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Heat exchangers make it possible to utilize energy efficiently, reducing the cost of energy production or consumption. For example, they can be used to improve the efficiency of gas turbines. Improving the efficiency of a heat exchanger directly affects the efficiency of the device for which it is used. One of the most effective ways to intensify heat exchange in a heat exchanger without a significant increase in mass-dimensional characteristics and changes in the input parameters of the flows is the introduction of turbulators into the heat exchangers. This article investigates the increase in efficiency of heat exchanger apparatuses by introducing turbulent lattice structures manufactured with the use of additive technologies into their design. The study is carried out by numerical modeling of the heat transfer process for two sections of the heat exchanger: with and without the lattice structure inside. It was found that lattice structures intensify the heat exchange by creating vortex flow structures, as well as by increasing the heat exchange area. Thus, the ratio of convection in thermal conductivity increases to 3.03 times. Also in the article, a comparative analysis of the results obtained with the results of heat transfer intensification using classical flow turbulators is carried out. According to the results of the analysis, it was determined that the investigated turbulators are more effective than classical ones, however, the pressure losses in the investigated turbulators are much higher.

This study proposes using connected automatic vehicles (CAVs) as traffic flow detectors to collect and exchange traffic flow data for heterogeneous traffic management and control. The proposed method includes the construction of a mathematical matrix to represent the status of the road section, the use of unsupervised machine learning to evaluate traffic data, and an improved generative adversarial imputation net (GAIN) to evaluate and impute missing traffic data. Next-generation simulation (NGSIM) data are used to verify the accuracy and robustness of the proposed method. One of the primary innovations of this study is the use of GAIN, a deep learning framework based on generative adversarial networks (GANs), to impute missing traffic data. GAIN has been shown to be more robust and stable when handling incomplete heterogeneous data than existing imputation methods. Additionally, this study contributes to the field by proposing the use of CAVs as sensors to detect mixed traffic flow, which could lead to more efficient and accurate traffic management and control. Experimental results demonstrate that the proposed method outperforms existing imputation methods, with a normalized root mean squared error and symmetric mean absolute percentage error of less than 0.2/0.3 and 0.08/0.13 in I-80 and Lankershim Boulevard, respectively. The findings of this study have important implications for the development and implementation of connected and automated vehicle technologies in the field of transportation.

Object detection is the fundamental task of vision-based sensors in environmental perception and sensing. To leverage the full potential of roadside 4D MMW radars, an innovative traffic detection method is proposed based on their distinctive data characteristics. First, velocity-based filtering and region of interest (ROI) extraction were employed to filter and associate point data by merging the point cloud frames to enhance the point relationship. Then, the Louvain algorithm was used to divide the graph into modularity by converting the point cloud data into graph structure and amplifying the differences with the Gaussian kernel function. Finally, a detection augmentation method is introduced to address the problems of over-clustering and under-clustering based on the object ID characteristics of 4D MMW radar data. The experimental results showed that the proposed method obtained the highest average precision and F1 score: 98.15% and 98.58%, respectively. In addition, the proposed method showcased the lowest over-clustering and under-clustering errors in various traffic scenarios compared with the other detection methods.

Abstract We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc. The mobility parameter derived from this data is used to model the infection rate. As a test case, we study the impact of the usage of the New York City subway on the spread of COVID-19 within the city during 2020. We show that utilizing subway transport data as an indicator of the general mobility trends within the city, and therefore as an indicator of the effective infection rate, improves the quality of forecasting COVID-19 spread in New York City. Our model predicts the two peaks in the spread of COVID-19 cases in NYC in 2020, unlike a standard SIR model that misses the second peak entirely.

Endometrial cancer (EC) is one of the three fatal tumors of the female reproductive system. Epigenetic alterations have been reported to be important in tumorigenesis, especially the chromatin accessibility changes and transcription factor binding differences. However, the regulatory mechanism underlying epigenetic alterations in EC development remains unclear. Here, we identified and characterized transcription factor binding site clustered regions (TFCRs) by integrating chromatin accessibility and transcription factor binding information. We totally identified 78,820 TFCRs and explored the relationship between TFCRs and regulatory elements, gene expression and mutation. Finally, we constructed a bioinformatic framework to identify candidate oncogenes and screened 13 candidate key genes, which may serve as potential diagnostic markers or therapeutic targets of EC.

Abstract Peripheral nerve injury is a serious medical problem with limited surgical and clinical treatment options. It is of great significance to integrate multiple guidance cues in one platform of nerve guidance conduits (NGCs) to promote axonal elongation and functional recovery. Here, a multi‐functional NGC is constructed to promote nerve regeneration by combining ordered topological structure, density gradient of biomacromolecular nanoparticles, and controlled delivery of biological effectors to provide the topographical, haptotactic, and biological cues, respectively. On the surface of aligned polycaprolactone nanofibers, a density gradient of bioactive nanoparticles capable of delivering recombinant human acidic fibroblast growth factor is deposited. On the graded scaffold, the proliferation of Schwann cells is promoted, and the directional extension of neurites from both PC12 cells and dorsal root ganglions is improved in the direction of increasing particle density. After being implanted in vivo for 6 and 12 weeks to repair a 10‐mm rat sciatic nerve defect, the NGC promotes axonal elongation and remyelination, achieving the regeneration of the nerve not only in anatomical structure but also in functional recovery. Taken together, the NGC provides a favorable microenvironment for peripheral nerve regeneration and holds great promise for realizing nerve repair with an efficacy close to autograft.

Abstract Peripheral nerve injury is a large‐scale problem that annually affects more than several millions of people all over the world. It remains a great challenge to effectively repair nerve defects. Tissue engineered nerve guidance conduits (NGCs) provide a promising platform for peripheral nerve repair through the integration of bioactive scaffolds, biological effectors, and cellular components. Herein, we firstly describe the pathogenesis of peripheral nerve injuries at different orders of severity to clarify their microenvironments and discuss the clinical treatment methods and challenges. Then, we discuss the recent progress on the design and construction of NGCs in combination with biological effectors and cellular components for nerve repair. Afterward, we give perspectives on imaging the nerve and/or the conduit to allow for the in situ monitoring of the nerve regeneration process. We also cover the applications of different postoperative intervention treatments, such as electric field, magnetic field, light, and ultrasound, to the well‐designed conduit and/or the nerve for improving the repair efficacy. Finally, we explore the prospects of multifunctional platforms to promote the repair of peripheral nerve injury.

As an important stage in the development of autonomous driving, mixed traffic conditions, consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), have attracted more and more attention. In fact, the randomness of human-driven vehicles (HDV) is the largest challenge for connected autonomous vehicles (CAV) to make reasonable decisions, especially in lane change scenarios. In this paper, we propose the problem of lane change decisions for CAV in low visibility and mixed traffic conditions for the first time. First, we consider the randomness of HDV in this environment and construct a finite state machine (FSM) model. Then, this study develops a partially observed Markov decision process (POMDP) for describing the problem of lane change. In addition, we use the modified deep deterministic policy gradient (DDPG) to solve the problem and get the optimal lane change decision in this environment. The reward designing takes the comfort, safety and efficiency of the vehicle into account, and the introduction of transfer learning accelerates the adaptation of CAV to the randomness of HDV. Finally, numerical experiments are conducted. The results show that, compared with the original DDPG, the modified DDPG has a faster convergence velocity. The strategy learned by the modified DDPG can complete the lane change in most of the scenarios. The comparison between the modified DDPG and the rule-based decisions indicates that the modified DDPG has a stronger adaptability to this special environment and can grasp more lane change opportunities.

In this paper, we report a two-layer five-port diffraction grating by a polarization-independent design under normal incidence. To be different from conventional five-port gratings, initially, we design a novel two-layer grating with operation in transmission. Next, we firstly design a polarization-independent grating with five-port splitting output. On the one hand, the accurate grating vector parameters are optimized using rigorous coupled-wave analysis (RCWA) approach. On the other hand, the inherent coupling mechanism happened in the grating region can be well explained by employing simplified modal method (SMM). More importantly, all reported conventional five-port beam splitter gratings were designed based on polarization dependent. Although they could obtain high diffraction efficiency for TE or TM polarization, it requires more complicated and time-consuming fabrication processes in practical grating applications. Thus, this work can open up a possible thought for designing polarization-independent multi-port beam splitter diffraction gratings.

Traffic safety is affected by many complex factors. Mind wandering (MW) is a fatal cause affecting driving safety and is hard to be detected and prevented due to its uncertain and complex occurrence mechanism. The aim of this study was to propose a framework for analyzing and predicting MW based on readily available driving status data. The data used in this study are the single-trip information collected by the questionnaire, which includes drivers’ personal characteristics, contextual information in which MW occurs, and in-vehicle environmental factors. After investigating the extent of factors that influence MW, these chosen factors are used to forecast MW. Based on these results, we select factors reliable to be obtained in real life to forecast MW. To verify that the new factors explored are useful in improving the forecast accuracy, the compared analysis is conducted with the results found by our approach and the existing approaches. We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. The result shows that the factors found in this paper can significantly improve forecast accuracy. The confusion matrix, ROC curves, and AUC are conducted, and the performance of the gradient boosting decision tree algorithm is better than other forecast approaches. The importance rankings of most factors obtained by the Gradient Boosting Decision Tree and questionnaire are the same.

Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.

Quasiperiodic signal is ubiquitous and entrenched in neuronal networks, and thus taking it into consideration is necessary. The Wiener process with the intensity of σ is used here to model randomly fluctuated phase in external weak quasiperiodic signal. The departure from the normal periodicity can be governed by the parameter σ. Then, the effects of randomly fluctuated phase of signal and time-periodic coupling intensity of synaptic junctions between neurons on propagation of weak quasiperiodic signal through feed-forward Hodgkin–Huxley network are explored in detail. Increasing σ makes more neurons fire simultaneously, and better synchronous state is observed. Consequently, the external weak quasiperiodic signal introduced into all neurons in the first layer can be effectively transmitted through the whole feed-forward network via synchronization mechanism. In the case of time-periodic synaptic coupling intensity, when oscillatory frequency of synaptic coupling intensity is equal precisely to average frequency of external quasiperiodic signal, the propagation of weak quasiperiodic signal is optimal. Additionally, rapid oscillation of synaptic coupling intensity hinders or even kills the propagation of quasiperiodic signal to great depths of neuronal network, provided σ is not large enough.

Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.

The amplitude-phase error caused by the channel inconsistency in the actual digital array radar and the mutual coupling error caused by the mutual coupling effect have a great influence on the performance of the array. This study analyses and models the error characteristics of amplitude-phase error and mutual-coupling error, respectively. Based on the narrow-band and wide-band array received signal models, the spatial signal arrival angle estimation accuracy and the beam-synthesising pattern are used as indicators to analyse the influence of errors on the array. Channel inconsistency and mutual coupling will cause the estimated signal arrival angle to deviate from the true signal direction and the spatial spectrum curve peak will become gentle. In addition, the beam pattern's sidelobe will raise, null orientation will deviate from the interference signal direction, and gain will raise.

This study proposes trusted measurement and evaluation models for intelligent transportation system products to provide technological means for the transport sector and government departments to detect the compliance and availability of intelligent transportation system products and consider the market access of intelligent transportation system products. Then, this study builds a trusted testing platform based on a hardware-in-loop system to test and validate trusted measurement and evaluation models of intelligent transportation system products. Finally, a traffic signal controller is tested and validated in the platform in different operation environments. The result shows that the trusted measurement model can accurately measure the trusted status in normal and non-normal operation conditions, and the trusted evaluation model can dynamically calculate the intelligent transportation system product’s trusted attributes, namely, the trusted evaluation values and comprehensive trusted evaluation values of the intelligent transportation system product, as the intelligent transportation system product’s operation environment changes.

More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation.

For traffic management under sudden disasters in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow management and control, so as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow management and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439 pcu/lane/h, 529 pcu/lane/h, 377 pcu/lane/h at 5 s, 10 s, 15 s data collection interval in a Sudoku traffic network respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10 s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster.

A light detection and ranging (LiDAR) sensor can obtain richer and more detailed traffic flow information than traditional traffic detectors, which could be valuable data input for various novel intelligent transportation applications. However, the point cloud generated by LiDAR scanning not only includes road user points but also other surrounding object points. It is necessary to remove the worthless points from the point cloud by using a suitable background filtering algorithm to accelerate the micro-level traffic data extraction. This paper presents a background point filtering algorithm using a slice-based projection filtering (SPF) method. First, a 3-D point cloud is projected to 2-D polar coordinates to reduce the point data dimensions and improve the processing efficiency. Then, the point cloud is classified into four categories in a slice unit: Valuable object points (VOPs), worthless object points (WOPs), abnormal ground points (AGPs), and normal ground points (NGPs). Based on the point cloud classification results, the traffic objects (pedestrians and vehicles) and their surrounding information can be easily identified from an individual frame of the point cloud. We proposed an artificial neuron network (ANN)-based model to improve the adaptability of the algorithm in dealing with the road gradient and LiDAR-employing inclination. The experimental results showed that the algorithm of this paper successfully extracted the valuable points, such as road users and curbstones. Compared to the random sample consensus (RANSAC) algorithm and 3-D density-statistic-filtering (3-D-DSF) algorithm, the proposed algorithm in this paper demonstrated better performance in terms of the run-time and background filtering accuracy.

Guanine-rich sequences in the genomes of herpesviruses can fold into G-quadruplexes. Compared with the widely-studied G3-quadruplexes, the dynamic G2-quadruplexes are more sensitive to the cell microenvironment, but they attract less attention. Pseudorabies virus (PRV) is the model species for the study of the latency and reactivation of herpesvirus in the nervous system. A total of 1722 G2-PQSs and 205 G3-PQSs without overlap were identified in the PRV genome. Twelve G2-PQSs from the CDS region exhibited high conservation in the genomes of the Varicellovirus genus. Eleven G2-PQSs were 100% conserved in the repeated region of the annotated PRV genomes. There were 212 non-redundant G2-PQSs in the 3′ UTR and 19 non-redundant G2-PQSs in the 5′ UTR, which would mediate gene expression in the post-transcription and translation processes. The majority of examined G2-PQSs formed parallel structures and exhibited different sensitivities to cations and small molecules in vitro. Two G2-PQSs, respectively, from 3′ UTR of UL5 (encoding helicase motif) and UL9 (encoding sequence-specific ori-binding protein) exhibited diverse regulatory activities with/without specific ligands in vivo. The G-quadruplex ligand, NMM, exhibited a potential for reducing the virulence of the PRV Ea strain. The systematic analysis of the distribution of G2-PQSs in the PRV genomes could guide further studies of the G-quadruplexes’ functions in the life cycle of herpesviruses.

This study presents methods of transit signal priority without transit-only lanes for a transit-based emergency evacuation in a sudden-onset disaster. Arterial priority signal coordination is optimized when a traffic signal control system provides priority signals for transit vehicles along an evacuation route. Transit signal priority is determined by “transit vehicle arrival time estimation,” “queuing vehicle dissipation time estimation,” “traffic signal status estimation,” “transit signal optimization,” and “arterial traffic signal coordination for transit vehicle in evacuation route.” It takes advantage of the large capacities of transit vehicles, reduces the evacuation time, and evacuates as many evacuees as possible. The proposed methods were tested on a simulation platform with Paramics V6.0. To evaluate and compare the performance of transit signal priority, three scenarios were simulated in the simulator. The results indicate that the methods of this study can reduce the travel times of transit vehicles along an evacuation route by 13% and 10%, improve the standard deviation of travel time by 16% and 46%, and decrease the average person delay at a signalized intersection by 22% and 17% when the traffic flow saturation along an evacuation route is 0.81.0, respectively.

A traditional traffic signal control system is established based on vehicular delay, queue length, saturation and other indicators. However, due to the increasing severity of urban environmental pollution issues and the development of a resource-saving and environmentally friendly social philosophy, the development of low-carbon and energy-efficient urban transport is required. This paper first defines vehicular trajectories and the calculation of vehicular emissions based on VSP. Next, a regression analysis method is used to quantify the relationship between vehicular emissions and delay, and a traffic signal control model is established to reduce emissions and delay using the enumeration method combined with saturation constraints. Finally, one typical intersection of Changchun is selected to verify the model proposed in this paper; its performance efficiency is also compared using simulations in VISSIM. The results of this study show that the proposed model can significantly reduce vehicle delay and traffic emissions simultaneously.