Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach

Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in a VUCA environment. With a heuristic exploration mechanism considering target location, path cost, and safety level, the upper layer performs simultaneous map exploration and route-planning to avoid trapping in a blind alley, similar to the cerebrum in the CNS. Using a local adaptive model fusing multiple discrepant strategies, the lower layer pursuits a balance between collision-avoidance and go-straight strategies, acting as the cerebellum in the CNS. We conduct simulation and real-world experiments on multiple platforms, including legged and wheeled robots. Experimental results demonstrate our algorithm outperforms the existing methods in terms of task achievement, time efficiency, and security.Comment: 8 pages, 10 figure

Systematic Analysis of Endometrial Cancer-Associated Hub Proteins Based on Text Mining

Objective. The aim of this study was to systematically characterize the expression of endometrial cancer- (EC-) associated genes and to analysis the functions, pathways, and networks of EC-associated hub proteins. Methods. Gene data for EC were extracted from the PubMed (MEDLINE) database using text mining based on NLP. PPI networks and pathways were integrated and obtained from the KEGG and other databases. Proteins that interacted with at least 10 other proteins were identified as the hub proteins of the EC-related genes network. Results. A total of 489 genes were identified as EC-related with P<0.05, and 32 pathways were identified as significant (P<0.05, FDR<0.05). A network of EC-related proteins that included 271 interactions was constructed. The 17 proteins that interact with 10 or more other proteins (P<0.05, FDR<0.05) were identified as the hub proteins of this PPI network of EC-related genes. These 17 proteins are EGFR, MET, PDGFRB, CCND1, JUN, FGFR2, MYC, PIK3CA, PIK3R1, PIK3R2, KRAS, MAPK3, CTNNB1, RELA, JAK2, AKT1, and AKT2. Conclusion. Our data may help to reveal the molecular mechanisms of EC development and provide implications for targeted therapy for EC. However, corrections between certain proteins and EC continue to require additional exploration

Association between oncogenic status and risk of venous thromboembolism in patients with non-small cell lung cancer

Abstract Background Preclinical data suggest that oncogene (EGFR and KRAS) events regulate tumor procoagulant activity. However, few studies have prospectively investigated the clinical relevance between the presence of EGFR or KRAS mutations and occurrence of venous thromboembolism(VTE) in patients with non-small cell lung cancer (NSCLC). Methods A total of 605 Chinese patients with newly diagnosed NSCLC were included and were followed for a maximum period of 4.5 years. EGFR and KRAS mutations were determined by amplification refractory mutation system polymerase chain reaction method at inclusion. The main outcome was objectively confirmed VTE. Results Of the 605 patients, 40.3% (244) had EGFR mutations and 10.2% (62) of patients had KRAS mutations. In multivariable analysis including age, sex, tumor histology, tumor stage, performance status, EGFR and KRAS status, EGFR wild-type (sub-distribution hazard ratio 1.81, 95% confidence interval 1.07–3.07) were associated with the increased risk of VTE. In competing risk analysis, the probability of developing VTE was 8.3% in those with and 13.2% in those without EGFR mutations after 1 year; after 2 years, the corresponding risks were 9.7 and 15.5% (Gray test P = 0.047). Conclusions EGFR mutations have a negative association with the risk of VTE in Chinese patients with NSCLC

A data mining framework within the Chinese NPPs operating experience feedback system for identifying intrinsic correlations among human factors

With the continuous increase in the number of operating nuclear power plants (NPPs) in China, the amount of operating experience feedback (OEF) increases significantly. On the other hand, the safe operation of NPPs has become an urgent problem that the National Nuclear Safety Administration (NNSA) must solve. To this end, NNSA established a nationalwide OEF system to improve the safety level of NPPs and strengthen the exchange of operating experience. Analyzing the human factors events (HFEs) is an important part of OEF and it is significant to improve human performance and prevent human error. Data mining has been recognized as an effective way to analyze data. With the continuous increase in operating event reports, data mining related to nuclear safety becomes a new domain of study. In this paper, we propose a data mining framework in support of the OEF system. The framework combines three statistical approaches (i.e., correlation analysis, cluster analysis and association rule mining) for identifying intrinsic correlations among human factors: correlation analysis measures the strength of linear relationship between human factors; cluster analysis classifies human factors into relevant groups; association rule mining identifies associations and causalities among human factors. For illustration, we apply the proposed framework to 162 human factors events (screened out from 313 events collected from the OEF system), and the results reflect the feasibility and effectiveness of the framework in identifying the intrinsic correlations among human factors. Besides, further suggestions for improving human performance and preventing human errors in NPPs are also discussed

In situ fabrication and investigation of nanostructures and nanodevices with a microscope

The widespread availability of nanostructures and nanodevices has placed strict requirements on their comprehensive characterization. Herein, in situ techniques are demonstrated to have created a rare opportunity to accurately analyze the intrinsic properties of individual nanostructures and to accomplish the smart design of nanodevices made from these nanostructures. This paper reviews recent developments in in situ fabrication and characterization technologies established within various types of microscopes and the rich information they may provide. The in situ techniques are shown to be important for exploration of many intriguing phenomena at the nanoscale which may then be followed by the smart integration of nanostructures into real functional devices. Successful in situ detection results are presented and discussed, especially in the areas of energy generation, biological imaging and water pumping. Finally, we conclude this article with an examination of the existing challenges and the outlook for this quickly emerging field

Comparable Effects of Strontium Ranelate and Alendronate Treatment on Fracture Reduction in a Mouse Model of Osteogenesis Imperfecta

Alendronate (Aln) has been the first-line drug for osteogenesis imperfecta (OI), while the comparable efficacy of Aln and strontium ranelate (SrR) remains unclear. This study is aimed at comparing the effects of SrR and Aln treatment in a mouse model of OI. Three-week-old oim/oim and wt/wt female mice were treated with SrR (1800 mg/kg/day), Aln (0.21 mg/kg/week), or vehicle (Veh) for 11 weeks. After the treatment, the average number of fractures sustained per mouse was significantly reduced in both SrR- and Aln-treated oim/oim mice. The effect was comparable between these two agents. Both SrR and Aln significantly increased trabecular bone mineral density, bone histomorphometric parameters (bone volume, trabecular number, and cortical thickness and area), and biomechanical parameters (maximum load and stiffness) as compared with the Veh group. Both treatments reduced bone resorption parameters, with Aln demonstrating a stronger inhibitory effect than SrR. In contrast to its inhibitory effect on bone resorption, SrR maintained bone formation. Aln, however, also suppressed bone formation coupled with an inhibitory effect on bone resorption. The results of this study indicate that SrR has comparable effects with Aln on reducing fractures and improving bone mass and strength. In clinical practice, SrR may be considered an option for patients with OI when other medications are not suitable or have evident contraindications

Improved LightGBM-Based Framework for Electric Vehicle Lithium-Ion Battery Remaining Useful Life Prediction Using Multi Health Indicators

To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the RMSE and prediction time of the proposed framework are 1.078% and 15.728 s under the total HIs. The performance of the proposed framework under a different number of HIs is also analyzed. The experimental results show that the proposed framework can achieve the optimal prediction accuracy (98.978%) under the HIs of resistances, loss modes, and isobaric voltage drop time.</p