Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs

Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.

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.

Application of cluster analysis and stepwise regression in predicting the traffic volume of lanes

Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.

Cluster Analysis of Morphologic Characteristic of Eight Geographical Populations of Rana Dybowskii

[ObJective] The research aimed to determine the geographic distribution map of system of Rana dybowskii. [Method] Four morphologic indices （body length, body weight, forelimb length, hindlimb length） of eight geographical populations of R.dybowskii which naturally distribute in Changhai Mountain and Xiaoxing＇an Mountain were measured. Measure results were variance analyzed and cluster analyzed. [Result] Variance analysis showed： the genetic branching among the Dongfanghong male population（ belongs to Wandashan） and Xiaoxing＇an Mountain male population and Changbai Mountain male population were significantly different （P〈0.05） ; the genetic branching between the Hebei female population （belongs to Xiaoxing＇an Mountain） and Changbai Mountain female population was significantly different （P〈0.05 ）. Cluster analysis showed ： male R.dybowskii can be divided into three groups ： the first group included Quanyang, Tianbei, Chaoyang and Ddkouqin, the second group included Tieli and Anshan, the third group included Dongfanghong; and the female R. dybowskii can be divided into three groups ： the first group included Quanyang and Chaoyang, the second group included Tianbei and Dakouqin, the third group included Hebei. [Condusion] The paper deduced that the Sanjiang Plain was the geographical origin center ofR. dybowskii which radiated to Changbai Mountain and Xiaoxing＇an Mountain along the adverse current of Songhua River basin, therefore, the current distribution pattern of R. dybowskii was formed.

Clustering Analysis on Large Grained Brassica napus Materials Based on the Optimized ACGM Markers

[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to Arabidopsis grain development and their homologous rape EST sequences. After electrophoresis, 18 pairs of ACGM primers were selected for the clustering analysis of 16 larger grained samples and four fine grained samples of rapeseed. [Result] PCR result showed that 2-6 specific bands were respectively amplified by each pair of primes, and all the bands were polymorphic and repeatable, suggesting that the optimized ACGM markers were useful for clustering analysis of B. napus species. Clustering analysis revealed that the 20 rapeseed samples were divided into three clusters A, B, and C at similarity coefficient 0.6. Then, the clusters A and B were further divided into five sub clusters A1, A2, A3, B1 and B2 at similarity coefficient 0.67. [Conclusion] This study will provide theoretical and practical values for rape breeding.

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.

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.

Development,hotspots and trend directions of groundwater numerical simulation:A bibliometric and visualization analysis

Groundwater is a vital component of the hydrological cycle and essential for the sustainable development of ecosystems.Numerical simulation methods are key tools for addressing scientific challenges in groundwater research.This study uses bibliometric visualization analysis to examine the progress and trends in groundwater numerical simulation methods.By analyzing literature indexed in the Web of Science database from January 1990 to February 2023,and employing tools such as Citespace and VOSviewer,we assessed publication volume,research institutions and their collaborations,prolific scholars,keyword clustering,and emerging trends.The findings indicate an overall upward trend in both the number of publications and citations concerning groundwater numerical simulations.Since 2010,the number of publications has tripled compared to the total before 2010,underscoring the increasing significance and potential of numerical simulation methods in groundwater science.China,in particular,has shown remarkable growth in this field over the past decade,surpassing the United States,Canada,and Germany.This progress is closely linked to strong national support and active participation from research institutions,especially the contributions from teams at Hohai University,China University of Geosciences,and the University of Science and Technology of China.Collaboration between research teams is primarily seen between China and the United States,with less noticeable cooperation among other countries,resulting in a diverse and dispersed development pattern.Keyword analysis highlights that international research hotspots include groundwater recharge,karst water,geothermal water migration,seawater intrusion,variable density flow,contaminant and solute transport,pollution remediation,and land subsidence.Looking ahead,groundwater numerical simulations are expected to play a more prominent role in areas such as climate change,surface water-groundwater interactions,the impact of groundwater nitrates on the environment and health,submarine groundwater discharge,ecological water use,groundwater management,and risk prevention.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Analysis of Patents Related to COVID-19-Based on Patent Clustering Model in Specific Fields

Objective To improve the efficiency of patent clustering related to COVID-19 through the topic extraction algorithm and BERT model,and to help researchers understand the patent applications for novel corona virus.Methods The weights of topic vector and BERT model vector were adjusted by cross-entropy loss algorithm to obtain joint vector.Then,k-means++algorithm was used for patent clustering after dimension reduction.Results and Conclusion The model was applied to patents for corona virus drugs,and five clustering topics were generated.Through comparison,it is proved that the clustering results of this model are more centralized and the differentiation between clusters is significant.The five clusters generated are visually analyzed to reveal the development status of patents for corona virus drugs.

Identifying a competency improvement strategy for infection prevention and control professionals:A rapid systematic review and cluster analysis

Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.

Cluster Analysis on Japonica Rice(Oryza sativa L.) with Good Eating Quality Based on SSR Markers and Phenotypic Traits

Diversity of 60 conventional japonica rice accessions with good eating quality at home and abroad was analyzed using SSR molecular markers, agronomic traits and taste characteristics. A total of 290 alleles were detected in the 60 accessions at 72 SSR loci with the high similarity coefficients varying between 0.600 and 0.924. The loci on chromosome 5 showed the greatest value in average allele number. Additionally, most of the SSR loci could detect 3 to 4 alleles. An UPGMA dendrogram based on the cluster analysis of the genetic similarity coefficients showed that the grouping trend of part of the rice accessions was geographic-related and most of the rice accessions in Jiangsu Province, China were clustered together. Furthermore, many domestic accessions from south and north origins in China were close to the foreign japonica rice varieties, as proved by their pedigree origin from the foreign high-quality sources. For taste characteristics, part of the accessions with excellent taste were clearly clustered into one category though they came from different geographical regions, which indicates that taste characteristics of some varieties were mainly genetically determined. In addition, the agronomic traits of japonica rice with good taste might be closely related with their geographical origins, but the relationship between superior taste characteristics and agronomic traits should be further clarified.

Establishment of HPLC Fingerprint, Cluster Analysis and Principle Component Analysis of Citri Reticulatae Pericarpium Viride

[Objectives] This study aimed to establish HPLC fingerprint and conduct cluster analysis and principle component analysis for Citri Reticulatae Pericarpium Viride. [Methods] Using the HPLC method, the determination was performed on XSelect~® HSS T3-C_(18) column with mobile phase of acetonitrile-0.5% acetic acid solution(gradient elution) at the flow rate of 1.0 mL/min. The detection wavelength was 360 nm. The column temperature was 25℃. The sample size was 10 μL. With peak of hesperidin as the reference, HPLC fingerprints of 10 batches of Citri Reticulatae Pericarpium Viride were determined. The similarity of the 10 batches of samples was evaluated by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012 edition) to determine the common peaks. Cluster analysis and principal component analysis were performed by using SPSS 17.0 statistical software. [Results] The HPLC fingerprints of the 10 batches of medicinal materials had total 11 common peaks, and the similarity was 0.919-1.000, indicating that the chemical composition of the 10 batches of medicinal materials was consistent. There were 11 common components in the 10 batches of medicinal materials, but their contents were different. When the Euclidean distance was 20, the 10 batches of samples were divided into two categories, S4 in the first category, and the others in the second one. When the Euclidean distance was 5, the second category could be further divided into two sub-categories, S1 and S10 in one sub-category, and S2, S3, S5, S6, S7, S8 and S9 in the other one. The principle component analysis showed that cumulative contribution rate of the two main component factors was 92.797%, and the comprehensive score of S7 was the highest with the best quality. [Conclusions] The results of HPLC fingerprinting, cluster analysis and principle component analysis can provide reference for the quality control of Citri Reticulatae Pericarpium Viride.

Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corre- sponding normal paraffin hydrocarbon （ including pristane and phytane） concentration was obtained by the internal standard methed. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated： there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the nomad paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

Vertical Migrating and Cluster Analysis of Soil Mesofauna at Dongying Halophytes Garden in Yellow River Delta

For the first time, we used Tullgren method made a study on vertical migrating and cluster analysis of the soil mesofauna in Dongying Halophytes Garden in the Yellow River Delta （YRD）, Shandong Province. The results showed that the soil mesofauna tended to gather on soil surface in most samples at most times, but the vertical migrating greatly varied in different seasons or environment conditions. Acari was the dominant group. The index of diversity of the soil fauna was correlated with the index of evenness. The Acari＇s number of individuals infected other species and numbers. Dominant group-Aeari made greater contribution to the result of cluster analysis, and there were significant differences between communities in different habitats by cluster analysis with both Bray-Curtis and Jaccard similarity coefficient.

Genetic Diversity among Parents of Hybrid Rice Based on Cluster Analysis of Morphological Traits and Simple Sequence Repeat Markers

The genetic diversity of 41 parental lines popularized in commercial hybrid rice production in China was studied by using cluster analysis of morphological traits and simple sequence repeat （SSR） markers. Forty-one entries were assigned into two clusters （i.e. early or medium-maturing cluster; medium or late-maturing cluster） and further assigned into six sub-clusters based on morphological trait cluster analysis, The early or medium-maturing cluster was composed of 15 maintainer lines, four early-maturing restorer lines and two thermo-sensitive genic male sterile lines, and the medium or late-maturing cluster included 16 restorer lines and 4 medium or late-maturing maintainer lines. Moreover, the SSR cluster analysis classified 41 entries into two groups （i.e, maintainer line group and restorer line group） and seven sub-groups. The maintainer line group consisted of all 19 maintainer lines, two thermo-sensitive genic male sterile lines, while the restorer line group was composed of all 20 restorer lines. The SSR analysis fitted better with the pedigree information. From the views on hybrid rice breeding, the results suggested that SSR analysis might be a better method to study the diversity of parental lines in indica hybrid rice.

The cluster analysis of the water masses in western Taiwan Strait from hydrologic And chemical factors

On the basis of mixture theory of concentration of Helland-Hansen (Mao et al, 1964; Helland-Hansen, 1916), this paper takes salinity as a conservative factor in the process of dilution and mixture and selects by relating analysis the bydrological and chemical factors which are closely related to salinity. Then making use of the Q type multi-dimensions cluster analysis, we get the results that the water masses in the western Taiwan Strait include the follying: the coastal water along Fujian, Zhejiang and Guangdong Provinces, the diluted fresh water of Minjiang, Jiulong and Hanjiang Rivers; the mixing water in the Taiwan Strait; upwelling cold/warm water to the northwest of the Taiwan Shoal and the upwelling water to the east of Guangdong. The mixing weter in the Taiwan Strait during spring and summer is composed of a Kuroshio branch, the surface weter of the South China Sea, outal wier along Fujian, Zhejiang and Guangdong Provinces. While in autunm and winter, it is mixed up from Kuroshio branch, the shelf weter in the East China Sea, and the coastal water along Fujian, Zhejiang and Guangdong. There is an obvious seasonal change of growth and decline in these water masses.

Regional Allocation of CO_2 Intensity Reduction Targets Based on Cluster Analysis

To meet China＇s CO2 intensity target of 40%-45% reduction by 2020 based on the 2005 level, a regional allocation method based on cluster analysis is developed. Thirty Chinese provinces are classified into six groups based on economy, emissions, and reduction potential indicators. Under the equity principle, the two most developed groups axe assigned the highest reduction targets （55% and 65%, respectively）. However, their reduction potent!al is limited. Under the efficiency principle, the two groups with the highest reduction potential take the highest targets （48% and 61%, respectively）, but their economy is relatively backward. When equity and efficiency are equally weighted, the 5th group with a prominent reduction potential takes the highest target （54%）, and the 2nd and the 3rd groups with large industry scales take the second highest target （49%）. However, under all the three allocation schemes, the targets are not greater than 40% for the 4th and the 6th groups, which have a relatively low economic ability, emissions, and reduction potential. Due to inconsistency between economic and reduction potential, corresponding market mechanisms and policy instruments should be established to ensure equity and efficiency of regional target allocation.

Application of cluster analysis to preventive maintenance scheme design of pavement

To quantitatively identify the maintenance demand for each highway segments in the pavement maintenance scheme design,a mathematical model of uniform segment division was established and an approach of applying cluster analysis theory to the uniform segment division and evaluation of pavement maintenance demand was proposed.The actual maintenance project of a highway carried out in Guangdong province was cited as an example to demonstrate the validity of the proposed method.It is proved that the cluster analysis can eliminate human factors in classification without being constrained by the quantities of samples,considering multiple pavement distress indexes and the continuity of samples.Thus it is evident that cluster analysis is an efficient analytical tool in uniform segment division and evaluation of maintenance demand.