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Intelligent loading systems are extensively employed in coal enterprises. Nevertheless, pre-loading customer vehicle scheduling predominantly depends on manual expertise. This frequently results in extended vehicle waiting periods, elevated carbon emissions, and reduced customer satisfaction, particularly in multi-customer scenarios. Therefore, this study introduces a multi-customer vehicle scheduling optimization approach for an intelligent coal loading system. Customer priorities are first identified to enhance satisfaction. Considering various customers and enterprise factors, the multi-customer vehicle scheduling model is established to minimize the total cost. The optimal vehicle scheduling scheme is obtained by using the enhanced sparrow search algorithm. The validity of the proposed approach is demonstrated through a case study of a coal mining enterprise. The results show that the total cost of the optimized plan was 79% lower than the traditional plan, which means a significant reduction in vehicle waiting time, and an improvement in customer satisfaction.

BackgroundMachine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. ObjectiveThis study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. ResultsA total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. ConclusionsMachine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

The macro-invertebrate is an important part of the aquatic food web of urban rivers, and it is of great significance in understanding its ecological suitability for the stability of river ecosystems. Previous studies, such as those that have conducted suitability index and canonical correspondence analyses (CCAs), have widely used a macro-invertebrate suitability analysis; however, these studies can only confirm a few coupling relationships between the environment and macro-invertebrates. In our study, one-way ANOVA, HCA, PCA and GAM models were used to explain the differences in the spatial and temporal distribution of environmental factors, as well as to reduce data redundancy. A response curve of the critical environmental factors and macro-invertebrates was constructed, and the nonlinear relationship between these factors and benthic animals was quantified to analyze the ecological threshold of the macro-invertebrates. The study area was the Majiagou River, Harbin, China. The results show that COD had significant seasonal differences due to complex hydrological conditions, and most of the water quality factors had spatial differences. The GAM model explained 60% of the Margalef diversity index (MDI) variance. The relationship between chlorophyll-a and MDI was unimodal, and MDI and NH4+-N essentially showed a negative correlation; when the total nitrogen (TN) value reached 5.8 mg/L, MDI reached its peak. When MDI was higher than the mean value, the chlorophyll-a range was 18.1 μg/L~83 μg/L. The NH4+-N was less than 1.8 mg/L, and TN was 1.8~6.8 mg/L. This study provides a reference for the comprehensive management of urban river ecosystems.

Impaired decidualization was recognized as one of the crucial pathomechanisms accounting for unexplained recurrent spontaneous abortion (URSA). Currently, the exact molecular mechanism and targeted clinical decision are still in exploration. Bushen Huoxue decoction (BSHXD) has previously been proved effective in treating URSA, but its mechanism remains to be elucidated. This study aimed to explore the regulation mechanism of BSHXD in decidualization from its intervention in autophagy so as to rationalize its potential as a novel therapeutic regime for URSA. Decidua tissues were collected from patients with URSA and healthy pregnant women who underwent legal terminations for non-medical reasons at the first trimester. Besides, cell line T-hESCs was utilized to establish induced decidualization model, and were randomly divided into ESC group, DSC group, 3-MA group, AMPK siRNA group, scrambled siRNA group and AMPK siRNA + BSHXD group. Transmission electron microscopy, Monodansylcadaverine (MDC) assay, qRT-PCR, immunohistochemistry, immunofluorescence and Western blotting were used to evaluate the level of decidualization, autophagy and activation of AMPK signaling pathway in decidua tissues and cell experiments. Experiments on decidua tissues showed that decidualization was impaired in URSA with inhibited autophagy. Besides, pAMPK T172 and pULK1 S556 were decreased, and pmTOR S2448 and pULK1 S757 were increased. Cell experiments showed that the level of autophagy increased during induced decidualization, but when autophagy was inhibited, decidualization was impaired. In addition, AMPK/mTOR/ULK1 affected decidualization by mediating autophagy, and BSHXD improved decidualization through this mechanism. In conclusion, this study clarified that the inhibition of autophagy mediated by AMPK/mTOR/ULK1 was associated with impaired decidualization, and the intervention of BSHXD on this pathological process may be a vital mechanism for its treatment of URSA. This study laid the foundation for further research and application of BSHXD.

This paper presents a complex product design-manufacturing-operations and maintenance integration method based on digital twin technology. This method aims to solve the problem of information silos in the design, manufacturing and operation and maintenance phases of complex products in the context of intelligent manufacturing and to integrate the design, manufacturing and operation and maintenance processes of complex products. To address the integration needs of complex product design, manufacturing, operation and maintenance business integration, a framework for complex product design-manufacturing-operation and maintenance integration based on the digital twin is first proposed, in addition to designing a model and operation mechanism for combining the virtual and real of the digital twin model. Then, the implementation of multistage collaborative design technology, data intelligent sensing technology, and data integration and fusion technology for the digital twin-based design-manufacturing-operations and maintenance integration processes are analyzed and discussed. Finally, a case study involving the fault prediction of key components of the bogie of an EMU demonstrated the integrated mode of operation in the design-manufacture-operation and maintenance process of the EMU. It verified the effectiveness of the proposed framework, process and methodology.

Background China has the largest number of diabetic patients in the world. Insulin resistance is a major pathogenetic mechanism and cause of type 2 diabetes. Recent studies have suggested potential relationship between triglyceride-glucose index (TyG) and its derivatives index and the development of type 2 diabetes. However, most available evidence is from cross-sectional study or longitudinal study, and there is a lack of studies in the northeast China. Objective To explore the association between TyG and its derivatives index called triglyceride glucose-body mass index (TyG-BMI) and the incidence risk of type 2 diabetes, analyze the predictive capacity of TyG and TyG-BMI for type 2 diabetes, so as to provide a scientific basis for the early screening of high risk population with type 2 diabetes. Methods A total of 6843 cases of physical examination in the health management center of the Second Affiliated Hospital of Dalian Medical University from January 2015 to December 2017 were included as research subjects. The baseline data of their physical examinations were collected. By the use of nested case-control study method, 209 patients with new-onset type 2 diabetes during follow-up from January 2018 to April 2021 were selected as the case group; 418 cases among those without new-onset type 2 diabetes, endocrine system diseases and malignancies were selected as the control group after 1∶2 matching by propensity score according to the same gender and age ±2 years. TyG and TyG-BMI were calculated based on baseline data. The Cox regression fitted conditional Logistic regression model was used to analyze the relationship between TyG, TyG-BMI and the development of type 2 diabetes. The restricted cubic spline regression model was used to analyze the dose-response relationship between TyG, TyG-BMI and type 2 diabetes. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of TyG and TyG-BMI for type 2 diabetes. Results BMI, AC, SBP, DBP, FPG, TG, TC, LDL-C, TyG, and TyG-BMI in the case group were higher than the control group, and HDL-C in the case group was lower than the control group (P0.05) , with an increasing shape in the dose-response relationship. The incidence risk of type 2 diabetes gradually increased when TyG and TyG-BMI were higher than 8.838 and 229.364, respectively. The area under the ROC curve (AUC) for TyG and TyG-BMI to predict type 2 diabetes was 0.696 〔95%CI (0.658, 0.732) 〕and 0.725〔95%CI (0.688, 0.760) 〕with the optimal cut-offs of 8.650 and 224.859, respectively. Conclusions Increased TyG and TyG-BMI levels are independent risk factors for type 2 diabetes, showing a linear dose-response relationship with type 2 diabetes, both of which have predictive value for type 2 diabetes. TyG-BMI may be a better prediction index considering the strength of risk association, AUC, and clinical impact of screening results.