Interval-Valued Intuitionistic Fuzzy-Rough Sets

This paper combines interval-valued intuitionistic fuzzy sets and rough sets.It studies rougheness in interval-valued intuitionistic fuzzy sets and proposes one kind of interval-valued intuitionistic fuzzy-rough sets models under the equivalence relation in crisp sets.That extends the classical rough set defined by Pawlak.

Uncertainty Representation of Ocean Fronts Based on Fuzzy-Rough Set Theory

Analysis of ocean fronts’ uncertainties indicates that they result from indiscernibility of their spatial position and fuzzi- ness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncer- tainties, then they are classified into three sets: with members one hundred percent belonging to the ocean front, belonging to the ocean front’s edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are util- ized to evaluate the ocean front’s degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts’ uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.

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。

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。

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。

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.

Arterial Hypertension-Related Factors within Custodial Settings of Southern Benin in 2023

Introduction: Hypertension, a non-communicable disease, is a major public health threat worldwide, accounting for a high level of morbidity and mortality. Although it has been extensively published among the general population, further research is needed to understand the reality of hypertension within the custodial setting. This study aimed to investigate the factors associated with arterial hypertension in custodial settings in southern Benin in 2023. Methods: This was a cross-sectional, descriptive, analytical study held in prisons in southern Benin from March to April 2023, involving inmates selected by two-stage random sampling. In the first stage, four prisons out of the six in the southern region of Benin were selected by simple random sampling. In the second stage, the prisoners were selected by systematic random sampling, with the sampling frame being the numbered list of eligible prisoners in each prison selected. Data collected by observation and questionnaire survey were analyzed using Stata 11 software. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg. Overweight was defined by a body mass index (weight/height2 (kg/m2) ≥ 25. Factors associated with hypertension were identified by multiple logistic regression, at a 5% threshold of significance. Results: Altogether 336 inmates aged 37.55 ± 1.72 years were surveyed. The prevalence of hypertension in custodial settings in southern Benin in 2023 was 31.32% (95% CI [17.06;52.57]). Associated factors were inmate age (ORa = 3.36 95% CI: [1.94;5.85]) and abnormal waist circumference (ORa = 2.61 95% CI [1.27;5.40]). Conclusion: The prevalence of arterial hypertension in prisons of southern Benin (31.32%) is high when compared with the national average (25.9% (22.5-29.3)). The ministries of the Interior and Health need to collaborate to involve inmates in preventive strategies for non-communicable diseases, including hypertension.

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.

Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019

The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening potential patients and early detection of cases. This could also save the lives of elderly patients who have a higher mortality rate from this disease.

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.

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.

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.

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.

Watson-Jones Anatomical Approach for Open Reduction and Internal Fixation of Proximal Femoral Fractures without Image Intensifier in a Low-Resource Setting

Introduction: Standard procedures for surgical fixation of proximal femoral fractures (PFF) require an image intensifier which in developing countries remains a luxury. We hypothesized that, with a well-codified technique, the Watson Jones approach (WJA) without image intensifier nor traction table, can allow open reduction and internal fixation (ORIF) of PFF using Dynamic hip screw (DHS), with satisfactory outcome. Patients and methods: Forty one consecutive patients (mean age 59.5 ± 21.6 years, 61% males) who were followed in a Teaching Hospital for PFF treated by ORIF using the WJA and DHS from January 2016 to December 2020 were reassessed. The outcome measures were the quality of the reduction, the positioning of the implants, the tip-apex distance (TAD), the rate and delay of consolidation, the functional results using Postel Merle d’Aubigné (PMA) score, the rate of surgical site infection (SSI) and the overall mortality. Logistic regression was used to determine factors associated with mechanical failure. Results: The mean follow-up period was 33.8 ± 15.0 months. Fracture reduction was good in 31 (75.6%) cases and acceptable in 8(19.5%) cases. Implant position was fair to good in 37 (90.2%) patients. The mean TAD was 26.1 ± 3.9 mm. Three patients developed SSI. Consolidation was achieved in 38 (92.6%) patients. The functional results were good to excellent in 80.5% of patients. The overall mortality rate was 7.3%. There were an association between mechanical failure and osteoporosis (p = 0.04), fracture reduction (p = 0.003), and TAD (p = 0.025). In multivariate logistic regression, no independent factors were predictive of mechanical failure. Conclusion: This study shows that ORIF using DHS for PFF via the Watson-Jones approach without an image intensifier can give satisfactory anatomical and functional outcomes in low-resource settings. It provides and validates a reliable and reproducible technique that deserves to be diffused to surgeons in austere areas over the world.

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.

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.

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.

Approximation Maintenance When Adding a Conditional Value in Set-Valued Ordered Decision Systems

The integration of set-valued ordered rough set models and incremental learning signify a progressive advancement of conventional rough set theory, with the objective of tackling the heterogeneity and ongoing transformations in information systems. In set-valued ordered decision systems, when changes occur in the attribute value domain, such as adding conditional values, it may result in changes in the preference relation between objects, indirectly leading to changes in approximations. In this paper, we effectively addressed the issue of updating approximations that arose from adding conditional values in set-valued ordered decision systems. Firstly, we classified the research objects into two categories: objects with changes in conditional values and objects without changes, and then conducted theoretical studies on updating approximations for these two categories, presenting approximation update theories for adding conditional values. Subsequently, we presented incremental algorithms corresponding to approximation update theories. We demonstrated the feasibility of the proposed incremental update method with numerical examples and showed that our incremental algorithm outperformed the static algorithm. Ultimately, by comparing experimental results on different datasets, it is evident that the incremental algorithm efficiently reduced processing time. In conclusion, this study offered a promising strategy to address the challenges of set-valued ordered decision systems in dynamic environments.

Significance of“Printing English”in the Curriculum Setting of Business English Majors(Publishing Direction)

With the development of the global economy and the quick growth of international exchanges,Business English major plays an increasingly important role in higher education.The training goal of Business English major(publishing direction)has gradually shifted from the improvement of studentslanguage abilities to the cultivation of more specialized business knowledge and publishing skills.As an important course for Business English majors(publishing direction),Printing English is of great significance for cultivating studentsprofessional quality,professional competitiveness,career development,and practical ability.From the perspective of curriculum setting,it is necessary to re-understand the importance of Printing English in the curriculum setting of Business English major(publishing direction),and put forward corresponding teaching methods and strategies.

miR-127-3p靶向调节SETD8对胃癌细胞增殖、迁移和侵袭的影响

目的探讨微小RNA(miR)-127-3p对胃癌细胞增殖、迁移及侵袭的影响及其作用机制。方法采用qRT-聚合酶链反应(PCR)检测150例胃癌患者胃癌组织、癌旁组织及人胃上皮细胞GES1、人胃癌细胞SGC7901、AGS、MKN-45、MGC-803中miR-127-3p表达水平。双荧光素酶报告基因检测miR-127-3p与含SET结构域赖氨酸甲基转移酶(SETD)8的靶向调控关系。将SGC7901细胞分为对照(Control)组、miR-127-3p模拟物(miR-127-3p mimics)组及miR-NC组、沉默SETD8表达(si-SETD8)组、si-NC组、miR-127-3p mimics+SETD8过表达(miR-127-3p mimics+SETD8)组及空载质粒(miR-127-3p mimics+pcDNA)组。噻唑蓝(MTT)法、划痕愈合实验、Transwell小室实验分别检测SGC7901细胞增殖、迁移与侵袭;Western印迹检测SGC7901细胞SETD8、基质金属蛋白酶(MMP)-2、MMP-9蛋白表达;裸鼠成瘤实验检测过表达miR-127-3p对裸鼠胃癌移植瘤生长的影响。结果miR-127-3p在胃癌组织与细胞中较癌旁组织和正常细胞显著下调(P<0.05),SETD8 mRNA在胃癌组织中较癌旁组织显著上调(P<0.05);胃癌组织中miR-127-3p、SETD8 mRNA表达水平呈负相关(r=-0.553,P<0.05);双荧光素酶实验证实miR-127-3p与SETD8存在靶向调控关系(P<0.05);过表达miR-127-3p或抑制SETD8表达显著抑制SGC7901细胞增殖、迁移与侵袭(P<0.05);过表达SETD8可部分逆转过表达miR-127-3p对SGC7901细胞增殖、迁移与侵袭的抑制作用(P<0.05);裸鼠成瘤实验表明过表达miR-127-3p可显著抑制胃癌移植瘤生长(P<0.05)。结论miR-127-3p在胃癌组织与细胞中表达下调,过表达miR-127-3p可靶向抑制SETD8表达而抑制胃癌肿瘤生长。