RiskFormer: Exploring the temporal associations between multi-type aberrant driving events and crash occurrence.

With the development of driving behavior monitoring technologies, commercial transportation enterprises have leveraged aberrant driving event detection results for evaluating crash risk and triggering proactive interventions. The state-of-the-art applications were established based upon instant associations between events and crash occurrence, which assumed crash risk surged with aberrant events. Consequently, the generated crash risk monitoring results merely contain discrete abrupt changes, failing to depict the time-varying trend of crash risk and posing challenges for interventions. Given the multiple types of aberrant events and their various temporal combinations, the key to depict crash risk time-varying trend is the analysis of multi-type events' temporal coupling influence. Existing studies employed event frequency to model combined influence, lacking the capability to differentiate the temporal sequential characteristics of events. Hence, there is an urgent need to further explore multi-type events' temporal coupling influence on crash risk. In this study, the temporal associations between multi-type aberrant driving events and crash occurrence are explored. Specifically, a contrastive learning method, fusing prior domain knowledge and empirical data, was proposed to analyze the single event temporal influence on crash risk. After that, a novel Crash Risk Evaluation Transformer (RiskFormer) was developed. In the RiskFormer, a unified encoding method for different events, as well as a self-attention mechanism, were established to learn multi-type events' temporal coupling influence. Empirical data from online ride-hailing services were employed, and the modeling results unveiled three distinct time-varying patterns of crash risk, including decay, increasing, and increasing-decay pattern. Additionally, RiskFormer exhibited remarkable crash risk evaluation performance, demonstrating a 12.8% improvement in the Area Under Curve (AUC) score compared to the conventional instant-association-based model. Furthermore, the practical utility of RiskFormer was illustrated through a crash risk monitoring sample case. Finally, applications of the proposed methods and their further investigations have been discussed.

Unveiling pre-crash driving behavior common features based upon behavior entropy.

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set.

In order to further improve the evaluation quality of enterprise operating efficiency, reduce the error items and invalid items of partition, and improve the objectivity of operating condition evaluation, this study takes listed enterprises as an example and proposes an evaluation method of operating efficiency based on association rule algorithm and data set. In this method, the results of operating efficiency are scientifically analyzed from horizontal and vertical dimensions. The operating cost of total assets of listed companies is taken as indicators, and the correlation test is carried out by Kendall's tau_b. From the longitudinal comparison results, it can be seen that only 12 of the 19 enterprises in the study have small-scale changes and increase year by year, accounting for 63.16%. At the same time, there are also 6 enterprises with an overall trend of decline, which objectively reflects the reasonable operation status and operation scale of enterprises in the study.

The catalytic activity of PtnCum (n = 1-3, m = 0-2) clusters for methanol dehydrogenation to CO.

A large number of experiments show that PtCu catalyst has a good catalytic effect on methanol decomposition. Therefore, density functional theory (DFT) was used to further study the dehydrogenation of methanol catalyzed by PtnCum (n = 1-3, m = 0-2). The energy diagrams of O-adsorption path and H-adsorption path were drawn. By calculation, the Pt is the active site of the whole reaction process, and the barrier energy of the rate-determining step is 11.09 kcal mol-1 by Pt2Cu, which is lower than that of Pt3 and PtCu2. However, the complete dehydrogenation product of methanol, CO, is easier to dissociate from PtCu2 clusters than from Pt3 and Pt2Cu clusters. Therefore, Cu doping can improve the catalytic activity and anti-CO toxicity of Pt to a certain extent.

SNHG8 is upregulated in esophageal squamous cell carcinoma and directly sponges microRNA-411 to increase oncogenicity by upregulating KPNA2.

BACKGROUND: The long noncoding RNA, small nucleolar RNA host gene 8 (SNHG8), is upregulated in multiple human cancer types. However, whether SNHG8 is aberrantly expressed in esophageal squamous cell carcinoma (ESCC) and its biological functions have yet to be elucidated. Thus, we aimed to determine the expression status of SNHG8 in ESCC, explore the effects of SNHG8 on the oncogenicity of ESCC, and investigate the potential underlying mechanisms. METHODS: SNHG8 expression in ESCC tissues and cell lines was determined via reverse-transcription quantitative polymerase chain reaction. The actions of SNHG8 on the malignant characteristics of ESCC were explored using CCK-8 assay, flow-cytometric analysis, Transwell migration and invasion assays, and tumor xenografts in nude mice. RESULTS: SNHG8 expression was significantly higher in ESCC tissues and cell lines. High SNHG8 expression was revealed to closely correlate with primary tumor invasion depth, lymph node metastases, TNM stage, and worse overall survival among patients with ESCC. Functional investigation showed that ablation of SNHG8 notably restricted ESCC cell proliferation, migration, and invasion while inducing apoptosis in vitro and hindered tumor growth in vivo. In the meantime, SNHG8 acted as a molecular sponge of microRNA-411 (miR-411) in ESCC. Furthermore, miR-411 exerted a tumor-suppressive effect on ESCC cells, and karyopherin alpha 2 (KPNA2) turned out to be a direct target gene of miR-411. Restoring KPNA2 expression neutralized the inhibitory effects of miR-411 overexpression on the malignant behaviors of ESCC cells. Moreover, silencing of miR-411 abrogated the influence of SNHG8 downregulation in ESCC cells. CONCLUSION: SNHG8 may play oncogenic roles in the malignancy of ESCC by sponging miR-411 to increase KPNA2 expression. The SNHG8-miR-411-KPNA2 pathway may be a novel target for the treatment of patients with ESCC and offer potential biomarkers for the diagnosis and prognosis of ESCC.

Improved two-cuff technique for orthotopic liver transplantation in rat.

BACKGROUND: The first orthotopic liver transplantation in rat (ROLT) was reported by Lee in 1973. Kamada innovatively applied cuff technique to ROLT in 1979. However, the operative procedures were highly demanding and the operative mortality was relatively high. The purpose of this study was to improve the model of ROLT, simplify operative procedures, and enhance the successful rate of operation. METHODS: Orthotopic liver transplantation was performed in 160 Wistar rats by improved two-cuff technique. The portal vein between donor and recipient was anastomosed with the cuff technique. The same method was used to anastomose the infrahepatic vena cava. The suprahepatic vena cava and the hepatic artery were anastomosed by microvascular suturing and the bile duct was anastomosed end to end by a Teflon catheter. RESULTS: The average time for donor operation, graft preparation and anhepatic phase was 31 minutes, 14 minutes and 13 minutes, respectively. The anastomosis time for the suprahepatic vena cava, portal vein, infrahepatic vena cava, hepatic artery and bile duct was 7 minutes, 2 minutes, 2 minutes, 8 minutes and 1 minute, respectively. The main causes for operative mortality were pneumothorax, anesthesia, air embolism and massive bleeding, and the successful rate of operation was 92.5%. The causes for death after operation were stoma bleeding, infection, biliary obstruction and graft failure. CONCLUSION: The improved two-cuff technique can reduce operative mortality, enhance survival rate, and serve as an ideal method for the establishment of animal model of ROLT.

[Dynamic changes of groundwater level and vegetation in water table fluctuant belt in lower reaches of Heihe River: coupling simulation].

Based on the 2006 investigation data in lower reaches of Heihe River, and by using logarithmic normal distribution model, the models about the vegetation cover of Populus euphratica and Tamarix ramossima and the groundwater level in study area were built, and the potential plant of the study area was simulated. The results showed that in the lower reaches of Heihe River, the optimal groundwater level and mean groundwater level for P. euphratica were 2.6 m and 3.6 m, and those for T. ramossima were 2.0 m and 3.0 m, respectively. The high cover P. euphratica distribution area was mainly concentrated in the near-banks of Donghe River and Xihe River, while higher cover T. ramossima was distributed in most parts of the study area. From the aspect of current groundwater level, T. ramossima should be the adaptive species in the study area. The similarities between the potential and actual spatial distribution of P. euphratica and T. ramossima were 43. 4% and 55. 6% , respectively, and the main reason for the lower similarity was that there existed a gypsum salt pan in soil, which blocked the vertical movement of soil water.