruiting Talents of NJUPT(NY221006,NY223051)Natural Science Foundation of the Jiangsu Higher Education Institutions of China(23KJB150025)State Key Laboratory of Analytical Chemistry for Life Science,Nanjing University(SKLACLS2311).
文摘Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore microenvironments.Since the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy storage.Among them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive interest.In this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and aptasensors.Notably,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary applications.Additionally,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.21978119,22202088)Key Research and Development Plan of Hainan Province(ZDYF2022SHFZ285)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB636)。
文摘Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.
基金This work is supported by the 2022 National Key Research and Development Plan“Security Protection Technology for Critical Information Infrastructure of Distribution Network”(2022YFB3105100).
文摘First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.
基金funded by the National Natural Science Foundation of China(Grant No.41861134008)Muhammad Asif Khan academician workstation of Yunnan Province(Grant No.202105AF150076)+6 种基金General program of Yunnan Province Science and Technology Department(Grant No.202105AF150076)Key Project of Natural Science Foundation of Yunnan Province(Grant No.202101AS070019)Key R&D Program of Yunnan Province(Grant No.202003AC100002)General Program of basic research plan of Yunnan Province(Grant No.202001AT070059)Major scientific and technological projects of Yunnan Province:Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan(No:202202AD080010)“Study on High-Level Hidden Landslide Identification Based on Multi-Source Data”of Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province(KLGDTC-2021-02)Guizhou Scientific and Technology Fund(QKHJ-ZK[2023]YB 193).
文摘Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.
