K-means Research Articles

Streptococcus canis transcriptomic modifications in host cell entry environments of human keratinocytes

BackgroundStreptococcus canis is a commensal bacterium in companion animals. This microorganism can infect humans who have been in deep contact with or bitten by pet dogs, suggesting that the skin/soft tissue is one of infection entry sites. To understand pathological process in human cells, we aimed to determine S. canis transcriptomic changes in invasive environments of human keratinocytes.MethodsWe selected one isolate from candidates with whole-genome sequences, based on re-obtained cell invasion ability (CIA) data into human keratinocytes along with bacterial cytotoxicity. RNA-sequencing was conducted for the samples at baselines and 2 h/5 hr post-inoculation using NovaSeq 6000. Global/differential gene expression analyses [principal component analysis (PCA)/k-means clustering analysis/differentially expressed gene (DEG) analyses] were performed. We classified DEGs into their functional categories. To validate transcriptomic results, we did quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assays.ResultsFU1 isolate was selected from seven candidates, based on re-obtained CIA data with less cytotoxicity. Total read bases of 6.17–9.02 Gbp were obtained by RNA-sequencing. PCA and k-means clustering analysis indicated clustering according to their inoculation times. Volcano plots and Venn diagrams revealed that S. canis invasion into keratinocytes produced altered distributions of many genes. Gene ontology enrichment analysis showed most of the gene expressions were downregulated. DEG functional analysis showed the downregulated DEGs belonging to energy production and conversion/carbohydrate transport and metabolism/amino acid transport and metabolism/nucleotide transport and metabolism, with the upregulated DEGs belonging to transcription. qRT-PCR assays for downregulated/upregulated expressions of four genes (pgk–slo/opuAA–kdpB) validated transcriptomic results.ConclusionOur observations suggest that S. canis can downregulate its metabolism-associated gene expressions in human keratinocyte environments. The observed gene expression changes can imply the latent infection in human cells. Further investigation is needed to elucidate the underlying mechanisms for the latent infection.

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico

Abstract. This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.

Levelling up in a ‘decentralised’ England: place-based or ‘one-size-fits-all’ strategies?

ABSTRACT English policymaking has traditionally been centralised, assuming homogenous needs across regions and contributing to economic inequalities. The Levelling Up Agenda aims to address these disparities through a place-based approach using devolved Combined Authority (CA) organisations. Despite being the first national strategy with an extensive place-based focus, it faces criticism as a potentially centralised political tool. Concerns also exist about whether Combined Authorities can deliver regional economic development as they have centrally controlled aspects. If regional planning approaches are identical, decentralisation through Combined Authorities would essentially be futile. This paper uses a novel quantitative method with Strategic Economic Plans to determine if regional planning inhibits a place-based or ‘one-size-fits-all’ approach, contributing data-driven insights to the debate regarding the efficacy of Combined Authorities. Through exploratory text analysis, hierarchical and K-means clustering, findings indicate regions adopt place-sensitive planning with varying sector priorities. A northern-southern and an inland-coastal distinction emerge, with London as an outlier. This suggests Combined Authorities are fit for purpose at this quantitative clustering level and do consider their geographical context in planning despite a devolved yet centrally controlled oxymoronic landscape. Collaborations like an inland and a coastal CA network are recommended to potentially maximise place-based growth.

A Dynamic Early Warning Model for Flash Floods Based on Rainfall Pattern Identification

AbstractFlash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Investigating formation water variability in the Búzios oilfield (Santos Basin) using cluster analysis and hydrogeochemical modeling

The characteristics of formation waters play a crucial role in production development projects within Pre-Salt oil fields in the Santos Basin, offshore southeastern Brazil. This study investigated the compositional variations of brines of the Búzios Field (northeast of the Santos Basin), proposing for the first time an extensive hydrochemical mapping of the Pre-Salt aquifers and identifying nuances of connectivity. A comprehensive methodology was employed, including analysis of pressure gradients, cluster analysis, and hydrogeochemical modeling to classify different types of brine and understand their behavior under reservoir conditions. A hydrochemical classification routine was applied to 129 formation water samples, identifying seven distinct clusters using the unsupervised k-means algorithm. This clustering process efficiently differentiated representative, non-representative, and contaminated samples. The hydrochemical composition of the formation waters varied among the four hydraulic compartments identified in the field, with their boundaries defined by permeability barriers. Compared with the central portion of the field (regional aquifer), where salinities predominantly range from 155,000 to 185,000 mg/L of NaCl, enrichment of specific ions was observed at the field's extremities, such as boron (northeast), barium and lithium (north), and bicarbonate (west). These hydrochemical differences lead the brine salinities at the field's extremities to exceed 225,000 mg/L of NaCl. Hydrogeochemical modeling provided insights into the pH levels of each brine under reservoir conditions, showing a decrease of between 23 and 32% compared to surface pH values. The speciation analysis showed positive strontianite and barite saturation indices in certain types of formation water, indicating potential precipitation. These areas are considered critical for inorganic scaling in production equipment.

Load profiles of residential off-grid solar systems on the Navajo Nation

Standalone off-grid electrical systems, no matter where they are deployed or for what user class, are designed based upon the load they are expected to serve. State-of-the-art computerized off-grid system design tools require the user to specify the expected load profile, that is, how the power consumption changes throughout the day. Often, this is at an hourly resolution, and some characterization of the distribution of power around the average values may be required. Specifying realistic and reasonable load profiles is a barrier to the appropriate design of standalone systems. This research extends previous studies on daily energy consumption of residential solar-powered off-grid systems on the Navajo Nation to provide hourly load profiles, statistical characteristics, and probabilistic models. The data analyzed come from 90 homes over a two-year period. K-means clustering is used to identify prototypical normalized load profiles when the data are grouped by year, season, weekday, and weekend. Eight parametric probability density functions are fit to the grouped data at an hourly resolution. Their fit to the data is evaluated using the Cramér-von Mises (CvM) statistic. The results show that the load profiles tend to be night-peaking and that Log Normal and Gumbel distributions can reasonably model variation in the data. The load profiles and probabilistic models can be used in off-grid design software and to synthesize load profiles for design and future research.

A hybrid model for missing traffic flow data imputation based on clustering and attention mechanism optimizing LSTM and AdaBoost

Reliable traffic flow data is not only crucial for traffic management and planning, but also the foundation for many intelligent applications. However, the phenomenon of missing traffic flow data often occurs, so we propose an imputation model for missing traffic flow data to overcome the randomness and instability bands of traffic flow. First, k-means clustering is used to classify road segments with traffic flow belonging to the same pattern into a group to utilize the spatial characteristics of roads fully. Then, the LSTM networks optimized with an attention mechanism are used as the base learner to extract the temporal dependence of the traffic flow. Finally, the AdaBoost algorithm is used to integrate all the LSTM-attention networks into a reinforced learner to impute the missing data. To validate the effectiveness of the proposed model, we use the PeMS dataset for validation, we impute the data with missing data rate from 10 to 60% under three missing modes, and we use multiple baseline models for comparison, which confirms that our proposed model improves the stability and accuracy of imputing the missing data of the traffic flow with different scenarios.

Longitudinal analysis of ARDS variability and biomarker predictive power in burn patients

Acute Respiratory Distress Syndrome (ARDS) is a severe complication in burn patients, characterized by rapid lung inflammation and hypoxemia. Managing ARDS is challenging due to its high mortality rates and the complex interplay of various pathophysiological factors. This study aims to investigate the heterogeneity of ARDS in burn patients admitted to the Intensive Care Unit (ICU) and evaluate the predictive efficacy of biomarkers in comparison to the PaO2/FiO2 (PF) ratio. A retrospective cohort study was conducted at the Burn Intensive Care Unit of Hangang Sacred Heart Hospital in Seoul, Korea, from July 2010 to December 2022, involving 2,318 patients. Longitudinal k-means clustering was employed to identify patient subgroups based on PF ratio trajectories. Biomarkers, including albumin, white blood cell (WBC) count, and lactate, were assessed for their predictive accuracy using multivariable logistic regression, area under the curve (AUC) analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Four distinct patient clusters were identified. Cluster A exhibited the highest mortality rate at 54.9%, while Cluster D showed the lowest at 7.9%. Albumin demonstrated significantly higher predictive accuracy in Cluster A (AUC: 0.905, NRI: 0.421) and Cluster C (AUC: 0.915, NRI: 0.845), outperforming the PF ratio in both groups. However, in Clusters B and D, biomarkers did not significantly improve upon the predictive power of the PF ratio. Across all clusters, the integration of biomarkers with the PF ratio led to modest improvements but did not consistently outperform the PF ratio as a standalone predictor. This study reveals substantial heterogeneity in ARDS progression among burn patients, with varying mortality rates and biomarker efficacy across clusters. While biomarkers such as albumin showed potential in specific subgroups, their overall contribution to predictive accuracy was limited. Further multicenter, prospective studies are required to validate these findings and develop more refined predictive models. Personalized treatment strategies, based on biomarker profiles and traditional clinical metrics, could enhance ARDS management in burn patients.

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations.

Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation. A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features. Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group. Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

A model to address the cold-start in peer recommendation by using k-means clustering and sentence embedding

In academia, research collaboration plays a vital role in enhancing the research quality and enriching the academic profile of the authors. Recommending appropriate collaborators from a vast scholarly database, particularly for newcomers, poses a challenging cold-start problem. This study addresses a cold-start problem in peer recommendation, considering a dynamic coauthorship graph as a network structure of academic collaborators. As the coauthorship graph is quite large and complex, an efficient indexing method is essential for speeding up the initial search of similar coauthors. The study introduces an efficient Global Inverted List (GIL) for indexing research areas and active authors in the coauthorship network. An attribute-based search and filtering mechanism is proposed to identify relevant collaborators, followed by the application of k-means clustering and doc2vec metrics to rank and select top recommendations. A cold user is associated with attributes that identify coauthors with similar research interests. For each attribute of the cold user, the model searches the associated authors from the GIL. Further, two filtering approaches are applied to refine the retrieved author list. The first ensures that the authors have a significant presence in the specified research areas, whereas the second one helps avoid recommending authors with only superficial connections to the cold user. The model creates a feature matrix of filtered authors using the publication features of authors. The k-means clustering applied to the feature matrix generates k clusters, among which the model chooses only those with seed nodes i.e. the clusters which are having seed nodes are selected for further process. Selected clusters are ranked using doc2vec metrics, with the top-ranked cluster providing the final recommendation. The model recommends the top L members of the selected cluster, where L is the length of the recommendations provided to the new user. Our extensive experiments show the efficacy of the proposed model.

Comparison of K-Means and FCM Algorithms to Optimize Spatiotemporal Pore Pressure Prediction of Earth Dams

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

Prioritizing conservation sites for multi-pond systems to maintain protection of water quality in a fragmented agricultural catchment

Precise targeting of conservation practices to the most effective sites in multi-pond systems (MPS) is critical for resource optimization and water quality improvement. Previous studies generally prioritized ponds for conservation practices considering nutrient removal efficiency. However, they have frequently overlooked the role of ponds in sediment interception and the impact of human activities and environmental factors around the pond. Herein, the present study developed and applied a novel framework for pond prioritization by integrating the Pressure-State-Response (PSR) model, graph theory, and K-mean clustering. The framework consists of three components. An indicator system is developed to represent the nutrient removal performance of any MPS, impacts on catchment sediment connectivity, external threats, and human-initiated conservation. A flow path network considering natural and artificial elements was constructed to calculate indicator values. A cluster analysis was conducted on the index values of different ponds, and a hierarchical sorting method were used to prioritize ponds. The framework was applied to the Guilinqiao Catchment, a typical fragmented agricultural catchment in the Yangtze River Basin, China. The study has quantified the Pressure, State, and Response indices of different ponds in this catchment, prioritized the ponds, and drawn recommendations for conserving MPSs based on field surveys and remote sensing data. Ponds with higher Pressure index, higher State index, and lower Response index scores should be targeted as conservation priorities. This framework provides an effective method for ensuring management of MPSs to sustainably maximize water cleanup capacity with limited resources.

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

An Optimized Public Opinion Communication System in Social Media Networks Based on K-means Cluster analysis

This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model’s performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness. The experimental results indicate that the proposed optimization model excels in multiple areas, particularly in dissemination depth and sentiment dissemination effectiveness. Specifically, in the three dimensions of dissemination speed, the proposed model achieves scores of 4.3, 4.2, and 4.4 in initial dissemination speed, decay speed, and peak dissemination speed, respectively. In the dimensions of user coverage, geographic coverage, and platform coverage under dissemination scope, the model scores 4.4, 4.5, and 4.3, demonstrating a broad dissemination capability. Additionally, in the dimensions of hierarchical dissemination depth and key node influence within dissemination depth, the model scores 4.3 and 4.5, indicating excellent performance in multi-level dissemination and key node activation. In sentiment dissemination effectiveness, the model receives scores of 4.4, 4.5, and 4.4 in emotional tendency change, polarity distribution, and diffusion intensity, showcasing its advantages in sentiment classification and dissemination. Sensitivity analysis further validates the model’s sensitivity to parameter settings, with experiments showing that reasonable adjustments to parameters such as the K value, PSO inertia weight, and learning factors can reduce the Sum of Squared Errors to 3209.72. Meanwhile, it can improve clustering purity to 0.822 and raise the Rand index to 0.623. Therefore, this study offers an efficient and reliable solution for public opinion monitoring on social media, providing valuable reference significance.

Exploring healthy knee kinematic phenotypes obtained through dynamic CT imaging: A cluster analysis study

Dynamic Computed Tomography (CT) emerges as a pivotal imaging modality for the assessment of knee joint kinematics. However, integrating dynamic CT into clinical practice necessitates a thorough understanding of healthy knee kinematics, as large variation in kinematics has been described within healthy populations. Therefore, this study aims to identify and describe healthy phenotypes with homogenous knee kinematics using a clustering approach. A total of 120 healthy knees from 64 participants underwent dynamic CT scanning during knee extension and flexion. Eight tibiofemoral (TF) and patellofemoral kinematic parameters were extracted, after which K-means clustering was applied to identify homogenous kinematic clusters. Kinematic phenotypes were obtained by calculating the median and interquartile range (IQR) for all kinematic parameters per cluster. Two distinct clusters were found, comprising 53 (Cluster 1) and 67 (Cluster 2) knees. Statistically significant differences between the clusters were found in six out of eight kinematic parameters. The most notable differences were observed in TF rotations, with cluster 1 exhibiting a greater amount of internal and adduction rotation of the tibia compared to cluster 2. The two kinematic phenotypes provide new insights into the nuanced variation within a healthy cohort and can serve as reference for future studies evaluating pathological kinematic phenotypes using dynamic CT.

Longitudinal stability of molecular endotypes of knee osteoarthritis patients

ObjectiveTo assess the longitudinal stability of biomarker-based molecular endotypes of knee osteoarthritis (KOA) participants from APPROACH and to evaluate the consistency of findings in an independent KOA population. MethodsNineteen biomarkers were measured longitudinally in 295 KOA participants from the APPROACH cohort. K-means clustering was used to identify the structural damage, inflammation, and low tissue turnover endotypes at the six-, 12-, and 24-month follow-ups. Endotype stability was defined as having the same independent endotype assignment longitudinally for patients with complete data (n = 226). Clinical and biochemical characteristics were compared between participants with longitudinally stable and unstable endotypes. The presence and longitudinal stability of the endotypes were evaluated in a different KOA population from the placebo arm of the oral salmon calcitonin trials. ResultsAn average overall longitudinal endotype stability of 55% (Fleiss’ Kappa of 0.53; 95% CI: 0.46, 0.60) was demonstrated. An average stability of 59% (range: 54% - 59%) was observed for the structural damage endotype (Fleiss’ Kappa 0.52; 95% CI: 0.45, 0.60), 54% (52% - 56%) for the inflammatory (Fleiss’ Kappa 0.61; 95% CI: 0.53, 0.68), and 50% (49% - 52%) for the low tissue turnover endotype (Fleiss’ Kappa 0.46; 95% CI: 0.39, 0.54). Participants with longitudinally unstable endotypes exhibited molecular properties of more than one endotype, which were detectable already at the first visit. ConclusionsOur study showed for the first time that more than half of KOA participants exhibited a longitudinally stable endotype, highlighting the applicability of biomarker-based endotyping in a clinical trial setting.

A clustering method for charging behaviors of electric vehicle users based on an improved k-means algorithm

Abstract The uncertainty of Electric vehicle users’ (EVUs) charging behaviors affect the power grid safety. Therefore, mining the charging behavior characteristics of EVUs can help the power grid to make optimal operation plans previously to smooth the load curve and improve stability. In this regard, a clustering strategy for EVUs is proposed, using an improved K-means algorithm based on the minimum variance theory(MVT). Additionally, the Monte Carlo algorithm is utilized to obtain the simulation results of EVUs. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness and reliability of the proposed cluster algorithm. The results indicate that the proposed cluster algorithm can accurately extract the characteristics of EVUs’ off-on grid behaviors and obtain the accurate daily charging demand representing the main charging rules among EVUs. The proposed algorithm results in a reduction of 69.2% of solution time compared to the traditional K-means algorithm.

Is rheumatoid arthritis always preceded by a symptomatic at-risk phase of arthralgia?

ObjectivesSecondary prevention of rheumatoid arthritis (RA) is generally considered potentially impactful because the entire RA population is believed to experience a symptomatic ‘pre-RA’ phase. We wondered whether this dogma is...

Inference of forensic body fluids/tissues based on mitochondrial DNA copy number: a preliminary study.

The inference of body fluids and tissues is critical in reconstructing crime scenes and inferring criminal behaviors. Nevertheless, present methods are incompatible with conventional DNA genotyping, and additional testing might result in excessive consumption of forensic scene materials. This study aims to investigate the feasibility of distinguishing common body fluids/tissues through the difference in mitochondrial DNA copy number (mtDNAcn). Four types of body fluids/tissues were analyzed in this study - hair, saliva, semen, and skeletal muscle. MtDNAcn was estimated by dividing the read counts of mitochondrial DNA to that of nuclear DNA (RRmt/nu). Results indicated that there were significant differences in RRmt/nu between different body fluids/tissues. Specifically, hair samples exhibited the highest RRmt/nu (log10RRmt/nu: 4.3 ± 0.28), while semen samples showed the lowest RRmt/nu (log10RRmt/nu: -0.1 ± 0.28). RRmt/nu values for DNA samples without extraction were notably higher (approximately 2.9 times) than those obtained after extraction. However, no significant difference in RRmt/nu was observed between various age and gender groups. Hierarchical clustering and Kmeans clustering analyses showed that body fluids/tissues of the same type clustered closely to each other and could be inferred with high accuracy. In conclusion, this study demonstrated that the simultaneous detection of nuclear and mitochondrial DNA made it possible to perform conventional DNA analyses and body fluid/tissue inference at the same time, thus killing two birds with one stone. Furthermore, mtDNAcn has the potential to serve as a novel and promising biomarker for the identification of body fluids/tissues.