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Based on four reanalysis datasets including CMA-RA, ERA5, ERA-Interim, and FNL, this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear. Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley (SYRV) during the summers (June-August) from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm, namely the t-distributed stochastic neighbor embedding method (t-SNE) and the k-means clustering method. The results are as follows: (1) The reproducibility of the 850 hPa wind fields over the SYRV using China's reanalysis product CMARA is superior to that of European and American products including ERA5, ERA-Interim, and FNL. (2) Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities. (3) The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal, but the intensity and the accompanying rainstorm have a clear diurnal variation: they are significantly stronger during daytime than those at nighttime. (4) Half (47%) of the shear lines can cause short-duration rainstorms (=20 mm (3h)-1), and shear line rainstorms account for one-sixth (16%) of the total summer short-duration rainstorms. Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing. (5) Under the influence of stronger water vapor transport and barotropic instability, shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothesis that unsupervised ML approach utilizing EMR could be used to develop a new model for detecting prevalent CVD in clinical settings., Methods: We included 155,894 patients (aged ≥ 18 years) discharged between January 2014 and July 2022, from Xuhui Hospital, Shanghai, China, including 64,916 CVD cases and 90,979 non-CVD cases. K-means clustering was used to generate the clustering models with k = 2, 4, and 8 as predetermined number of clusters k = 2, 4, and 8. Bayesian theorem was used to estimate the models' predictive accuracy., Results: The overall predictive accuracy of the 2-, 4-, and 8-classification clustering models in the training set was 0.856, 0.8634, and 0.8506, respectively. Similarly, the predictive accuracy of the 2-, 4-, and 8-classification clustering models in the testing set was 0.8598, 0.8659, and 0.8525, respectively. After reducing from 19 dimensions to 2 dimensions by principal component analysis, significant separation was observed for CVD cases and non-CVD cases in both training and testing sets., Conclusion: Our findings indicate that the utilization of EMR data can support the development of a robust model for CVD detection through an unsupervised ML approach. Further investigation using longitudinal design is needed to refine the model for its applications in clinical settings., Competing Interests: Declarations. Ethics approval and consent to participate: This study was performed in accordance with the guidelines of the Declaration of Helsinki. The study design was approved by the Ethics Committee of Shanghai Xuhui Central Hospital (approval no.: 2023033), and the institutional review board waived the requirement to obtain the informed consent. Consent for publication: All authors have approved for its publication. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Assessing groundwater quality is essential for achieving sustainable development goals worldwide. However, it is challenging to conduct hydrochemical analysis and water quality evaluation by traditional methods. To fill this gap, this study analyzed the hydrochemical processes, drinking and irrigation water quality, and associated health risks of 93 groundwater samples from the Sichuan Basin in SW China using advanced unsupervised machine learning, the Combined-Weights Water Quality index, and Monte-Carlo simulations. Groundwater samples were categorized into three types using the self-organizing map with the K-means method: Cluster-1 was Ca-HCO 3 type, Cluster-2 was dominated by Ca-HCO 3 , Na-HCO 3 , and mixed Na-Ca-HCO 3 types, Cluster-3 was Ca-Cl and Ca-Mg-Cl types. Ion ratio diagrams revealed that carbonate dissolution and silicate weathering primarily influenced the hydrochemical characteristics. Cluster-1 samples exhibited high NO 3 - contents from intensive agricultural activities. Cluster-2 samples with high Na + contents were characterized by positive cation exchange, while Cluster-3 samples with elevated Ca 2+ and Mg 2+ contents were influenced by reverse cation exchange. Combined-Weights Water Quality Index indicated that 62.37% of total samples were suitable for drinking, predominantly located in the central part of the study area. Irrigation Water Quality Index revealed that 33.34% of total samples were suitable for irrigation, mainly in the northeastern region. NO 3 - concentration and electrical conductivity (EC) value were the main indicators with the highest sensitivity for drinking and irrigation suitability, respectively. Probabilistic health risk assessments suggested that a significant portion of the groundwater samples posed a health risk greater than 1 to children (63%) and adults (52%) by Monte-Carlo simulation. The high-risk areas (hazard index >4), primarily in the eastern region, are closely associated with nitrate distribution. Sensitivity analysis demonstrated that NO 3 - concentration is the primary indicator accounting for health risks. Reducing the application of nitrogen-based fertilizers on cultivated land is the most effective approach to improve drinking quality and mitigate the associated health risks to the population. This study's findings aim to produce a novel groundwater quality evaluation for promoting the sustainable management and utilization of groundwater resources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Conventional supervised and unsupervised machine learning models used for landslide susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of recorded landslide samples, and the subjective and random selection of non-landslide samples. To overcome these drawbacks, a semi-supervised multiple-layer perceptron (SSMLP) is innovatively proposed with several processes: (1) an initial landslide susceptibility map (LSM) is produced using the multiple-layer perceptron (MLP) based on the original recorded landslide samples and related environmental factors; (2) the initial LSM is respectively classified into five areas with very high, high, moderate, low and very low susceptible levels; (3) some reasonable grid units from the areas with very high susceptible level are selected as new landslide samples to expand the original landslide samples; (4) reasonable non-landslide samples are selected from the areas with very low susceptible level; and (5) the expanded landslide samples, reasonable selected non-landslide samples and related environmental factors are put into the MLP once again to predict the final LSM. The Xunwu County of Jiangxi Province in China is selected as the study area. Conventional supervised machine learning (i.e. MLP) and unsupervised machine learning (i.e. K-means clustering model) are selected for comparisons. The comparative results indicate that the SSMLP model has a considerably higher LSP performance than the MLP and K-means clustering in Xunwu County. The SSMLP model successfully addresses the drawbacks existed in the conventional machine learning for LSP. [ABSTRACT FROM AUTHOR]

During the online shopping process, customer reviews strongly influence consumers' buying behaviour. Fake reviews are increasingly utilized to manipulate products' reputations. Automatically and effectively identifying fake reviews has become a salient issue. This study proposes a novel individual-group-merchant relation model to automatically identify fake reviews on e-commerce platforms, which focuses on the behavioural characteristics of the stakeholders. Three groups of indicators are proposed, i.e., individual indicators, group indicators and merchant indicators. An unsupervised matrix iteration algorithm is utilized to calculate the fake degree values at individual, group and merchant levels. To validate the model, an empirical study of fake review identification on a Chinese e-commerce platform is implemented. A total of 97,804 reviews related to 93 online stores and 9558 different reviewers are randomly selected as the test data. The experimental results show that the F-measure values of the proposed method in identifying fake reviewers, online merchants and groups with reputation manipulation are 82.62%, 59.26% and 95.12%, respectively. The proposed method outperforms the traditional methods (e.g. Logistic Regression and K nearest neighbour) in identifying fake reviews. It suggests that the combinations of the behaviour indicators with content analysis can effectively improve the performances of the fake review identification. The proposed method is more scalable to large datasets and easier to be employed, as it does not require manual labelling training set and it eliminates the training of classification models. This study greatly contributes to purifying the Chinese environment of business competition and establishing a better regulatory mechanism for credit manipulation in China. [ABSTRACT FROM AUTHOR]

Purpose: Mismatch between partial imported prostheses and Chinese anatomy affects the clinical outcome of the procedure. The purpose of this study was to measure the anatomical dimensions of Chinese distal femurs to provide guidance for the design of more compatible distal femoral prostheses., Methods: A total of 406 healthy distal femurs were reconstructed and measured. Consistency of these measurements and differences in sides, gender, and populations were examined. Parameter correlations were analyzed, and pairs with strong correlations underwent linear regression analysis. The design of distal femoral prostheses was referenced from the results of K-means and hierarchical clustering analysis., Results: Ten parameters were measured, including the trans-epicondylar axis, width of the distal femur (ML), anteroposterior diameter of the distal femur (AP), etc. The intra-class correlation coefficient ranged from 0.795 to 0.999 for intra-observer consistency, and from 0.796 to 0.998 for inter-observer consistency. Males exhibited significantly larger parameters than females, except for the posterior condylar angle (all P values < 0.05). Compared to other populations, substantial differences were observed for most parameters, such as ML, AP, width of lateral femoral condyle, etc. (all P values < 0.05). Clustering analysis suggested that distal femoral prostheses should include at least five sizes to adequately accommodate the sampled population. ML sizes for males were 68, 70, 83, 73, and 89 mm, and for females 64, 65, 71, 67, and 77 mm. AP sizes for males were 56, 60, 60, 64, and 64 mm, and for females 48, 52, 54, 57, and 58 mm., Conclusions: Chinese distal femur morphology, as analyzed using 3D techniques, varies significantly between genders and when compared with international data. For improved patient fit, the creation of five or more distal femur prostheses, diversified by gender and size and informed by the associated morphological parameters, is recommended., Level of Evidence: IV., (© 2023. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Background: Pancreatic adenocarcinoma (PAAD) is one of the most lethal carcinomas, and the current histopathological classifications are of limited use in clinical decision-making. There is an unmet need to identify new biomarkers for prognosis-informative molecular subtyping and ultimately for precision medicine., Methods: We profiled genomic alterations for 608 PAAD patients in a Chinese cohort, including somatic mutations, pathogenic germline variants and copy number variations (CNV). Using the CNV information, we performed unsupervised consensus clustering of these patients, differential CNV analysis and functional/pathway enrichment analysis. Cox regression was conducted for progression-free survival analysis, the elastic net algorithm used for prognostic model construction, and rank-based gene set enrichment analysis for exploring tumor microenvironments., Findings: Our data did not support prognostic value of point mutations in either highly mutated genes (such as KRAS, TP53, CDKN2A and SMAD4) or homologous recombination repair genes. Instead, associated with worse prognosis were amplified genes involved in DNA repair and receptor tyrosine kinase (RTK) related signalings. Motivated by this observation, we categorized patients into four molecular subtypes (namely repair-deficient, proliferation-active, repair-proficient and repair-enhanced) that differed in prognosis, and also constructed a prognostic model that can stratify patients with low or high risk of relapse. Finally, we analyzed publicly available datasets, not only reinforcing the prognostic value of our identified genes in DNA repair and RTK related signalings, but also identifying tumor microenvironment correlates with prognostic risks., Interpretation: Together with the evidence from genomic footprint analysis, we suggest that repair-deficient and proliferation-active subtypes are better suited for DNA damage therapies, while immunotherapy is highly recommended for repair-proficient and repair-enhanced subtypes. Our results represent a significant step in molecular subtyping, diagnosis and management for PAAD patients., Funding: This work was supported by the National Natural Science Foundation of China (grant numbers 81470894, 81502695, 81672325, 81871906, 82073326, 82103482 and 32170663), the Shanghai Sailing Program (grant number 20YF1426900), and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (awarded to H.F.)., Competing Interests: Declaration of Competing Interest The authors declare no potential conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Electric Submersible Pump (ESP) is currently widely employed to help enhance production for nonlinear-flowing well with high production and high water cut well. However, ESP broken shaft is common in the oil industry. The broken shaft leads to production disruptions, resulting in significant economic losses. The objective of this paper is to evaluate Principal Component Analysis (PCA) as an unsupervised machine learning technique to detect the cause of the breakage of the ESP shaft. This method was successfully applied in the Penglai block of Bohai Oilfield in China to detect the ESP shaft fracture in real-time. A two-dimensional plot of scores of Principal Component 1 and Principal Component 2 can be used to identify different clusters of the stable region, unstable region and failure region. By this means, potential ESP shaft fracture will be found when the cluster starts deviating away from the stable region. Moreover, a PCA diagnostic model is built to predict the time at which the ESP shaft fracture occurs and to determine the main decision variable most responsible for ESP broken shaft. This paper demonstrates that the application of the PCA method performs well in monitoring the ESP operation system and predicts the impending breakage of ESP shaft with high accuracy. • ESP broken shaft is common in the oil industry, which leads to production disruptions, resulting in huge economic losses. • Principal Component Analysis is used as an unsupervised machine learning method to predict the breakage of the ESP shaft. • A plot of scores of Principal Component 1 and 2 can identify different clusters of stable, unstable and failure regions. • Once a PCA diagnostic model is built, the variable most responsible for the breakage of the ESP shaft will be determined. • By implementing T 2 and SPE equations, the PCA model has excellent accuracy in predicting the ESP shaft breakage time. • PCA can be used as an important pre-processing method and as an unsupervised machine learning technique to predict the developing ESP failures. [ABSTRACT FROM AUTHOR]

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.