Strong Rules Research Articles

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.探究周楣声《灸绳》医案灸法隐性知识显性化及其知识图谱构建的方法。收集《灸绳》医案数据，基于Python3.8.6进行频数统计分析，利用Gephi9.2软件进行复杂网络分析，登录古今医案云平台V2.3.5进行社团分析，并运用Neo4j3.5.25图数据库进行关联图谱和权值图谱的分析及验证。出现频率≥10%的疾病系统分别为外科、五官、运动、消化和呼吸系统。疾病系统下属疾病以痈、关节炎、腰椎间盘突出症、头痛为主。治疗常用灸法为熏灸、吹灸、直接灸和温针灸。复杂网络分析所得腧穴核心组方为压痛穴、至阳、三阴交、大椎、足三里、灵台、心俞、指尖、合谷，基本与高频腧穴相一致。社团分析共得到6个社团，分别与不同疾病相对应。关联图谱分析得到强关联规则穴对13条，至阳-大椎、压痛穴-灵台的关联性最强，与压痛穴关联性较高的腧穴为至阳、灵台、大椎、足三里、三阴交。高频疾病系统权值图谱中，外科系统疾病权值排名第一的关系为熏灸-痈-压痛穴，五官系统疾病权值排名第一的关系为吹灸-喉炎-合谷。关联图谱及权值图谱所得结论与数据挖掘结果相一致，可作为未来研究灸法诊疗知识库的有效途径。.

Strong Allee Effect Synaptic Plasticity Rule in an Unsupervised Learning Environment.

Synaptic plasticity, or the ability of a brain to change one or more of its functions or structures at the synaptic level, has generated and is still generating a lot of interest from the scientific community especially from neuroscientists. These interests went into high gear after empirical evidence was collected that challenged the established paradigm that human brain structures and functions are set from childhood and only modest changes were expected beyond. Early synaptic plasticity rules or laws to that regard include the basic Hebbian rule that proposed a mechanism for strengthening or weakening of synapses (weights) during learning and memory. This rule, however, did not account for the fact that weights must have bounded growth over time. Thereafter, many other rules that possess other desirable properties were proposed to complement the basic Hebbian rule. In particular, a desirable property in a synaptic plasticity rule is that the ambient system must account for inhibition, which is often achieved if the rule used allows for a lower bound in synaptic weights. To that regard, in this letter, we propose such a synaptic plasticity rule that is inspired by the Allee effect, a phenomenon often observed in population dynamics. We show that properties such as synaptic normalization, competition between weights, decorrelation potential, and dynamic stability are satisfied. We show that in fact, an Allee effect in synaptic plasticity can be construed as an absence of plasticity.

Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.

STRUCTURAL COMPLEXITY OF MOLECULAR, CHAIN, AND LAYERED CRYSTAL STRUCTURES OF NATURAL AND SYNTHETIC ARSENIC SULFIDES

A ladder-wise calculation scheme has been developed for the structural complexity of heterodesmic crystal structures, with crystal interpreted as a system of contacting molecules, chains, and layers. In the last stage of ladder-wise calculation the structural complexity of the main motif is summed with the complexity of the contacts beyond the main motif in correspondence with the strong additivity rule. The application potential of the scheme is demonstrated, and the calculation results for the crystal structures of natural and synthetic arsenic sulfides are presented. The coordination of molecules and chains that is necessary for calculating the complexity of contacts beyond the main motif is determined by the method of Voronoi–Dirichlet polyhedra.

Road Accident Prediction Model Using Machine Learning

Abstract: Last year, the number of traffic accidents declined to 3.66 lakh,the lowest level in the last 20 years. The strong rules buried in these common item sets frequently reveal the relationship between influencing variables of accidents, which may be exploited to break them and limit the incidence of accidents. The guidelines may also be used to investigate common accident sites, and appropriate security improvements can be implemented to prevent accidents and, as a result, enhance thecity's traffic safety. In general, association rule mining can generate many weak rules; thus, the study first devised a technique for calculating the minimum Support value of training parameters, then proposed a method for extracting strong rules automatically. The experiment results revealed that the strategies provided in the research are successful. As a result, an automatic modelling technique based on association rules was developed to help promote the successful use of association rule mining in intelligent transportation systems.

International retailer performance: Disentangling the interplay between rule of law and culture

Understanding the complexity of institutional factors in host countries and adapting strategies accordingly is crucial for international retailers, given the high failure rate of global operations. This paper investigates the impact of formal institutions (i.e., legal environments) and informal institutions (i.e., cultural values) on the performance of international retailers. We analyzed the data on 144 international retailers and 565 subsidiaries owned by these retailers, using three measures of retailer performance: sales per square meter, return on equity, and Tobin's Q. Our findings show that retailer performance is influenced by the interactions of the host country's rule of law and three cultural dimensions. Specifically, strong rule of law enhances the performance of international retailers in host countries, especially with high power distance or high uncertainty avoidance. In highly collectivistic countries, retailers can also build strong relationships to govern transactions. Results provide key insights that explain international retailers’ performance in different countries and deliver guidelines for retailer strategies.

Pseudospin-selective Floquet band engineering in black phosphorus

Time-periodic light field has emerged as a control knob for manipulating quantum states in solid-state materials1-3, cold atoms4 and photonic systems5 through hybridization with photon-dressed Floquet states6 in the strong-coupling limit, dubbed Floquet engineering. Such interaction leads to tailored properties of quantum materials7-11, for example, modifications of the topological properties of Dirac materials12,13 and modulation of the optical response14-16. Despite extensive research interests over the past decade3,8,17-20, there is no experimental evidence of momentum-resolved Floquet band engineering of semiconductors, which is a crucial step to extend Floquet engineering to a wide range of solid-state materials. Here, on the basis of time and angle-resolved photoemission spectroscopy measurements, we report experimental signatures of Floquet band engineering in a model semiconductor, black phosphorus. On near-resonance pumping at a photon energy of 340-440 meV, a strong band renormalization is observed near the band edges. In particular, light-induced dynamical gap opening is resolved at the resonance points, which emerges simultaneously with the Floquet sidebands. Moreover, the band renormalization shows a strong selection rule favouring pump polarization along the armchair direction, suggesting pseudospin selectivity for the Floquetband engineering as enforced by the lattice symmetry. Our work demonstrates pseudospin-selective Floquet band engineering in black phosphorus and provides important guiding principles for Floquet engineering of semiconductors.

What drives bank income smoothing? Evidence from Africa

We investigate whether banks use loan loss provisions to smooth income and whether this behaviour is influenced by foreign bank presence, ownership and institutional quality differences across African countries. We examine 370 banks from 21 African countries from 2002 to 2021. We find evidence that African banks use LLPs to smooth their income when they are more profitable during economic boom or recession. Income smoothing is persistent (i) among banks with a widely dispersed ownership, (ii) among banks with strong government ownership and (iii) among banks with weak government ownership. Income smoothing is also persistent in African countries that have greater corruption control, better regulatory quality and political stability. In contrast, moderate concentrated ownership reduces bank income smoothing. Bank income smoothing is also reduced in African countries that have strong rule of law, high government effectiveness, strong foreign bank presence and strong voice and accountability institutions. The implication is that effective corporate governance and institutional quality can constrain the extent of income smoothing by African banks.

Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke Correction

Using AI technology to improve teaching and learning is an important goal of educational sustainability. By mining the correlation between knowledge points, the discrete knowledge points can be integrated to improve the knowledge density and reduce the learning task. In addition, the successful experiences of similar learners can be shared, thus shortening the learning path of new learners. To change the common situation of irregular writing stroke order, to teach and correct stroke order effectively, this study uses association rules to explore the potential correlation between error-prone Chinese characters based on a large number of learners’ writing records, and then summarizes and sorts out a set of error-prone Chinese characters based on this. Every Chinese character contained in an error-prone category has a common error-prone feature. By correcting this error, it can be extended to every Chinese character of this category, and the learning efficiency of Chinese character strokes can be improved tens of times. In the training and testing system with a Chinese character error-prone character set, combined with the improved collaborative filtering algorithm, a learner-based personalized error-prone Chinese character recommendation model was proposed. Experimental results showed that the Apriori algorithm with lift measure can excavate effective strong association rules and provide an important reference for the character set table. The improved collaborative filtering algorithm can make use of the similarity between learners, share successful learning experiences, provide a personalized recommendation service for error-prone Chinese characters, and the recommendation performance is higher than that of the traditional collaborative filtering model. In the test of different types of learning groups, there are obvious differences between the independent pre-test and the post-training test, which effectively corrects the irregular writing habits, and further indicates that the excavation of knowledge correlation and the combination of learners’ similarity can effectively improve the efficiency and effect of teaching and learning.

Using improved feature extraction combined with RF-KNN classifier to predict coal and gas outburst

Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models.

Pathways to women’s electoral representation: the global effectiveness of legislative gender quotas over time

ABSTRACT This inquiry investigates the effectiveness of legislative gender quotas on women’s electoral representation and how it evolves over time and across countries. Using 190 countries spanning between 1947 and 2015, the findings suggest that quotas’ effectiveness is conditional on types and features, varies with time and imprinted with countries’ historical representation status. Reserved seats usher in more women when featuring high legislative thresholds and the effects are stable; Candidate quotas signal countries’ historical disadvantage but show an acceleration effect on women’s representation over time. Strong placement rules increase women’s representation, yet the effects decelerate. Strong sanctions indicate countries’ historical disadvantage, nevertheless, accelerate women’s representation over time. The interactive dynamics between time and quota types and features prove nuanced, presenting good room for policy learning and future policy design.

PS-P12-2: NEEDS ASSESSMENT OF LEADERSHIP AND GOVERNANCE IN CARDIOVASCULAR HEALTH IN NEPAL

Objective: Good governance and leadership to address non-communicable diseases and minimization of their risk factors is crucial to improve healthy life expectancy particularly in low- and middle-income countries. The objective of this study was to understand and document cardiovascular disease (CVD) programs and policy formulation process and epitomize the challenges and opportunities for leadership and good governance for the health system to address non-communicable diseases particularly cardiovascular diseases in Nepal. Design and method: A national level task force was formed to coordinate and steer the overall needs assessment process. A qualitative study design was adopted using The Health System Assessment Approach Manual. Eighteen indicators under six topical areas of leadership and governance in cardiovascular health were assessed. Health laws, policies, regulatory standards, planning and strategy documents and reports on civil society engagement were reviewed. Seven key stakeholders from ministry, professional councils and non-governmental organizations working on CVD were identified and in depth interviews were conducted. Field notes and tape records of interview were compiled and transcribed. Strength, weakness, opportunities and threats in each topical area of leadership and governance were analyzed. Results: Voice and accountability exist in planning for health from the local level. The government has shown strong willingness and has a strategy to work together with the private and non-government sectors in health however, the coordination with non-state actors during policy formulation has not been effective. There are strong rules in place for regulatory quality, control of corruption and maintaining financial transparency. The government frequently relies on evidence generated from large-scale surveys for health policy formulation and planning but research in cardiovascular health has been below par. There is a scarcity of CVD-specific treatment protocols at all levels of government coupled with weak reporting from the private sector. Expensive health care with ineffective cost control over drugs and services, medical malpractice with commission system and lack of interest among donors to invest in CVD were important challenges for strong leadership and governance. Conclusions: Despite plenty of opportunities, much homework is needed to improve leadership and governance in cardiovascular health in Nepal. The government needs to designate a workforce for specific programs to help monitor the enforcement of health sector regulations, allocate enough funding to encourage CVD research and work towards developing CVD-specific guidelines, protocols, and capacity building. The government needs to leverage the opportunities associated with the current decentralized health system.

Occupation, transformation, communication : Interaction between the geographic environment, regional administration, and traffic in Northern Transdanubia during the Roman Period. The ALP project

When the expanding Roman Empire reached today’s Transdanubia and incorporated the area as Pannonia province, this part of the Carpathian Basin got under a strong central rule for the first time. The economic and social framework that developed determined not only the history but also the structure of the territory in the centuries to come. Our understanding of how the economy, society, or simply life was organised in the territory of the former empire is constantly improving. The presented research project focuses on settling in Pannonia, that is, in North-western Transdanubia throughout the Roman Period, by investigating the topic using non-destructive methods instead of excavations. It aims to reveal how the new power situation and economic structure affected the environment and how the new economic units emerged as a result of the mixing of a new immigrant population, the slow but constant inflow of which was related to the military occupation of the region, and the local population. The focus, in this case, has shifted from the limes, a zone that required constant military presence, to the hinterland, where steady food production secured a reliable supply for the army.

Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

With the rapid development of the economy, the scale of the power grid is expanding. The number of power equipment that constitutes the power grid has been very large, which makes the state data of power equipment grow explosively. These multi-source heterogeneous data have data differences, which lead to data variation in the process of transmission and preservation, thus forming the bad information of incomplete data. Therefore, the research on data integrity has become an urgent task. This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system. According to the characteristics and data sources of the massive data generated by power equipment, the fuzzy mining model of power equipment data is established, and the data is divided into numerical and non-numerical data based on numerical data. Take the text data of power equipment defects as the mining material. Then, the Apriori algorithm based on an array is used to mine deeply. The strong association rules in incomplete data of power equipment are obtained and analyzed. From the change trend of NRMSE metrics and classification accuracy, most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend, and will not fluctuate greatly with the growth of the missing rate. The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets, and the filling effect fluctuates greatly with the increase of the missing rate, that is, with the increase of the missing rate, the improvement effect of the model for the existing filling methods is higher than 4.3%. Through the incomplete data clustering technology studied in this paper, a more innovative state assessment of smart grid reliability operation is carried out, which has good research value and reference significance.

Незавершенный Лувр: общее место как способ присвоения

In contemporary public culture, there is a stereotypical interpretation of the history of the Louvre Palace as multiple successive attempts to achieve a ‘Grand design’ that dates back to the 16th century. However, this vision of the history of the Louvre does not emerge until the 19th century, although the narrative of its unfinishedness has existed for much longer. This article analyses the origins and transformation of this narrative throughout the 17th and 18th centuries. Despite the linguistic continuity associated with the stability of the very expression achever le Louvre (to finish the Louvre), the meaning that was invested in the idea of completion underwent significant changes. Originating in the context of the establishment and glorification of Louis XIV’s strong royal rule, the narrative was subsequently borrowed by critics of his great-grandson Louis XV’s reign and played a significant role in establishing the royal palace as a national treasure. By the 1760-s, in public discussions about the Louvre its completion becomes an allegory of a process of civilisation carried out in the name of the national interest. The changes that took place contributed greatly to the subsequent interpretation of the history of the Louvre and facilitated an ambitious project to merge it with the Tuileries Palace.

Fiscal rules as institutional tools for public debt management in the European Union Member States

Abstract This paper aims to assess the impact of the second-generation numerical fiscal rules on the effectiveness of public debt management in the Member States of the European Union. The research was conducted using dynamic panel models on a sample of 27 EU Member States over the period 2008–2021. The effectiveness of public debt management was determined by the level of public debt servicing costs, considering not only the impact of the quality of numerical fiscal rules on interest payments, but also other factors influenced by these rules, such as the quality of fiscal policy, the solvency of public finances and the quality of institutional governance. The motivation for this topic was to evaluate the effectiveness of the second-generation numerical fiscal rules following the changes made to their design in the context of the reconstruction of the EU fiscal surveillance system after the global economic and financial crisis of 2008–2010. The research has found that strong numerical fiscal rules improve the effectiveness of public debt management. In addition, stable fiscal policy and higher solvency of public finances, as well as political stability and the absence of violence, are conducive to lower public debt servicing costs. This paper enriches the literature by extending it with a new approach to fiscal rules, highlighting their multifaceted impact on the quality of public debt management.

Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm

The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distributes daily consumer goods. The algorithm used is Frequent Pattern – Growth which is one of the algorithms in data mining used to find relationships in large data based on the number of occurrences of these data relationships. The Association Rule Mining method can be used in the retail business field, known as Market Basket Analysis. The application used for testing is Rapidminer 9.10. The research stages include: data collection, data preparation, FP-Growth algorithm implementation, result analysis and conclusions. The results of the tests carried out resulted in 819 rules with a total of 85 rules. The results of grouping strong rules based on the combination and number of products that produce information that is expected to be used as recommendations to promote products with discount, cross-selling, up-selling, product bundling and other types of promotions to increase product sales.

Prevalence of internet gaming disorder among medical students at medical college in Jamnagar, Gujarat

Background: Smartphone usage became drastically increased in the past two decades. Spending abnormally more time playing games can lead to development of internet gaming disorder (IGD) which leads to negative impact on health and lifestyle. Aim and Objectives: The aim of this study was to find out the prevalence of IGD and smartphone usage patterns in medical students. Materials and Methods: The study was held in M. P. Shah Government Medical College in Jamnagar. Total 206 1st year medical students were included for this study. Pre-validated self-made questionnaire and pre-structured IGD-20 questionnaire were used in this study. Results: The prevalence of IGD in medical students is 1.46%. In male population, the prevalence of IGD was higher (1.6%) compared to female population (1.23%). Conclusion: IGD can be a great disaster if remain unnoticed. It can change one’s lifestyle gradually. Preventive measures such as parental locking system and strong rules for mobile phone use in university hours should be implied.

Deeds and Words: Farmers’ Attitude-Paradox in Collective Action for Small-Scale Irrigation

We explore the mechanisms of the attitude-behavior paradox and how multiple stakeholders strategize to compromise their attitudes and behaviors. Through an instrumental variable probit model, we examine the effect of income heterogeneity and social ties on the farmers' attitude-behavior paradox for collective action. The empirical results demonstrate that weak and strong ties, income heterogeneity, interaction terms, education, community environment, and community rules negatively affect the paradox, whereas water stealing and water use conflicts positively affect it. After dividing the paradox into two forms, we find that weak ties, the interaction terms thereof, negatively affect the paradox for "having negative attitude but do have behavior", while income heterogeneity negatively affects the paradox for "having positive attitude but no behavior". We contribute to the understanding of mechanisms whereby economic incentives and social structures interplay in addressing the above paradox. We conclude by discussing the implications for policies to overcome this social dilemma.

Regional Geological Disasters Emergency Management System Monitored by Big Data Platform

In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property.