Curriculum Development and Teachers for Cultivating OECD Student Agency in Japan:From an Examination of Educational Thought,Educational System,History,and Contemporary Science and Mathematics Education Practices

The purpose of this paper is to examine the development of a curriculum in Japan to foster the agency(OECD Student Agency)necessary for children living in the future,as outlined in the OECD Education2030 Project,and the role of teachers who implement this curriculum.Therefore,I aim to achieve this goal by taking an overview of educational thought,educational systems and history,and examining several initiatives in the OECD Project and the situation of a Japanese high school that practices science and mathematics education.As a result of analyzing previous research and the narratives obtained from interviews with Teacher A(pseudonym)of a certain F High School(pseudonym),it was found that school education should not simply teach subjects,but should aim to enable student to help student exercise agency in the future.It was suggested that it is not necessary to add content to the curriculum,but rather to prepare to provide students with competencies that will enable them to use the content in various contexts,situations,and circumstances,suggesting that in order to discover such important competencies,teachers and other related parties are required to implement curricula that allow students to slowly and carefully explore the competencies surrounding the content,and to conceive and build curricula that include both hidden and intended curriculum that allows student to reflect on the curriculum they have achieved.

Vocational Identity and Agency of Chinese College Students: Effects of Filial Piety and Implications in Career Education

Balancing family obligations and personal aspirations is crucial for college students in making life decisions.This critical review examined empirical studies on the effects of filial piety on college students’vocational identity and agency.According to identity capital theory,college students need a sense of agency to construct identity consciously and adapt to adulthood.Filial piety may profoundly influence this process as the core value and internalized social structure for the role of being an adult child in a Chinese family.However,no studies have been conducted from the identity capital perspective.This review found that filial piety had direct effects on college students'vocational identity and also indirect effects by shaping specific agency competencies.The impact of filial piety on the agency was more likely to occur through providing normative guidance about adult roles,influencing how college students view family as a supportive resource,and perpetuating parenting experiences.Based on these findings,this article suggested further examining the mediating role of agency between filial piety and identity development,as well as the effects of filial piety on the psychological foundation of agency development.This review suggested enhancing the agency of college students and encouraging reflections on filial piety in career education.

Effect of Brisk Walking on Self-Care Agency or Care Dependency among Colorectal Cancer Patients with Permanent Stoma

Objective:The purpose of this study was to determine the effectiveness of brisk walking as an intervention for self-care agency and care dependency in patients with permanent colorectal cancer stoma.Method:This study adopted a quasi-experimental research design,specifically a non-equivalent control group pre-test and post-test design.Utilizing the Exercise of Self-Care Agency Scale(ESCA)and Care Dependency Scale(CDS),a survey was administered to 64 patients from a hospital in Shandong Province.The statistical methods used for analyzing data included frequency,mean,standard deviation(SD),independent t-test,P-value calculation,and dependent t-test.Result:After two months of a brisk walking exercise program,participants in the experimental group had a higher level of self-care agency than before the experiment(P<0.05),and their level of care dependency was significantly reduced(P<0.05).Participants in the control group also showed higher levels of self-care agency(P<0.05)and lower levels of care dependency(P<0.05)after two months compared to their levels before the two months.Conclusion:The brisk walking program had a positive impact on patients’self-care agency and reduced their care dependency.

基于ER Rule的多分类器汽车评论情感分类研究

该文针对汽车评论语料的情感二分类问题,提出一种基于证据推理规则的多分类器融合的情感分类方法。在情感特征构建方面,通过实验对比不同特征模型对分类结果的影响,并改进传统的TFIDF权重计算方法。同时,在此基础上使用ER Rule融合不同分类器进行文本情感极性分析,并考虑各分类器的权重和可靠度。最后,爬取汽车网站上的评论数据对上述方法进行测试,并用公开的中文酒店评论语料数据进行了验证,结果表明该方法能够有效集成不同分类器的优点,与传统机器学习分类算法相比,其结果在Recall,F1值和Accuracy三个指标上得到了提高,与目前流行的深度学习算法和集成学习算法相比,其结果总体占优。

Profile of Declared Work Accidents at the Parakou Agency of the National Social Security Fund, 2015-2021, Benin

Introduction: Work accidents (WA) have consequences for both the individual and the company. The objective of this study is to study the profile of work accidents declared to the Parakou agency of the National Social Security Fund (CNSS) from 2015 to 2021. Method: This was a descriptive cross-sectional study with retrospective data collection. It concerned workers affiliated with the CNSS in Parakou, victims of declared WA whose file was available and exploitable. The sampling was exhaustive. Data was collected from registers and worker records. The variables studied were socio-demographic and related to the accident. They were analyzed using R version 4.1 software. Results: The annual incidence rate of WA varied from 5.8% in 2015 to 1.7% in 2021. The victims were 90.6% male. There were more subjects aged 28 to 37 years (37.1%). Workers and laborers were the most concerned. Fractures (22.9%) were the major lesions. At 38.5% they sat in the thoracic limbs and at 22.5% in the pelvic limbs. The majority (63.6%) of these accidents occurred in the workplace. The processing industry sector was mainly (52.1%) the place of accidents. Conclusion: The prevalence of WA declared to the agency of the National Social Security Fund of Parakou from 2015 to 2021 is decreasing. The most affected were young people. It is important to assess their socio-economic impacts.

Isolating the female agency‑driven development factor in external sovereign emerging market debt

The underlying literature hypothesises and provides randomised evidence for the positive impact of promoting the broad-based inclusion,empowerment,and representation of women on regional ecosystems.This study seeks to isolate a female agencydriven development factor in external sovereign emerging market debt and finds evidence for superior risk-adjusted returns from tilting towards female agency leaders.We propose the female agency factor as an additional scope in the modern investor’s toolbox of holistic credit assessment,allowing investors to isolate the issuers which are the most effective sovereign transmission mechanisms of sustainable development capital.This contribution to the corpus supports the notion of integrating sustainability factors into portfolio construction and reinforces the argument for supporting femaleled development from a financial markets’perspective.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Environmental complaint insights through text mining based on the driver,pressure,state,impact,and response(DPSIR)framework:Evidence from an Italian environmental agency

Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

Study on the Medication Rules of Traditional Chinese Medicine Prescriptions for the Treatment of New Crown Pneumonia Based on Data Mining Technology

[Objectives]This study was conducted to analyze the medication rules of clinical prescriptions of traditional Chinese medicine decoction pieces for the treatment of novel coronavirus pneumonia(COVID-19)during the epidemic in multiple regions based on data mining technology,so as to provide a reference for the treatment of COVID-19 with traditional Chinese medicine.[Methods]The traditional Chinese medicine prescriptions used since the outbreak of COVID-19 in Hubei Province during the fight against the epidemic from February 25,2020 to February 14,2022,the traditional Chinese medicine prescriptions used by Guizhou traditional Chinese medicine expert team aiding Hubei Province,the traditional Chinese medicine prescriptions for rehabilitation and conditioning of patients in Ezhou of Hubei Province after discharge,the traditional Chinese medicine prescriptions for the prevention and treatment of COVID-19 in Guizhou Province,and the traditional Chinese medicine prescriptions for the treatment of COVID-19 collected from the end of 2019 to the present from the Chinese database of CNKI were collected as the data of this study.Excel was used to establish a database and enter it into the TCM inheritance calculation platform V3.5,and the association rules and k-means clustering algorithm were used to analyze the frequency of herbal medicines in prescriptions during the treatment of COVID-19,the frequency of four natures,five flavors,meridian distribution,and drug combinations.[Results]A total of 1859 COVID-19 patients treated with traditional Chinese medicine were included,and the proportion of males was higher than that of females,and middle-aged and elderly people were the most common group.A total of 2170 prescriptions of traditional Chinese medicine were included,involving a total of 383 traditional Chinese medicines.High-frequency medicines included poria,Radix Bupleuri,Radix Scutellariae,Herba Pogostemonis,Fructus Forsythiae,Flos Loniceraeetc.The four natures were mainly concentrated in cold,warm and neutral,and the five flavors were mainly concentrated in bitter,pungent and sweet.The herbal medicines were mainly attributed to the lungs and stomach meridians,and were mainly of heat-clearing,exterior syndrome-relieving and diuresis-promoting and damp-clearing types.A total of 24 high-frequency herbal combinations and 35 association rule were excavated,and 3 types of formulas were obtained by cluster analysis.[Conclusions]The analysis results and medicine combinations obtained in the formulas are consistent with the traditional Chinese medicine treatment theory of COVID-19 caused by wind-heat filth accompanied with damp and toxin.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Advancements in Medication Rule for Pulmonary Nodules: A Review of Current Research Progress

This paper reviewed the literature on medication rule of pulmonary nodules in recent years. It is found that contemporary doctors pay more attention to regulating Qi, clearing heat and detoxifying, eliminating phlegm, dissolving phlegm and dissipating masses. They use mild drugs, cold and warm treatments in parallel, combining the tastes of pungent, bitterness, and sweetness at the same time. The treatment focuses on the five viscera with emphasis on the lung meridian while also considering the spleen and stomach functions as well as soothing liver stagnation. This information aims to provide some reference for clinical treatment of pulmonary nodules.

Heterogeneous information fusion recognition method based on belief rule structure

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.

Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Composite Fractional Trapezoidal Rule with Romberg Integration

The aim of this research is to demonstrate a novel scheme for approximating the Riemann-Liouville fractional integral operator.This would be achieved by first establishing a fractional-order version of the 2-point Trapezoidal rule and then by proposing another fractional-order version of the(n+1)-composite Trapezoidal rule.In particular,the so-called divided-difference formula is typically employed to derive the 2-point Trapezoidal rule,which has accordingly been used to derive a more accurate fractional-order formula called the(n+1)-composite Trapezoidal rule.Additionally,in order to increase the accuracy of the proposed approximations by reducing the true errors,we incorporate the so-called Romberg integration,which is an extrapolation formula of the Trapezoidal rule for integration,into our proposed approaches.Several numerical examples are provided and compared with a modern definition of the Riemann-Liouville fractional integral operator to illustrate the efficacy of our scheme.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.