Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy Sets

This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in this.Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects.Five anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this study.Based on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is picked.As a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical approaches.The entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM methodologies.Sensitivity analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed method.The smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.

VR环境下SET GO教学法的构建及在耳鼻喉临床教学的应用

SET GO教学法也称为应诊录像教学法,就是在教学过程中,通过观察操作者的视频录像,进行教学的教学法。由于耳、鼻、喉的结构复杂,内部空间狭小,增加了视频录制的难度。在此背景下,吉林药学院附属医院将VR技术融入SET GO教学法中,借助VR技术录制成三维视频,使录制的视频清晰度更高,为SET GO教学法的实施提供了可行性。具体的教学环节分为前期教学、VR模拟操作及录制、VR录像加工、See和Else教学环节、Think教学环节、Goals和Offers教学环节等。通过以上环节的有序实施,提高了耳鼻喉科的临床教学效果。如在视频录制质量评价和SET GO教学效果评价方面,学生各项指标评价的良好率均在90%以上。建议进一步推广和应用。

基于SET-AHP-TRIZ的坐式康复训练器设计研究

为解决现有坐式康复训练器形式与功能单一、操作使用缺乏智能化等问题,文章构建了基于SET-AHP-TRIZ的设计思路。首先,对产品的现状、用户进行市场调研,采用SET分析法确定目标产品的机会缺口;其次,应用AHP法对需求点进行定量计算,以确定创新过程中需要着重改善的问题;最后,通过应用TRIZ理论分析出现的矛盾问题,并总结其矛盾类型,利用发明原理解决问题,进行坐式康复训练器设计实践,减少了设计过程主观因素的影响,使设计结果更为科学合理,以期提高用户的满意度。

SETD5介导AKT1磷酸化调控结肠癌细胞的迁移和5-FU敏感性

目的:探讨含SET结构域蛋白5(SETD5)对结肠癌细胞增殖、迁移和对5-氟尿嘧啶(5-FU)药物敏感性的影响及机制。方法:常规培养结肠癌细胞,用Lipofectamine 2000将siSETD5-NC、si-SETD5-1~3质粒转染至HT-29细胞中,将其分为对照组(未处理)、si-SETD5-NC组、si-SETD5组和si-SETD5+SC79组,si-SETD5+SC79组HT-29细胞转染质粒的同时用10µmol/L SC79处理。qPCR法检测NCM460、HT-29和LoVo细胞中SETD5 mRNA表达,流式细胞术、细胞划痕法、WB法和CCK-8法分别检测各组HT-29细胞的凋亡情况、迁移能力、相关蛋白的表达,以及对5-FU的敏感性。结果:SETD5 mRNA在HT-29、LoVo细胞中均呈高表达(均P<0.01)。在HT-29细胞中成功地敲减了SETD5 mRNA(P<0.01)。敲减SETD5 mRNA可明显抑制HT-29细胞的增殖活性(P<0.01)、迁移能力(P<0.01)、相关蛋白(SETD5、p-PI3K、p-AKT1、p-mTOR蛋白)的表达(均P<0.01)、促进细胞凋亡(P<0.01),且提高其对5-FU的敏感性(P<0.01),这些作用均可被AKT激活剂SC79部分阻挡(P<0.05或P<0.01)。结论:SETD5在HT-29、LoVo细胞中高表达,SETD5通过PI3K/AKT1通路促进结肠癌HT-29细胞的增殖、迁移,且降低其对5-FU的敏感性,SETD5是结肠癌临床诊断、治疗的潜在靶点。

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

MTA2、SET8蛋白在喉癌中的表达及与临床病理特征的关系

目的探讨MTA2、SET8蛋白在喉癌中的表达及与临床病理特征的关系。方法收集喉癌患者205例作为研究对象,采用免疫组织化学染色法检测MTA2、SET8阳性蛋白,采用Real-time PCR检测MTA2、SET8 mRNA表达水平。分析MTA2、SET8蛋白表达与喉癌患者临床病理特征的关系。Spearson相关性分析MTA2蛋白与SET8蛋白表达之间的关系。结果喉癌组MTA2、SET8蛋白阳性表达率(56.10%、54.15%)显著高于癌旁组,差异均有统计学意义(χ^(2)=27.926,P<0.001;χ^(2)=28.277,P<0.001);喉癌组MTA2、SET8 mRNA表达水平高于癌旁组,差异均具有统计学意义(t=25.005,P<0.001;t=20.390,P<0.001)。喉癌组织中MTA2、SET8蛋白阳性表达与性别、年龄、是否吸烟、临床分型均无关(P>0.05),与淋巴结是否转移、病理分级、临床分期有关(P<0.05)。Spearson相关性分析显示,MTA2蛋白与SET8蛋白表达呈现正相关(γ=0.477,P<0.05)。结论MTA2、SET8蛋白在喉癌中表达明显上升,且与喉癌的发展密切相关,预示其可能成为诊断分子的潜能。

Evaluation of Industrial IoT Service Providers with TOPSIS Based on Circular Intuitionistic Fuzzy Sets

Industrial Internet of Things(IIoT)service providers have become increasingly important in the manufacturing industry due to their ability to gather and process vast amounts of data from connected devices,enabling manufacturers to improve operational efficiency,reduce costs,and enhance product quality.These platforms provide manufacturers with real-time visibility into their production processes and supply chains,allowing them to optimize operations and make informed decisions.In addition,IIoT service providers can help manufacturers create new revenue streams through the development of innovative products and services and enable them to leverage the benefits of emerging technologies such as Artificial Intelligence(AI)and machine learning.Overall,the implementation of IIoT platforms in the manufacturing industry is crucial for companies seeking to remain competitive and meet the ever-increasing demands of customers in the digital age.In this study,the evaluation criteria to be considered in the selection of IIoT service provider in small andmedium-sized(SME)manufacturing enterprises will be determined and IIoT service providers alternatives will be evaluated using the technique for order preference by similarity to an ideal solution(TOPSIS)method based on circular intuitionistic fuzzy sets.Based on the assessments conducted in accordance with the literature review and expert consultations,a set of 8 selection criteria has been established.These criteria encompass industry expertise,customer support,flexibility and scalability,security,cost-effectiveness,reliability,data analytics,as well as compatibility and usability.Upon evaluating these criteria,it was observed that the security criterion holds the highest significance,succeeded by cost-effectiveness,data analytics,flexibility and scalability,reliability,and customer support criteria,in descending order of importance.Following the evaluation of seven distinct alternatives against these criteria,it was deduced that the A6 alternative,a German service provider,emerged as the most favorable option.The identical issue was addressed utilizing sensitivity analysis alongside various multi-criteria decision-making(MCDM)methods,and after comprehensive evaluation,the outcomes were assessed.Spearman’s correlation coefficient was computed to ascertain the association between the rankings derived from solving the problem using diverse MCDM methods.

Development of a High Early Strength Non-fuorine and Non-alkaline Flash Setting Admixture and Flash Mechanism

To solve the problems of high rebound rate and strength reversion of shotcrete,a non-fluorine and non-alkaline liquid flash setting admixture(FSN)with low rebound and high early strength was synthesized under 60-65℃water bath environment through orthogonal test design and taking setting time and compressive strength as indicators.The experimental results show that the optimum mass ratio of FSN is aluminum sulfate:diethanolamine:triethanolamine:pseudo-boehmite:lithium carbonate:water=57%:8%:0.05%:2%:2%:31%.When FSN is added with 7%of the mass of portland cement,the cement paste can be initially set in 3 min and finally set in 5 min.The compressive strength of mortar is 1.2 MPa at 6 h,18.0 MPa at 1 d,and more than 100%at 28 days;The microscopic analysis shows that the rapid release of Li^(+),NH^(4+),and CO_(3)^(2-)ions by FSN in the paste solution effectively shortens the induction period of high C_(3)S content in Portland cement,directly forms early coagulation AFt crystals in FSN and CH dominated by Al_(2)(SO_(4))_(3),and forms a large number of C-S-H gels in the later stage,so that the cement can quickly coagulate and harden,and the strength in the later stage is not retracted.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Age and geochemical evolution of granite magmatism in Olkhon region from Caledonian syncollisional ore-free granite to the rare metal granite and pegmatite of Middle Paleozoic intraplate setting

The detailed description of two granite complexes in the Olkhon subterrane is given.The Early Paleozoic Sharanur complex was formed by granitization of gneisses of the Olkhon series.It includes migmatites,granite-gneisses,granites and pegmatites of normal alkalinity;they belong to the type of syncollisional granites.The Middle Paleozoic Aya granite complex includes mother Aya massif of amazonite-bearing granites and several types of rare-metal pegmatites.They have elevated alkalinity,low of Ba,Sr,and high LILE and HFSE elements contents.The Aya pegmatites lie in northwest cracks of stretching and associated with the rise of the territory under the influence of the North Asian plume.These cracks and pegmatites mark the beginning of a new intraplate geodynamic setting.Two geochemical types are distinguished among the pegmatites of this complex.These are amazonite pegmatites of Li-F type with Ta mineralization and complex type pegmatite with Be-Rb-Nb-Ta and Li-F mineralization(the Ilixin vein).The Tashkiney pegmatite vein is similar to Ilixin,but lies in the gneisses of the Olkhon series.It shows high concentrations of Be,Nb,Ta,as well as W,Sn,but lacks Li and F,due to a greater depth and higher temperature of the melt crystallization of this pegmatite.

An Intelligent MCGDM Model in Green Suppliers Selection Using Interactional Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Sets

Green supplier selection is an important debate in green supply chain management(GSCM),attracting global attention from scholars,especially companies and policymakers.Companies frequently search for new ideas and strategies to assist them in realizing sustainable development.Because of the speculative character of human opinions,supplier selection frequently includes unreliable data,and the interval-valued Pythagorean fuzzy soft set(IVPFSS)provides an exceptional capacity to cope with excessive fuzziness,inconsistency,and inexactness through the decision-making procedure.The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers(IVPFSNs)and create two interaction operators-the intervalvalued Pythagorean fuzzy soft interaction weighted average(IVPFSIWA)and the interval-valued Pythagorean fuzzy soft interaction weighted geometric(IVPFSIWG)operators,and analyze their properties.These operators are highly advantageous in addressing uncertain problems by considering membership and non-membership values within intervals,providing a superior solution to other methods.Moreover,specialist judgments were calculated by the MCGDM technique,supporting the use of interaction AOs to regulate the interdependence and fundamental partiality of green supplier assessment aspects.Lastly,a statistical clarification of the planned method for green supplier selection is presented.

Quantum algorithm for minimum dominating set problem with circuit design

Using quantum algorithms to solve various problems has attracted widespread attention with the development of quantum computing.Researchers are particularly interested in using the acceleration properties of quantum algorithms to solve NP-complete problems.This paper focuses on the well-known NP-complete problem of finding the minimum dominating set in undirected graphs.To expedite the search process,a quantum algorithm employing Grover’s search is proposed.However,a challenge arises from the unknown number of solutions for the minimum dominating set,rendering direct usage of original Grover’s search impossible.Thus,a swap test method is introduced to ascertain the number of iterations required.The oracle,diffusion operators,and swap test are designed with achievable quantum gates.The query complexity is O(1.414^(n))and the space complexity is O(n).To validate the proposed approach,qiskit software package is employed to simulate the quantum circuit,yielding the anticipated results.

On Multi-Granulation Rough Sets with Its Applications

Recently,much interest has been given tomulti-granulation rough sets (MGRS), and various types ofMGRSmodelshave been developed from different viewpoints. In this paper, we introduce two techniques for the classificationof MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novelapproximation space is established by leveraging the underlying topological structure. The characteristics of thenewly proposed approximation space are discussed.We introduce an algorithmfor the reduction ofmulti-relations.Secondly, a new approach for the classification ofMGRS based on neighborhood concepts is introduced. Finally, areal-life application from medical records is introduced via our approach to the classification of MGRS.

Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach,the Einstein hybrid structure of q-rung orthopair fuzzy soft set.This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations,especially in areas affected by floods.Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set,the q-rung orthopair fuzzy soft set(q-ROFSS)adequately incorporates unclear and indeterminate facts.The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft Einstein hybrid weighted average(q-ROFSEHWA)operator and its specific characteristics.Moreover,our stated operator is implementing intelligentmulti-criteria group decision-making(MCGDM)methodology.Floods are severe natural catastrophes that raise the risk of diseases and epidemics,particularly those caused by contaminants in the water,such as gastrointestinal diseases,respiratory infections,vector-borne diseases,skin infections,and water-borne parasites.The designed MCGDM strategy tackles the prevalence of certain conditions in flood-affected patients.A comparative investigation determined that the suggested method for detecting water-borne infectious disease due to floods is more effective and productive than conventional methods because of its logical structure.

Prospective randomized study to evaluate and compare the post-procedural sexual function in patients undergoing semi-rigid ureterorenoscopy for distal ureteric stones in three different operating room settings

Objective:To evaluate and compare the effect of semi-rigid ureterorenoscopy on post-procedural sexual function with three different operating room settings.Methods:In this prospective randomized study,consecutive sexually active patients with normal pre-operative sexual function undergoing uncomplicated semi-rigid ureterorenoscopy for distal ureteric stones were randomized into three groups,with three different operating room settings.Procedure-related anxiety and sexual function were assessed pre-operatively using Amsterdam Preoperative Anxiety and Information Scale and Brief Sexual Function Inventory(in males)and Female Sexual Function Index-6(in females),respectively.All the participants were stented following the procedure,and the stent was removed after 3 weeks.Post-procedural sexual function and general discomfort were assessed and compared between three groups at 1 week,3 weeks,and 12 weeks.The effect of surgery-related anxiety,preoperative sexual function,age,and general discomfort(including stent-related discomfort)on post-procedural sexual function were analyzed using multiple regression analysis.A p-value of less than 0.05 was considered statistically significant.Results:Totally,327 eligible patients were randomized into three groups.The group of patients who underwent the procedural with a screen separating the operating area from the patient vision,while the patient could watch the endoscopy through a separate monitor,had better post-procedural sexual function compared to those who had total vision of the operating area as well as to those whose eyes were blocked.This difference was statistically significant.This post-procedural reduction in sexual function could not be attributed to in situ stent alone.Conclusions:Our study showed that semi-rigid ureterorenoscopy can have significant negative effect on sexual function,which can be reduced with proper preoperative counseling and an ideal operating room settings.

Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

A Hybrid Level Set Optimization Design Method of Functionally Graded Cellular Structures Considering Connectivity

With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly.However,a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures,potentially resulting in diminished efficiency or macroscopic failure.A Hybrid Level Set Method(HLSM)is proposed,specifically designed to enhance connectivity among non-uniform microstructures,contributing to the design of functionally graded cellular structures.The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.Initially,an interpolation algorithm is presented to construct transition microstructures seamlessly connected on both sides.Subsequently,the algorithm enables the morphing of non-uniform unit cells to seamlessly adapt to interconnected adjacent microstructures.The method,seamlessly integrated into a multi-scale topology optimization framework using the level set method,exhibits its efficacy through numerical examples,showcasing its prowess in optimizing 2D and 3D functionally graded materials(FGM)and multi-scale topology optimization.In essence,the pressing issue of interface connections in complex structure design is not only addressed but also a robust methodology is introduced,substantiated by numerical evidence,advancing optimization capabilities in the realm of functionally graded materials and cellular structures.