Practice and Thinking of Agricultural Science and Technology Experts Serving the Grassroots to Promote Rural Revitalization

Provincial agricultural research institutes are local agricultural research institutions.They are familiar with local agricultural and ru-ral conditions,and have advantages in scientific and technological achievements,technical reserves and the construction of scientific and tech-nological talent support systems.In the process of promoting the implementation of the rural revitalization strategy,all the responsibilities and obligations of the agricultural research institute are to identify the focus of scientific and technological support and talent support in rural revital-ization,and break the bottleneck and constraints of rural revitalization.By sorting out the current situation of agricultural science and technolo-gy experts serving the grassroots in the problem of talent support for rural revitalization,this paper analyzed the existing problems and put for-ward countermeasures and recommendations.

International experts consensus guidelines on robotic liver resection in 2023

The robotic liver resection(RLR)has been increasingly applied in recent years and its benefits shown in some aspects owing to the technical advancement of robotic surgical system,however,controversies still exist.Based on the foundation of the previous consensus statement,this new consensus document aimed to update clinical recommendations and provide guidance to improve the outcomes of RLR clinical practice.The guideline steering group and guideline expert group were formed by 29 international experts of liver surgery and evidence-based medicine(EBM).Relevant literature was reviewed and analyzed by the evidence evaluation group.According to the WHO Handbook for Guideline Development,the Guidance Principles of Development and Amendment of the Guidelines for Clinical Diagnosis and Treatment in China 2022,a total of 14 recommendations were generated.Among them were 8 recommendations formulated by the GRADE method,and the remaining 6 recommendations were formulated based on literature review and experts’opinion due to insufficient EBM results.This international experts consensus guideline offered guidance for the safe and effective clinical practice and the research direction of RLR in future.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Perception of ChatGPT among Chinese Experts: A Thematic Analysis of a Semi-structured Interview

ChatGPT,as an emergent technique,has become a heat throughout the world.Currently,it was not known entirely to the public.To gain knowledge of how experts perceive and experience ChatGPT,a semi-structured interview was conducted.By doing thematic analysis of their interview reports,it found that the experts construed the chatbot not the same as some media advertised.They didn’t regard it as a threat to their occupation,but as a helper or an assistant both in life and work.Neither did they restrict its use in education or other fields.They viewed the challenges of ChatGPT in a welcomed and positive way.

Expert consensus on irrigation and intracanal medication in root canal therapy

Chemical cleaning and disinfection are crucial steps for eliminating infection in root canal treatment. However, irrigant selection or irrigation procedures are far from clear. The vapor lock effect in the apical region has yet to be solved, impeding irrigation efficacy and resulting in residual infections and compromised treatment outcomes.

Leading industry experts to give keynote presentations at ITMA forums

April 2023–Two industry forums on nonwovens,and textiles and colourants will be reprised at ITMA 2023 which will be held in Milan this June.The ITMA Nonwovens Forum and ITMA Textile Colourants and Chemicals Forum will feature renowned experts who will offer insights into current challenges and share ideas on how the textile industry can achieve sustainability by leveraging innovative technologies.

Interpretation of Chinese Expert Consensus on the Diagnosis and Management Strategy of Patients with Statin Intolerance: A guiding file for helping to lipid management for Chinese population

It is well-known that elevated low-density lipoprotein cholesterol(LDL-C)is a causal risk factor for atheroscler-otic cardiovascular disease(ASCVD),statins are cornerstone drugs for the cause-based treatment of ASCVD,which has created a new era for ASCVD therapy.However,statin intolerance is not clinically uncommon,which there are several issues with confu-sion and misunderstandings.Hence,a file named Chinese Expert Consensus on the Diagnosis and Management Strategy of Pa-tients With Statin Intolerance,like a navigator,has recently been published written by a team of experts from the Cardiovascular Metabolic Medicine Professional Committee,Expert Committee of the National Center for Cardiovascular Diseases aiming to en-hance the standardized clinical application of statins and improve the prevention and clinical outcome.In this article,author briefly summarized the key points of above consensus in order to helping to comprehending the content of the consensus sugges-tions.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

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.

Agricultural experts’attitude towards precision agriculture:Evidence from Guilan Agricultural Organization,Northern Iran

Identifying factors that influence the attitudes of agricultural experts regarding precision agriculture plays an important role in developing,promoting and establishing precision agriculture.The aim of this study was to identify factors affecting the attitudes of agricultural experts regarding the implementation of precision agriculture.A descriptive research design was employed as the research method.A research-made questionnaire was used to examine the agricultural experts’attitude toward precision agriculture.Internal consistency was demonstrated with a coefficient alpha of 0.87,and the content and face validity of the instrument was confirmed by a panel of experts.The results show that technical,economic and accessibility factors accounted for 55%of the changes in attitudes towards precision agriculture.The findings revealed that there were no significant differences between participants in terms of gender,field of study,extension education,age,experience,organizational position and attitudes,while education levels had a significant effect on the respondent’s attitudes.

利用Pure Experts Portal展示科研成果与拓展合作

Pure Experts Portal是一个基于网页的集成化科研信息系统,是一个全新的展示与发现科研信息的工具。用户可以在其中拥有完善的科研信息档案,也可以在全球范围内方便地查找领域内的专家。结合山东大学PureExpertsPortal的实例,详细阐述了该系统的功能、核心技术及数据基础,介绍了该系统的实施过程和运行维护的相关工作。

Chinese Association for the Study of Pain:Experts consensus on ultrasound-guided injections for the treatment of spinal pain in China(2020 edition)

Spinal pain(SP)is a common condition that has a major negative impact on a patient’s quality of life.Recent developments in ultrasound-guided injections for the treatment of SP are increasingly being used in clinical practice.This clinical expert consensus describes the purpose,significance,implementation methods,indications,contraindications,and techniques of ultrasound-guided injections.This consensus offers a practical reference point for physicians to implement successfully ultrasound-guided injections in the treatment of chronic SP.

Climate change impacts and forest adaptation in the Asia–Pacific region: from regional experts' perspectives

Expert opinions have been used in a variety of fields to identify relevant issues and courses of action. This study surveys experts in forestry and climate change from the Asia–Pacific region to gauge their perspectives on the impacts of climate change and on the challenges faced by forest adaptation in the region, and explores recommendations and initiatives for adapting forests to climate change. There was consensus regarding the impacts of climate change on forest ecosystems and on economic sectors such as agriculture and forestry. Respondents also indicated a lack of public awareness and policy and legislation as challenges to addressing climate change. However, the results indicate differences in opinion between regions on the negative impacts of climate change and in satisfaction with actions taken to address climate change,highlighting the need for locally specific policies and research. The study presents specific recommendations to address issues of most concern, based on subregion and professional affiliation throughout the Asia–Pacific region.The results can be used to improve policy and forest management throughout the region. This research will also provide valuable suggestions on how to apply research findings and management recommendations outside of the AP region. The conclusions should be communicated relative to the level of the research and the target audience,ensuring that scientific findings and management recommendations are effectively communicated to ensure successful implementation of forest adaptation strategies.

基于Design Expert的煤系高岭土离心增白试验

黑岱沟矿区矸石的高岭土质量分数高达70%。从煤系资源综合利用的角度出发,利用离心选矿法脱除以铁钛类为主的高密度致色杂质,提高煤系高岭土的煅烧白度。采用Design Expert进行四因素三水平的Box-Behnken正交试验设计;探究离心转速、入料速度、入料浓度和入料时间对煅烧白度及精矿产率的影响,拟合二次方程关系模型;给出煅烧白度及精矿产率与显著因素之间的响应曲面图,发现煅烧白度主要受离心转速影响,随离心转速的增加,煅烧白度先升高后降低,而精矿产率主要受入料时间影响,随入料时间的延长,精矿产率线性增加;最终结合Design Expert模拟结果,探究最佳离心增白试验条件,即离心转速为800 r/min,入料速度为8.4 L/min,入料浓度20%,入料时间100 s时,得到最佳高岭土煅烧白度(78.80%)和精矿产率(86.31%)。

The consensus on liver autotransplantation from an international panel of experts

IntroductionMore than 1.0 million patients worldwide are diagnosed with space-occupying lesions in the liver every year, with the number approaching 0.5 million per year in China, and only 20% of the lesions are resectable Due to a lack of available donors, only a limited number of patients underwent allogeneic liver transplantation, the remaining patients simply receive palliative care. Therefore, discovering new options for treating these patients is a high priority. Liver autotransplantation （LAT） is a surgical technique that adopts liver transplantation skills to radically treat spaceoccupying hepatic lesions, benign or malignant,

Determining winner in multi-attribute procurement auction:a method based on technical and business experts' evaluation information

A method for solving the winner determination problem （WDP） in multi-attribute procurement auctions is proposed, based on technical and business experts＇ evaluation information. Firstly, on the background of procurements in China, a multi-attribute pro- curement auction mechanism is presented, where technical and business experts participate in the bid evaluation. Then, the concept of TOPSIS is used to determine the positive and negative ideal points of the WDP according to bid prices, the technical and business experts＇ evaluation information. Further, the closeness coefficient of each bidder （candidate supplier） is obtained by calculating the distances to the positive and negative ideal points. Thus, the winning supplier can be determined according to the closeness coefficients. Finally, a numerical example is used to illustrate the use of the proposed method.

Expert Consensus on Nutritional Support for Children with Congenital Heart Disease(2023 Edition)

The second edition of the expert consensus on pediatric nutrition was formed based on a global update of pedia-tric nutrition guidelines or consensus worldwide,the management of congenital heart disease,and the results of multi-center clinical nutrition research for congenital heart disease following thefirst Chinese consensus edition of 2016.The consensus was also shaped by the results of three discussion sessions and two questionnaires con-ducted by the 13-member collaboration group.This process was informed by both clinical guidelines and expert consensus.The quality of literature,both in English and Chinese,and the level of recommendations were evaluated using the Grading of Recommendations Assessment,Development,and Evaluations(GRADE)system.

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

Heteroscedastic Laplace mixture of experts regression models and applications

Mixture of Experts(MoE)regression models are widely studied in statistics and machine learning for modeling heterogeneity in data for regression,clustering and classification.Laplace distribution is one of the most important statistical tools to analyze thick and tail data.Laplace Mixture of Linear Experts(LMoLE)regression models are based on the Laplace distribution which is more robust.Similar to modelling variance parameter in a homogeneous population,we propose and study a new novel class of models:heteroscedastic Laplace mixture of experts regression models to analyze the heteroscedastic data coming from a heterogeneous population in this paper.The issues of maximum likelihood estimation are addressed.In particular,Minorization-Maximization(MM)algorithm for estimating the regression parameters is developed.Properties of the estimators of the regression coefficients are evaluated through Monte Carlo simulations.Results from the analysis of two real data sets are presented.