The Evolutionary Rules of Production System

The competition among modem enterprises has been converted from products to capability. As the basis of competition, production system can＇t win the market unless it takes advantage of its capability in competition. Here in this article, the evolutionary rules of production system contributing to the establishment, renovation, reform, invention and promotion of the production of modem enterprises are observed.

A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.

Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System

As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.

An Application Expert System for Evaluating Effective Factors on Trust in B2C WebsitesTrust, Security, ANFIS, Fuzzy Logic, Rule Based Systems, Electronic Commerce

In new environments of trading, customer's trust is vital for the extended progress and development of electronic commerce. This paper proposes that in addition to known factors of electronic commerce B2C websites such a design of websites, security of websites and familiarity of website influence customers trust in online transactions. This paper presents an application of expert system on trust in electronic commerce. Based on experts’ judgment, a frame of work was proposed. The proposed model applies ANFIS and Mamdani inference fuzzy system to get the desired results and then results of two methods were compared. Two questionnaires were used in this study. The first questionnaire was developed for e-commerce experts, and the second one was designed for the customers of electronic websites. Based on AHP method, Expert Choice software was used to determine the priority of factors in the first questionnaire, and MATLAB and Excel were used for developing the fuzzy rules. Finally, the fuzzy logical kit was used to analyze the generated factors in the model. Our study findings show that trust in EC transactions is strongly mediated by perceived security.

Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

Improving Rule Base Quality to Enhance Production Systems Performance

Production systems have a special value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. Therefore, it is of high importance to consider different techniques to improve their performance. In this research, rule base is the component of the production system that we aim to focus on. This work therefore seeks to investigate this component and its relationship with other components and demonstrate how the improvement of its quality has a great impact on the performance of the production system as a whole. In this paper, the improvement of rule base quality is accomplished in two steps. The first step involves re-writing the rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be obtained easily. The second step involves augmenting the rule base with inference short-cut rules devised from the long inference chains. These inference short-cut rules have a great impact on the performance of the production system. Finally, simulations are performed on randomly generated rule bases with different sizes and goals to be proved. The simulations demonstrate that the suggested enhancements are very beneficial in improving the performance of production systems.

A Rule Management System for Knowledge Based Data Cleaning

In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.

Applying DNA Computation to Error Detection Problem in Rule-Based Systems

As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.

Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

我国法治体系建设中的立法质量评估与分析——基于2020年28省份法学专家的调研数据

从中国特色社会主义法律体系建设进入法治体系建设的新时代,立法质量成为法治建设的重要内容。从法理角度观察,我国立法质量建设不是从无到有的建设,而是提升法律规范性品质的建设,为此要构建一套符合我国法治建设实际的立法质量评估体系。基于详实的问卷调查数据,本评估体系将立法质量判断标准建立在指标得分及指标之间内在逻辑的双重基础之上,改变了以往仅凭指标简单加权平均来评估立法成效的方式,实现了立法整体发展水平和立法中各项要素发展情况的刻度化测评,从而有助于把握立法规律和现状,准确评估当前立法水平,以提升立法的规范性品质。

Optimization of Intraday Trading Strategy Based on ACD Rules and Pivot Point System in Chinese Market

Various trading strategies are applied in intraday high-frequency market to provide investors with reference signals to be on the right side of market at the right time. In this paper, we apply a trading strategy based on the combination of ACD rules and pivot points system, which is first proposed by Mark B. Fisher, into Chinese market. This strategy has been used by millions of traders to achieve substantial profits in the last two decades, however, discussions concerning on the methods of calculating specific entry point in this trading strategy are rare, which is crucial to this strategy. We suggest an improvement to this popular strategy, providing the calculating and optimizing methods in detail to verify its effectiveness in recent Chinese futures market. Because of the high liquidity and low commissions in stock index futures market, this trading strategy achieves substantial profits .However, given the less liquidity in commodity futures market, profits decrease and even be neutralized by the relatively high commissions.

DRG付费下重点稽核违规行为智能审核和监控规则库比较研究

目的通过对重点稽核违规行为智能审核和监控规则库进行规则类别、重点稽核行为、规则与行为对应程度比较分析,为完善我国DRG付费下医保重点稽核违规行为智能审核和监控提供参考。方法检索收集2019年5月20日—2023年12月31日各地医保局发布的DRG付费管理办法等政策文件及数据库中DRG付费智能审核监控规则的相关文献,针对其中与DRG付费下重点稽核违规行为相关的规则,采用文献分析法和比较分析法进行归纳和分析。结果DRG付费下重点稽核违规行为智能审核规则可分为逻辑判断类和参数指标类两大类,审核规则与重点稽核行为的对应程度各异。结论(1)统一全国重点稽核规则,保证规则的普适性、全面性;(2)深挖区域违规行为现状,强化地方特色;(3)逻辑判断类规则“提质”与参数指标类规则“增量”共进;(4)强化新型违规行为的科学研究,建立更具针对性的监管规则。

Applying Fuzzy Rule-Based System on FMEA to Assess the Risks on Project-Based Software Engineering Education

Project-based learning has been in widespread use in education. However, project managers are unaware of the students’ lack of experience and treat them as if they were professional staff. This paper proposes the application of a fuzzy failure mode and effects analysis model for project-based software engineering education. This method integrates the fuzzy rule-based system with learning agents. The agents construct the membership function from historical data. Data are processed by a clustering process that facilitates the construction of the membership function. It helps students who lack experience in risk assessment to develop their expertise in that skill. The paper also suggests a classification technique for a fuzzy rule-based system that can be used to judge risk based on a fuzzy inference system. The student project will thus be further enhanced with respect to risk assessment. We then discuss the design of experiments to verify the proposed model.

A rule induction algorithm for incomplete information system

Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.

Operating Rule Classification System of Water Supply Reservoir Based on Learning Classifier System

An operating rule classification system based on learning classifier system （LCS）, which learns through credit assignment （bucket brigade algorithm, BBA） and rule discovery （genetic algorithm, GA）, is established to extract water-supply reservoir operating rules. The proposed system acquires an online identification rate of 95% for training samples and an offline rate of 85% for testing samples in a case study. The performances of the rule classification system are discussed from the rationality of the obtained rules, the impact of training samples on rule extraction, and a comparison between the rule classification system and the artificial neural network （ANN）. The results indicate that the LCS is feasible and effective for the system to obtain the reservoir supply operating rules.

习近平法治思想中的“中华法系”概念

中华法系萌生发展于中华大地,为千百年来国家治理与社会运行作出了重要贡献。但作为一个法学概念,“中华法系”发端于19世纪末传入中国的“法族”概念,其内涵在中华民族艰辛探索法治道路过程中经历了长期建构与重构。法系与法律系统、法律体系,法律、道德及其相互关系,发展方向、未来命运及其决定因素是“中华法系”概念演进过程中形成的基本论题,最终表达的是中国法治的过去和未来如何在法治实践中获得存在的意义。“中华法系”概念百余年演进历程中围绕基本论题展开的讨论,表征着概念本身所具有的民族性、实践性与时代性。习近平总书记全面回顾了“中华法系”概念的演进历程与基本论题,深刻揭示了其丰富的制度内涵、思想内涵与文明内涵,并赋予其新的时代意义。习近平法治思想中的“中华法系”概念具有鲜明的主体性、范式性、引领性,是具有标识意义的重要概念。

南京市口腔医院前置审方系统的建立及应用体会

构建南京市口腔医院前置审方系统。调取2022年7月-2023年6月该系统启动后共404037张处方作为研究对象,统计不合理处方。系统预审弹窗率3.68%(14872/404037),医师主动修改率40.10%(5976/14872),药师干预率4.31%(349/8090),药师干预成功率98.57%(344/349),系统刚性拦截率1.06%(4278/404037),处方合理率98.85%(399410/404037)。主要不合理原因为重复用药、适应证不适宜或无适应证、遴选药物不适宜等。前置审方系统启用后不合理处方数量逐步减少。

论更加完善的中国特色社会主义法治体系的内涵及建设路径

在中国特色社会主义新时代,要进一步完善以宪法为核心的中国特色社会主义法律体系,加快建设更加完善的中国特色社会主义法治体系。建设更加完善的法治体系是法治中国建设的总目标,也是法治中国建设的总抓手,更是法治中国建设的总依托。更加完善的中国特色社会主义法治体系的基本内容主要包括:明晰中央和地方立法的位阶及判断标准,构建规范高效可行的法律体系形式结构,构建政府职权法定的行政法律规范体系。建设更加完善的中国特色社会主义法治体系的主要路径是:建设与宪法相适应的党内法规体系,建设更加严格规范公正文明的执法体系,建设更加公正高效权威的司法审判体系,建设更加严密的社会主义法治监督体系。

中国与东南亚合作应对电信网络诈骗犯罪策略优化:结构性错位与现代化融合

跨境电信诈骗犯罪是一种全新的高智商高科技型犯罪,当前各国犯罪侦破和司法审查仍处于经验积累阶段。近年来,部分东南亚国家尤其是澜沧江—湄公河流域国家电信网络诈骗案发数量骤增。中国公安机关会同海关总署等部门通过与缅甸等国家执法部门开展大规模反电诈行动,在缅北等地区取得阶段性成果,但电信诈骗犯罪活动仍有较大活动空间,且有向司法监管薄弱国家或地区转移的趋势,这种以境外第三国为犯罪源头,专门针对境内外中国公民财产为诈骗目标的新型网络犯罪亟需予以持续关注。新型网络电信诈骗团伙对社会经济安全、公民财产安全产生持续系统性威胁。跨境电信诈骗犯罪组织具有高科技化、善变多变和反侦查意识强等特征,电信诈骗犯罪在各国间面临罪与非罪的差异、罪名与刑罚的差异,各国间法治差异、应对的不对称性和结构性错位问题给跨国电信诈骗治理带来障碍,同时也存在跨境证据收集原则差异与证据转化难问题。文章提出按照预防为先的应对思路,加快构建涉外法治体系为切入,共同推进国家间涉外法治现代化融合发展,深化跨境侦防一体化建设与完善跨境诉讼制度。通过跨境“侦查—防范”体系的涉外法治与国际法治保障相结合,立法上扩大跨境技术侦查的证据来源范畴,优化跨境证据转化和有效利用,统筹协调预防犯罪,密切双边法治合作基础,推动澜沧江—湄公河次区域各国大数据警务合作发展,共同提升跨境电信诈骗犯罪治理效能。