方正电子获“G7 Expert”称号并取得相关认证资格

近日，方正电子色彩管理服务工程师乔泉、高建波顺利通过美国印刷行业权威机构“美国国际数码企业联盟”组织的“G7 Expert”（即可以帮助印刷企业认证“G7 Master”资质的专业人员）认证考试，并获得“G7专家”的称号，这意味着方正电子从此成为国内为数不多的具有“G7”资格的企业之一。

方正电子获“G7 Expert”称号并取得“G7”认证资格

方正电子色彩管理服务工程师乔泉、高建波在经过严格筛选淘汰之后顺利通过美国印刷行业权威机构“美国国际数码企业联盟”（简称IDE Alliance）组织的“G7Expert”（G7专家，即可以帮助印刷企业认证“G7 Master”资质的专业人员）认证考试，

基于Design-Expert的电链锯锯齿结构参数优化

为提高电链锯的锯切效率,对电链锯锯齿的齿形结构参数进行研究,以单位锯切功为电链锯锯切效率的衡量指标,通过对电链锯锯切过程的仿真研究,得出不同结构锯齿的单位锯切功。以电链锯锯齿中的外形前角、侧刃楔角和顶刃楔角等结构参数作为影响因子,采用Box-Benhnken中心组合试验方法设计多因素正交试验,使用Design-expert软件进行数据分析得出最优齿形结构参数组合。研究结果表明,所选取的齿形结构参数对单位锯切功影响程度由大到小顺序依次为外形前角、侧刃楔角、顶刃楔角,并获得最优齿形结构参数组合,外形前角为10.92°、侧刃楔角为45.7°、顶刃楔角为45.41°。

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.

企业技术难题与专家精准对接语言模型Expert-ChatGLM

传统方式下,企业引进技术领域的专家时,通常通过简历等信息筛选。但该方式存在同质化高、精准匹配难等问题。为此,建立一个能实现专家与企业需求精准对接的模型迫在眉睫。近年来,随着人工智能的发展,出现了很多大型语言模型,如OpenAI的ChatGPT、斯坦福的Alpaca和清华大学的ChatGLM-6B。虽然这些模型在指令和聊天方面取得了成绩,但对于专家与企业的精准对接尚有不足。因此,提出了专家与企业精准对接语言模型Expert-ChatGLM,该模型对研究采集构建的专家知识图谱以及专家对话数据集进行了微调,能够准确理解企业需求并提供精准的专家信息。该模型能提高寻找专家的效率和质量,具有很大的发展潜力。

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.

Fully Completed Spherical Fuzzy Approach-Based Z Numbers(PHIModel)for Enhanced Group Expert Consensus

This study aims to establish an expert consensus and enhance the efficacy of decision-making processes by integrating Spherical Fuzzy Sets(SFSs)and Z-Numbers(SFZs).A novel group expert consensus technique,the PHImodel,is developed to address the inherent limitations of both SFSs and the traditional Delphi technique,particularly in uncertain,complex scenarios.In such contexts,the accuracy of expert knowledge and the confidence in their judgments are pivotal considerations.This study provides the fundamental operational principles and aggregation operators associated with SFSs and Z-numbers,encompassing weighted geometric and arithmetic operators alongside fully developed operators tailored for SFZs numbers.Subsequently,a case study and comparative analysis are conducted to illustrate the practicality and effectiveness of the proposed operators and methodologies.Integrating the PHI model with SFZs numbers represents a significant advancement in decision-making frameworks reliant on expert input.Further,this combination serves as a comprehensive tool for decision-makers,enabling them to achieve heightened levels of consensus while concurrently assessing the reliability of expert contributions.The case study results demonstrate the PHI model’s utility in resolving complex decision-making scenarios,showcasing its ability to improve consensus-building processes and enhance decision outcomes.Additionally,the comparative analysis highlights the superiority of the integrated approach over traditional methodologies,underscoring its potential to revolutionize decision-making practices in uncertain environments.

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.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

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.

German Automobile Expert:Cooperation Instead of Tariffs

“Europe has missed this development opportunity because we are short of a long-term vision.”MORE than 40 exhibitors from 15 major vehicle brands around the world,representing the latest cutting-edge technologies and models,gathered at the seventh China International Import Expo(CIIE),which was held in Shanghai from November 5 to 10.

British Law Expert:South China Sea Islands Are Chinese Territory

The History and Sovereignty of the South China Sea Islands clarifies the sovereignty over the South China Sea Islands with irrefutable facts.THE English edition of The History and Sovereignty of the South China Sea Islands was released by New Star Press under China International Communications Group at the 2024 Beijing International Book Fair on June 20.Prior to this,the Chinese version of the book,also published by New Star Press,had attracted wide attention from the international community.

基于Design-Expert的碱激发粉煤灰建筑外墙腻子配合比优化试验研究

为了拓展工业固体废弃物粉煤灰的回收利用途径,减少对环境造成的污染,增加其在筑材料中回收利用率。在建筑外墙腻子中加入碱性激发剂(Ca(OH)_(2)、Na_(2)SiO_(3))、粉煤灰,研究碱性激发剂的激发效果。同时通过Design-Expert设计试验,分析主要组分对腻子漆膜粘结强度、吸水量的影响。结果表明:加入碱性激发剂5%,粉煤灰32.5%时,吸水量减少14%,粘结强度增加43%。乳胶粉对吸水量、粘结强度的影响非常显著(P<0.0001),其次是粉煤灰的加入量。通过加入碱性激发剂、提高乳胶粉占比,粉煤灰掺量能达到32%~65%,原配比中水泥的掺量相对减少0%~16%。

Expert consensus on the technical specifications of the in vitro skin penetration test of cosmetic product by Franz diffusion cell

Background:Diffusion cell test is one of the most commonly used in vitro model to detect the percutaneous absorption of cosmetic ingredients.Although PRC State Administration of Quality Supervision and Quarantine has issued the guideline for the in vitro test method for the percutaneous absorption of drug molecules,there is still a lack of guideline for the percutaneous absorption test of cosmetic components.Objective:In order to improve the standardized level and make the in vitro percutaneous absorption test of cosmetic ingredients more comparable,based on the international and domestic experience,Transdermal Drug Delivery Committee of the World Federation of Chinese Medicine Societies organized the experts to discuss and formed this consensus as reference for the percutaneous absorption test of cosmetic components.Materials and Methods:To standardize the in vitro diffusion cell test technique for transdermal penetration of functional cosmetic ingredients,the Transdermal Drug Delivery Professional Committee of the World Federation of Societies of Traditional Chinese Medicine has organized many discussions among experts.

基于ANSYS和Design-Expert的农用挖掘机斗杆机构优化设计

JKW-20山地农用挖掘机斗杆机构存在设计冗余,在保证安全可靠的前提下,为解决斗杆机构材料冗余问题,实现轻量化改进,建立了农用挖掘机挖掘装置斗杆机构耦合模型,通过ANSYS软件静力学仿真及拓扑优化,确定了斗杆机构拓扑优化新结构。由于拓扑优化结构不符合实际生产要求,基于优化结果设计斗杆变量,确定优化目标函数,利用Design-Expert进行斗杆机构尺寸优化求解。由优化结果对比可知:运用Design-Expert优化后的斗杆结构相对原结构具备更轻的质量和更大的强度,该方法不仅节约了JKW-20山地农用挖掘机的生产成本,还可为类似的机构设计提供优化思路。

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%)。

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 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.