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.

支持或反对:社交媒体用户观点的形成机理

探究社交媒体用户观点形成机理,揭示影响网民观点形成的重要因素,可深入理解网络用户的行为模式,洞察网络舆论本质。文章以“双减”话题微博数据为数据源,基于主题模型和机器学习的情感分析方法挖掘用户观点;从意见氛围、社交关系、前期行为特征和用户属性等维度构造特征变量,基于LightGBM建立不同主题下的情感预测模型,采用SHapley Additive exPlanations(SHAP)对特征重要性进行排序,发现影响网民观点形成的重要因素。研究发现:前期行为特征对用户观点形成的影响最大,意见氛围次之,社交关系即好友关系影响较弱;个体在情感倾向上更易跟随规模较大的传播主体,而不是崇拜权威。

角色演变视角下辟谣信息对社交媒体意见领袖形成的影响——基于舆论领导法则

[目的/意义]探究辟谣信息对社交媒体用户的影响,特别是对意见领袖形成现象机制的挖掘。构建社交媒体用户角色演变行为模型,研究人员可以更好地了解意见领袖的形成原因,刻画辟谣信息对社交媒体用户的作用机制,有助于对参与辟谣的“准意见领袖”进行合理引导,以提高社交媒体舆情管理的效率和准确性。[方法/过程]首先,对微博社交媒体平台中的特定主题事件进行跟踪,识别并筛选出“新生意见领袖”群体,并利用质性访谈对此群体进行深度访谈。然后,基于程序化扎根理论方法,利用NVivo 12软件进行扎根编码构建模型,并通过理论饱和度检验验证模型的完整性。最后,对社交媒体用户角色演变的原因进行深入分析和探讨。[结果/结论]在辟谣信息作用下,个体主观因素、社会环境因素和辟谣信息因素对一般用户向新生意见领袖的演变行为产生重要影响,其中个体主观因素在辟谣信息的作用下对意见领袖形成的影响更加显著,自我认知清晰、高情绪需求且情绪稳定的个体更容易成为影响力持久的意见领袖;社会公众认可、实质性奖励和政策法律支持共同构成社会环境因素,是意见领袖形成的重要保障;辟谣信息因素中的辟谣信息热度、可信度和发布质量等方面都会直接影响“潜在意见领袖”用户群体对辟谣事件的把握,进而干预其参与辟谣的决定。

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.

网络舆情事件链演化分析

为防范舆情风险,分析网络舆情的细粒度演化,提出一种去冗余的衍生事件内容关联演化分析框架。通过文本挖掘技术从海量文本流中提取主要的衍生事件,将舆情内容压缩到人工可判读的数量级;利用词移距计算相邻两个时间片上的衍生事件相似度,构建反映演化关系的衍生事件链图。以“上海特斯拉自燃”事件为例进行事件链演化分析,得到微博网络舆情事件发展不同阶段涉事主体在各个衍生事件中的话题转移关系,最后鲁棒性分析的结果验证了该分析方法具有降低微博短文本冗余信息的能力,提高了事件演化关联识别分析的准确性。该研究方法为舆情事件的事后复盘、同类舆情事件的预判和介入、衍生事件的科学研判提供了决策支持。

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。

论备案审查意见的督促纠错功能

2023年《立法法》修改赋予了全国人民代表大会专门委员会、常务委员会工作机构在审查法规、条例“同宪法或者法律相抵触”或“认为存在合宪性、合法性问题”时,可向制定机关提出“审查意见”的职权。地方人大“备案审查条例”也作出了相应的规定。“审查意见”作为强化人大备案审查职能的一项重要举措,一方面,“审查意见”刚性效力远强于修法前的“研究意见”;另一方面,“审查意见”能有效督促制定机关及时纠错。作为备案审查外部督促手段,“审查意见”表现为以“沟通”“协商”方式,形成兼具法律和政治双重督促效果。为进一步强化人大备案审查中的督促纠错功能,还需明确审查机关“督促”与制定机关“纠错”工作联系机制,明晰“审查意见”的督促对象、督促内容、督促方式等具体细则。

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。