Identification of key subsystems for urban rail vehicles based on fuzzy comprehensive evaluation

Abstract Identification of key subsystems for urban rail vehicles is important for the selection of maintenance strategy. The fuzzy comprehensive evaluation technique is applied to determine the key subsystems of urban rail vehicles. Firstly, the vehicle is divided into nine subsystems according to the module partition method. Then, the degrees of occurrence, severity, detection and maintenance cost are chosen as the evaluation factors that are quantified based on fuzzy theory and collected historical data. Finally, the calculation model of critical degree is established based on the fuzzy comprehensive evaluation method. The proposed approaches are applied to Guangzhou Metro Corporation, and five key subsystems are selected. The experiment results, which are consistent with those of most knowledgeable engineers and experts, indicate the validity of the proposed method

MAPK-RAP1A Signaling Enriched in Hepatocellular Carcinoma Is Associated With Favorable Tumor-Infiltrating Immune Cells and Clinical Prognosis

BackgroundMAPK-RAP1A signaling, which is involved in cancer progression, remains to be defined. Upregulation of MAPK-RAP1A signaling accounts for most cancers that harbor high incident rate, such as non-small cell lung cancer (NSCLC) and pancreatic cancer, especially in hepatocellular carcinoma (HCC). MAPK-RAP1A signaling plays an important function as clinical diagnosis and prognostic value in cancers, and the role of MAPK-RAP1A signaling related with immune infiltration for HCC should be elucidated.MethodsMicroarray data and patient cohort information from The Cancer Genome Atlas (TCGA; n = 425) and International Cancer Genome Consortium (ICGC; n = 405) were selected for validation. The Cox regression and least absolute shrinkage and selection operator (LASSO) were used to construct a clinical prognostic model in this analysis and validation study. We also tested the area under the curve (AUC) of the risk signature that could reflect the status of predictive power by determining model. MAPK-RAP1A signaling is also associated with tumor-infiltrating immune cells (TICs) as well as clinical parameters in HCC. The GSEA and CIBERSORT were used to calculate the proportion of TICs, which should be beneficial for the clinical characteristics (clinical stage, distant metastasis) and positively correlated with the survival of HCC patients.ResultsHCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), and STMN1, RAP1A, FLT3, HSPA8, ANGPT2, and PGF were used as candidate biomarkers for risk scores of HCC. To determine the molecular mechanism of this signature gene association, Gene Set Enrichment Analysis (GSEA) was proposed. Cytokine–cytokine receptor interaction, TGF-β signaling pathway, and Intestinal immune network for IgA production gene sets were closely related in MAPK-RAP1A gene sets. Thus, we established a novel prognostic prediction of HCC to deepen learning of MAPK-RAP1A signaling pathways.ConclusionOur findings demonstrated that HCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), which may be a novel prognostic prediction of HCC

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

PurposeFerroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.MethodsFerroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.ResultsWe identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.ConclusionOur novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM

Impact of a new teaching model on the fine cosmetic suturing operation and quantitative assessment of the training effect on plastic surgeons

Background: Traditional lecture-based teaching (TLT) has long been the primary method of teaching plastic suturing techniques and even surgical education. It has been challenging to adapt this approach to fit the educational objectives of plastic surgery, which is a very practical science. Additionally, it is mainly teacher-led, and the course content is teacher-driven, which has disadvantages such as difficulty in motivating students and disconnection from clinical practice. Therefore, we developed a video point-to-point teaching (VPT) method and teamwork-based teaching (TBT) to study the effect of the new teaching model on fine cosmetic suturing operation (FCSO) and training outcomes for plastic surgeons. Methods: We selected 30 junior doctors from the Department of Plastic and Reconstructive Surgery of the Chinese PLA General Hospital. All trainees were randomly assigned to three groups: TLT, VPT, and TBT. All trainees had their performances photographed, and a senior attending physician was appointed as a rater. We rated the process and results of FCSO according to a uniform rubric following the double-blind principle to compare the effects of different teaching modes on the trainees’ FCSO and differences in training outcomes. Results: There was no significant effect of video recording on trainees’ FCSO (P>0.05). The total scores of the first suturing in the three groups were as follows: TLT group (13.18 ​± ​1.66), VPT group (13.63 ​± ​1.97), and TBT group (13.50 ​± ​2.26), with no significant difference among the groups (P>0.05), indicating that the starting level of the trainees in the three groups was basically the same. There was no significant difference (P>0.05) between the VPT (20.30 ​± ​2.17) and TBT (20.38 ​± ​2.29) groups, but both of these groups were significantly better than the TLT group (16.43 ​± ​1.86, P<0.01). Conclusion: The TBT and VPT methods are significantly better than TLT. However, the TBT method is more economical and optimal for teachers and better utilizes students’ initiative in learning and operation, which improves the teaching level and training efficiency

Preliminary design and optimization of a solar-driven combined cooling and power system for a data center

Aiming at the demand of reducing cost, increasing efficiency and green low-carbon development in data center, a novel solar-driven combined cooling and power system is constructed by coupling supercritical power cycle with transcritical refrigeration cycle. The heliostat field is designed, and the models on thermal, economic, environmental and comprehensive performances are established. On this basis, system performances under design conditions are studied, and the system parameters are analyzed. Thereafter, optimal system performances at typical days in four seasons are compared based on genetic algorithm. The results indicate that: under design conditions, the system energy and exergy efficiencies are 29.47% and 16.21% respectively. The total system capital cost is $1.2576 × 108. The carbon emissions are reduced by 257,420 kg per day. The comprehensive performance evaluation index (CPEI) can reach up to 47.63%. CPEI increases with the increase of turbine inlet pressure. With the increase of high-pressure compressor (HPC) inlet pressure and evaporation temperature, CPEI increases first and then decreases. Furthermore, CPEI shows a continuous downward trend as the load rate of data center increases. The system performances at autumn equinox and spring equinox are more similar, while those at summer solstice and winter solstice are quite different from those of spring equinox

Analytical models for channel potential, drain current, and subthreshold swing of short-channel triple-gate FinFETs

An analytical model for channel potential, subthreshold drain current, and subthreshold swing of the short-channel fin-shaped field-effect transistor (FinFET) is obtained. The analytical model results are verified against simulations, good agreement is observed. The explicit expressions for drain current and subthreshold swing make the model suitable to be embedded in circuit simulation and design tools.Published versio

Physicochemical Characteristics of Individual Aerosol Particles during the 2015 China Victory Day Parade in Beijing

During the 2015 China Victory Day parade control periods, the air quality in Beijing hit the best record, leading to 15 continuous good days with an average PM2.5 mass concentration 18 μg/m3, which provided a unique opportunity to study the ambient aerosols in megacity Beijing. The morphology and elemental composition of aerosol particles were investigated by transmission electron microscopy coupled with energy dispersive X-ray spectrometry (TEM-EDX). Five types of individual particles were identified, including homogeneous mixed S-rich particles (HS; 44.9%), organic coated S-rich particles (CS; 34.3%), mineral particles (10.5%), soot aggregates (7.21%) and organic particles (3.2%). The number percentage of secondary particles (including HS and CS) accounted for a large proportion with 79.2% during the control periods. The average diameter of secondary particles increased with relative humidity (RH), being 323 nm, 358 nm and 397 nm at the RH 34%, 43% and 53%, respectively, suggesting that the high RH might favor the growth of secondary particles. The higher proportion of CS particles may show great atmospheric implications and the CS particles may be formed by the condensation of secondary organic aerosols on pre-existing S-rich particles

Analytical models for the electric potential, threshold voltage and drain current of long-channel junctionless double-gate transistors

Analytical models for the electric potential, threshold voltage and drain current of long-channel junctionless (JL) double-gate (DG) field-effect transistors (FET) are presented. A regional method is used to solve the Poisson equation under different gate biases, and the electric potential is obtained. With the potential model, an analytical expression for the threshold voltage is achieved. An expression for the drain current is derived from the potential model. The analytical results are compared with simulations, and excellent agreements are observed. The models accurately describe the characteristics of JLDG FETs, and they are very helpful for the design and optimization of devices