Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Prevalence of Occupational Injury and Knowledge of Post-Exposure Prophylaxis Accessibility among Healthcare Workers in Mogadishu, Somalia

Introduction: Healthcare workers in Mogadishu, Somalia face significant occupational injury risks, particularly needle stick injuries, with 61.1% reporting incidents. This poses a serious threat to their health, leading to infections such as hepatitis B, hepatitis C, and HIV. Despite the high prevalence of injuries, awareness of Post-Exposure Prophylaxis (PEP) accessibility is relatively high, with 84.0% of respondents aware of it. However, there are gaps in knowledge and implementation, as evidenced by variations in availability of PEP. Improving workplace safety measures, providing comprehensive training on injury prevention and PEP protocols, and ensuring consistent availability of PEP in healthcare facilities are crucial steps to safeguard the well-being of healthcare workers in Mogadishu, Somalia. Methods: A cross-sectional study was conducted among hospital workers in Mogadishu, Somalia, focusing on professionals from various healthcare facilities. The study targeted nurses, doctors, laboratory personnel, and pharmacists. Purposive sampling was employed, resulting in a sample size of 383 calculated using Fisher’s sample size formula. Data were collected using coded questionnaires entered into Microsoft Excel 2019 and analyzed with SPSS software to generate frequencies and proportions, presented through frequency tables and pie figures. Results: The study in Mogadishu, Somalia, examined the prevalence of occupational injuries and knowledge of Post-Exposure Prophylaxis (PEP) accessibility among healthcare workers. Findings indicate a high prevalence of injuries, with 61.1% reporting incidents, predominantly needle stick injuries (60.6%). Despite the majority seeking prompt medical attention (72.0%), work-related illnesses affected 53.2% of respondents, notably work-related stress (59.5%). While most received training on injury and illness prevention (68.9%), gaps exist in PEP awareness, with 16.0% unaware of it. Nonetheless, 84.0% were aware, predominantly through health facilities (52.0%). Availability of PEP was reported by 71.3% in healthcare facilities, with variations in shift availability. The majority reported guidelines for PEP use (55.7%). Efforts are needed to bolster PEP awareness and ensure consistent availability in healthcare facilities to safeguard worker health. Conclusion: High prevalence of occupational injuries among healthcare workers, with needle stick injuries being the most common (60.6%). Despite this, 84.0% of respondents were aware of Post-Exposure Prophylaxis (PEP), primarily learning about it from health facilities (52.0%). While 71.3% reported the availability of PEP in their facility, 28.7% noted its unavailability. These results emphasize the need for improved education and accessibility of PEP to mitigate occupational injury risks.

Cultivate Talents with Both Noble Qualities and Knowledge of Chinese and Western Culture——Exclusive Interview with Professor Yin Meiqun, Dean of the Business School of Beijing International Studies University

Although it was frigid outside mid-winter, classrooms in the Business School of Beijing International Studies University are still as warm as spring,because of the passionate teachers and students. Professors were conducting lectures in English, as the students listen carefully and occasionally answered the questions posed in fluent English.If hearing their voices only, audiences might easily mistake that they were in a university in an English-speaking country.'

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

AI赋能哲学社会科学研究、传播及评价

人工智能正在改变整个人类社会,丰富人们的文化生活,促进科技创新与发展,是各行各业“新质化”的促进剂。人工智能可为哲学社会科学提供高效的研究工具和研究场景,将深刻影响哲学社会科学研究、传播及评价的方方面面。加速人工智能在哲学社会科学研究、传播及评价领域的深入应用,提升哲学社会科学研究效率,促进哲学社会科学的创新与发展,构建自主知识体系,是当下哲学社会科学工作者的重要使命。为推进AI赋能哲学社会科学(AI4SS),2024年9月28日,由西华大学、四川省社会科学学术期刊协会主办,西华大学学术期刊部、四川省社会科学重点研究基地四川学术成果分析与应用研究中心承办的“AI赋能社科研究与评价天府论坛”在西华大学举行。来自南京大学、中国人民大学、四川大学等高校与科研机构的20位专家学者,从不同学科、研究领域出发,就人工智能赋能哲学社会科学研究、传播及评价进行了深入交流。文章为20位专家学者在论坛上所作报告的主要观点。这些报告议题包括AI赋能人文社科研究方法的变革,法学、教育学、经济学、图书情报学、新闻传播等社会学科视域下的人工智能,AI辅助社会科学研究的学术伦理,人工智能在医疗健康、大宗商品价格风险监测、大学生高阶思维能力培养等方面的应用,AI赋能学术期刊出版与传播,AI赋能哲学社会科学评价新模式、新平台、新场景,等等。这些议题共同描绘了一个由AI技术驱动的多学科融合与创新的新时代。

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.

Occupational exposures to blood and body fluids (BBF): Assessment of knowledge, attitude and practice among health care workers in general hospitals in Lebanon

Healthcare workers (HCWs) who are employed in traditional health care workplaces face a serious danger that may threaten their life;it is their exposure to blood and body fluids (BBF). In Lebanon, the introduction of a hospital accreditation system has put a particular emphasis on staff safety, and on the evaluation of professional practice (EPP) programs. Methods: A cross-sectional survey was conducted amongst 277 HCWs working in 4 general hospitals in South Lebanon. Objective: 1) describe the prevalence and the risk factors for occupational exposure to BBF among HCWs;2) evaluate knowledge, attitude, and practices of HCW concerning blood-borne pathogens and adherence to universal safety precautions. Results: The mean age of the respondents was 32.14 years (SD = 10.33), 57.4% were females. 43.3% of HCWs expressed that they use gloves all the time for every activeity of care. 67.1% were aware that needles should not be recapped after use;registered nurses and nursing students were more aware than physicians and nursing assistants (nurse) in this subject. 30% of HCWs declared having had at least one occupational exposure to BBF;62.7% of all accidental exposure was reported to the department responsible for managing exposures. Percutaneous injuries were the most frequently reported. Vaccination coverage was 88.4% for hepatitis B, and 48.4% against influenza. The source patient was tested in 43.4% of reported BBF exposures. Accidental exposure to BBF was more frequent in older people (OR = 3.42;p = 0.03) and the more experienced. Subjects working in intensive care unit ward reported more exposure to BBF (OR = 3;p = 0.04). Participants incurring exposure to BBF resorted to different measures after the injury suggesting a lack of a uniform policy for post-exposure prophylaxis. Conclusion: Exposure to BBF represents an important and frequently preventable occupational hazard for HCWs in Lebanon that requires continuous EPP of HCWs, and a comprehensive approach for prevention and management.

Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.

Frequency Hopping Spread Spectrum Security Improvement with Encrypted Spreading Codes in a Partial Band Noise Jamming Environment

Frequency Hopping Spread Spectrum (FHSS) system is often deployed to protect wireless communication from jamming or to preclude undesired reception of the signal. Such themes can only be achieved if the jammer or undesired receiver does not have the knowledge of the spreading code. For this reason, unencrypted M-sequences are a deficient choice for the spreading code when a high level of security is required. The primary objective of this paper is to analyze vulnerability of linear feedback shift register (LFSRs) codes. Then, a new method based on encryption algorithm applied over spreading codes, named hidden frequency hopping is proposed to improve the security of FHSS. The proposed encryption security algorithm is highly reliable, and can be applied to all existing data communication systems based on spread spectrum techniques. Since the multi-user detection is an inherent characteristic for FHSS, the multi-user interference must be studied carefully. Hence, a new method called optimum pair “key-input” selection is proposed which reduces interference below the desired constant threshold.

Knowledge, Attitude, and Practice of Using Helmets in Children amongst Parents to Prevent Head Injuries: A Cross-Sectional Study in Riyadh, Saudi Arabia

Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-sectional study, done by online survey using a snowball sampling technique, the number of included responses were 386 parents (Male and female) living in Riyadh Aged 21 - 60 years old or above. Results: The study showed that there is a difference in Parents’ belief in the importance of helmet use while riding a Bicycle vs Motorcycle/Quad bike and that was affected by parents’ education level, almost all the people who answered the survey (76.7%) agree that it is important for their children to wear a helmet when riding both a Bicycle and a Motorcycle or Quadbike with a cumulative percentage of (93.8%). And almost all agreed on multiple approaches to help increase helmet use be it by forcing rental shops to give out helmets, forcing sellers to recommend the use of helmets, increasing awareness campaigns, and imposing fines for not wearing helmets. Conclusions: This study is the first to explore Family helmet use while riding Bicycles and Motorcycles/Quad bikes. Although Parent’s belief in the importance of helmet use for their children was high, it is clear that the level of practice is low. With that the risk of head injuries might be high, our findings suggest that safety interventions for increasing pediatric helmet use are needed to increase helmet use and reduce the risk of head injury and hospitalization.

组织理论视角的知识共享影响因素整合模型研究——基于湖南省IT企业的实证分析

以湖南省内21家IT企业知识管理实践活动为研究对象,基于组织理论分析框架,从组织结构特征、组织文化和组织互动关系构建并检验了知识共享影响因素模型。研究发现,组织正式化程度、复杂化程度和集权化程度对企业知识共享行为都有负向影响;官僚型文化不利于知识共享行为的产生,虽然支持型文化和创新型文化都有利于知识共享,但创新型文化的影响更大;跨部门或个体之间的信任和组织承诺对知识共享行为有积极影响作用。

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

Knowledge, attitudes, and practices regarding Covid-19 and their relationship with Covid-19 booster vaccination status among women with infertility

Objective:To elucidate the relationship among knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status among women with infertility.Methods:This questionnaire-based cross-sectional study was performed online and offline among women with infertility who visited an infertility clinic in Jakarta,Indonesia.We assessed the patient’s knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status and sociodemographic profile.Results:A total of 178 subjects participated in this study,and most participants(92.6%)had received booster Covid-19 vaccines.From the questionnaire,74.2%had good knowledge,and 99.4%had good attitudes regarding Covid-19;however,only 57.9%of patients had good practices.A weak positive correlation existed between knowledge and attitudes(r=0.11,P=0.13)and a moderate negative correlation between attitudes and practices(r=-0.44,P=0.56).Participants’knowledge about vaccines and infertility was correlated with booster vaccination status(P=0.04).Academic background(P=0.01)and attitudes(P=0.01)were also correlated with booster vaccination status.The significant determinants of hesitance of receiving Covid-19 booster vaccines were high school education or below(OR=0.08,95%CI 0.02-0.36)and poor practices(OR=0.21,95%CI 0.05-0.95).Conclusions:The majority of the participants had received the Covid-19 booster vaccine and had good knowledge and attitudes but poor practices regarding Covid-19.Most participants had poor knowledge about the relationship between infertility and the Covid-19 vaccine.The general population should be more informed and reminded about practices to prevent Covid-19 and the relationship between vaccination and fertility to increase the number of people who receive Covid-19 booster vaccines.

Assessment of disease specific knowledge and health-relatedquality of life among United States military veterans withinflammatory bowel disease

AIM To evaluate the association between patientdisease knowledge of inflammatory bowel disease （IBD）and health related quality of life （HRQoL） and identifypatient and disease related predictors of patientknowledge of IBD.METHODS： We performed a cross-sectional study ofIBD patients with an established diagnosis of IBD longerthan 3 mo prior to enrollment. The Crohn＇s and colitisknowledge score （CCKNOW） and short inflammatorybowel disease questionnaire （SIBDQ） were selfadministeredto assess patient knowledge of IBDand HRQoL, respectively. Demographic and diseasecharacteristics were abstracted from the electronicmedical record. The correlation between CCKNOWand SIBDQ scores was assessed by a linear regressionmodel. Associations of patient knowledge and thevariables of interest were calculated using ANOVA.RESULTS： A total of 101 patients were recruited.Caucasian race, younger age at diagnosis, and having a college or post-graduate degree were significantlyassociated with higher CCKNOW scores. Patients withCD had higher CCKNOW scores compared to patientswith ulcerative colitis and inflammatory bowel diseasetype unclassified, P 〈 0.01. There was no significantcorrelation between overall CCKNOW and SIBDQ scores（r^2 = 0.34, P = 0.13）. The knowledge sub-domain ofdiet in CCKNOW was negatively correlated with HRQoL（r^2 = 0.69, P 〈 0.01）.CONCLUSION： IBD diagnosis at a younger age inaddition to Caucasian race and higher education weresignificantly associated with higher knowledge aboutIBD. However, patient knowledge of IBD was notcorrelated with HRQoL. Further studies are required tostudy the effect of patient knowledge of IBD on otherclinical outcomes.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.

Knowledge Discovery from Dynamic Data on a Nonlinear System

A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.

Intelligent System for Parallel Fault-Tolerant Diagnostic Tests Construction

This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.

Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network

This paper proposes machine learning techniques to discover knowledge in a dataset in the form of if-then rules for the purpose of formulating queries for validation of a Bayesian belief network model of the same data. Although do-main expertise is often available, the query formulation task is tedious and laborious, and hence automation of query formulation is desirable. In an effort to automate the query formulation process, a machine learning algorithm is lev-eraged to discover knowledge in the form of if-then rules in the data from which the Bayesian belief network model under validation was also induced. The set of if-then rules are processed and filtered through domain expertise to identify a subset that consists of “interesting” and “significant” rules. The subset of interesting and significant rules is formulated into corresponding queries to be posed, for validation purposes, to the Bayesian belief network induced from the same dataset. The promise of the proposed methodology was assessed through an empirical study performed on a real-life dataset, the National Crime Victimization Survey, which has over 250 attributes and well over 200,000 data points. The study demonstrated that the proposed approach is feasible and provides automation, in part, of the query formulation process for validation of a complex probabilistic model, which culminates in substantial savings for the need for human expert involvement and investment.

Improving the Patient Discharge Planning Process through Knowledge Management by Using the Internet of Things

The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.

开放教育工商管理专业教学设计原则思考

本文采用系统研究的方法,从教育理论与社会实践两个方面较为全面地对开放教育工商管理专业学生的知识、能力、素质结构与内容进行了分析,论文研究基本结论是应从社会对开放教育学生的期望要求、社会未来发展以及开放教育的对象自身需求这三个方面来确定开放教育工商管理专业学生的知识、能力、素质结构的培养内容,并根据上述内容指出:在进行教学设计时应遵循适应性原则、系统性原则、现实性与前瞻性兼顾的原则、社会发展与个体全面发展有机统一原则、科学性开放性和实践性相统一原则、应用性为主的原则、专科本科不同学历层次纵向贯通以及不同专业横向衔接等七个原则。