A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechanical properties.Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification,material selection has become a crucial component of design for engineers.This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC,and GRA approaches.The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches:qualitative and quantitative.This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σtechnique,with the decision based on the highest performance,the lightest weight,and the most environmentally friendly criteria.The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase.In the future study,selection technique may have been more exhaustive if more information from other factors had been added.

Study on Evaluation of the Rainstorm Disaster in Fujian Province Based on Spectral Clustering Model with Grey Correlation Analysis

[ Objective] The research aimed to study assessment index system of the rainstorm disaster in Fujian Province based on spectral cluste- ring model with grey correlation analysis. [Method] According to meteorological disaster yearbook in Fujian Province, by comprehensively consider- ing disaster-inducing factor, disaster-inducing environment, disaster-sustaining body and regional disaster-prevention level, evaluation index system of the regional rainstorm disaster in Fujian was established. By spectral clustering model based on grey correlation analysis, dsk zoning of the rain- storm disaster was conducted in each area of Fujian. Finally, effect and application of the clustering model were analyzed by case research. [ Re- sult] In order to dig immanent connection among regional characteristics and improve disaster-preventing linkage performance of the evaluation unit, a spectral clustering model based on grey correlation analysis was used to conduct risk zoning of the rainstorm disaster in Fujian Province. Moreo- ver, combined weight was introduced to judge each evaluation index, so as to adjust clustering model. By case study, rainstorm disaster levels in 67 counties were obtained. Internal characteristics of each type were analyzed, and main correlation factors of each type were extracted. It was compared with statistical result of the rainstorm disaster, verifying validity and feasibility of the model. [ Conclusion] The method was feasible, and its evaluated result had better differentiation and decision accuracv.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

Statistical Analysis of Abilities to Give Consent to Health Data Processing

The recent pandemic crisis has highlighted the importance of the availability and management of health data to respond quickly and effectively to health emergencies, while respecting the fundamental rights of every individual. In this context, it is essential to find a balance between the protection of privacy and the safeguarding of public health, using tools that guarantee transparency and consent to the processing of data by the population. This work, starting from a pilot investigation conducted in the Polyclinic of Bari as part of the Horizon Europe Seeds project entitled “Multidisciplinary analysis of technological tracing models of contagion: the protection of rights in the management of health data”, has the objective of promoting greater patient awareness regarding the processing of their health data and the protection of privacy. The methodology used the PHICAT (Personal Health Information Competence Assessment Tool) as a tool and, through the administration of a questionnaire, the aim was to evaluate the patients’ ability to express their consent to the release and processing of health data. The results that emerged were analyzed in relation to the 4 domains in which the process is divided which allows evaluating the patients’ ability to express a conscious choice and, also, in relation to the socio-demographic and clinical characteristics of the patients themselves. This study can contribute to understanding patients’ ability to give their consent and improve information regarding the management of health data by increasing confidence in granting the use of their data for research and clinical management.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Grey Relational Analysis between Chlorophyll a and Environmental Factors in ShaHu Lake

[Objective] The aim was to explore effects of environmental factors on the content of Chlorophyll a in ShaHu Lake.[Method] Based on the data in Shahu Lake from November in 2007 to September in 2008,the relationship between chlorophyll a and environmental factors like water temperature,pH,secchi-depth （SD）,total nitrogen,total phosphorus and potassium permanganate index was studied by grey relational analysis method.[Result] The main environmental factors affecting the content of Chlorophyll a in ShaHu Lake were in order of water temperature potassium permanganate index 〉total nitrogen 〉pH〉 total phosphorus 〉SD.[Conclusion] The research provides reference for the control of eutrophication and the reasonable development and utilization of Shahu Lake.

ESTIMATION METHOD FOR AIRCRAFT SIMILARITY BASED ON FUZZY THEORY AND GREY INCIDENCE ANALYSIS

An estimation method for aircraft similarity based on fuzzy theory and grey incidence analysis is presented. This estimation method is made up of the triangular fuzzy transforming model of linguistic variables and the method of grey incidence analysis. Nine feature attributes of aircraft are selected to estimate the similarity between the new aircraft and the existing aircraft. A new aircraft X and other six existing aircrafts are taken as examples. Analyses show that similarity estimation results obtained from the method are in accordance with practice.

Insulation fault diagnosis based on group grey relational grade analysis method for power transformers

Utilising dissolved gases analysis, a new insulation fault diagnosis methodfor power transformers is proposed. This method is based on the group grey relational grade analysismethod. First, according to the fault type and grey reference sequence structure, some typicalfault samples are divided into several sets of grey reference sequences. These sets are structuredas one grey reference sequence group. Secondly, according to a new calculation method of the greyrelational coefficient, the individual relational coefficient and grade are computed. Then accordingto the given calculation method for the group grey relation grade, the group grey relational gradeis computed and the group grey relational grade matrix is structured. Finally, according to therelational sequence, the insulation fault is identified for power transformers. The results of alarge quantity of instant analyses show that the proposed method has higher diagnosis accuracy andreliability than the three-ratio method and the traditional grey relational method. It has goodclassified diagnosis ability and reliability.

Grey Correlation Analysis of Physiological Traits Related to Drought Tolerance in Pennisetum sp.

The drought resistance of Pennisetum sp. was analyzed under different drought stress conditions simulated by various concentrations of PEG-6000. Changesofa series of indexes such as chlorophyll content, water content, relative electrical conductivity of osmotic solution, MDA content, proline content, soluble polysaccharide content and soluble protein content in the leaves were measured.The correlation of these indexes with the drought resistance of Pennisetum sp. was an- alyzed by the method of grey correlation analysis. The results showed that the chlorophyll, water content, relative electrical conductivity and soluble protein con- tentexhibited obvious negative correlation with the stress concentration, which de- creasedfollowing the increase of PEG-6000 concentration. The soluble polysaccha- ride contentshowed an increasing trend,the MDA content showed a rise at first fol- lowed by a declinetrend, while the prolinecontent was just opposite to MDA. The changes of the 3 indexes showed obvious positive correlation with thestress con- centration. The correlation of these indexes with the drought resistance of Pennise- tum sp. were ranked as follows： leaf water content （0.980 8）, chlorophyll content （0.967 9）, MDA content （0.876 0）, soluble polysaccharide content （0.839 5）, soluble protein content（0.827 5）, relative electrical conductivity of osmotic solution（0.670 8）, and proline content（0.571 3）.

Genetic Diversity and Clustering Analysis of 48Cultivars of Olea euyopaea L.

Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei＇s genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group（UPMGA）method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources.

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

Application of Grey Relational Analysis in Regional Water Environment Assessment——Taking Jin River Basin as An Example

According to the data from four water monitoring stations at Meihewan, Shimawan, Tanwan and Jinbin, the water environment at the downstream basin of Jin River was evaluated through grey reIational analysis in this study. The resuIts showed that the water environment was severeIy contaminated by phosphorus, am-monia nitrogen and permanganate.

Analysis of Driving Factors of Grain Output in Henan Province Based on Grey Correlation Model

Henan Province is an important grain production base, whose stable production is related to the economic development and social stability significantly. To summarize the primary factors of grain producing, we used grey correlation analysis method to analyze 10 correlative factors such as grain yield, grain yield per unit area, effective irrigated area, rural employed persons, grain planting area, consumption of chemical fertilizer in 25 years. The results showed order of synthetic degree of incidence: effective irrigated area>rural electricity consumption>consumption of chemical fertilizer>disaster crop area>grain yield per unit of area>total power of agricultural machinery>afflicted crop area>grain planting area>number of the rural employed persons. Finally, according to synthetic degree of incidence between influential factors and total grain output respectively, a series of measures and the suggestions was proposed for grain production in Henan Province, which will also provide the scientific basis and help for the government correct decision-making.