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A simple, broad-scope rhodium(I)/chiral diene catalytic system for challenging asymmetric intramolecular cyclopropanation of various tri-substituted allylic diazoacetates was successfully developed. The low coordination state Rh(I)-complex exhibits an extraordinarily high degree of tolerance to the variation in the extent of substitution of the allyl double bond, thus allowing the efficient construction of a wide range of penta-substituted, fused-ring cyclopropanes bearing three contiguous stereogenic centers, including two quaternary carbon stereocenters, in a highly enantioselective manner with ease at catalyst loading as low as 0.1 mol%. The stereoinduction mode of this Rh(I)-carbene-directed asymmetric intramolecular cyclopropanation was investigated by DFT calculations, indicating that π-π stacking interactions between the aromatic rings of chiral diene ligand and diazo substrate play a key role in the control of the reaction enantioselectivity.

A detection and tracking algorithm based on improved YOLOv5 is proposed for the poor recognition and tracking of obscured targets and small-sized targets. The K-means ++ algorithm is used to cluster to obtain new anchor values; the CIOU-NMS is introduced to improve the missed detection problem when the target is obscured; the CBAM is proposed to be embedded into the Backbone and Neck part to improve the feature extraction capability of the algorithm for small targets. DeepSORT is chosen as the multi-target tracker to plot the motion trajectory of the target in real time. The experimental results show that the improved algorithm has a 2.1% improvement in detection accuracy and a detection speed of 32.32/s, satisfying real-time efficient detection with better tracking.

Microstructure of the fusion zone of steel/nickel-alloy dissimilar metal welds (DMWs) for nozzle buttering was investigated by optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and electron back-scattered microscopy (EBSD). The results showed that the dissimilar joint was complete, without welding defects. The structures of the fusion zone included the beach structure along the fusion boundary, the peninsula structures connected with the fusion boundary, and the island structures in the weld. The distribution of these three types of structures near the fusion boundary was uneven. The beach structure was formed because of the insufficient mixing and solidification of the molten liquid base material and the filler metal, with the width ranging between 0 and 150 μm. The peninsula and island structures were formed by the undercooling of the insufficiently mixed liquid base material and filler metal that entered the weld because of the convection and scouring of the weld pool. The composition of the three structures depended on the degree of mixing of the liquid base material and the filler metal, with a dilution ratio between 40 and 60%. The degree of dilution for the beach, peninsula, and island structures decreased in turn. With an increase in the dilution ratio, the initial solidification temperature of the corresponding composition increased significantly. When the dilution ratio exceeded 94.5%, the initial solidification phase transformed from the face-centered cubic γ-austenite into a body-centered cubic ferrite, with island structures solidified in the form of ferrites in the weld near the fusion boundary. The austenite grain orientations at weld side are dispersed with 75% large (> 15°) misorientation in frequency and the overall texture orientation distributes dispersedly with deviating from the direction.

The crash injury severity prediction of traffic accident is the prerequisite for driving decisions under dangerous conditions. This paper puts forward a new wrapper feature selection algorithm to i...

Simple Summary Pyroptosis is a type of programmed cell death accompanied by inflammation. Although the dysregulation of pyroptosis has been reported to be involved in carcinogenesis, its function in cancer progression and therapy remains largely unknown and controversial because of the inconsistency across different cancer types. This study provides the most complete gene set of pyroptosis-related genes (PRGs), depicts their expression changes across 31 cancer types for the first time, and constructs a novel prognostic risk model to predict cancer patient survival. In addition, the effects of pyroptosis on immune cell infiltration and immunotherapy were dissected at the pan-cancer level. Small-molecule compounds, which may be beneficial to immunotherapy, were screened on the basis of differentially expressed PRGs. These results lay the foundation for the study of pyroptosis in cancer. Abstract Pyroptosis is a newly characterized type of programmed cell death. However, its function in cancer progression and its response to treatments remain controversial. Here, we extensively and systematically compiled genes associated with pyroptosis, integrated multiomics data and clinical data across 31 cancer types from The Cancer Genome Atlas, and delineated the global alterations in PRGs at the transcriptional level. The underlying transcriptional regulations by copy number variation, miRNAs, and enhancers were elucidated by integrating data from the Genotype-Tissue Expression and International Cancer Genome Consortium. A prognostic risk model, based on the expression of PRGs across 31 cancer types, was constructed. To investigate the role of pyroptosis in immunotherapy, we found five PRGs associated with effectiveness by exploring the RNA-Seq data of patients with immunotherapy, and further identified two small-molecule compounds that are potentially beneficial for immunotherapy. For the first time, from a pyroptosis standpoint, this study establishes a novel strategy to predict cancer patient survival and immunotherapeutic outcomes.

This paper first analysis the shortcomings of the existing traditional numerical solution algorithm to solve the two-way green wave bandwidth problem, then improves the traditional numerical solution algorithm, constructs the objective function, proposes a trunk phase difference optimization method to seek the maximum two-way green wave bandwidth, constructs the model and solves it using Lingo optimization software, which can overcome the disadvantages of artificially fixed speed and common signal period and the lack of connection between green wave speed and common signal period. It can overcome the disadvantages of artificially fixed speed and common signal period and the lack of connection between green wave speed and common signal period, and optimize the signal period, phase difference, green signal ratio and green wave speed simultaneously. Meanwhile, the simulation analysis by VISSIM software verifies that the method can significantly reduce the stopping delay and improve the intersection capacity.

Over the past years, the metal-catalyzed dearomative cycloaddition of 3-nitroindoles and 2-nitrobenzofurans have emerged as a powerful protocol to construct chiral fused heterocyclic rings. However, organocatalytic dearomative reaction of these two classes of heteroarenes has become a long-standing challenging task. Herein, we report the first example of phosphine-catalyzed asymmetric dearomative [3+2]-cycloadditio of 3-nitroindoles and 2-nitrobenzofurans, which provide a new, facile, and efficient protocol for the synthesis of chiral 2,3-fused cyclopentannulated indolines and dihydrobenzofurans by reacting with allenoates and MBH carbonates, respectively through a dearomative [3+2]-cycloaddition.

Pyroptosis is a type of programmed cell death (PCD) accompanied by an inflammatory reaction and the rupture of a membrane. Pyroptosis is divided into a canonical pathway triggered by caspase-1, and a non-canonical pathway independent of caspase-1. More and more pyroptosis-related participants, pathways, and regulatory mechanisms have been exploited in recent years. Pyroptosis plays crucial roles in the initiation, progression, and metastasis of cancer and it affects the immunotherapeutic outcome by influencing immune cell infiltration as well. Extensive studies are required to elucidate the molecular mechanisms between pyroptosis and cancer. In this review, we introduce the discovery history of pyroptosis, delineate the signaling pathways of pyroptosis, and then make comparisons between pyroptosis and other types of PCD. Finally, we provide an overview of pyroptosis in different cancer types. With the progression in the field of pyroptosis, new therapeutic targets and strategies can be explored to combat cancer.

Autonomous vehicles (AVs) are supposed to make appropriate strategies to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable. In a typical dilemma scenario of this paper, we have to make hard decisions from two evils, sparing a child with red-light running behavior or crashing into a well-equipped motorcycle driver without violation of the traffic law. Combined with the existing traffic laws and the accident liability judgment cases in China, 12 typical dilemma scenarios are established. In order to acquire data to analyze the driving decision-making factors involving ethics and legal under the moral dilemma of AVs, we conduct a series of experiments in virtual reality environments. Furthermore, key factors are extracted to characterize the driving decisions under different scenarios and quantified by the gray relation entropy analysis method. Particularly, the ethical factor is represented by the quantity and type of collision targets, the legal factor is qualified by rights of way. The results showed that priority levels of the influencing factors in each moral dilemma were considerably different. Nevertheless, when the quantity of collision targets on two sides is equal, more participants prefer to protect the ones that comply with traffic rules. The research results can provide the basis for designing moral algorithms for the AVs.

This study investigated the relationship between personality states and driving behavior from a dynamic perspective. A personality baseline was introduced to reflect the driver's trait level and can be used as a basic reference for the dynamic change of personality states. Three kinds of simulated scenarios triggered by pedestrian crossing the street were established using a virtual reality driving simulator. Fifty licensed drivers completed the driving experiments and filled in the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) questionnaire to measure the drivers&rsquo, personality baselines. Key indicators were quantified to characterize the five types of personality states by K-means clustering algorithm. The results indicated that the high-risk situation had a greater impact on the drivers, especially for drivers with openness and extroversion. Furthermore, for the drivers of extroverted personality, the fluctuation of personality states in the high-risk scenario was more pronounced. This paper put forward a novel idea for the analysis of driving behavior, and the research results provide a personalized personality database for the selection of different driving modes.

In this paper, taking the typical dangerous driving behaviors of following the preceding car too close and abnormal ways to change lane as the research object, the identification indicators are selected. Using the UC-win/Road software to build the scene, the samples to be identified were obtained, and support vector machine regression and BP neural network recognition models were constructed respectively to identify dangerous driving behaviors and compare their accuracy. The results showed that the driving behavior recognition model based on support vector regression can effectively identify the driving behavior, and the accuracy of discriminant inference was significantly higher than that based on BP neural network, both reaching over 92%. At the same time, the average prediction time was also obvious reduced. The research results have important theoretical and practical significance for the performance evaluation of behavior recognition models and the development of road traffic safety.

The development of commercial-scale thin-film composite (TFC) hollow fiber membranes and modules for reverse osmosis (RO) has been lagging behind spiral wound TFC flat-sheet membranes and modules for many years. The delay arises from the challenges of synthesizing a defect-free polyamide selective layer on hollow fibers in modules. In order to solve this problem, a novel interfacial polymerization method is presented in this work to fabricate 1-inch TFC hollow fiber membrane modules for RO applications. By circulating the m-phenylenediamine (MPD) solution on the lumen side under a positive pressure and circulating the trimesoyl chloride (TMC) solution at the atmosphere pressure, a strong, uniform and defect-free polyamide selective layer is formed on the inner surface of hollow fiber membranes in modules. Under pressure, not only MPD monomers are evenly distributed on the inner surface of each hollow fiber substrate in modules, but also sufficient MPD monomers can retain in the substrate pores for further reaction with TMC monomers. The best RO performance obtained by this newly developed TFC hollow fiber membrane module has a water permeability of 4.49 L m−2 h−1 bar−1 and a NaCl rejection of ~99%, using a feed solution of 1000 ppm NaCl under a transmembrane pressure of 20 bar. The novel interfacial polymerization method may provide useful insights to fabricate TFC hollow fiber membrane modules for desalination of brackish water.

Preceding vehicle detection is still a challenge for unmanned driving technology. Deep learning has achieved great success in target detection. Among them, the Faster R-CNN algorithm is a classic representative. However, the algorithm still has some room for improvement in detection accuracy. By analyzing the problems of Faster R-CNN in the detection of occluded vehicles, taking the target detection post-processing algorithm Soft-NMS as the research object, two new penalty coefficients, inverse proportional penalty coefficient and exponential penalty coefficient, were proposed. It further improves the algorithm’s detection accuracy of the blocked vehicle in front.

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

The first enantioenriched synthesis of 1,1,3-trisubstituted (trifluoromethyl)indene derivatives, bearing a quaternary stereogenic carbon center, is reported using a simple chiral sulfinamide phosphine-catalyzed asymmetric allylic alkylation of a mixture of indenes with Morita–Baylis–Hillman carbonates. The resulting derivatives can serve as a valuable synthetic building block for some drugs and natural products. Broad substrate scope and high regio- and enantioselectivity of this reaction were particularly remarkable.

In order to accurately simulate vehicles' running states of lane changing and more effectively reflect its characteristics, based on the driving experience, this paper introduced three changing rules and their corresponding lane changing benefits and safety factors, and summarized the lateral and longitudinal motion of vehicles as the interaction result between lane changing benefits and safety factors in the lane changing process. Taking the basic idea of artificial potential field for reference, the lane changing benefits and safety factors are abstracted respectively as the attractive and repulsive force perceived by drivers, and then the lane changing model corresponding to each lane changing rule was established after analyzing. Finally, the measured data were used to calibrate and test the parameters of lane changing model of different rules. The results showed that the testing errors of the models are within a reasonable range, and the changing trend of lane changing of different rules can be well fitted.

In order to improve the accuracy of laser radar for dynamic obstacle detection, and ensure the safety of autonomous vehicles, the method of dynamic obstacle method of wireless sensor and 3 dimensional laser radar Velodyne based on information fusion of vehicle identification and a multi feature detection and tracking are put forward. A preliminary model of mobile obstacle is established by using lidar, and the model information includes center position, length and width; at the same time, in order to reduce the number of sensor nodes to reduce the energy consumption, the tracking area is designed, and the moving objects are modeled the second time by using the wireless sensors' data in the tracking area, and if data consistency is lower, data fusion and model updating are performed; and the optimized Euclidean algorithm is combined with the corresponding features to complete the tracking of obstacles. The calculation results show that the method proposed in this paper can solve the problem that the accuracy of detection and modeling is not high for occlusion, and it can guarantee the driving safety of autonomous vehicles.

A novel phosphine-catalyzed, highly enantioselective umpolung addition of trifluoromethyl ketimines to Morita–Baylis–Hillman carbonates was developed and it provides facile access to optically active trifluoromethyl amines with a chiral tertiary stereocenter under mild reaction conditions. The salient features of this reaction include general substrate scope, mild reaction conditions, good yields, high enantioselectivity, ease of scale-up to gram scale, and further transformations of the products.

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0&ndash, 20 mph) to middle (20&ndash, 45 mph) and then to the high range (45&ndash, 75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

Autonomous vehicles (AVs) are supposed to identify obstacles automatically and form appropriate emergency strategies constantly to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable, and AVs are required to make difficult decisions involving ethical and legal factors under emergency situations. In this paper, the ethical and legal factors are introduced into the driving decision-making (DDM) model under emergency situations evoked by red light-running behaviors. In this specific situation, 16 factors related to vehicle-road-environment are considered as impact indicators of DDM, especially the duration of red light (RL), the type of abnormal target (AT-T), the number of abnormal target (AT-N) and the state of abnormal target (AT-S), which indicate legal and ethical components. Secondly, through principal component analysis, seven indicators are selected as input variables of the model. Furthermore, feasible DDM, including braking + going straight, braking + turning left, braking + turning right, is taken as the output variable of the model. Finally, the model chosen to establish DDM is the T-S fuzzy neural network (TSFNN), which has better performance, compared to back propagation neural network (BPNN) to verify the accuracy of TSFNN.

According to the traffic problems such as traffic congestion and buses moving slowly, the paper based on the idea of bus priority to introduced the per capita minimum delay of signal control method. In this paper, the intersection of Huaguang Road and Liuquan Road in Zibo Zhangdian was used as an example to propose a bus signal timing plan. By considering the impact of traffic delays and designing the phase of the intersection. It can reduce the per capita delay and improve service levels. Using Trans Modeler to simulate the timing scheme, the results showed that the optimized scheme can effectively reduce the per capita delay and ensure priority access for buses.

Presently, the development of the city doesn't pay enough attention to building bus stations. Fund raising has some difficulty; it is an obstacle to the development of public transport. Through the analysis of the shortcomings of the traditional investment and financing way, puts forward to bus station investment and financing system reform. From the domestic and foreign experience, determine the direction of the reform. According to the actual situation, combined with "people-oriented" concept, gradually completing the investment and financing system of marketization reform. Base on the model of investment of urban infrastructure, this paper discusses the characteristics of various models, analysis and comparison for bus station of the investment and financing mode, find suitable ways with the situation of China of the investment and financing methods, for the success of the bus station construction provided protection.