Fuzzy C-means Cluster Analysis Research Articles

An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.

In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.

Predicting Financial Failure of Public Teaching Hospitals: An Application of Fuzzy Clustering Analysis

Purpose –To develop a novel prediction model and determine the variables that best predict hospitals’ financial failures. Design/methodology/approach –A fuzzy C-means clustering analysis was conducted using financial ratios from MoH Hospitals’ financial databases. Hospital Fuzzy Financial Health Scores (HF-HFS) then were calculated the as the degree of financial failure measured through the selected n-cluster model. Findings –The results show that the number of teaching hospitals experiencing financial failure has increased. HF-FHS scores also were compared with the modified Altman Z scoresa commonly used financial distress measure in the hospital service sector. The HF-FHS scores in good and poor financial conditions differ significantly. The HF-FHS scores were also strongly correlated with the Altman Z scores. Discussion –The rising trend in the number of hospitals experiencing financial difficulties over the years may cause some hospitals in financial distress to fail to fulfill their obligations, which may disrupt the services provided. In such a situation, good management skills, which are the most important factor reducing the financial risks of hospitals, will not work after a certain point.Conclusions –The findings indicate Fuzzy C Means clustering as a viable option to evaluate the financial failure of hospitals compared to more traditional methods such as Altman Z scores. Hospitals still face financial pressures due to market and structural factors such as the global budget repayment system, pricing and collection time, and insufficient competition conditions.Predicting hospitals’ financial failures can help develop managerial policies and strategies to recognize and combat risks, improveperformance, and improve the current situation of hospitals.

The Influence of Augmented Reality Mobile App on Electronics Engineering Students' Self-Competence

Augmented reality (AR) is currently becoming educational trend by offering visualization of learning and making abstract concepts more concrete. Therefore, this research aimed to investigate the effect of AR technology based on mobile application on self-competence of electronics engineering students. It also considered the role of self-regulated learning, intrinsic learning motivation, and perceived usefulness as moderating factors. A cross-sectional survey with quantitative method was conducted on 848 electronics engineering education students at Universitas Negeri Padang, Indonesia. The data were obtained through questionnaires, and Structural Equation Model (SEM) analysis was conducted using SmartPLS 3. Furthermore, a fuzzy C-means clustering analysis was carried out using JASP software. The results showed that perceived usefulness had a significant positive effect on students' self-competence and moderated the significant positive relationship between self-regulated learning and self-competence. These empirical results showed that an improved level of self-regulated learning and beneficial application of technology could increase self-competence.

Projected fuzzy [formula omitted]-means clustering algorithm with instance penalty

At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

BackgroundMultimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years.MethodsA retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group.Results3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories.ConclusionsIn a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning.Trial registration numberNCT05717309.

Multivariate analysis for data mining to characterize poultry house environment in winter

The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into three growth phases (weeks 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All three clustering methods were found to be suitable for grouping data, with FCM outperforming the other two. Using data clustering, the broiler house spaces were divided into three, two, and two subspaces (clusters) for weeks 1–3, 4–6, and 7–9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

Development of a fabric classification system using drapability and tactile characteristics

AbstractWhen producing clothing using virtual fitting technology or purchasing textile and clothing products online, it is challenging to make judgments or communicate information about sensory characteristics, such as drapability and tactile sensations, as there are no clear objective indicators for these factors. Therefore, the study aims to develop a classification system for the sensory properties of fabrics using drapability and tactile characteristics as quantitative indicators. The developed system was verified through subjective evaluations by an expert group, and it was found to be meaningful in reflecting classification levels in practice. The drapability and tactile sensation (softness; TS7) of the fabric were classified using fuzzy c-means cluster analysis, and the results were confirmed through a subjective evaluation by experts. The classification system was then used to predict the classification group, constituted by drapability and tactile characteristics, from mechanical properties using an artificial neural network. The network was trained on 534 fabric samples for drapability and tactile sensation (softness), and it correctly predicted 202 samples out of 243 validation data, with a forecasting accuracy of 83.5%. The developed classification system enables predictions and judgments about subjective characteristics like fabric drapability and tactile sensation based on the mechanical property values of various samples.

Physiological Reactions Profiling in Polygraph Testing: Insights from Fuzzy C-Means Clustering Analysis

Physiological Reactions Profiling in Polygraph Testing: Insights from Fuzzy C-Means Clustering Analysis

Clusters of medical specialities around patients with multimorbidity – employing fuzzy c-means clustering to explore multidisciplinary collaboration.

Background: The increasing prevalence of multimorbidity challenges hospital care organization. Hospital care is structured around medical specialties and focused on separate treatment of individual organ systems. Consequently, patients with multimorbidity visit multiple healthcare professionals and have high healthcare utilization with a risk of interactions and contradictory treatments if multidisciplinary coordination is absent. Organizational structures for multidisciplinary collaboration between medical specialists in case of multimorbidity are, however, limited. To facilitate multidisciplinary, coordinated care and to prevent or minimize adverse health outcomes, it might be useful to organize hospital care around specific patterns of multimorbidity or medical specialties. Therefore, we hypothesized that the use of cluster analysis and real clinical data could be helpful to unravel groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity. 
 Method: We performed a cross-sectional cohort study and used retrospectively collected electronic health record (EHR) from a Dutch hospital in 2017. We extracted EHR data of 22,133 patients with multimorbidity aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnosis. We used a fuzzy c-means clustering algorithm and explored the patients’ membership degree factors to each cluster to distinguish subgroups of patients with a specific pattern of involved medical specialties.
 Results: The mean age was 67.9 years (interquartile range (IQR):20.9), and 56.0% was female. We identified six clusters and 22 subgroups. The clusters were: 1. dermatology/ plastic surgery, 2. six specialties, 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ rehabilitation/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one cluster only (11 subgroups) and the remaining subgroups were combinations of two clusters. The prevalence of specific diagnosis groups, patient characteristics and healthcare utilization seemed to differ between subgroups. For instance, patients of the subgroup with high involvement of internal medicine, cardiology and geriatrics; was characterized by cardiometabolic diseases. These patients had the highest average age (median:73 years, IQR:16.2), number of diagnoses (median:5, IQR:3), and number of outpatient visits (median:7, IQR:6). Patients of the subgroup with involvement of both the cardiologist and pulmonologist had lower number of diagnoses (median:3, IQR:1) and outpatient visits (median: 5:, IQR:4). This subgroup was mostly characterized by pulmonary and heart diseases, which can both represent with symptoms as dyspnea. Thus, the subgroups and their characteristics can provide clues about potential target populations that might benefit from (more) multidisciplinary collaboration, such as a formalization of a cardiometabolic or dyspnea outpatient clinic. 
 Conclusion: Our study shows that it is possible to use fuzzy c-means cluster analysis and real clinical data to identify clinically relevant clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity. With this strategy hospitals, healthcare professionals, and data scientists can further analyze which of these subgroups are target populations with multimorbidity that might benefit from improved multidisciplinary collaboration or new models of care.

Seasonal variations of potentially toxic elements (PTEs) in drinking water and health risk assessment via Monte Carlo simulation and Sobol sensitivity analysis in southern Iran's largest city

The escalating concern over the presence and health implications of potentially toxic elements (PTEs) in drinking water has underscored the need for rigorous risk assessments. Our study aimed to quantify both the non-carcinogenic and carcinogenic health risks associated with exposure to selected PTEs—namely arsenic (As), chromium (Cr), and cadmium (Cd). Also, we evaluated ingestion and skin contact exposures to risks during summer and winter using metrics such as the hazard quotient (HQ), hazard index (HI), and cancer risk (CR) for children, adult males, and adult females. For all demographic groups and exposure pathways, the HQ values remain below the established safety threshold (HQ < 1). Notably, As consistently had the highest average HI value across children, male adults, and female adults. Seasonal variations were statistically significant (p < 0.05) for As and Cr, but not Cd. During the summer, the average total carcinogenic risks (TCR) from drinking water exposure were 7.61 × 10–6, 8.94 × 10–6, and 1.12 × 10–5 for children, male adults, and female adults, respectively. In the winter, these values were 1.18 × 10–5, 1.40 × 10–5, and 1.75 × 10–5, respectively. The fuzzy C-means clustering analysis provided insights into our dataset's Cr, Cd, and As distribution patterns. Results indicate that As, Cr, and Cd mean concentrations were below the World Health Organization health-based guidelines. The CR values for children and adults from drinking water exposure were slightly above or below the US Environmental Protection Agency’s standards. These findings can inform research and policy-making regarding the risk of PTEs in drinking water and highlight the need to monitor Shiraz water regularly.

Binary structure constrained gravity inversion based on seismic first arrival travel time data

Gravity exploration method is one of the important methods for deep mineral resource exploration, but gravity data inversion has limited resolution ability in the vertical direction. In order to improve the vertical resolution of gravity data inversion, we propose a binary structure constrained gravity inversion method based on seismic first arrival travel time data. This method effectively reconstructs a density model with high vertical resolution by transferring the structural information of a high-resolution velocity model reconstructed by seismic data inversion to gravity data inversion through the binary structure constrained technique. This strategy eliminates the need to integrate both gravity and seismic methods into a single inversion framework, avoiding both the difference in convergence speeds between the two methods, as well as getting rid of the complexity associated with calculating structural coupling terms. Theoretical simulations show that the fuzzy c-means cluster analysis technique can accurately extract the target region of the velocity model reconstructed by seismic data inversion. Under the constraint of seismic structural information, the resolution of reconstructed density model is much higher than that of separate gravity data inversion, which proves that high resolution seismic information can improve the vertical resolution of gravity data inversion. Compared with the traditional cross-gradient joint inversion, the binary structure constrained gravity inversion method can further improve the resolution of the density model, especially in the reconstruction of the anomaly interface, which verifies that the method has certain effectiveness.

Clusters of medical specialties around patients with multimorbidity – employing fuzzy c-means clustering to explore multidisciplinary collaboration

BackgroundHospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals’ realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity.MethodsWe conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients’ membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses.ResultsWe found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups.ConclusionOur study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.

Multimorbidity patterns in COVID-19 patients and their relationship with infection severity: MRisk-COVID study.

Several chronic conditions have been identified as risk factors for severe COVID-19 infection, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess their relationship with infection severity/mortality. The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were subjected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within each stratum. Bivariate analyses were performed to assess the relationship between severity/mortality and age, sex, and multimorbidity clusters. Severe infection was reported in 9.5% (95% CI: 9.0-9.9) of the patients, and death occurred in 3.9% (95% CI: 3.6-4.2). We identified multimorbidity clusters related to severity/mortality in most age groups from 21 to 65 years. In males, the cluster with highest percentage of severity/mortality was Heart-liver-gastrointestinal (81-90 years, 34.1% severity, 29.5% mortality). In females, the clusters with the highest percentage of severity/mortality were Diabetes-cardiovascular (81-95 years, 22.5% severity) and Psychogeriatric (81-95 years, 16.0% mortality). This study characterized several multimorbidity clusters in COVID-19 patients based on sex and age, some of which were found to be associated with higher rates of infection severity/mortality, particularly in younger individuals. Further research is encouraged to ascertain the role of specific multimorbidity patterns on infection prognosis and identify the most vulnerable morbidity profiles in the community. NCT04981249. Registered 4 August 2021 (retrospectively registered).

Multimorbidity patterns and 18-years transitions from normal cognition to dementia and death: A population-based study.

Several chronic diseases accelerate cognitive decline; however, it is still unknown how different patterns of multimorbidity influence individuals' trajectories across the cognitive continuum. We aimed to investigate the impact of multimorbidity and of specific multimorbidity patterns on the transitions across cognitive stages (normal cognition, cognitive impairment, no dementia[CIND], dementia) and death. We included 3122 dementia-free individuals from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Using fuzzy c-means cluster analysis, multimorbid participants were classified into mutually exclusive groups characterized by commonly coexisting chronic diseases. Participants were followed up to 18 years to detect incident CIND, dementia or death. Transition hazard ratios, life expectancies and time spent in different cognitive stages were estimated using multistate Markov models. At baseline, five multimorbidity patterns were identified: neuropsychiatric, cardiovascular, sensory impairment/cancer, respiratory/metabolic/musculoskeletal and unspecific. Compared to the unspecific pattern, the neuropsychiatric and sensory impairment/cancer ones showed reduced hazards of reverting from CIND to normal cognition (HR 0.53, 95%CI 0.33-0.85 and HR 0.60, 95%CI 0.39-0.91). Participants in the cardiovascular pattern exhibited an increased hazard of progression from CIND to dementia (HR 1.70, 95%CI 1.15-2.52) and for all transitions to death. Subjects with the neuropsychiatric and cardiovascular patterns showed reduced life expectancy at age 75, with an anticipation of CIND (up to 1.6 and 2.2 years, respectively) and dementia onset (up to 1.8 and 3.3 years, respectively). Multimorbidity patterns differentially steer individual trajectories across the cognitive continuum of older adults and may be used as a risk stratification tool. This article is protected by copyright. All rights reserved.

Application of spatial data and 3S robotic technology in digital city planning

This article uses spatial data and 3S (Spatial, Surveying, and Remote Sensing) technology to enhance digital city planning. The methodology integrates WebGIS Big data, statistical feature extraction techniques, and strategic planning to create a comprehensive framework for digital urban planning. Spatial information point calibration ensures accurate spatial positioning during the planning process. At the same time, data fusion and fuzzy C-means clustering analysis are utilized to detect and analyze WebGIS data within the digital city planning context. The proposed model incorporates a piecewise fitting method within the Big data integration scheduling framework for digital city spatial planning. A C/S (Client/Server) architecture and ARM-embedded technology have been developed to establish a robust digital city planning system to support this approach. This system encompasses modules for WebGIS information collection, bus control, database management, human-machine interaction, and data processing terminals. Simulation results demonstrate that the method significantly reduces delays in digital city planning and design. When analyzing a data scale of 100, the method exhibits an 83.4% lower delay than the fuzzy method. Although delays increase with larger data scales, even at a scale of 400, the method still offers a 43.1% reduction compared to the fuzzy method. Across varying data scales, the proposed method consistently maintains approximately 60% lower latency than the rough set method. This method showcases superior intelligence and exhibits strong capabilities in accessing and scheduling WebGIS data effectively.

Image Processing and Feature Extraction for Hull Structure GMAW Based on Weld Pool Visual Sensing

Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum‐visual‐sensing acquisition system and an image‐processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut‐off composite dimming and near‐infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high‐speed charge‐coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image‐processing fusion model based on the spatial information fuzzy C‐means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image‐processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual‐sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM‐Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.

Specific multimorbidity patterns modify the impact of an exercise intervention in older hospitalized adults.

Different multimorbidity patterns present with different prognoses, but it is unknown to what extent they may influence the effectiveness of an individualized multicomponent exercise program offered to hospitalized older adults. This study is a secondary analysis of a randomized controlled trial conducted in the Department of Geriatric Medicine of a tertiary hospital. In addition to the standard care, an exercise-training multicomponent program was delivered to the intervention group during the acute hospitalization period. Multimorbidity patterns were determined through fuzzy c-means cluster analysis, over 38 chronic diseases. Functional, cognitive and affective outcomes were considered. Three hundred and six patients were included in the analyses (154 control; 152 intervention), with a mean age of 87.2 years, and 58.5% being female. Four patterns of multimorbidity were identified: heart valves and prostate diseases (26.8%); metabolic diseases and colitis (20.6%); psychiatric, cardiovascular and autoimmune diseases (16%); and an unspecific pattern (36.6%). The Short Physical Performance Battery (SPPB) test improved across all patterns, but the intervention was most effective for patients in the metabolic/colitis pattern (2.48-point difference between intervention/control groups, 95% CI 1.60-3.35). Regarding the Barthel Index and the Mini Mental State Examination (MMSE), the differences were significant for all multimorbidity patterns, except for the psychiatric/cardio/autoimmune pattern. Differences concerning quality of life were especially high for the psychiatric/cardio/autoimmune pattern (16.9-point difference between intervention/control groups, 95% CI 4.04, 29.7). Patients in all the analyzed multimorbidity patterns improved with this tailored program, but the improvement was highest for those in the metabolic pattern. Understanding how different chronic disease combinations are associated with specific functional and cognitive responses to a multicomponent exercise intervention may allow further tailoring such interventions to older patients' clinical profile.

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System.

Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management.

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

In order to locate the single-phase-to-ground fault in distribution network, a research method of single-phase-to-ground fault location and recovery technology in distribution network is proposed. According to the characteristics of single-phase-to-ground fault in distribution network, the fault characteristics of zero-sequence current and three-phase current transient records are extracted by generalized S transform of correlation coefficient, and multi-source redundant information is formed. Fuzzy C-means clustering analysis method is used to fuse multiple fault features online location strategy. The simulation results show that the fuzzy clustering based on FCM changes continuously with the accumulation of samples, and the sample center changes with the enrichment of the samples. When the arc suppression coil is grounded with different initial phase angle transition resistance and intermittent fault, the state of each measuring point can be correctly identified, and the accurate energization rate is 97%. The test results show that the system can achieve accurate positioning for all sections.

Hydrological characteristics of Australia: national catchment classification and regional relationships

• Australian subcatchments are described by forty climate and intrinsic attributes. • Fuzzy c-means classified the national subcatchment dataset into eight clusters. • Most subcatchments have a very high membership coefficient to one cluster. • Each cluster is typified by specific streamflow behaviour and flood risk. • Subcatchments with poor cluster membership levels highlight interactive complexity. A continent-wide classification study of Australian catchments was undertaken to group drainage basins with comparable flow characteristics based on forty metrics from the themes of climate, topography, surface condition and hydrogeology. A principal component analysis reduced the multi-collinear indices to nine principal components (PCs). A fuzzy c-means cluster analysis on the PCs resulted in the delineation of eight subcatchment clusters with similar physiography, and 82.5% of subcatchments displaying a membership coefficient greater than 0.7. Subcatchments with poor cluster membership levels may be significant in highlighting complexity in the interaction between climatic controls and local geomorphological and land use variability. The clusters are distinguished by different attributes in several key subcatchment variables and each group is described in terms of its flow and flood profile. These interpretations are supported by separately derived streamflow signatures from gauged catchments. Cluster and PC distribution mapping revealed distinct spatial and drainage relationships between the subcatchments relevant to the assessment of flood behaviour. The spatial pattern of the river basin regionalisation via physical catchment similarity closely resembles the modified Köppen climate classification, with differences highlighting the importance of non-climate characteristics on streamflow behaviour in Australia. The catchment classification is an Australia-wide regionalisation scheme, incorporating datasets with disparate spatial distribution and ungauged subcatchments. This study therefore provides the framework for more detailed streamflow analysis by applying consistent methods across all catchments in Australia, and has implications for flood risk prediction, mitigation and planning.