Search

Objective Metro shield tunnel construction may cause surrounding land subsidence, affecting the surrounding environment. Traditional land subsidence prediction models are difficult to comprehensively consider the influencing factors of land subsidence. Therefore, in order to improve the prediction accuracy of land subsidence, the CNN(convolutional neural network)-LSTM(long-short-term memory) joint neural network is used to predict the land subsidence caused by shield tunnel construction. Method With the monitored land subsidence data of a metro section as the research object, CNN is used to connect the influencing parameters (including compressive modulus, cohesion, internal friction angle, Poisson′s ratio, soil thickness, tunnel buried depth, and construction parameters) and monitored land subsidence data. The LSTM neural network is used to analyze the land subsidence, and a land subsidence prediction model based on the CNN-LSTM joint neural network is established. Simultaneous consideration of the multiple factor influence on land subsidence prediction is explored. Result & Conclusion Using CNN has a good effect on extracting the parameter features related to land subsidence. The prediction accuracy of the established CNN-LSTM model is 3% higher than that of the LSTM model alone, and 9% higher than that of the traditional BP (back propagation) neural network model. The prediction accuracy of the established CNN-LSTM model reaches 93% when predicting the short time land subsidence in single measuring point, and the predicted value is in good agreement with the monitored value.

Objective To evaluate the potential biological functions of non-viral neutralizing antibodies associated with linear epitopes of the SARS-CoV-2 spike protein. Methods The linear epitope S2-78 with the highest response frequency and longest antibody retention time in COVID-19 patients was selected according to previous reports. After immunizing mice with keyhole limpet hemocyain conjugated S2-78 polypeptide(KLH-S2-78), the serum anti-S2-78 IgG antibody level, mouse behavioral phenotype and the density of microglia in the brain were detected. Results KLH-S2-78 immunized mice developed high titers of anti-S2-78 IgG antibodies(the highest titer was 25 600, A450=0.305 5), a psychomotor-like behavioral phenotype with defective sensorimotor gating, impaired olfactory function, and impaired spontaneity. The density of microglia significantly increased in the cortex and hippocampus(P＜0 .05), suggesting a state of central inflammatory stress. Conclusions Anti-S2-78 IgG antibody may cause neuroinflammatory damages in the brain tissue by stimulating the host inflammatory response.

Cyclobutanes are popular structural units in bioactive compounds and versatile intermediates in synthetic chemistry, but their synthesis is challenging owing to high ring strain. In this study, a novel method for highly regio‐ and diastereoselective synthesis of fluoroalkylcyclobutanes bearing vicinal quaternary and tertiary stereocenters is realized by a photocatalytic 4‐exo‐trig cyclization cascade of thioalkynes or trifluoromethylalkenes. Density functional theory calculations reveal that a unique fluorine effect, arising from hyperconjugative π→σ*C‐F interactions, accounts for the regio‐reversed radical addition at the sterically hindered alkene carbon, which facilitates an unprecedented 4‐exo‐trig ring closure. This chemistry enables the direct and controllable construction of medicinally valuable quaternary‐carbon‐containing cyclobutanes from readily available raw materials, nicely complementing the existing methods. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into three working conditions, that is, enclosure construction, earthwork excavation, and basement support construction. The attention mechanism and residual update are integrated into the artificial neural network (ANN) model, and the root-mean-square error, average absolute error, and determination coefficient are used as the evaluation indices of the model. The artificial neural network prediction model LSTM-RA-ANN for building settlements in deep foundation pit construction was then established. The prediction performance of the model was also analysed under different working conditions, and the influences of the main factors (including the soil parameter, monitoring point location, activation function, hyperparameter, and input number) on the evaluation index was further explored. The results indicate that the performances of the established LSTM-RA-ANN model are closely related to the construction conditions, the predicted settlements agree well with the monitored ones in three working conditions with the greatest errors occurring at a later time of the working conditions, and the prediction accuracy of the great–small order corresponds to basement support, enclosure construction, and earthwork excavation respectively. The farther the monitoring point is from the edge of the pit, the better the model performance is. The activation function, initial learning rate, and maximum iteration batch have a great influence on the evaluation indices of the model, while the number of input points has little effect on the evaluation indices. These results may serve as a reference for the safe construction and normal operation of foundation pit engineering. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively. [ABSTRACT FROM AUTHOR]

Background The association between vitamin D/calcium and risk of ovarian cancer is still a debatable point. The aim of our study was to systematically investigate the association between vitamin D/calcium, and the risk of ovarian cancer and estimate their dose-response association quantitatively. Methods PubMed, EMBASE, and Web of Science databases were searched to identify relevant observational studies. Two investigators screened citations and extracted data independently. Data were extracted and the association between vitamin D/calcium and ovarian cancer risk was estimated by calculating pooled relative risks (RRs). Subgroup analyses, publication bias estimation, and dose-response analyses were carried out as well. Results In total, 21 articles involving 980,008 participants were included in our present study. No significant association was observed between total vitamin D intake and ovarian cancer risk (RR: 1.02; 95% CI, 0.89-1.16, p = 0.81). Further subgroup analysis suggested that neither dietary vitamin D intake (RR: 0.80; 95% CI, 0.62-1.03, p = 0.09) nor supplementary vitamin D intake (RR: 0.98; 95% CI, 0.85-1.13, p = 0.80) was associated with the risk of ovarian cancer. As for calcium, total calcium intake was found to be statistically inversely associated with ovarian cancer risk in case-control studies (RR: 0.73; 95% CI, 0.63-0.86, p < 0.001) but not in cohort studies (RR: 1.05; 95% CI, 0.90-1.24, p = 0.52). Besides, supplementation with calcium plus vitamin D was not effective for the prevention of ovarian cancer (p = 0.98). Of note, dose-response analysis based on cohort studies suggested a potential inverse U-shape relationship between calcium intake (including total calcium and dietary calcium) and ovarian cancer risk, which indicated that low dose of calcium intake might reduce ovarian cancer risk while high dose of calcium intake might not. Conclusions Taken together, vitamin D could not decrease the risk of ovarian cancer. The role of calcium intake was not proven for reducing ovarian cancer risk. Besides, no evidence showed combinative use of calcium and vitamin D have additional benefits for ovarian cancer prevention., Author(s): Jiawei Xu [sup.1] , Kelie Chen [sup.1] , Fan Zhao [sup.2] , Dongdong Huang [sup.3] , Honghe Zhang [sup.3] , Zhiqin Fu [sup.4] , Jinming Xu [sup.5] , Yongfeng [...]

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in discretionary lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose the Parameterized Soft Actor–Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis with Parameterized Soft Actor–Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining zero collision rates. However, at high traffic density levels, although both algorithms result in collisions, PASAC-PIDLag has a much lower collision rate than PASAC. [ABSTRACT FROM AUTHOR]

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining the battery state of charge within safe limits, which presents a challenging task. Traditional reinforcement learning struggles with safety constraints, and the penalty method often leads to suboptimal performance. This study introduces Lagrangian-based parameterized soft actor–critic (PASACLag), a novel safe hybrid-action reinforcement learning algorithm for HEV energy management. PASACLag utilizes a unique composite action representation to handle continuous actions (e.g., engine torque) and discrete actions (e.g., gear shift and clutch engagement) concurrently. It integrates a Lagrangian method to separately address control objectives and constraints, simplifying the reward function and enhancing safety. We evaluate PASACLag's performance using the World Harmonized Vehicle Cycle (901 s), with a generalization analysis of four different cycles. The results indicate that PASACLag achieves a less than 10% increase in fuel consumption compared to dynamic programming. Moreover, PASACLag surpasses PASAC, an unsafe counterpart using penalty methods, in fuel economy and constraint satisfaction metrics during generalization. These findings highlight PASACLag's effectiveness in acquiring complex EMS for control within a hybrid action space while prioritizing safety. [ABSTRACT FROM AUTHOR]

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works. [ABSTRACT FROM AUTHOR]