Using Mathematical Classification Methods to Analyze and Standardize Man-Made Territorial Risk

Applying hierarchical grey relation clustering analysis to geographical information systems – A case study of the hospitals in Taipei City

Key messageSessile oak (Quercus petraea (Matt.) Liebl.) provenance variation was assessed in a multisite test based on traits of economic and ecological relevance in France. While climatic drivers generated genetic clines at a range-wide scale, provenance variation in France was mainly shaped by past silvicultural regimes. We developed a multitrait approach to facilitate decision-making for seed sourcing. A set of provenance clusters is proposed, supporting recommendations for plantation programmes.ContextAmong broadleaves, sessile oak (Quercus petraea) is likely to spread in the context of current climate change and is increasingly planted in France. Seed sourcing is of the utmost importance for ensuring plantation success and adaptation. The selection of appropriate seed sources is highly challenging when the future climate conditions of plantation areas are uncertain.AimsWe aimed at identifying drivers of provenance variation in Q. petraea and to build provenance clusters based on traits of adaptive and economic value, to ultimately support decision-making in seed sourcing.MethodsWe analysed a multisite provenance test established 30 years ago and comprising a large collection of Q. petraea provenances by performing phenotypic assessments of survival, growth, phenology, and stem-quality traits. We analysed climate-trait correlations at a range-wide scale and used multivariate statistics [multivariate mixed models, principal component analysis (PCA)] and classification methods [hierarchical clustering analysis (HCA), K-means method] to generate an overall clustering of french provenances.ResultsProvenance effects were highly significant regardless of the trait considered, whereas interaction effects between provenance and other experimental sources of variation were minor compared to provenance and environmental variance. There was limited variation between provenances collected in the same forest in comparison to origins of different forests. We found sharp temperature-driven genetic clines for growth and phenology-related traits at a range-wide scale. The multitrait classification approach grouped the French provenances into 11 clusters, with the members of each cluster having similar trait values. Overall, the cluster composition of provenances poorly matched the provenance regions identified on ecological grounds, but rather mirrored the silvicultural regimes implemented in the source stands in the past.ConclusionsTwo of the provenance clusters (comprising a total of 34 provenances) were identified as potentially useful sources of reproductive material. We recommend mixing seeds of different provenances from a given cluster to ensure the maintenance of diversity and to enhance adaptability to future climatic conditions.

5241652 System for performing rule partitioning in a rete networkWilliam Barabash, William Yerazunis assigned to Digital Equipment Corporation

A combined approach has been developed for the identification and determination of quinolone antibiotics in pharmaceuticals using solid-phase fluorescence digital colorimetry and chemometric analysis. The eigen fluorescence of quinolones and quinolone-sensitized fluorescence of europium(III) have been studied on various matrices. The paper proposes a test-system consisting of four indicator zones. Under irradiation with ultraviolet light (365 nm) of quinolone solutions applied to the indicator zones of test systems, there are observed blue fluorescence on cellulose paper (CP) and plates for high performance thin layer chromatography (HPTLC) and pink (CP–Eu, HPTLC–Eu) fluorescence. The measurement of the fluorescence intensity on the surface of the matrices has been carried out using a smartphone. The possibility of using chemometric analysis, which makes it possible to reduce the analysis time and visualize research data, has been shown. The data array was processed by principal component methods, hierarchical cluster analysis, and the kmean method using the XLSTAT software. The identification and assessment of the quantitative content of quinolone antibiotics in tablet forms have been carried out using chemometric analysis. The calibration dependences in the РСА and k-mean methods have a logarithmic form in the ranges of the determined concentrations of 0.5–250 μg/ml (R2 ≥ 0.98). A technique for the determination of fluoroquinolones in pharmaceuticals has been proposed. The relative standard deviation does not exceed 0.09.

Rheumatoid arthritis (RA) is one of the most prevalent chronic inflammatory and angiogenic diseases. The aim of this study was to evaluate the anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenoids isolated from Daphne genkwa. LC-MS was used to identify diterpenes isolated from D. genkwa. The anti-inflammatory and anti-angiogenic activities of eight diterpenoids were evaluated on LPS-induced macrophage RAW264.7 cells and TNF-α-stimulated human umbilical vein endothelial cells (HUVECs) using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The eight diterpenes isolated from D. genkwa were identified as yuanhuaphnin, isoyuanhuacine, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuagine, isoyuanhuadine, yuanhuadine, yuanhuaoate C and yuanhuacine. All the eight diterpenes significantly down-regulated the excessive secretion of TNF-α, IL-6, IL-1β and NO in LPS-induced RAW264.7 macrophages. However, only 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl markedly reduced production of VEGF, MMP-3, ICAM and VCAM in TNF-α-stimulated HUVECs. HCA obtained 4 clusters, containing 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, isoyuanhuacine, isoyuanhuadine and five other compounds. PCA showed that the ranking of diterpenes sorted by efficacy from highest to lowest was 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuaphnin, isoyuanhuacine, yuanhuacine, yuanhuaoate C, yuanhuagine, isoyuanhuadine, yuanhuadine. In conclusion, eight diterpenes isolated from D. genkwa showed different levels of activity in LPS-induced RAW264.7 cells and TNF-α-stimulated HUVECs. The comprehensive evaluation of activity by HCA and PCA indicated that of the eight diterpenes, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl was the best, and can be developed as a new drug for RA therapy.

Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .

<p><em>This Research aim of comparing the modified K-means method for classification processing of training models (Supervised) with the K-NN method using the Mango Arumanis, Golek, Madu and Gedong classes. Feature extraction used in processing classification methods is based on shape characteristics consisting of metric and eccentricity. The research results obtained that the percentage precision of the K-NN method was Arumanis: 0.9%, Madu: 0.9%, Gedong: 0.9%, Golek: 0.8%, and the K-Means method was Arumanis: 0.7%, Honey: 0.9%, Gedong: 0.9%, Golek 0.6%. The recall percentage of the K-NN method is Arumanis: 0.90%, Madu: 0.75%, Gedong: 1.00%, Golek: 0.89% and the K-means method is Arumanis: 0.70%, Madu: 0 .64%, Gedong: 1.00%, Golek: 0.86%. The accuracy percentage of the K-NN classification method is Arumanis: 94.59%, Madu: 89.74%, Gedong: 97.22%, Golek: 92.11% and the K-Means method is Arumanis: 83.78%, Madu: 83.78%, Gedong: 96.88%, Golek: 86.11%. For global precision, recall and accuracy values, the K-NN method is greater than the K-Means method. Thus, the K-Means classification method which was modified to use supervised training data is still not as good as the K-NN method in classifying mango fruit types. It is hoped that the accuracy of the method for classifying mango fruit plant types by extracting shape characteristics can obtain uniform shape quality.</em></p><p align="left"><strong><em>Keywords</em></strong><em>: Image Processing; Feature Extraction; K-means, K-NN; Metric, Eccentricity</em></p><h1 align="center">Abstrak</h1><p>Penelitian dengan tujuan komparasi Metode <em>K-means</em> yang dimodifikasi untuk pemrosesan klasifikasi model pelatihan (<em>Supervised</em>) dengan MetodeK-NN mempergunakan kelas Mangga Arumanis, Golek, Madu dan Gedong. Ekstraksi ciri yang dipergunakan dalam pemrosesan metode klasifikasi berdasarkan ciri bentuk yang terdiri dari <em>metric</em> dan <em>eccentricity</em>. Hasil penelitian memperoleh presentase <em>precision </em>metode K-NN adalah Arumanis: 0,9%, Madu: 0,9%, Gedong: 0,9%, Golek: 0,8%, dan<em> </em>metode K-Means adalah Arumanis: 0,7%, Madu: 0,9%, Gedong: 0,9%, Golek 0,6%. Presentase <em>recall</em> metode K-NN adalah Arumanis: 0,90%, Madu: 0,75%, Gedong: 1,00%, Golek: 0,89% dan metode K-means adalah Arumanis: 0,70%, Madu: 0,64%, Gedong: 1,00%, Golek: 0,86%.<em> </em>Presentase <em>Accuracy</em> metode klasifikasi K-NN adalah Arumanis: 94,59%, Madu: 89,74%, Gedong: 97,22%, Golek: 92,11% dan metode K-Means adalah Arumanis: 83,78%, Madu: 83,78%, Gedong: 96,88%, Golek: 86,11%. Untuk nilai <em>precision</em>, <em>recall</em> dan <em>accuracy</em> secara global adalah metode K-NN lebih besar daripada metode K-Means. Dengan demikian, metode klasifikasi <em>K-Means</em> yang dimodifikasi untuk dapat mempergunakan data pelatihan (<em>supervised</em>) masih belum mampu sebaik Metode K-NN dalam klasifikasi jenis buah mangga. Diharapkan akurasi metode klasifikasi jenis tanaman buah mangga dengan ekstraksi ciri bentuk dapat memperoleh kualitas bentuk yang seragam.</p><p><strong>Kata kunci</strong>: <em>Pengolahan Citra, Ekstraksi Ciri, K-means, K-NN, Metric, Eccentricity</em></p>

Women still being a minority in engineering majors, they are reported to face discriminatory treatment in a collegiate environment. "Chilly climate," referring to such a sexist environment, may have a negative impact on women's mental health, academics, and careers. But, what exactly is it that female students in engineering perceive as chilly, and how chilly is it? This study aimed to explore the chilly campus climate perceived by female undergraduate engineering students in South Korea using the concept mapping method. Semi-structured interviews were conducted with 13 participants enrolled for more than four semesters at four-year coeducational universities. After extracting 52 representative statements, the participants were asked to classify them according to content similarity and rate the influence of each statement on their perception of the chilly climate. For concept mapping analysis, multidimensional scaling analysis (ALSCAL), hierarchical cluster analysis (Ward's method), and non-hierarchical cluster analysis (K-means method) were performed. Fifty-two statements were extracted under the following four clusters: (i) "Exclusion and alienation inherent in the culture (Cluster 1)," (ii) "Sexual objectification and lack of gender sensitivity (Cluster 2)," (iii) "Male-centered academic situations (Cluster 3)," and (iv) "Prejudice and generalization (Cluster 4)." A concept map was two-dimensional: an X-axis named "context dimension," with "task: academic" and "non-task: social" at both ends, and a Y-axis named "sexism dimension", having "explicit" and "implicit" at both ends. The order of higher scores in the influence rating is as follows: Cluster 2, Cluster 3, Cluster 1, and Cluster 4. This study is significant because it conceptualizes the subjective experience of minorities in a collegiate environment and provides influence rating results for prioritized measures. The findings will be helpful in formulating educational policies, psychological counseling, and social advocacy activities. Future research should target larger populations, and cover more diverse cultures, majors, and age groups.

Pipelines are the most important way to transport large amounts of dangerous substances as oil and gas, through long distances, due to their advantages in terms of safety and low cost. However, failures and leaks in pipelines may happen and sometimes they generate catastrophic consequences. In this paper we propose an approach for the risk assessment of oil and gas pipeline defects leveraging machines learning algorithms and multi-criteria decision methods (MCDM), with the objective of accompanying decision-makers for prioritizing risk mitigation activities. The pipeline defects risk assessment approach proposed is based on some machines learning algorithms, which allows to cluster ILI (In Line Inspection) data performed by smart pigs in a group of clusters by using K-means method, then, two classifications methods (decision trees and neural network) are applied on clusters in order to construct a classification model of defects risk on pipe in three level (High, Medium and Low) according to theirs criticizes. The discovered models are assessed using cross validation, which allows choosing a model based on a decision tree as a pipeline defects risk classification and prediction model. For scheduling maintenance and reparation operations we apply the multi-criteria decision method AHP (Analytical Hierarchy Process) in order to rank-order defects which belong to the High class according to theirs criticizes degree.

BackgroundGaucher disease (GD) is a rare, autosomal recessive lysosomal storage disorder that adversely affects life expectancy and health-related quality of life (HRQOL). Although HRQOL questionnaires are available for type 1 GD, they are not suitable for patients with the neuronopathic types 2 and 3 GD who have neurological symptoms that develop during early childhood or adolescence. Here we report the development of a language-validated HRQOL questionnaire specifically for patients with neuronopathic types 2 and 3 GD in Japan, which is the first step toward HRQOL questionnaire provision for all types of GD in the future.MethodsIn February and March 2021, semi-structured interviews were conducted by the authors (supported by qualified interviewers) with patients and/or their caregivers (for patients < 16 years old) who were recruited from a Japanese patient association, the Association of Gaucher Disease Patients in Japan. Qualitative analysis of interview transcripts was used to identify major themes and key topics within those themes. Hierarchical cluster analysis and co-occurrence network analysis were performed to map relationships between commonly occurring words. The study is registered at the UMIN Clinical Trials Registry (https://www.umin.ac.jp/ctr/index.htm [UMIN000042872]).ResultsThree main themes emerged from qualitative analysis: treatment status, patient burden, and social support systems. Key topics within each theme included hearing impairment, visual impairment, difficulty swallowing, difficulty speaking, involuntary movement of extremities, epileptic seizures, and body aches (treatment status); anxiety about symptoms, difficulty with exercise and work, anxiety about continuing treatment, anxiety about going out, and tiredness from hospital visit or treatment (patient burden); and dissatisfaction about government service, lack of social support, and information exchange in the patient association (social support systems). Commonly used words and the relationships between words identified through the hierarchical cluster and co-occurrence network analyses supported these themes and topics.ConclusionsThe themes and topics identified in this analysis were specific to patients with types 2 and 3 GD and will be used to inform the development of a HRQOL questionnaire specifically for patients with all GD types.

BackgroundKnee osteoarthritis (KOA) is a pathological condition prevalent in the general population. Exercise and pharmaceutical approaches are first-line treatments and the end-stage treatment is total knee replacement (TKR). Interestingly, around...

The purpose of this study is to classify the territories of the forest fund of the Republic of Karelia and Murmansk region at the level of central forestries on the basis of multifactorial assessment of timber resources, natural and production conditions, and road infrastructure. Forest exploitation zoning was carried out taking into account 20 variables. For this purpose, factor and hierarchical cluster analysis, k-means method and discriminant analysis were successively applied. Based on statistical methods, all central forestries were distributed into 9 clusters: 4 large clusters (No. 2, 3, 8, 9), including several forestries, and 5 (No. 1, 4–7), each formed by one forestry. Forestries of cluster No. 2 had large annual allowable cut (AAC), significant average forest reserve per hectare and hardwood reserve. Forestries of cluster No. 3 were characterized by small AAC and forest reserves per hectare. Cluster No. 8 forestries were characterized by average AAC and forest stock per hectare, high proportion of forests in stagnant moisture conditions. Forestries of cluster No. 9 were intermediate in their characteristics between the forestries of clusters No. 2 and No. 8. The forestry of cluster No. 1 is close to the forestry of cluster No. 2 in its characteristics, but its forests are more productive. Forestries of small clusters No. 4, 6, 7 have the highest forest stock per hectare. Differences in some variables did not allow them to be combined into one cluster. The forestry of cluster No. 5 is similar in terms of natural and production conditions to the forestry of cluster No. 3 but had a small estimated harvesting area for the coniferous component. The results of the study can be considered when solving the issues of organization and development of forestry and forest industry complex in the Republic of Karelia and Murmansk region. For citation: Budnik P.V., Baklagin V.N., Galaktionov O.N. Forest Exploitation Zoning of Forest Subarctic Territories. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 6, pp. 114–135. (In Russ.). https://doi.org/10.37482/0536-1036-023-6-114-135

Ecological-health risks assessment and source apportionment of potentially toxic elements (PTEs) in surface dust near copper mine.

Hierarchical clustering analysis is one basic multivariate statistical instrument which is used in classifying research in multifarious fields widely. The most three common clustering methods are Single Linkage Method, Group Average Method and Ward Method in hierarchical clustering analysis. As it is about to confirm the sources, approaches and conditions of water leakage in BaYi tunnel, this article analyses the advantages, the disadvantages and the applicable conditions of these three methods taking BaYi tunnel as an example to hierarchical clustering analyse in hydrochemical classification. The results indicate that the Single Linkage Method is not capable dealing with complicated actual situations and the Ward Method is applicable in samples of narrow amount while the Group Average Method can be used competently in general conditions.

Critical food issues of the eighties : Edited by Marylin Chou and David P. Harmon, Jr Pergamon Press, New York, 1979, 404 pp. $32.50