With the increasingly widespread application of large-scale energy storage battery systems, the demand for battery safety is rising. Research on how to detect battery anomalies early and reduce the occurrence of thermal runaway (TR) accidents has become particularly important. Existing research on battery TR warning algorithms can be mainly divided into two categories: model-driven and data-driven methods. However, the common model-driven methods are often of high complexity, with poor versatility and low early warning capability; and the common data-driven methods are mostly based on neural networks, requiring substantial training costs, with better early warning capabilities but higher false alarm probabilities. To address the limitations of existing works, this paper proposes a combined data-driven and model-based algorithm for accurate battery TR warnings. Specifically, the K-Means algorithm serves as the data-driven module, capturing outliers in battery data, and the Bernardi equation serves as the model-driven module used to evaluate battery temperature. Ultimately, the outputs of the weighted model-driven module and data-driven module are combined to comprehensively assess whether the battery is abnormal. The proposed algorithm combines the advantages of model-driven and data-driven approaches, achieving a 25 min advance warning for thermal runaway, with a significantly reduced probability of false alarms.

Chronic exposure to the hypobaric hypoxia environment of plateau could influence human cognitive behaviours which are supported by dynamic brain connectivity states. Until now, how functional connectivity (FC) of the brain network changes with altitudes is still unclear. In this article, we used EEG data of the Go/NoGo paradigm from Weinan (347 m) and Nyingchi (2950 m). A combination of dynamic FC (dFC) and the K-means cluster was employed to extract dynamic FC states which were later distinguished by graph metrics. Besides, temporal properties of networks such as fractional windows (FW), transition numbers (TN) and mean dwell time (MDT) were calculated. Finally, we successfully extracted two different states from dFC matrices where State 1 was verified to have higher functional integration and segregation. The dFC states dynamically switched during the Go/NoGo tasks and the FW of State 1 showed a rise in the high-altitude participants. Also, in the regional analysis, we found higher state deviation in the fronto-parietal cortices and enhanced FC strength in the occipital lobe. These results demonstrated that long-term exposure to the high-altitude environment could lead brain networks to reorganize as networks with higher inter- and intra-networks information transfer efficiency, which could be attributed to a compensatory mechanism to the compromised brain function due to the plateau environment. This study provides a new perspective in considering how the plateau impacted cognitive impairment.

BACKGROUND: Angiogenesis plays an important role in colon cancer (CC) progression.
OBJECTIVE: To investigate the tumor microenvironment (TME) and intratumor microbes of angiogenesis subtypes (AGSs) and explore potential targets for antiangiogenic therapy in CC.
METHODS: The data were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. K-means clustering was used to construct the AGSs. The prognostic model was constructed based on the differential genes between two subtypes. Single-cell analysis was used to analyze the expression level of SLC2A3 on different cells in CC, which was validated by immunofluorescence. Its biological functions were further explored in HUVECs.
RESULTS: CC samples were grouped into two AGSs (AGS-A and AGS-B) groups and patients in the AGS-B group had poor prognosis. Further analysis revealed that the AGS-B group had high infiltration of TME immune cells, but also exhibited high immune escape. The intratumor microbes were also different between the two subtypes. A convenient 6-gene angiogenesis-related signature (ARS), was established to identify AGSs and predict the prognosis in CC patients. SLC2A3 was selected as the representative gene of ARS, which was higher expressed in endothelial cells and promoted the migration of HUVECs.
CONCLUSIONS: Our study identified two AGSs with distinct prognoses, TME, and intratumor microbial compositions, which could provide potential explanations for the impact on the prognosis of CC. The reliable ARS model was further constructed, which could guide the personalized treatment. The SLC2A3 might be a potential target for antiangiogenic therapy.

BACKGROUND: Prior typing methods fail to provide predictive insights into surgical complexities for extrahepatic choledochal cyst (ECC). This study aims to establish a new classification system for ECC through clustering of imaging results. Additionally, it seeks to compare the differences among the identified ECC types and assess the levels of surgical difficulty.
METHODS: The imaging data of 124 patients were automatically grouped through a K-means clustering analysis. According to the characteristics of the new grouping, corrections and interventions were carried out to establish a new classification. Demographic data, clinical presentations, surgical parameters, complications, reoperation, and prognostic indicators were analyzed according to different types. Factors contributing to prolonged surgical time were also evaluated.
RESULTS: A new classification system of ECC: Type A (upper segment), Type B (middle segment), Type C (lower segment), and Type D (entire bile duct). The incidences of comorbidities (calculus or infection) were significantly different (P = 0.000, P = 0.002). Additionally, variations in the incidence of postoperative biliary stricture were statistically significant (P = 0.046). The operative time was significantly different between groups (P = 0.001). Age, BMI > 30, classification, and the presence of combined stones exhibit a significant association with prolonged operative time (P = 0.002, P = 0.000, P = 0.011, P = 0.011).
CONCLUSIONS: In conclusion, our utilization of machine learning-driven cluster analysis has enabled the creation of a novel extrahepatic biliary dilatation typology. This classification, in conjunction with factors like age, combined stone occurrence, and obesity, significantly influences the complexity of laparoscopic choledochal cyst surgery, offering valuable insights for improved surgical treatment.

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of \'water quality distance\' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.

Informed by the biopsychosocial framework, our study uses the Chinese Longitudinal Healthy Longevity Survey (CLHLS) dataset to examine cognitive function trajectories among the oldest-old (80+). Employing K-means clustering, we identified two latent groups: High Stability (HS) and Low Stability (LS). The HS group maintained satisfactory cognitive function, while the LS group exhibited consistently low function. Lasso regression revealed predictive factors, including socioeconomic status, biological conditions, mental health, lifestyle, psychological, and behavioral factors. This data-driven approach sheds light on cognitive aging patterns and informs policies for healthy aging. Our study pioneers non-parametric machine learning methods in this context.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

UNASSIGNED: Gene expression data is typically high dimensional with a limited number of samples and contain many features that are unrelated to the disease of interest. Existing unsupervised feature selection algorithms primarily focus on the significance of features in maintaining the data structure while not taking into account the redundancy among features. Determining the appropriate number of significant features is another challenge.
UNASSIGNED: In this paper, we propose a clustering-guided unsupervised feature selection (CGUFS) algorithm for gene expression data that addresses these problems. Our proposed algorithm introduces three improvements over existing algorithms. For the problem that existing clustering algorithms require artificially specifying the number of clusters, we propose an adaptive k-value strategy to assign appropriate pseudo-labels to each sample by iteratively updating a change function. For the problem that existing algorithms fail to consider the redundancy among features, we propose a feature grouping strategy to group highly redundant features. For the problem that the existing algorithms cannot filter the redundant features, we propose an adaptive filtering strategy to determine the feature combinations to be retained by calculating the potentially effective features and potentially redundant features of each feature group.
UNASSIGNED: Experimental results show that the average accuracy (ACC) and matthews correlation coefficient (MCC) indexes of the C4.5 classifier on the optimal features selected by the CGUFS algorithm reach 74.37% and 63.84%, respectively, significantly superior to the existing algorithms.
UNASSIGNED: Similarly, the average ACC and MCC indexes of the Adaboost classifier on the optimal features selected by the CGUFS algorithm are significantly superior to the existing algorithms. In addition, statistical experiment results show significant differences between the CGUFS algorithm and the existing algorithms.

OBJECTIVE: The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques.
METHODS: Data were obtained from Japanese male participants enrolled in a prospective cohort study between August 2008 and August 2010. A final dataset of 447 male participants was used for analysis. Dimension reduction using uniform manifold approximation and projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to evaluate the association between dietary patterns and the incidence of hypertension.
RESULTS: We identified four dietary patterns: \'Low-protein/fiber High-sugar,\' \'Dairy/vegetable-based,\' \'Meat-based,\' and \'Seafood and Alcohol.\' Compared with \'Seafood and Alcohol\' as a reference, the protective dietary patterns for hypertension were \'Dairy/vegetable-based\' (OR 0.39, 95% CI 0.19-0.80, P = 0.013) and the \'Meat-based\' (OR 0.37, 95% CI 0.16-0.86, P = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding.
CONCLUSIONS: This study finds that relative to the \'Seafood and Alcohol\' pattern, the \'Dairy/vegetable-based\' and \'Meat-based\' dietary patterns are associated with a lower risk of hypertension among men.

With the increased use of online English courses, the quality of the course directly determines its efficacy. Recently, various industries have continuously employed Internet of Things (IoT) technology, which has considerable scene adaptability. To better supervise the specific content of English courses, we discuss how to apply multi-source mobile Internet of Things information technology to the practical evaluation system of English courses to boost the performance of English learning evaluation. Therefore, by analyzing the problems of existing English course evaluation and the characteristics of multi-source mobile Internet of Things information technology, this article designs an English course practical evaluation system based on multi-source data collection, processing, and analysis. The system can collect real-time student voices, behavior, and other data through mobile devices. Then, analyze the data using cloud computing and data mining technology and provide real-time learning progress and feedback. We can demonstrate that the accuracy of the evaluation system can reach 80.23%, which can effectively improve the efficiency of English learning evaluation, provide a new method for English teaching evaluation, and further improve and optimize the English education teaching content to meet the needs of the actual teaching environment.