Expert Knowledge-Based Apparel Recommendation Question and Answer System

Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.

Application of an expert knowledge system in the study of forest spatial patterns

For the sake of exploring how the pattern of Chinese pine （Pinus massoniana Lamb） community changed after the invasion of the pine wood nematode （Bursaphelenchus xylophilus （Steiner ＆ Buhrer） Niclde） in Zhoushan, Zhejiang Province, we established a test area in the local Chinese pine community. Landsat5 TM images from 1991 and 2006 were integrated with auxiliary data from field investigation and spectral data as additional sources of information. A method of expert knowledge classifier was applied to establish the expert knowledge dataset of the main vegetation cover types from which we obtained a forest type distribution map. The spatial patterns and stability of the forest, before and after the invasion of the pine wood nematode, were analyzed in terms of community patterns. The results indicated that the predominant coniferous forest type changed to a mixed forest. As a result, the forest structure became complex and the interaction between coniferous forest patches became weakened over the period from 1991 to 2006. Therefore, the resistance of the forest eco-system to plant diseases and insect pests and the stability of forest eco-system enhanced.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Expert knowledge maps for knowledge management: a case study inTraditional Chinese Medicine research

OBJECTIVE:To design a model to capture information on the state and trends of knowledge creation,at both an individual and an organizational level,in order to enhance knowledge management.METHODS:We designed a graph-theoretic knowledge model,the expert knowledge map(EKM),based on literature-based annotation.A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model.RESULTS:The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs,expert graphs,and expert-knowledge biography.CONCLUSION:Our model could help to reveal thehot topics,trends,and products of the research done by an organization.It can potentially be used to facilitate knowledge learning,sharing and decision-making among researchers,academicians,students,and administrators of organizations.

Prioritizing sustainable development goals and linking them to ecosystem services:A global expert’s knowledge evaluation

The implementation of strategies to achieve the Sustainable Development Goals(SDGs)is frequently hindered by potential trade-offs between priorities for either environmental protection or human well-being.However,ecosystem services(ES)-based solutions can offer possible co-benefits for SDGs implementation that are often overlooked or underexploited.In this study,we cover this gap and investigate how experts from different countries value the SDGs and relate them with ES.A total of 66 countries participated to the survey,and answers were grouped into three macro-regions:Asia;Europe,North America,and Oceania(ENO);Latin America,Caribbean and Africa(LA).Results show that the most prioritized SDGs in the three macro-regions are usually those related to essential material needs and environmental conditions,such as SDG2(Zero Hunger),SDG1(No Poverty),and SDG6(Clean Water).At a global scale,the number of prioritized synergies between SDGs and ES largely exceeded trade-offs.The highest amount of synergies was observed for SDG1(No Poverty),mainly with SDG2,SDG3(Good Health),SDG5(Gender Equality),and SDG8(Economic Growth).Other major synergies among SDGs include SDG14-15(Life below water-Life on land),SDG5-10(Gender Equity-Reduced Inequality),and SDG1-2(No poverty-Zero Hunger).At a global scale,SDG15,SDG13,SDG14,and SDG6 were closely related to ES like climate regulation,freshwater,food,water purification,biodiversity,and education.SDG11(Sustainable Cities)and SDG3 were also relevant in Asia and in LA,respectively.Overall,this study shows the potential to couple future policies that can implement SDGs’strategies while adopting ES-based solutions in different regions of the world.

Data-enhanced revealing of trends in Geoscience

Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Expert Classification Method Based on Patch-Based Neighborhood Searching Algorithm

The expert knowledge has been widely used to improve the remotely sensed classification accuracy. Generally, the ex-pert classification system mainly depends on DEM and some thematic maps. The spatial relationship information in pixel level was commonly introduced into the expert classification. Because the geographic objects were found spatially dependent relationship to a certain degree, the commonly used basic unit of spatial relationship information in pixel greatly limited the efficiency of spatial in-formation. A patch-based neighborhood searching algorithm was proposed to implement the expert classification. The homogene-ous spectral unit, patch, was used as the basic unit in the spatial object granularity, and different types of patches' relationship in-formation were obtained through a spatial neighborhood searching algorithm. And then the neighborhood information and DEM data were added into the expert classification system and used to modify the primitive classification errors. In this case, the classi-fication accuracies of wetland, grassland and cropland were obviously improved. In this work, water was used as base object, and different types of water extraction methods were tested to get a result in a high accuracy.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

A geomorphological model of susceptibility to the effect of human interventions for environmental licensing determination(SHIELD)

Almost every country requires some form of environmental licensing prior to the inception of development projects that may affect the integrity of the environment and its social context.We developed a new conceptual and methodological model to instruct the assessment of the potential impacts posed by proposed projects.Susceptibility to Human Interventions for Environmental Licensing Determination(SHIELD)includes a novel geomorphological interpretation of the Environmental Impact Assessment(EIA).It considers the impact of human interventions on geomorphological processes and landscape functioning in the context of the entire ecosystem,going further than the classical concept of vulnerability.Estimated susceptibility of the site informs the screening stage,allowing local conditions to help define the criteria used in the process.Similarly,the level of detail of the environmental baseline is scoped by considering the degree of disturbance of natural processes posed by human intervention.Testing this geomorphological susceptibility model on different kinds of environments would allow shifting the environmental licensing practices from the prevailing anthropocentric and static conception of the environment towards an Ecosystem Approach.SHIELD addresses the need to improve the screening and scoping stages that form the basis of the rest of any EIA.SHIELD introduces several innovations to EIA including the incorporation of fuzzy logic,a preassembled database of contributions form experts,and a shifting of emphasis from the type of proposed intervention to the type of environment and its relative susceptibility.

Using citizen science data to improve regional bird species list:A case study in Shaanxi,China

An accurate and updated regional bird species checklist is the foundation for biodiversity research and conservation.However,with ongoing climate and landscape changes,tracking the distributions of bird species is challenging,and expert-curated species lists are often limited regarding survey area and timeliness.Birdwatching in China is becoming increasingly popular,and observations recorded by citizen birders are quickly increasing as well.Assessing the value of these data for improving regional species lists and studying bird distribution needs a detailed and quantitative comparison of citizen science data and expert-curated data.We collected observation reports from the China Bird Report Center,the largest online open platform for sharing bird sightings in China.We focused on reports from 2016 to 2019 in Shaanxi Province.For expert-curated species lists,we used three sources:the latest bird field guide published by local ornithologists,the province list from Avibase,and a list generated from overlaying distribution range from BirdLife International with the outline of Shaanxi Province.In addition,we also compared the bird sighting coordinates with the species distribution maps from BirdLife International.Surprisingly,species checklists from different sources have considerable discrepancies,even among lists based on expert knowledge.Including birdwatching data,there are 616 bird species in total,but less than half of the species(294)appear in all checklists,and 17.2%of species are unique to one list.One hundred sixty-three species lack birdwatching records,but birdwatching identified 39 species new to the province.One hundred thirty-six bird species have sighting locations outside the distribution ranges from BirdLife International,suggesting that updates might be needed.The data also showed a clear trend of bird species shifting to higher latitudes than their traditional distributions.While being inadequate for generating a regional species checklist on its own,birdwatching data in China can be a valuable source for complementing expert knowledge.In particular,the coordinate information of bird sighting can help track species distribution shifts.On the other hand,comparing expert-curated lists to birdwatching data can generate a species list for targeted birdwatching and monitoring,which will improve the quality of the birdwatching data in the future.

An efficient Bayesian network for differential diagnosis using experts’knowledge

Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment.For this purpose,different classification methods based on data,experts’knowledge and both are considered in some cases.Besides,feature reduction and some clustering methods are used to improve their performance.Design/methodology/approach-First,the performances of classification methods are evaluated for differential diagnosis of different diseases.Then,experts’knowledge is utilized to modify the Bayesian networks’structures.Analyses of the results show that using experts’knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification.A total of ten different diseases are used for testing,taken from the Machine Learning Repository datasets of the University of California at Irvine(UCI).Findings-The proposed method improves both the computation time and accuracy of the classification methods used in this paper.Bayesian networks based on experts’knowledge achieve a maximum average accuracy of 87 percent,with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implications-The proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/value-This study presents the usefulness of experts’knowledge in the diagnosis while proposing an adopted improvement method for classifications.Besides,the Bayesian network based on experts’knowledge is useful for different diseases neglected by previous papers.

Analyzing the Corrosion Wastage over Some of the Bulk Carriers＇ Member Locations

In this paper, well-known and structured Monte Carlo simulation technique has been employed in predicting the amounts of the corrosion wastage over some bulk carriers＇ structural elements in different points of time during their exploitation life. As a base for the realization of the simulations, the appropriate statistical data collected over the group of ten bulk carriers have been used. Both longitudinal and transversal ships＇ hull structural elements have been taken into the consideration. Due to some experts＇ knowledge in this domain, the critical hull zones are identified and certain interventions are done in the pre-processing of the input data to the Monte Carlo simulations, all with the aim of achieving better convergence between simulation results and the experts＇ expectations in this field.

An Alternative to the Adversarial:Studies on Challenges of Court-appointed Experts

At present,experts have become a mainstay of modern litigation,although criticisms suggest that the problems of how to fit expert knowledge comfortably into the method of adversarial fact-finding are numerous,significant,and without simple solutions.Concerns about partisanship and lack of scientific competence by adjudicators to evaluate contradictory expert testimony have been widely recognized in the traditional use of party-called expert witnesses.While such concerns cannot be wholly ameliorated,there may be alternative mechanisms that can help.One solution would be to call for the use of neutral court-appointed experts,to create a nonpartisan source of expert knowledge.A system of neutral court-appointed experts is an advisory tribunal to the court that could deliver“those general truths,applicable to the issue,which they may treat as final and decisive.”However,no matter in which country,the choice of appointing neutral experts still seems to be a rare option for trial judges to consider and exercise.An obvious question would be:Why are neutral experts not used more frequently at trial?This paper did a study on court-appointed experts,with a focus on challenges that such mechanism faces.Part Ⅰ examines problems in the traditional use of expert witnesses in an adversarial system.Part Ⅱ discusses the incentives to make greater use of court-appointed experts in a typical adversarial system and to what extent such mechanism would solve difficulties within the traditional use of party-called expert witnesses.Part Ⅲ further explores and analyzes obstacles that a typical neutral expert system nowadays encounters when it operates in practice.Taking all analysis together,Part IV makes an overall evaluation of the mechanism of court-appointed experts.

A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment.Belief Rule Base(BRB)is an interpretable expert system gradually applied in fault diagnosis.However,the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features.In addition,the interpretability of BRB-based fault diagnosis is destroyed in the optimization process,which reflects in two aspects:deviation from the initial expert judgment and over-optimization of parameters.To solve these problems,a new interpretable fault diagnosis model based on BRB and probability table,called the BRB-P,is proposed in this paper.Compared with the traditional BRB,the BRB-P constructed by the probability table is more accurate.Then,the interpretability constraints,i.e.,the credibility of expert knowledge,the penalty factor and the rule-activation factor,are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P.A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.

Assessing habitat quality of the mountain nyala Tragelaphus buxtoni in the Bale Mountains, Ethiopia

Populations of the endangered mountain nyala Tragelaphus buxtoni are significantly threatened by the loss of critical habitat. Population estimates are tentative, and information on the species＇ distribution and available habitat is required for for-mulating immediate management and conservation strategies. To support management decisions and conservation priorities, we integrated information from a number of small-scale observational studies, interviews and reports from multiple sources to define habitat parameters and create a habitat quality model for mountain nyala in the Bale Mountains. For our analysis, we used the FunConn model, an expertise-based model that considers spatial relationships （i.e., patch size, distance） between the species and vegetation type, topography and disturbance to create a habitat quality surface. The habitat quality model showed that approxi- mately 18,610 km^2 （82.7% of our study area） is unsuitable or poor habitat for the mountain nyala, while 2,857 km^2 （12.7%） and 1,026 km^2 （4.6%） was ranked as good or optimal habitat, respectively. Our results not only reflected human induced habitat deg-radation, but also revealed an extensive area of intact habitat on the remote slopes of the Bale Mountain＇s southern and southeast- ern escarpments. This study provides an example of the roles that expert knowledge can still play in modem geospatial modeling of wildlife habitat. New geospatial tools, such as the FunConn model, are readily available to wildlife managers and allow them to perform spatial analyses with minimal software, data and training requirements. This approach may be especially useful for species that are obscure to science or when field surveys are not practical .