BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available at https://github.com/Petrichor625/BATraj-Behavior-aware-Model

Combining modified Graeb score and intracerebral hemorrhage score to predict poor outcome in patients with spontaneous intracerebral hemorrhage undergoing surgical treatment

ObjectiveSpontaneous intracerebral hemorrhage (sICH) is a frequently encountered neurosurgical disease. The purpose of this study was to evaluate the relationship between modified Graeb Score (mGS) at admission and clinical outcomes of sICH and to investigate whether the combination of ICH score could improve the accuracy of outcome prediction.MethodsWe retrospectively reviewed the medical records of 511 patients who underwent surgery for sICH between January 2017 and June 2021. Patient outcome was evaluated by the Glasgow Outcome Scale (GOS) score at 3 months following sICH, where a GOS score of 1–3 was defined as a poor prognosis. Univariate and multivariate logistic regression analyses were conducted to determine risk factors for unfavorable clinical outcomes. Receiver operating characteristic (ROC) curve analysis was performed to detect the optimal cutoff value of mGS for predicting clinical outcomes. An ICH score combining mGS was created, and the performance of the ICH score combining mGS was assessed for discriminative ability.ResultsMultivariate analysis demonstrated that a higher mGS score was an independent predictor for poor prognosis (odds ratio [OR] 1.207, 95% confidence interval [CI], 1.130–1.290, p < 0.001). In ROC analysis, an optimal cutoff value of mGS to predict the clinical outcome at 3 months after sICH was 11 (p < 0.001). An increasing ICH-mGS score was associated with increased poor functional outcome. Combining ICH score with mGS resulted in an area under the curve (AUC) of 0.790, p < 0.001.ConclusionmGS was an independent risk factor for poor outcome and it had an additive predictive value for outcome in patients with sICH. Compared with the ICH score and mGS alone, the ICH score combined with mGS revealed a significantly higher discriminative ability for predicting postoperative outcome

Ships Detection in SAR Images Based on Anchor-Free Model With Mask Guidance Features

Ship targets in synthetic aperture radar (SAR) images have various scales. The detection model based on anchor boxes requires manual design of candidate boxes, which are fixed and cannot completely match all kinds of targets. Instead, large of anchor boxes with different sizes also result in large amounts of computing resources being consumed. Another potential issue comes from complex background information of near-coast scenes, which leads to ship targets being unrecognized because the background contains similar appearing objects. Therefore, this article proposes an anchor-free detection model based on mask guidance features, which achieves detection mainly through three modifications. First, feature maps of multiple scales are fused to obtain high-resolution feature maps containing rich semantic information. Second, a transformer encoder module is introduced to focus on the context relationship between the target object and the global image and to enhance the dependence between ship targets. Third, the mask guide feature is used to highlight the positions of the target in the feature map, and a loss function in the mask guide mechanism is designed to optimize the mask feature map to reduce false detections and missed detections. Testing the model on the public dataset SAR ship detection dataset, the model's detection accuracy reached 96.17%, with its accuracy on small-size ships reaching 96.11% and 97.84% on large ships

Analysis of risk factors for mental health problems of inpatients with chronic liver disease and nursing strategies: A single center descriptive study

Abstract The number of patients with chronic liver disease (CLD) is large. The social and economic burdens due to CLD have increased. The mental health problems of patients with CLD are prominent and deserve our attention and care. This study analyzed 320 patients with CLD who were hospitalized between January 2018 and January 2020. Questionnaire surveys were used to assess mental health status, including the Self‐Rating Anxiety Scale (SAS), Self‐Rating Depression Scale (SDS), and Symptom Checklist‐90 (SCL‐90). At the same time, basic data and potential related factors were collected. Data were analyzed using descriptive statistics and logistic regression. Among the 320 patients with CLD, 240 (75%) had mental health problems; among the total patients, education levels, occupations, course of disease, annual hospitalizations, complications, and nursing satisfaction were significantly different between the two groups (p < .05). The education levels and occupations of the group without mental health problems were significantly different within the group (p < .05). The SCL‐90 found that the four factors with the highest scores were anxiety (ANX: 33.3%), depression (DEPR: 20.4%), somatization (SOM: 12.9%), and sleep and diet (SD: 9.6%). Logistic regression analysis showed that education levels, course of disease, annual hospitalizations, complications, and nursing satisfaction levels were independent risk factors for the mental health of patients with CLD. Model fitness was checked using the Hosmer–Lemeshow test. The receiver operating characteristic (ROC) curve showed that the area under the curve was 0.84. Patients with CLD have prominent mental health problems and experience many risk factors. It is necessary to adopt individualized psychological interventions and care to improve the quality of life of these patients

The Dilemma of C-Rate and Cycle Life for Lithium-Ion Batteries under Low Temperature Fast Charging

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management

Co-Cultivation of Two Bacillus Strains for Improved Cell Growth and Enzyme Production to Enhance the Degradation of Aflatoxin B1

Bacillus sp. H16v8 and Bacillus sp. HGD9229 were identified as Aflatoxin B1 (AFB1) degrader in nutrient broth after a 12 h incubation at 37 °C. The degradation efficiency of the two-strain supernatant on 100 μg/L AFB1 was higher than the bacterial cells and cell lysate. Moreover, degradations of AFB1 were strongly affected by the metal ions in which Cu2+ stimulated the degradation and Zn2+ inhibited the degradation. The extracellular detoxifying enzymes produced by co-cultivation of two strains were isolated and purified by ultrafiltration. The molecular weight range of the detoxifying enzymes was 20–25 kDa by SDS-PAGE. The co-culture of two strains improved the total cell growth with the enhancement of the total protein content and detoxifying enzyme production. The degradation efficiency of the supernatant from mixed cultures increased by 87.7% and 55.3% compared to Bacillus sp. H16v8 and HGD9229, individually. Moreover, after the degradation of AFB1, the four products of the lower toxicity were identified by LC-Triple TOF-MS with the two proposed hypothetical degradation pathways

A multi-omics analysis reveals CLSPN is associated with prognosis, immune microenvironment and drug resistance in cancers

Abstract Background Immunotherapy is effective only in limited patients. It is urgent to discover a novel biomarker to predict immune cells infiltration status and immunotherapy response of different cancers. CLSPN has been reported to play a pivotal role in various biological processes. However, a comprehensive analysis of CLSPN in cancers has not been conducted. Methods To show the whole picture of CLSPN in cancers, a pan-cancer analysis was conducted in 9125 tumor samples across 33 cancer types by integrating transcriptomic, epigenomic and pharmacogenomics data. Moreover, the role of CLSPN in cancer was validated by CCK-8, EDU, colony formation and flow cytometry in vitro and tumor cell derived xenograft model in vivo. Results CLSPN expression was generally upregulated in most cancer types and was significantly associated with prognosis in different tumor samples. Moreover, elevated CLSPN expression was closely correlated with immune cells infiltration, TMB (tumor mutational burden), MSI (microsatellite instability), MMR (mismatch repair), DNA methylation and stemness score across 33 cancer types. Enrichment analysis of functional genes revealed that CLSPN participated in the regulation of numerous signaling pathways involved in cell cycle and inflammatory response. The expression of CLSPN in LUAD patients were further analyzed at the single-cell level. Knockdown CLSPN significantly inhibited cancer cell proliferation and cell cycle related cyclin-dependent kinase (CDK) family and Cyclin family expression in LUAD (lung adenocarcinoma) both in vitro and in vivo experiments. Finally, we conducted structure-based virtual screening by modelling the structure of CHK1 kinase domain and Claspin phosphopeptide complex. The top five hit compounds were screened and validated by molecular docking and Connectivity Map (CMap) analysis. Conclusion Our multi-omics analysis offers a systematic understanding of the roles of CLSPN in pan-cancer and provides a potential target for future cancer treatment