An Approach to Evaluate the Implementation Effects of Advanced Manufacturing Technology Based on the Specialists' Opinion Selection

Since domestic corporations firstly introduced into and implemented the Advanced Manufacturing Technology (AMT),how to scientifically evaluate the implementation effects of AMT has been an urgent problem.Through the review of for- eign relative studies,this paper constructs an indicator system used to evaluate the implementation effects of AMT.Operational performance,Organizational or managerial performance,Satisfactory performance,and Competitive performance are indicators of first level in the system,and 19 indicators of second level have been selected to estimate the implementation effects of AMT.In this study,a model based on Minkowski distance discriminance to select the specialists’ opinion was designed to evaluate the indica- tor system,and the measure’s validity and practicability was proved by a case study to a diesel engine factory.

基于Faculty Opinions最新推荐评分的生物医学科技论文评价指标相关性研究

目的/意义分析不同生物医学科技论文数据库中的各类评价指标,挖掘其中隐含关联。方法/过程以Faculty Opinions推荐的文献为基础,分别从Altmetric、Scopus、WOS数据平台获取各类代表性指标,运用SPSS软件进行斯皮尔曼相关性比较分析。结果/结论不同学科类别文章Faculty Opinions最新推荐评分中,RS与WSS的相关度最高,RCR与RS相关度中等,Scopus各指标均与社会计量学各指标高度相关,Scopus与WOS各指标之间的相关度较高。

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Opinions of Students on the Contribution of the DIVA 3D Virtual Dissection Table to the Teaching of Anatomy at the Faculty of Medicine and Odontostomatology of Bamako

Objective: Study the contribution of the DIVA 3D dissection table in the teaching of anatomy at the Faculty of Medicine and Odontology of Bamako. Material and Methods: This was a qualitative study carried out from November 1 to December 30, 2023 at the clinical and morphological anatomy laboratory of the Faculty of Medicine and Odontostomatology of Bamako. Included in this study were students who participated in practical and tutorial sessions. The variables studied during this study were: the previous performance of dissection on a cadaver by the students, the opinion of the students on dissection on a cadaver, the replacement of dissection on a cadaver by virtual dissection in the absence of a body, the level student satisfaction. Results: We surveyed 130 participants. The average age was 22 ± 0.2 years with extremes of 17 and 29 years. 95.3% of participants were students. According to 66.7% of participants, virtual dissection is a good palliative in the absence of a corpse. 95.3% of participants found using the virtual dissection table easy with an average of 7.88 ± 1.4. The overall assessment was well rated by 99.3% of participants. Conclusion: According to the results of this study, the virtual dissection table should be improved by integrating commented videos. The use of the DIVA 3D virtual dissection table during practical and tutorial sessions is well appreciated by the students. We believe that the teaching of anatomy using 3D digital technology should be included in the study programs of the Faculty of Medicine and Odontostomatology.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Opinion of Stakeholders on the Management of Ecotourism in the Benue National Park of the North Region of Cameroon

Local communities living around protected areas stand high chance of benefiting from economic development and livelihood improvement through ecotourism if well planned. The objective of this study was to contribute to the understanding of stakeholders’ opinions on the valorization of ecotourism in order to propose alternative sustainable strategies. The study used a research method based on purposive sampling. Semi-structured questionnaires were administered to 125 household heads and 10 stakeholders were also interviewed with a question guide. The results were inputted into a student statistical package (SPSS) version 16. The results were analyzed using descriptive statistics and Chi-square was used to compare means. The results revealed that the test of dependence (Chi2) between the variables showed a significant difference at (p < 0.05). 51.9% of the respondent in ZIC 5 said that ecotourism has increased employment while respondents in the other sectors (ZIC 1, 4 and 9) who make up 76% of the respondents said ecotourism-related jobs in the park are seasonal and not frequent. Regarding the dependence of local communities on the park’s resources, 25% of ZIC 5 is highly dependent on the park, whereas, in the other sectors, only 18% are dependent on the park for their household income. This result is explained by the inaccessibility of local communities to the parks. The results of the impacts of ecotourism on the livelihoods of communities in localities reflect a general feeling of dissatisfaction with 88.8% of respondents manifesting their dissatisfaction with park authorities. Thus, 87.2% of respondents think that ecotourism does not bring significant economic benefits to the host communities. If ecotourism was planned, the benefits that accrue from activity could boost the economy of the area and support the conservation of the park, which so far remains insignificant. However, the strategy for enhancing ecotourism should be a participatory approach.

Kinetic Modeling of an Opinion Model on Social Networks

It is commonly accepted that, on social networks, the opinion of the agents with a higher connectivity, i.e., a larger number of followers, results in more convincing than that of the agents with a lower number of followers. By kinetic modeling approach, a kinetic model of opinion formation on social networks is derived, in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd type model with three opinions, ±1, 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. The asymptotic mean opinion of a social network is determined in terms of the initial opinion and the connectivity of the agents.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

A Knowledge-Integrate Cross-Domain Data Generation Method for Aspect and Opinion Co-Extraction

To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.

新闻舆论在网络暴力治理中的功能思考——从沃尔特·李普曼的《Public Opinion》谈起

网络暴力通常以言论暴力、舆论极化的形式出现。网络暴力破坏了网络生态,并引起各国的普遍重视。由于新闻媒体议程及其报道框架影响着舆论的形成,新闻舆论对网络舆论的引导也就十分重要。当代人的“媒介化生存”产生了宏大的“拟态环境”,新闻议程对公众议程的塑造日益深重,为权威信源、新闻真相等介入网络舆论治理提供了可能性。主流媒体在场,可以消弭网络空间暴戾之气;权威新闻舆论,可以引导网络空间情感宣泄;发掘新闻真相,能够疏导网络舆论。同时,网络暴力的治理尚需多元力量的参与。

The Protection of Right of Minors to Express Their Opinions in Family Litigations

The right of minors to express their opinions in family litigations refers to minors,who have a certain degree of mental capacity and assertiveness,having the right to express their opinions on the arrangements or decisions related to themselves when the courts handle familial litigation cases involving their interests,and to request the courts hear their opinions.In essence,the right of minors to express their opinions in family litigations belongs to the basic procedural rights of minors as well as judicial beneficiary rights.As for the protection of the right of minors to express their opinions in family litigations,recognizing the status of minors as independent subjects of rights is the logical premise,the procedural guarantee for the rights of the substantive parties involved is the direct cause,and ensuring the best interests of children is the fundamental reason.In order to protect the right of minors to express their opinions in family litigations,China should standardize the mechanisms for judges to hear the opinions of minors directly,improve the mechanisms for family investigators,and establish mechanisms for representing the interests of minors.

Supervision of Public Opinion under the Background of Social Co-governance-Take the Changsheng Vaccine Incident as an Example

Objective To provide reference for the news media to give play to the role of public opinion supervision in time based on the background of drug safety and social co-governance.Methods The method of case analysis was used to make a retrospective study on the Changsheng vaccine incident in 2018.Then the role of mainstream media,pharmaceutical media,and self-media in the supervision of public opinion was investigated.Results and Conclusion Both mainstream and pharmaceutical media played an excellent role in supervising the Changchun Changsheng vaccine incident.However,the content published by some pharmaceutical media was hard to understand by ordinary people.Besides,the role of self-media in public opinion supervision was polarized.Some self-media closely kept pace with mainstream media in public opinion supervision.Other self-media unilaterally pursued the click rate,publishing false information to guide wrong public opinion.The news media should optimize the supervision efficiency of drug safety.On the one hand,pharmaceutical media should pay attention to the fact that readers may not understand the difficult terms because they are not professional.On the other hand,self-media practitioners should improve their professional quality so that they will not publish some fake news to mislead public opinion.

IP属地化政策对舆情评论的影响研究——基于微博用户属地公开的准自然实验

[目的/意义]实证评估公开属地政策能否以及如何对舆情评论产生影响,为网络舆情研究、平台优化和治理提供启示。[研究设计/方法]基于自我监控理论和隐私计算理论构建研究模型和假设,以微博“IP属地”功能的推出为准自然实验,运用双重差分法和断点回归模型探究IP属地化政策对舆情评论的影响。[结论/发现]政策引发用户自我监控效应和风险感知效应,不仅整体改善了评论情感倾向、评论可读性和共情性,也能够显著抑制评论情感极性,该结论在一系列稳健性检验后仍然成立。政策影响在不同用户群体之间存在显著差异,相较处于事件热点中心的用户,属地公开更能促使处于非热点中心的用户做出改变。政策同时加剧评论中的地域歧视现象。[创新/价值]揭示了IP属地化政策对在线评论行为的作用,为清朗网络环境提供新视角,丰富了IP属地化与舆情评论行为影响因素的相关研究。

企业诚信建设与注册会计师审计意见

在全社会强调传承诚信文化的背景下,已有文献研究了诚信建设带给企业的诸多后果,却鲜有研究从审计市场角度分析企业诚信的影响。本文基于诚信的三个基础因素“言”“行”和“言行不一致”,利用年报文本和监管处罚数据构建了企业诚信指标,分析了企业诚信对审计意见的影响与机制。研究发现,企业诚信降低了注册会计师出具非标意见的可能性,该现象在非国有企业中更加突出。企业诚信通过减少财务报告错报、抑制盈余操纵和加强内部控制的方式,最终影响了其对审计意见的判断。进一步地,当客户的应收账款和存货占比越高、客户越重要时,企业诚信更能减少非标审计意见出具。本研究有助于促进企业从审计市场角度理解诚信建设的重要性。

机构投资者调研与公司审计意见购买行为:刺激效应还是抑制效应?

公司管理层实施审计意见购买行为及其监管治理是审计学界与监管部门长期关注的重要问题。机构投资者调研作为投资者发掘公司内部信息并缓解信息不对称的一种非正式制度安排,会抑制还是刺激公司实施审计意见购买行为?本文基于我国上市公司的机构投资者调研数据,通过实证研究发现:机构投资者调研会刺激公司实施审计意见购买行为,参与调研机构数量越多、调研频数越高,被调研公司的审计意见购买动机越强,审计意见激进度也越高。进一步分析发现,在机构投资者调研的刺激效应下,公司主要通过选聘“中小”事务所实施审计意见购买,其目的是掩盖公司盈余管理及经营风险的“不利信号”。在缓解内生问题、排除替代性解释、考虑审计轮换制度影响等一系列稳健测试之后,上述结论依然成立。因此,建议会计师事务所应重视由机构投资者调研衍生的客户公司审计风险,市场监管部门应加强公司审计师选聘行为监管,提高机构投资者调研的外部治理效应。

“再塑造”与“高风险”:生成式人工智能对舆论安全的影响

[研究目的]探讨对信息内容具有强理解、强生成能力的生成式人工智能,将如何改变既有舆论生产流程,如何推动舆论传播发展,以及对舆论安全带来的影响。[研究方法]该研究结合既有研究,系统性分析生成式人工智能对舆论生产与传播的影响,揭示其可能对舆论安全造成的多方位冲击,并在此基础上提出完善舆论安全的举措。[研究结论]生成式人工智能将简化舆论生产流程,加速舆论格局转变,强化资本对舆论的影响力。基于该技术开展的意识形态侵蚀行为更加难以防御,对该技术的大规模使用将让舆论生态更混乱,舆论治理更困难,虚假信息生产与传播更加隐蔽,并外溢出难以预估的危险。为此需构建多方参与的舆论生产与传播监管格局,强化舆论引导能力,传递优质价值观。

舆论动力学模型综述

社交网络为信息传播提供了媒介,导致了舆论的快速发展。控制舆论的发展方向是舆论动力学的核心问题之一。然而,舆论动力学模型主要通过研究主体意见更新的方式,来推理出舆论演化的规律。针对目前的舆论动力学模型进行了分类,分析了各自的优缺点,及其在不同领域的应用,并总结了舆论动力学的未来研究方向。该研究有助于理解舆论演化的规律,从而可以为政府等机构控制舆论导向提供较好的指导。

ChatGPT网络舆情特征多维度演化分析

[研究目的]ChatGPT在世界范围内刷爆网络,引起全球的关注和热议。互联网上针对ChatGPT产生多种不同的声音,从广泛社会舆论视角探究ChatGPT网络舆情特征的多维度演化规律,有利于为新兴科技类产品的舆情研判和利益相关者决策提供支撑。[研究方法]基于传播学视角构建了ChatGPT网络舆情特征多维度演化分析框架,以微博为数据获取平台,使用意见领袖影响力、LDA主题挖掘以及SnowNLP情感分析方法对ChatGPT网络舆情多维度特征演化展开分析。[研究结论]ChatGPT网络舆情发展分为缓慢萌发期、火爆出圈期、波动下降期以及稳定发展期四个阶段;初期意见领袖中包含科技科普博主、权威媒体以及社会公众人物,后期自媒体和名人的意见领袖作用逐渐被权威媒体和网络平台媒体所取代;热点主题数量增加,且主题不断细化和深化。公众对ChatGPT的情感以积极乐观为主。ChatGPT网络舆情的情感倾向与主题演变趋势相一致。