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ABSTRACTSpaceborne photon-counting light detection and ranging (LiDAR) have been extensively applied in shallow-water bathymetry. The density of underwater topographic photons (UTP) varies and is discontinuous due to sunlight noise, beam intensity, and seabed reflectivity, which differ from the land photon distribution due to the attenuation of water. Therefore, a general method for extracting and grading UTP is still lacking. We propose an active contour method combined with a variable convolution kernel method to calculate the photon range by considering the energy contributions of adjacent photons. Adaptive parameters under different observation conditions were determined to obtain the optimal convolution kernel using a kernel ridge regression model. This implies that the number of photons contained in the buffer zone was largest after the extracted UTP was fitted to a curve. Quantitative and qualitative verifications proved that the method performed well under different conditions. The photons obtained by the energy functional and the curve obtained by the fitting method were then used to grade the photons. Finally, an online developed UTP dataset and extraction framework were proposed to provide an applicable method for current and subsequent spaceborne photon-counting LiDAR.

Abstract Background A comprehensive assessment of combined modifiable risk factors in relation to infectious diseases among individuals with hypertension is lacking, and the potential mechanisms of these associations remain unclear. To investigate the prospective associations of a combination of lifestyle behaviors and cardiometabolic factors with the risk of infectious diseases among individuals with hypertension and to estimate whether and to what extent blood biomarkers mediate these associations. Methods This cohort study included 147,188 participants with hypertension and complete data on modifiable risk factors from the UK Biobank. Health score was constructed from eight modifiable risk factors, including four lifestyle behaviors (diet, physical activity, smoking, and sleep duration) and four cardiometabolic factors (body mass index, blood lipids, blood glucose, and blood pressure). Cox proportional hazards regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the health score and infectious diseases. The mediation analysis was performed to assess the potential intermediation effects of blood biomarkers. Results Over a median follow-up of 12.5 years, 27,398 participants with infectious diseases were documented, with 960 respiratory infectious diseases and 7940 digestive infectious diseases. After adjusting for potential confounders, the HR (95% CI) for the highest versus the lowest quartile of health score was 0.64 (0.62, 0.66) for infectious diseases, 0.72 (0.60, 0.86) for respiratory infectious diseases, and 0.66 (0.62, 0.71) for digestive infectious diseases. Stratified factors including duration of hypertension did not modify the associations between the health score and infectious diseases. In addition, biomarkers including inflammation and renal function collectively explained 46.60% of the associations between the combined lifestyle factors and infectious disease risk among individuals with hypertension. Conclusions and relevance Ideal management of combined modifiable risk factors was associated with lower risks of infectious diseases and might produce profound changes in blood biomarkers among individuals with hypertension. Additionally, specific biomarkers appeared to serve as an intermediate between combined lifestyle factors and infectious diseases. These insights highlighted the important role of a combination of healthy lifestyle and favorable cardiometabolic status in reducing disease burden and facilitated the understanding of biological mechanisms underlying modifiable risk factors with infectious diseases.

Abstract Sleep quality is crucial for the personal well-being of healthcare professionals and the health outcomes of their patients. This study aims to explore the relationship between psychological resilience (PR), perceived social support (PSS), psychological distress (comprising anxiety,depression,and stress), and sleep quality. It also examines whether PSS and psychological distress function as chain mediators between PR and sleep quality. A cross-sectional online survey was conducted using a convenient sampling method, with 454 participants included. The survey instruments included the Connor and Davidson Resilience Scale, the Perceived Social Support Scale, the 21-item Depression Anxiety Stress Scale, and the Pittsburgh Sleep Quality Index. Structural equation modeling revealed that PR significantly predicted sleep quality of Chinese medical staff. Psychological distress was identified as a mediating factor between PR and sleep quality. However, PSS did not directly mediate the relationship between PR and sleep quality. Instead, PSS and psychological distress were found to play a chain mediating role in the relationship between PR and sleep quality. This study provides new insights into the impact of PR on sleep quality, highlights the importance of PSS and psychological distress, and suggests practical implications for enhancing sleep quality among medical staff.

Abstract Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

Abstract Background and purpose Cerebrovascular events during thrombolysis in cardiac arrest (CA) caused by pulmonary embolism (PE) is a life-threatening condition. However, the balance between cerebrovascular events and thrombolytic therapy in PE-induced CA remains a great challenge. Methods In this study, we reported three unique cases regarding main concerns surrounding cerebrovascular events in thrombolytic therapy in PE-induced CA. Results The patient in the case 1 treated with thrombolysis during CPR and finally discharged neurologically intact. The patient in the case 2 received delayed thrombolysis and died eventually. The patient in the case 3 was contraindicated to thrombolysis due to the complication of subarachioid hemorrahage and died within days. Conclusions Our case series highlights three proposed approaches to consider before administering thrombolysis as a treatment option in PE-induced CA patients: (1) prolonging the resuscitation, (2) administering thrombolysis promptly, and (3) ruling out cerebrovascular events.

Strategic minerals are the cornerstone of national security and stable development. China’s strategic mineral imports are highly dependent on maritime transport, and ensuring smooth maritime routes is crucial for safeguarding the security of strategic mineral imports. This study analyzes the overall pattern of China’s strategic mineral imports and maritime routes. Taking the iron ore maritime import network as an example, the study evaluates the importance and failure impacts of key maritime routes, summarizes the risk sources of important maritime routes, and proposes corresponding security strategies. The research finds that China’s strategic mineral imports are highly dependent on maritime routes. Based on the case study of iron ore maritime transport, the importance and failure impacts of maritime routes are identified. It is suggested to build differentiated and refined response strategies, focus on the protection of the Cape of Good Hope and the Lombok Strait transport corridor network, and pay attention to the safe navigation of large vessels. The risks of maritime routes for China’s strategic minerals mainly come from three aspects: navigational passages, international disputes and war threats, and non-traditional security issues. In response, multi-domain response strategies can be implemented, including building multiple strategic pivot nodes, constructing a multidimensional early warning system and an alternative route response mechanism, strengthening the construction of cross-border rail – sea intermodal transport corridors, conducting preventive diplomatic work, improving the maritime security system, and implementing hierarchical and differentiated risk-response measures.

The rapid development of maritime transport and the gradual increase in the number of ports, ships and shipping routes can produce direct economic benefits for regional development, and a profound grasp of the actual situation of maritime transport makes it possible to make practical plans for economic development and thus rationally develop and use marine resources. However, there is a lack of research on maritime transport, so this study is based on the AIS, waterways, routes and ports data in the near-shore sea area of Guangdong Province. Using GIS spatial analysis technology and various mathematical models, we refer to the evaluation method of road traffic dominance. It analyses and proposes the evaluation indexes of maritime traffic advantages, such as the density of maritime traffic network and the proximity of ports. Based on the actual situation of the study regions, the indicators were quantitatively evaluated and analyzed. The economic development level of each region was also quantitatively evaluated using principal component analysis, and the study regions were classified based on the coupling-coordination model. The results show that the coastal area of Guangdong Province is divided into four types of zones according to the coupling-coordination type of zoning criteria. Corresponding development suggestions are put forward for different zones, and the research results provide certain practical guidelines for promoting the benign cycle development of maritime traffic and economy, and have important guiding significance and application value for the organization and safety of maritime traffic.

Abstract Septic cardiomyopathy is one of the most severe and common complications in patients with sepsis and poses a great threat to their prognosis. However, the potential mechanisms and effective therapeutic drugs need to be explored. The control of cardiac cell death by miRNAs has emerged as a prominent area of scientific interest in the diagnosis and treatment of heart disorders in recent times. In the present investigation, we discovered that overexpression of miR-31-5p prevented LPS-induced damage to H9C2 cells and that miR-31-5p could inhibit BAP1 production by binding to its 3’-UTR. BRCA1-Associated Protein 1 (BAP1) is a ubiquitin carboxy-terminal hydrolase. BAP1 upregulation blocked effect of miR-31-5p on H9C2 cell injury. Moreover, BAP1 inhibited the expression of solute carrier family 7 member 11 (SLC7A11) by deubiquitinating histone 2 A (H2Aub) on the promoter of SLC7A11. Furthermore, overexpression of miR-31-5p and downregulation of BAP1 inhibited SLC7A11 mediated ferroptosis. In addition, the downregulation of SLC7A11 reversed the inhibitory effect of miR-31-5p on the expression of myocardial injury and inflammatory factors, and cell apoptosis was reversed. In conclusion, these results indicate that miR-31-5p alleviates malignant development of LPS-induced H9C2 cell injury by targeting BAP1 and regulating SLC7A11 deubiquitination-mediated ferroptosis, which confirmed the protective effect of miR-31-5p on H9C2 cell injury and revealed potential mechanisms that may provide new targets for treatment of septic cardiomyopathy.

An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AI-based RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on high-performance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research.

Six-degree-of-freedom industrial robots, known for their low cost and high flexibility, have been extensively applied in optical processing. Precise pose control in robot-based optical processing systems depends on the accurate calibration of the tool coordinate system. However, in robot magnetorheological finishing (Robot-MRF) systems, the spherical shape of the polishing wheel poses significant challenges in precisely identifying the working point on the tool’s surface. Traditional calibration methods, such as the four-point or six-point techniques, fail to accurately calibrate the tool coordinate system for MRF tools. To overcome this limitation, a laser tracker-based calibration method is proposed for parameter calibration of the Robot-MRF system. Experimental results show that this method achieves a maximum repeatability error of just 0.0505 mm, significantly improving the stability and reliability of the calibration results and meeting the high-precision processing requirements of MRF technology.

Resumo Fundamento: O antibiótico quimioterápico antraciclina doxorrubicina (DOX) pode induzir cardiotoxicidade cumulativa e levar à disfunção cardíaca. RNAs não codificantes longos (lncRNAs) podem funcionar como importantes reguladores na lesão miocárdica induzida por DOX. Objetivo: Este estudo tem como objetivo investigar o papel funcional e o mecanismo molecular do RNA antisense lncRNA OXCT1 1 (OXCT1-AS1) na lesão celular miocárdica induzida por DOX in vitro. Métodos: Cardiomiócitos humanos (AC16) foram estimulados com DOX para induzir um modelo de lesão celular miocárdica. A expressão de OXCT1-AS1, miR-874-3p e BDH1 em células AC16 foi determinada por RT-qPCR. A viabilidade das células AC16 foi medida pelo ensaio XTT. A citometria de fluxo foi empregada para avaliar a apoptose de células AC16. Western blotting foi utilizado para avaliar os níveis proteicos de marcadores relacionados à apoptose. O ensaio repórter de luciferase dupla foi conduzido para verificar a capacidade de ligação entre miR-874-3p e OXCT1-AS1 e entre miR-874-3p e BDH1. O valor de p

The ice, cloud, and land elevation satellite-2 (ICESat-2) is the only satellite that produces photon-counting light detection and ranging data, and is equipped with the advanced topographic laser altimeter system. ICESat-2 provides sea surface height product; however, its approach of the product is unsuitable for areas with sub-surface signals. Conventional denoising methods applied to sea surface photon data of variable density involve the use of different empirical parameters. Considering the distribution of sea surface signal photons, we propose a general open-source method using a dual-signal unmixing parameter (DSUMP), which incorporates the Gaussian distribution of dual-signal peaks to determine the sea surface range. This method facilitates the direct extraction of sea surface photons under various observation conditions—day or night, strong or weak beams, and including or excluding seabed photons—without requiring any variable parameters. The elevation error by DSUMP within 0.1m accounts for more than 97%. The mean absolute error is within 0.01 m compared to sea surface photons obtained via manual extraction. Different model parameters show stable denoising accuracy, only affects operating efficiency. The proposed method introduces a novel denoising technique for extracting sea surface elevation from ICESat-2 altimetry data, and its applicability can be extended to various point cloud data with similar distributions.

Coral reef ecosystem is gradually being threatened, thus monitoring coral reefs using remote sensing is of great significance. There are difficulties in identifying and classifying coral reefs. We propose a radiative transfer model with deep learning (RTDL) to improve the accuracy of the coral reef classification. This model combines the radiative transfer theory with deep learning methods to consider the nonlinear fitting ability of the model and the constraints of the physical model, which improves the classification accuracy. This model utilizes ICESat-2 data to obtain the underwater topography photons (UTP) by active contours with variable convolution kernel model and retrieves water depth by combining the UTP with the Sentinel-2 images. An exponential model is used for reinforcing the reflectivity of the seabed features, which is executed by deep learning models. This study uses multidimensional and multitemporal images to illustrate the generalization and robustness of the RTDL model compared with other methods. The result indicates that the RTDL model obtains remarkable achievements in the classification of coral reef landforms. The method increases the accuracy by 5%, mean intersection over union by 13%, especially for areas, such as reef front slopes and lagoons, where the identification accuracy is increased by 26% for the reef front slopes, by 15% for sandbars, and by 9% for lagoons, compared with the traditional method across 14 areas. The model provides a stable classification framework with physically meaningful for identifying coral reef areas, providing robust method support for the conservation of their ecosystems.

Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician $K$ factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.

Radio propagation environment plays a critical role in the performance of wireless communication systems, and understanding channel characteristics is vital for ensuring reliable communication links and optimizing system performance. Ray tracing is an effective method to investigate propagation characteristics in a complex environment, and how to quickly and accurately obtain environmental information needs to be solved. This paper presents dynamic environment reconstruction and ray tracing simulation in railway tunnel environment based on Simultaneous Localization and Mapping (SLAM) algorithm and Poisson reconstruction algorithm. Accurate channel parameters are obtained and analyzed based on ray tracing simulation. Both straight and curved tunnels are considered and investigated, and the results show the channel characteristics in complex railway tunnel environments.

Passive backscatter communication can free wireless sensors from limited-life batteries and heavy manual maintenance, and will also reduce their hardware cost.Passive backscatter communication is one of the key technologies to realize the passive internet of things (IoT).Due to the limited resources of passive sensor nodes, how to use coding technology to improve system reliability has become a challenging problem for passive IoT.The coding and decoding technologies of passive nodes in the passive IoT were focused on.Firstly, the development history of traditional channel coding and the existing RFID channel coding and decoding schemes were systematically expounded.Then, three suitable coding schemes were selected for passive backscatter communication technology.Finally, the performance of these three encoding schemes were compared by simulation and the advantages and disadvantages were summarized.

Typhoons and other marine meteorological disasters often bring significant losses to human beings, and their data are characterized by multiple sources and scales, making traditional visualization methods unable to accurately express the characteristics and movement trends of the disasters. To address the above problems, this study proposes a typhoon dynamic visualization method based on the integration of vector and scalar fields. To address the above problems, this study proposes a typhoon dynamic visualization method based on the integration of vector and scalar fields. The method uses the ray casting method to visualize the volume rendering of typhoon scalar data, proposes a hybrid interpolation method to improve the visualization efficiency, and introduces the Sobel operator to achieve the edge enhancement of the volume rendering effect. Meanwhile, a particle system approach is used for dynamic visualization of typhoon vector data, where the tedious particle motion calculation is divided into two parts: parallel tracking and dynamic rendering to improve the visualization efficiency, and the Lagrangian field representation of the particle system is achieved. The experimental results show that the typhoon visualization method proposed in this study has better comprehensive visual effects, with a rendering frame rate greater than 44, and is able to stably and smoothly express the continuous spatio-temporal dynamic visualization features of typhoon.This study is conducive to the understanding of the evolution law of marine meteorological disasters and the adoption of disaster prevention and mitigation measures, and is of great significance for the expression of marine meteorological data such as typhoons and the analysis of their spatial and temporal changes.

Abstract A reliable train‐to‐ground wireless communication system is proposed for the foundation of the safe operation of Hyperloop. The wireless channel characteristics of Hyperloops at millimetre‐wave (mmWave) bands are analysed based on the ray‐tracing method. Considering the reflection paths and line of sight path, the channel transfer function expression is derived for each multipath and then the channel impulse response is obtained. On this basis, the investigation and analysis of the large‐scale and small‐scale channel characteristics, including the path loss, shadow fading, delay spread, and angular spread of 4 different mmWave frequency carriers, that is, 28/38/45/60 GHz, are conducted. Simulation results show that the path loss increases as the frequency increases while the time delay spread and angular spread almost keep unchanged. The relevant research results will contribute to the design of future Hyperloop wireless communication systems.

A core transcription regulatory circuitry (CRC) is an interconnected self-regulatory circuitry that is formed by a group of core transcription factors (TFs). These core TFs collectively regulate gene expression by binding not only to their own super enhancers (SEs) but also to the SEs of one another. For most human tissue/cell types, a global view of CRCs and core TFs has not been generated. Here, we identified numerous CRCs using two identification methods and detailed the landscape of the CRCs driven by SEs in large cell/tissue samples. The comprehensive biological analyses, including sequence conservation, CRC activity and genome binding affinity were conducted for common TFs, moderate TFs, and specific TFs, which exhibit different biological features. The local module located from the common CRC network highlighted the essential functions and prognostic performance. The tissue-specific CRC network was highly related to cell identity. Core TFs in tissue-specific CRC networks exhibited disease markers, and had regulatory potential for cancer immunotherapy. Moreover, a user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed, which contained the detailed information of CRCs and core TFs used in this study, as well as other interesting results, such as the most representative CRC, frequency of TFs, and indegree/outdegree of TFs.

With the advantage of programmable electromagnetic properties, Reconfigurable Intelligent Surfaces (RISs) have drawn wide attention from both industry and academia. RIS-assisted communication systems can promote huge wireless channel quality improvement and remarkable coverage enhancement. This paper proposes general pathloss model, radiation pattern and mirror beam effect of 1-bit RIS at sub-6 GHz band. Field trails have been carried out in outdoor and indoor deployment scenarios. The proposed model is validated through extensive simulations and field-trial measurements. In addition, an optimized RIS phase-shit design process for the mirror beam elimination is proposed and validated with simulations. The proposed theoretical model and measurement results can promote future research and application in RIS-assisted communications.

With the development of High-Speed Rail (HSR), countries and individual passengers alike have enjoyed far ranging benefits as a result – economic, social, environment and in added convenience. One of the important parts of HSR construction is the signaling system, where wireless communications play a key role in the transmission of train control data. Channel estimation has a significant impact on the quality of the wireless communication, however, whose performance is degraded due to the fast mobility of HSR. This paper focuses on the channel estimation technology in HSR. We first summarize the key challenges for HSR channel estimation, especially the Inter-Carrier Interference (ICI) faced by Orthogonal Frequency Division Multiplexing (OFDM) systems. Then we provide a comprehensive review of existing pilot-aided channel estimation schemes from three points: channel model, estimation algorithm, joint channel estimation and ICI mitigation schemes. Lastly, we present the challenges of channel estimation for the Orthogonal Time Frequency Space (OTFS) system and Reconfigurable Intelligent Surface (RIS), which are promising techniques for HSR systems in the future sixth Generation (6G) wireless communication.

With the development of deep learning in satellite remote sensing image segmentation, convolutional neural networks have achieved better results than traditional methods. In some full convolutional networks, the number of network layers usually increases to obtain deep features, but the gradient disappearance problem occurs when the number of network layers deepens. Many scholars have obtained multiscale features by using different convolutional calculations. We want to obtain multiscale features in the network structure while obtaining contextual information by other means. This article employs the self-attention mechanism and auxiliary loss network (SAMALNet) structure to solve the above problems. We adopt the self-attention strategy in the atrous spatial pyramid pooling module to extract multiscale features while considering the contextual information. We add auxiliary loss to overcome the gradient disappearance problem. The experimental results of extracting aquaculture areas in the Jiaozhou Bay area of Qingdao from high-resolution GF-2 satellite images show that, in general, SAMALNet achieves better experimental results compared with UPS-Net, SegNet, DeepLabv3, UNet, DeepLabv3+, and PSPNet network structures, including recall 96.34%, precision 95.91%, F1 score 96.12%, and MIoU 92.60%. SAMALNet achieved better results extracting aquaculture area boundaries than the other network structures listed above. The high accuracy of the aquaculture area can provide data support for the rational planning and environmental protection of the coastal aquaculture area and promote more rational usage of the coastal aquaculture area.

The mobile data traffic has been exponentially growing during the last several decades. This was enabled by the densification of the network infrastructure in terms of increased cell density (i.e., Ultra-Dense Network (UDN)) and/or the increased number of active antennas per Access Point (AP) (i.e., massive Multiple-Input Multiple-Output (mMIMO)). However, neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation (6G) wireless communications due to the inter-cell interference and large quality-of-service variations. Cell-Free (CF) mMIMO, which combines the best aspects of UDN and mMIMO, is viewed as a key solution to this issue. In such systems, each User Equipment (UE) is served by a preferred set of surrounding APs cooperatively. In this paper, we provide a survey of the state-of-the-art literature on CF mMIMO. As a starting point, the significance and the basic properties of CF mMIMO are highlighted. We then present the canonical framework to discuss the essential details (i.e., transmission procedure and mathematical system model). Next, we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms. After that, we discuss the practical issues in implementing CF mMIMO and point out the potential future directions. Finally, we conclude this paper with a summary of the key lessons learned in this field.

A new path loss model for high-speed rail (HSR) in the 5G communication system is constructed in this paper. The model is identified to obtain an accurate mathematical representation of path loss multipath propagation in line of sight of HSR scenarios. The grey box modelling utilization of Generalized Reduced Gradient (GRG) and Genetic algorithm (GA) is applied to find the unknown parameters of the constructed path loss model since some uncertainties in obtaining the corresponded parameters are unavoidable to be collected in the field. Both algorithms achieve excellent results in finding the unknown parameter values with RMSE and MAPE evaluation which are converging finally to 2.779 and 1.701 %. The visualization of fitting plots is also presented, and GA provides a better-adjusted agreement with the measurement dataset of HSR. Accordingly, the constructed path loss model is successfully validated since it is capable of following the dynamic characteristic of the original HSR path loss measurement. The path loss model can then be utilized for the future dense deployment of HSR infrastructures for the 5G communication network.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).

2020 marked the first year of the era of 5G commercialization in China and the inception of worldwide research in 6G.Compared with 5G networks, 6G networks will have larger information capacity, higher transmission rate, lower transmission delay, larger number of connected devices, higher spectrum efficiency, higher energy efficiency, and can support applications with higher mobile speed.Therefore, 6G networks will involve many new key technologies and also face many challenges.The background, development, typical applications, performance indicators, network architecture, key technologies and future challenges of 6G were systematically reviewed, which are expected to provide some reference for future 6G networks related research.

Reconfigurable intelligent surfaces (RISs) have attracted significant attention due to their capability in customizing wireless communication environments to improve system performance. In this study, we investigate the performance of an RIS-assisted multi-user multiple-input single-output wireless communication system, considering the impact of channel aging caused by users’ relative movements. In particular, first, we propose a model incorporating the joint effects of channel aging and channel estimation errors to investigate the performance of the RIS-assisted system. Then, we derive novel closed-form expressions for characterizing the sum spectral efficiency with zero-forcing precoding. From our analysis, we unveil that an increase in the temporal channel correlation coefficient, the number of base station antennas, and the received power at the users could help improve system performance. Furthermore, increasing the number of reflecting elements M of the RIS generally yields a good system performance, but with a diminishing return when M is sufficiently large. Finally, simulation results are presented to validate the accuracy of the analytical results.

Firstly, the different characteristics of terahertz waves compared to lower frequency bands were summarized, including the influence of meteorological factors and rough surfaces of materials on THz waves.Next, it was proposed to use ray-tracing (RT) technology to calibrate the three-dimensional (3D) environment model and electromagnetic parameters of materials in the target scenarios through very limited channel measurement data.Then, the reversed parameters were utilized to perform in a similar but more general scenario, instead of a large amount of channel measurement data.Finally, the channel characteristics were analyzed by extraction of the channel key parameters, such as path loss, shadow fading, Rician K-factor, delay spread, angular spread, and doppler parameters in a mobile scenario.Representing two ends of 6G THz use cases from indoor scenario to outdoor scenario, case studies were made for wireless connections on a desktop and vehicular communications, respectively.As for the outdoor scenario, the influences of meteorological factors were additionally considered.The work is of great significance for the design and evaluation of the THz communication system.

Urban rail traffic is an important part of modern transportation infrastructure.As a new generation of mobile communication technology, 5G can provide high data rate and low latency wireless transmission, which helps to improve the efficiency and service quality of urban rail traffic system.Due to the complexity of urban rail traffic scenarios, accurate communication scenario classification, channel characterization and channel models are required to provide theoretical support for the design of urban rail traffic 5G communication systems.The classification of 5G urban rail traffic radio propagation scenarios to support channel measurements and modeling was proposed.Current status of urban rail traffic channel measurements and modeling was shown, and the current challenges were analyzed.The applications of artificial intelligence in channel feature extraction and channel modeling were discussed, and the 5G urban rail traffic channels by considering reconfigurable intelligent surface and unmanned aerial vehicle were analyzed.Finally, the research on 5G urban rail traffic channel modeling at millimeter wave frequency band was described.

Abstract Background Advanced non‐small cell lung cancer (NSCLC) accounts for a high proportion of lung cancer cases. Targeted therapy improve the survival in these patients, but acquired drug resistance will inevitably occur. If tumor downstaging is achieved after targeted therapy, could surgical resection before drug resistance improve clinical benefits for patients with advanced NSCLC? Here, we conducted a clinical trial showing that for patients with advanced driver gene mutant NSCLC who did not progress after targeted therapy, salvage surgery (SS) could improve progression‐free survival (PFS). Herein, we retrospectively reviewed our former clinical trial and thoracic cancer database in our medical institutions. Methods We identified patients with advanced driver gene mutant NSCLC treated with targeted therapy plus SS or targeted therapy alone in our former clinical trial and our thoracic cancer database from July 2016 to July 2019. PFS was compared between the targeted therapy plus SS group and the targeted therapy only group using the log‐rank test. Results We identified 73 patients with driver gene mutant NSCLC who were treated with targeted therapy and 18 treated with targeted therapy plus SS.Among the 18 patients treated with targeted therapy plus SS, there were no obvious perioperative complications and deaths. Targeted therapy followed by SS resulted in a significantly longer PFS compared with targeted therapy alone (23.4 months VS 12.9 months, p = 0.0004). Conclusions Salvage surgery after tumor downstaging is a promising therapeutic strategy for some patients with advanced (stage IIIB–IV) NSCLC and may offer a new therapeutic option for multidisciplinary comprehensive treatment of lung cancer.

Abstract The OFDM system with multiple transceiver antennas is attractive to increase the spectrum or energy efficiency. Depending on the channel state information (CSI), precoding techniques offer array and multiplexing gain in such systems. However, if terminal equipment is fast moving, the CSI feedback would be outdated when it is applied to data transmission. This paper proposes a novel channel prediction method applicable for high‐speed environment. Benefiting from the proposed structure, this method greatly improves the prediction accuracy, and meanwhile retains the low feedback overhead. Numerical results show that the performance of the proposed method outperforms conventional precoding methods in high‐speed scenarios.

Abstract In November 2019, the GaoFen‐7(GF‐7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high‐precision long‐range three‐dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U‐Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF‐7 data processing and related research on footprint images.

As a new generation of application infrastructure and strategic emerging technologies, the internet of things (IoT) is an inevitable trend to be integrated into the rapid development of high-speed railways (HSR). This article first introduces the concept and key technologies of the HSR IoT, and then analyzes the HSR’s requirements for the IoTs. Based on these concepts, a system architecture of the HSR IoT is proposed to expand the in-depth applications of IoT in various fields of the HSR industry. Finally, future development trends of the HSR IoT are discussed. This article is expected to provide some useful insights into the study and evolution of HSR IoT.

An improved fractional-order nonlinear active disturbance rejection speed control (FO-NADRC) method is proposed for hydraulic turbine regulating systems (HTRSs) with a mechanical delay. First, the mathematical model of an HTRS with a mechanical time delay is established. Based on the principle of coordinate transformation, the state space equation of an HTRS with a time delay is transformed into the controllable normative mathematical model. Second, a new nonlinear function is proposed for the extended state observer ESO) that improves the observation accuracy and suppresses the high-frequency oscillation of the HTRS. Third, a new fractional-order state error feedback law (FO-SEFL) is proposed by introducing double adjustable parameters. Fourth, according to the improved ESO and the FO-SEFL, a novel FO-NADRC is designed for the HTRS. Furthermore, the Popov-Lyapunov robust stability analysis method is used to analyze the stability of the hydraulic turbine regulating control systems with mechanical delay. Finally, numerical simulation experiments demonstrate the effectiveness and superiority of the proposed control scheme.

A magnetic field sensor with temperature insensitivity implemented by two fiber Bragg gratings (FBGs) structure is presented and experimentally demonstrated. In the proposed sensing probe, a Terfenol-D piece is bonded with the same type of two FBGs in different direction relative to magnetic field. Two center wavelengths of the notches of FBG filters are mainly determined by the magnetic field, and the two FBGs experience different magnetic-field-induced strain and the same thermal expansion, leading to the linear relationship between the wavelength drift difference and the magnetic field and temperature insensitivity. By monitoring the shift of the wavelength drift difference, the magnetic field measurement for thermal-insensitive interrogation of the magnetic field sensor can be realized. Owing to its compact and temperature independent advantages, the proposed sensor has potential application especially in the unstable direction of magnetic field. Moreover, the sensor can also serve the purpose of temperature measurement by monitoring one of the center wavelengths. The measured sensitivity of the magnetic field intensity of the proposed sensor is 8.77 pm/mT.

Radio access technologies (RATs) are a key topic in railways, enabling them a better service rendering in terms of shorter headways between trains, higher safety levels and higher customer satisfaction. Very often, these railway RATs need a lot of time to be developed, tested and put into service, which implies a lack of efficiency and bottlenecks in the evolution of railway systems. To solve this situation, an emulation platform that considers both the physical layer and the network (this is, able to emulate the end-to-end chain) is envisaged in the EmulRadio4Rail project. Therefore, the physical layer of many railway scenarios must be emulated, which is a remarkable challenge because railways are very diverse. We see Tapped-Delay Lines (TDL) models as the most efficient way for emulation with the available hardware. In the literature, there are many TDL-based channel models for all the scenarios we considered but one: tunnels. Therefore, in order to fill this gap, we develop a novel TDL model for railway tunnels, considering the impact of the rolling stock (both high-speed railway (HSR) and subway trains). The proposed model allows the full characterization of this scenario in terms of power-delay-profile (PDP), Doppler spectrum and fading characteristics.

The development of the Internet of things has led to the deployment and application of various real-time monitoring systems (such as intelligent driving) and status update systems (such as industrial control).For the above systems,the freshness of information is particularly important.If the destination receives outdated information,it may reduce the accuracy and reliability of the system’s decision-making and cause severe security risks.In the future 6G network,the freshness of information will become increasingly important for information update applications.In order to characterize the freshness of information effectively,the academic world has proposed the concept of age of information (AoI).Recently,AoI has quickly become a new performance index and research hotspot of wireless systems.Firstly the research history of information age was reviewed.Then,the key issues of AoI introduced in different network models (such as single-user networks,single-hop networks,and multi-user networks) were analyzed.Finally the future research trend was prospected.

There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.

The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.

MicroRNAs (miRNAs) are small non-coding RNAs, which play important roles in regulating various biological functions. Many available miRNA databases have provided a large number of valuable resources for miRNA investigation. However, not all existing databases provide comprehensive information regarding the transcriptional regulatory regions of miRNAs, especially typical enhancer, super-enhancer (SE), and chromatin accessibility regions. An increasing number of studies have shown that the transcriptional regulatory regions of miRNAs, as well as related single-nucleotide polymorphisms (SNPs) and transcription factors (TFs) have a strong influence on human diseases and biological processes. Here, we developed a comprehensive database for the human transcriptional regulation of miRNAs (TRmir), which is focused on providing a wealth of available resources regarding the transcriptional regulatory regions of miRNAs and annotating their potential roles in the regulation of miRNAs. TRmir contained a total of 5,754,414 typical enhancers/SEs and 1,733,966 chromatin accessibility regions associated with 1,684 human miRNAs. These regions were identified from over 900 human H3K27ac ChIP-seq, ATAC-seq, and DNase-seq samples. Furthermore, TRmir provided detailed (epi)genetic information about the transcriptional regulatory regions of miRNAs, including TFs, common SNPs, risk SNPs, linkage disequilibrium (LD) SNPs, expression quantitative trait loci (eQTLs), 3D chromatin interactions, and methylation sites, especially supporting the display of TF binding sites in the regulatory regions of over 7,000 TF ChIP-seq samples. In addition, TRmir integrated miRNA expression and related disease information, supporting extensive pathway analysis. TRmir is a powerful platform that offers comprehensive information about the transcriptional regulation of miRNAs for users and provides detailed annotations of regulatory regions. TRmir is free for academic users and can be accessed at http://bio.liclab.net/trmir/index.html.

The study of population spatialization has provided important basic data for urban planning, development, environment and other issues. With the development of urbanization, urban residential buildings are getting higher and higher, and the difference between urban and rural population density is getting larger and larger. At present, most population spatial studies adopt the grid scale, and the population in buildings is evenly divided into various grids, which will lead to the neglect of the population distribution in vertical space, and the authenticity is not strong. In order to improve the accuracy of the population distribution, this paper studied the spatial distribution of population at the building scale, combined the digital surface model (DSM) and the digital elevation model (DEM) to calculate the floor of buildings, and proposed a new index based on the total floor area of residential buildings, called residential population index (RPI). RPI is directly related to the number of people a building can accommodate, so it can effectively estimate the population of both urban and rural areas even if the structure of urban and rural buildings is very different. In addition, this paper combined remote sensing monitoring data with geographic big data and adopted principal component regression (PCR) method to construct RPI prediction model to obtain building-scale population distribution data of Qingdao in 2018, providing ideas for population spatialization research. Through field sampling survey and overall assessment, the results were basically consistent with the actual residential situation. The average error with field survey samples is 14.5%. The R2 is 0.643 and the urbanization rate is 69.7%, which are all higher than WorldPop data set. Therefore, this method can reflect the specific distribution of urban resident population, enhance the heterogeneity and complexity of population distribution, and the estimated results have important reference significance for urban management, urban resource allocation, environmental protection and other fields.

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.

Green tide is a harmful marine ecological phenomenon caused by the explosive proliferation or high aggregation of some macroalgae, and can cause significant impacts on ecological environments and economies. An effective emergency disposal plan can significantly improve disposal capacity and reduce total costs. At present, the formulation of emergency disposal plans for green tide disasters usually depends on subjective experience. The primary purpose of this paper is to develop a decision-support model based on intelligent algorithms to optimize the type and number of resources when making emergency disposal plans so as to improve the reliability and efficiency of decision making. In order to simulate the decision-making environment more realistically, the drift motion of green tide is considered in this model. Two intelligent algorithms, the Genetic Algorithm (GA) and the improved Non-Dominated Sorting Genetic Algorithm-II (IMNSGA-II), are used to solve the model and find appropriate emergency disposal plans. Finally, a case study on the green tide disaster that occurred in Qingdao (Yellow Sea, China) is conducted to demonstrate the effectiveness and optimization of the proposed model. Through the model proposed in this paper, the overall response time and cost can be reduced in green tide disaster emergency operations.

Ginsenoside Rg3 is a steroidal saponin isolated from Panax ginseng. Previous studies have shown that Rg3 treatment downregulates the activity of rapamycin complex 1 (mTORC1) activity and inhibits the growth of cancer cells. However, the inhibitory effect of Rg3 on cancer cells is associated with high concentrations of Rg3 that are difficult to achieve in vivo. The human cervix adenocarcinoma HeLa cells were treated with Rg3. The protein levels of AMP-activated protein kinase alpha (AMPKα), protein kinase B(Akt), ribosomal S6 protein(S6), and Erk were determined by immunoblotting analyses. We used a fluorescent probe to detect reactive oxygen species (ROS) production in living cells. The oxygen consumption rate (OCR) was examined by the Seahorse Extracellular Flux Analyzer. The content of adenosine triphosphate (ATP) was measured by ATPlite kit and Mitotracker was applied to detect the mitochondria. We showed that at lower concentrations, Rg3 activates mTORC1 independent of AKT and AMP-activated protein kinase (AMPK). Rg3 promotes mitochondrial biogenesis and function, increases the oxygen consumption of mitochondria and the content of ATP. This effect is in contrast to that of high concentrations of Rg3, which inhibits cell growth. These findings demonstrate a pro-growth activity of Rg3 that acts through mTORC1 and mitochondrial biogenesis and suggest a dose-dependent effect of Rg3 on tumor cell growth.

In recent years, with the spectrum resources in low frequency bands becoming increasingly insufficient, the carrier frequency of wireless communication has started its evolution to the Terahertz (THz) frequency band, and meanwhile the THz communication system has become one of the research hot spots. In this paper, we investigate the problem of concurrent transmission scheduling for THz wireless backhaul network. To increase the weighted sum of completed flows with their quality of service (QoS) requirements satisfied in THz wireless backhaul network, we propose an algorithm of QoS-aware bandwidth allocation and concurrent scheduling (IHQB). Considering the priority of the flow, the reciprocal of the priority is taken as the weight of the flow. In this algorithm, QoS awareness and bandwidth allocation are exploited to achieve more successfully scheduled flows and higher network throughput. The concurrent scheduling with the maximum independent set (MIS) is allocated to different frequency bands. And the mentioned MIS is obtained from the conflict graph which has been established under the conditions of half-duplex and interference threshold. Simulation results show that IHQB performs better than other algorithms in terms of the weighted sum of completed flows and system throughput, especially compared with STDMA.

Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.

The local connection characteristics of convolutional neural network (CNN) are linked with the local spatial correlation of image pixels for water depth retrieval in this article. The method has greater advantages and higher precision than traditional retrieval methods. Traditional remote sensing empirical models require manual extraction of retrieval factors and the process is complex. This article proposes a model based on CNN, which uses different remote sensing images in four spectral bands, red, green, blue, and near-infrared, to retrieve the water depth. In general, CNN is mostly used for image recognition and classification tasks, which can make full use of the local spatial correlation between pixels. The method in this article exploits this feature of CNN for water depth retrieval, taking into consideration the nonlinear relationship between the radiance value and water depth value from adjacent and central pixels. In this article, remote sensing image data, measured water depth data, and lidar sounding data are used as input data to build the model. Then, the retrieval error is analyzed and the parameters are adjusted to further optimize the model. Quantitative analysis and experimental results show that the accuracy of the CNN model in shallow sea areas retrieval is improved by more than 50%. The mean absolute error can reach within 0.8 m. Finally, the model is shown to be highly portable and capable of retrieving water depth data with resolution equal to the spatial resolution of the remote sensing image using only a small amount of input water depth data.

Free-space optical (FSO) links are considered as a cost-efficient way to fill the backhaul/fronthaul connectivity gap between millimeter wave (mmWave) access networks and optical fiber based central networks. In this paper, we investigate the end-to-end performance of dual-hop mixed FSO/mmWave systems to address this combined use. The FSO link is modeled as a Gamma-Gamma fading channel using both heterodyne detection and indirect modulation/direct detection with pointing error impairments, while the mmWave link experiences the fluctuating two-ray fading. Under the assumption of both amplify-and-forward and decode-and-forward relaying, we derive novel closed-form expressions for the outage probability, average bit error probability (BER), ergodic capacity, effective capacity in terms of bivariate Fox's H-functions. Additionally, we discuss the diversity gain and provide other important engineering insights based on the high signal-to-noise-ratio analysis of the outage probability and the average BER. Finally, all our analytical results are verified using Monte Carlo simulations.

The enhancement of the carrying capacity of high-speed railway and the acceleration of train speed lead to the proliferation of signal interference in the communication network, which leads to the degradation of network performance and user service quality. To address the issue, we first classify the cell users in high-speed railway wireless communication environment based on Fuzzy C-Means algorithm and user mobility prediction model, and divide communicating users on trains into cell center users and edge users. Then, differential power distribution schemes are implemented for center users and edge users according to the classification results. Finally, the Max-SINR based interference alignment algorithm is used to realize the interference management. Simulation results show that the proposed algorithm fully takes into account the mobility of high-speed train users and effectively manages interference, which improves the high-speed railway wireless communication network performance significantly.

Automatic Identification System (AIS) is an automatic tracking system based on reports provided by AIS transponders, which can report the static and dynamic information of the ship, such as name, call sign, position and speed etc. However, AIS can be manually turned off, making it impossible to find the trajectory of the ship when AIS is turned off, which results in difficulties in maritime supervision. This article uses shore-based radar trajectory data to present a new method of asynchronous trajectory matching based on piecewise space-time constraints (PTSCTM), which is used to reconstruct AIS trajectory and discover the abnormal behavior of ships. The Ramer-Douglas-Peucker (RDP) algorithm is used to extract feature points of the trajectory, and the trajectory is segmented according to feature points. The method uses space-time constraints to determine candidate trajectory matching points in each trajectory segment and calculates the optimal matching point from the candidate set through the space and time distance. The method asynchronously calculates the similarity of trajectory segments from multi-source trajectories and calculates the trajectory similarity to achieve multi-source trajectory matching. The measured data show that the model can effectively measure the similarity of multi-source trajectories to find the optimal radar trajectory as a matching result when AIS is turned off, and the calculation accuracy and efficiency of the model is higher than the existing methods. According to the asynchronous trajectory matching method, the monitoring method of staying ships in the Qingdao lancelet reserve area is proposed. The result shows that many ships stayed in the area through radar signals when AIS was turned off. The result can provide auxiliary support for maritime supervisory and legal departments, which helps to strengthen maritime ship supervision, detect maritime illegal crimes and protect the marine environment.