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Long-distance high-speed train localization based on distributed optical fiber sensors (DOFS) has been a challenging issue due to the large-scale heterogeneous sensor nodes. It requires a competent localization algorithm to be capable of strong generalization and quick response. This paper proposes a cooperative multi-classifier network (CMCN) for locating HSTs based on heterogeneous DOFS signals by adaptive modeling of the local characteristics. The proposed CMCN is composed of adaptive feature extraction, lightweight base classifiers and spatial boostrap aggregating (SBA). First, the heterogeneous signals are adaptively transformed to an optimal intrinsic mode function for extracting the statistical features of base classifiers. The base classifiers are constructed based on dynamic soft-margin support vector machine to model local characteristics without computationally burdensome kernel functions by introducing a dynamic penalty factor. The factor is automatically initialized by evaluating the regional consistency before training. Furthermore, the SBA estimates the location of HSTs based on the local states of nodes. It can cooperate with base classifiers for enhanced accuracy by searching for the interval with maximum regional consistency. Finally, a trial is conducted in a high-speed railway in China in long-term running of 92 days. The results prove feasibility and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at Peking University in Beijing, China, aimed to establish a noninvasive model for screening metabolic dysfunction-associated steatotic liver disease (MASLD) in children aged 6-14 years. MASLD is becoming increasingly prevalent among children, especially in regions with high obesity rates. The study identified the waist-to-height ratio (WHtR) as the optimal noninvasive index for predicting MASLD, with a cutoff value of >= 0.48. The combination of WHtR and lipid accumulation product (LAP) was recommended for individual MASLD screening. The study also proposed three MASLD screening recommendations based on obesity prevalence in different regions. [Extracted from the article]

A study conducted at Ningbo First Hospital in China aimed to assess the feasibility of using computerized tomography (CT) imaging and a support vector machine (SVM) algorithm to diagnose secondary pulmonary fungal infections (PFIs) in patients with hematological malignancies (HM). The study included 100 patients with HM and secondary PFI in the training group, and 80 patients with the same underlying disease in the test group. The most common pathogen responsible for PFIs was Aspergillus, followed by Candida, Pneumocystis jirovecii, Mucor, and Cryptococcus. The SVM algorithm demonstrated robust predictive capability in diagnosing secondary PFIs in conjunction with HM. [Extracted from the article]

A recent study conducted by researchers at Tianjin University of Commerce in China has developed a multimodal prediction model for identifying suicidal attempts in patients with major depressive disorder (MDD). The model utilizes demographic, depressive symptoms, and brain structural imaging data to accurately distinguish MDD patients with and without suicidal attempts. The model achieved a balanced accuracy of 77.78%, sensitivity of 83.33%, and specificity of 70.37%. This research not only provides a new prediction model for early intervention in MDD patients but also identifies three important brain structural phenotypes that can be targeted for treatment in MDD patients with suicidal attempts. [Extracted from the article]

A recent study conducted by researchers at Henan University of Science and Technology in China explores the use of machine learning and neuroimaging techniques to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) groups. The researchers constructed a white matter connectivity network based on diffusion tensor imaging (DTI) and used support vector machine (SVM) algorithms for classification. The study found that this approach achieved competitive results, with an area under the curve (AUC) of 0.94 for AD/MCI and 0.95 for MCI/NC. The findings suggest that this method could assist in the early diagnosis and intervention of AD. [Extracted from the article]

A recent study conducted at Xuanwu Hospital of Capital Medical University in Beijing, China, explored the use of machine learning algorithms for the accurate diagnosis of Parkinson's disease (PD). The researchers trained and tested various models using data from different medical centers in China. The support vector machine (SVM) algorithm demonstrated the best performance in differentiating between healthy controls and PD patients. The study identified several important variables and concluded that SVM is a precise and clinician-friendly algorithm for predicting PD in Chinese patients. [Extracted from the article]

A recent study conducted by researchers at the University of Electronic Science and Technology of China explored the use of Raman spectroscopy as a method for screening breast cancer. The study collected blood serum samples from 333 participants, including breast cancer patients, individuals with benign lesions, and healthy controls. By utilizing machine learning techniques, the researchers were able to develop a classification model that accurately distinguished between the different sample groups with a prediction accuracy of up to 98%. The study suggests that Raman spectroscopy has the potential to be a valuable tool for rapid breast cancer screening. [Extracted from the article]

Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to build a support vector machine (SVM) model to evaluate the city air quality level, using the three main air pollutants selected as evaluation index. Design/methodology/approach – PM10, SO2, NO2 are the most important three air pollutants and their concentration data are selected as the influencing factor. And the SVM model is build and used to evaluate the air quality level of 29 major cities in China 2011. The cross-validation is adopted to select optimal penalty parameters and optimal kernel function, and the classification accuracies achieved under different normalization methods and kernel functions are compared in the end. Findings – The study found, the parameters and kernel functions chosen by the SVM model have influence on the model's prediction accuracy. Through continuous optimization of model parameters, finally it is found that the model performs better with [0, 1] normalization method and RBF kernel function. It proves that SVM classification model is effective in dealing with the problem of city air quality evaluation. Practical implications – The result of this study shows that the SVM classification model can be well applied to predict the city air quality level by using air pollutants concentration data as evaluation index. It can help the government and relevant department issue corresponding environmental policy and environmental protection measures. Originality/value – The qualitative and quantitative study method are combined in this paper, on the basis of predecessors’ research results, as well as careful analysis to select evaluation index. The SVM classification model build is simulated by using Matlab technique, beyond comparing the accuracy, its outcomes and its efficiency in classification are demonstrated. [ABSTRACT FROM AUTHOR]