New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

The Theory of Membership Degree of Γ-Conclusion in Several n-Valued Logic Systems

Based on the analysis of the properties of Γ-conclusion by means of deduction theorems, completeness theorems and the theory of truth degree of formulas, the present papers introduces the concept of the membership degree of formulas A is a consequence of Γ (or Γ-conclusion) in Lukasiewicz n-valued propositional logic systems, Godel n-valued propositional logic system and the R0 n-valued propositional logic systems. The condition and related calculations of formulas A being Γ-conclusion were discussed by extent method. At the same time, some properties of membership degree of formulas A is a Γ-conclusion were given. We provide its algorithm of the membership degree of formulas A is a Γ-conclusion by the constructions of theory root.

Asymptotically Necessary and Sufficient Quadratic Stability Conditions of T-S Fuzzy Systems Using Staircase Membership Function and Basic Inequality

Asymptotically necessary and sufficient quadratic stability conditions of Takagi-Sugeno (T-S) fuzzy systems are obtained by utilizing staircase membership functions and a basic inequality. The information of the membership functions is incorporated in the stability analysis by approximating the original continuous membership functions with staircase membership functions. The stability of the T-S fuzzy systems was investigated based on a quadratic Lyapunov function. The asymptotically necessary and sufficient stability conditions in terms of linear matrix inequalities were derived using a basic inequality. A fuzzy controller was also designed based on the stability results. The derivation process of the stability results is straightforward and easy to understand. Case studies confirmed the validity of the obtained stability results.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Comprehensive Evaluation of New Maize Varieties for Grain and Fodder Based on Membership Function Method

To screen new maize(Zea mays L.)varieties suitable for food and fodder dual-purpose in Du'an Yao Autonomous County of Guangxi,the agronomic characters,yield and quality indexes of 12 new maize varieties were measured,and the correlation between various indexes were analyzed,and the comprehensive performance of tested varieties was evaluated by membership function method.The results showed that Guidan 671 had the highest grain yield and whole-plant biomass at 10908 and 49965 kg/hm^(2),respectively,and the second was Zhaoyu 215 with a grain yield and whole-plant biomass of 10086 and 47175 kg/hm^(2),respectively.Grain yield was highly significantly positively correlated with ear diameter and 100-grain weight(P<0.01),and significantly correlated with whole-plant biomass,starch content,ear length and grain number per row(P<0.05);and the whole-plant biomass was highly significantly correlated with the number of grains per row(P<0.01),and significantly correlated with starch content,panicle length,plant height and panicle height(P<0.05).The comprehensive performance scores of the tested varieties from high to low were Guidan 671,Zhaoyu 215,Guidan 669,Guidan 6208,Guidan 666,Guidan 6205,Guidan 660,Guidan 6203,Guidan 6206,Guidan 162,Guidan 668 and Guidan 673.According to the values of membership function and combined with various indexes,Guidan 671 and Zhaoyu 215 had good comprehensive performance,and could be used as the first choice for food and fodder dual-purpose maize varieties in Du'an Yao Autonomous County.

Decision Making Based on Valued Fuzzy Superhypergraphs

This paper explores the defects in fuzzy (hyper) graphs (as complex (hyper) networks) and extends the fuzzy(hyper) graphs to fuzzy (quasi) superhypergraphs as a new concept.We have modeled the fuzzy superhypergraphsas complex superhypernetworks in order to make a relation between labeled objects in the form of details andgeneralities. Indeed, the structure of fuzzy (quasi) superhypergraphs collects groups of labeled objects and analyzesthem in the form of the part to part of objects, the part of objects to the whole group of objects, and the whole tothe whole group of objects at the same time.We have investigated the properties of fuzzy (quasi) superhypergraphsbased on any positive real number as valued fuzzy (quasi) superhypergraphs, considering the complement of valuedfuzzy (quasi) superhypergraphs, the notation of isomorphism of valued fuzzy (quasi) superhypergraphs based onthe permutations, and we have presented the isomorphic conditions of (self complemented) valued fuzzy (quasi)superhypergraphs. The concept of impact membership value of fuzzy (quasi) superhypergraphs is introducedin this study and it is applied in designing the real problem in the real world. Finally, the problem of businesssuperhypernetworks is presented as an application of fuzzy valued quasi superhypergraphs in the real world.

Limb Lengthening and Reconstruction Society orthopedic surgeons in the United States:An analysis of geographical distribution,academic,leadership,and demographic characteristics

BACKGROUND The Limb Lengthening and Reconstruction Society(LLRS)is a premier orthopedic specialty organization that promotes limb reconstruction for all ages.LLRS membership characteristics,however,are poorly reported.This study delineates orthopedic surgeon LLRS members’demographic traits,academic achievement,leadership attainment,and geographical distribution across the United States.AIM To inform aspiring orthopedic professionals,as well as to promote growth and diversity in both the LLRS organization and overarching field.METHODS This cross-sectional study examined United States LLRS members’academic,leadership,demographic,and geographical attributes.After reviewing the 2023 LLRS member directory,Google search results were matched to the listings and appended to the compiled data.Sex and ethnicity were evaluated visually utilizing retrieved images.The Hirsch index(H-index)of academic activity,residency and fellowship training,other graduate degrees,leadership positions,practice type(academic or non-academic),and spoken languages were categorized.LLRS members per state and capita determined geographic distribution.The Mann Whitney U test was applied to compare H-index between males and females,as well as to assess member differences pertaining to affiliation with academic vs non-academic practice facilities.RESULTS The study included 101 orthopedic surgeons,78(77.23%)Caucasian and 23(22.77%)non-Caucasian,79(78.22%)male and 22(21.78%)female.Surgeons with DO degrees comprised only 3.96%(4)of the cohort,while the vast majority held MDs[96.04%(97)].Mean H-index was 10.55,with male surgeons having a significantly higher score(P=0.002).Most orthopedic surgeons(88.12%,)practiced in academic centers.Of those professionals who occupied leadership positions,14%were women,while 86%were men.Additionally,19(37.25%)United States regions and the District of Columbia lacked an LLRS-member orthopedic surgeon.Total per capita rate across the United States was 0.30 LLRS orthopedic surgeons per 1 million people.CONCLUSION Over 21%of LLRS members are women,surpassing prior benchmarks noted in orthopedic faculty reporting.LLRS members’high research productivity scores imply field dedication that can refine expertise in the limb lengthening and reconstruction space.Gender disparities in leadership remain,however,necessitating greater equity efforts.A low rate of LLRS representation per capita must be addressed geographically as well,to affect improvements in regional care access.This study can serve to support aspiring orthopedic professionals,inform diversity,leadership,and field advancement strategies,and maintain the continued goal of enhanced patient care worldwide.

Are You Willing to Pay to Go to the Supermarket?

In recent yea rs,t raditiona l offline supermarkets are facing big challenges,and warehouse membership superma rkets(hereinafter referred to as membership supermarkets)are quickly gaining p opu l a r it y.Ma ny t r ad it iona l supermarkets have become membership supermarkets,and even many retail enterprises have entered this field.

The New Year Ushers in New Members to BRICS

At the beginning of this year,the BRICS countries welcomed a wave of new members.On New Year’s Day,Saudi Arabia,Egypt,the United Arab Emirates,Iran and Ethiopia became full members,doubling the number of BRICS members from five to ten.At present,BRICS,as a multilateral cooperation organization of developing countries,is on the road towards substantial expansion and rapid development.In both historical and practical terms,the expansion of BRICS membership has profound historical logic and contemporary implications,which will have a significant impact on the world’s pattern and international order.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

基于Membership的高可用性Linux集群

介绍了Ｌｉｎｕｘ集群高可用性（ＨＡ）技术的应用背景和体系结构，重点讲述了集群Ｍｅｍｂｅｒｓｈｉｐ技术可以显著提高集群系统的可用性，并且提出了可配置的Ｍｅｍｂｅｒｓｈｉｐ服务（ＭＢＬＣ）来实现Ｌｉｎｕｘ集群的高可用性的方案。

基于Membership拓展的权限机制设计

给出一种基于ASP.NET中的Membership拓展与传统的RBAC机制相结合形成的一种权限机制,它能够实现在Web页面中对于角色与权限的有效控制访问,并且改进了Membership的对权限控制力度不够的缺陷以及传统RBAC机制不能有效利用ASP.NET控件的劣势。最后,在"研究生信息管理系统"项目中实现这种权限机制,充分验证了其优势以及可行性和有效性。

Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System

A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted distance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.

Feature Selection Based on Adaptive Fuzzy Membership Functions

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

Multi-attribute decision making model based on optimal membership and relative entropy

To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

On Income Distribution Mechanism of Farmer Cooperatives in the Context of Membership Heterogeneity

With reference to several possible solutions to the issue of two subject allocation,using the Accumulation Point analysis method in Game Theory,this paper analyzed the income distribution mechanism between large farmers and small farmers in farmer cooperatives in the context of membership heterogeneity. It found that,in the practice of the income distribution in farmer cooperatives,there possibly exists equalization solution,pure utility solution,Nash solution and Kalai-Smorodinsky solution and it will be affected by social conventions. Finally,it made an empirical analysis using five cases of farmer cooperatives.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.