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Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliantly trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the proportion of active violations to 0%, demonstrating its effectiveness., Comment: 14 pages, 17 figures

In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly embedding the color signals into the geometry representation. Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task. Our key contribution is a 2D-3D cross-modality learning algorithm that embeds the deep features learned from color signals to the geometry representation. With our designed 2D-3D projection module, the pixel features in a square region centered at correspondences perceived from images are effectively correlated with point clouds. In this way, the overlapped regions can be inferred not only from point cloud but also from the texture appearances. Adding color is non-trivial. We compare against a variety of baselines designed for adding color to 3D, such as exhaustively adding per-pixel features or RGB values in an implicit manner. We leverage Predator [25] as the baseline method and incorporate our proposed module onto it. To validate the effectiveness of 2D features, we ablate different 2D pre-trained networks and show a positive correlation between the pre-trained weights and the task performance. Our experimental results indicate a significant improvement of 6.5% registration recall over the baseline method on the 3DLoMatch benchmark. We additionally evaluate our approach on SOTA methods and observe consistent improvements, such as an improvement of 2.4% registration recall over GeoTransformer as well as 3.5% over CoFiNet. Our study reveals a significant advantages of correlating explicit deep color features to the point cloud in the registration task., Comment: accepted to ECCV2022; code at https://github.com/Gardlin/PCR-CG

In the traditional food safety traceability systems, there are many problems such as lack of shared data, centralization and islanding of information storage. Since blockchain systems have the characteristics of decentralization, tamper-resistance, and trustworthiness, this paper proposes a food traceability system based on Hyperledger Fabric blockchain.Then Hyperledger Fabric was described in detail. The technical principles and characteristics of Hyperledger Fabric blockchain technology was clarified. Comprehensive practical use of food safety traceability system and detailed design of food traceability system based on Hyperledger Fabric blockchain were expounded. Finally, the food traceability system based on the Hyperledger Fabric blockchain was set up，and the related functional test and performance test of the system verified the feasibility and effectiveness of the plan properly.

The planar integration design of the antenna system for soldier terminals is presented in this paper based on the requirements of the miniaturization and high integration. A four-antenna system including recieving and transmitting Ad-Hoc network antennas, satellite navigation and communication antennas is proposed to solve the 3D packaging and low-integration problems. The conventional 3D Ad-Hoc network antennas and circular polarized helix antennas are replaced by planar printed monopoles and horizontal dipoles with linear polarization, respectively. Simulated and Measured results show that the planar four-antenna system realizes onmidirectional radiation in horizontal plane in Ad-Hoc network frequency band and upper space coverage in satellite navigation and communication band. This antenna system can be applied in soldier terminals to improve its integration.

High loadings of nitrate (NO3-) in the aerosol over China significantly exacerbate the air quality and pose a great threat to ecosystem safety through dry–wet deposition. Unfortunately, limited ground-level observation data make it challenging to fully reflect the spatial pattern of NO3- levels across China. Until now, long-term monthly particulate NO3- datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3- dataset at 0.25∘ resolution over China during 2005–2015 was developed by assimilating surface observations, satellite products, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient-boosting decision tree (GBDT), and extreme gradient-boosting (XGBoost) methods. The new developed product featured an excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 µg N m−3) and mean absolute error (MAE: 0.81 µg N m−3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scales. Moreover, the dataset displayed good agreement with (R2=0.85, RMSE=0.74 µg N m−3, and MAE=0.55 µg N m−3) some unlearned data collected from previous studies. The spatiotemporal variations in the developed product were also shown. The estimated NO3- concentration showed the highest value in the North China Plain (NCP) (3.55±1.25 µg N m−3); followed by the Yangtze River Delta (YRD) (2.56±1.12 µg N m−3), Pearl River Delta (PRD) (1.68±0.81 µg N m−3), and Sichuan Basin (1.53±0.63 µg N m−3), and the lowest one in the Tibetan Plateau (0.42±0.25 µg N m−3). The higher ambient NO3- concentrations in the NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3- pollution in the Sichuan Basin. The lowest ambient NO3- concentration in the Tibetan Plateau was contributed by the scarce anthropogenic emission and favourable meteorological factors (e.g. high wind speed). In addition, the ambient NO3- concentration showed a marked increasing tendency of 0.10 µgNm-3yr-1 during 2005–2014 (p), while it decreased sharply from 2014 to 2015 at a rate of −0.40 µgNm-3yr-1 (p). The ambient NO3- levels in Beijing–Tianjin–Hebei (BTH), YRD, and PRD displayed gradual increases at a rate of 0.20, 0.11, and 0.05 µgNm-3yr-1 (p) during 2005–2013, respectively. The gradual increases in NO3- concentrations in these regions from 2005 to 2013 were due to the fact that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase in energy consumption. Afterwards, the government further strengthened these emission reduction measures and thus caused the dramatic decreases in NO3- concentrations in these regions from 2013 to 2015 (p). The long-term NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005–2015 are open access at https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c).

High loadings of nitrate (NO3−) in the aerosol over China significantly exacerbates the air quality and poses a great threaten on ecosystem safety through dry/wet deposition. Unfortunately, limited ground-level observation data makes it challenging to fully reflect the spatial pattern of NO3− level across China. Up to date, the long-term monthly NO3− datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating surface observation, satellite product, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). The new developed product featured excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 μg/m3) and mean absolute error (MAE: 0.81 μg/m3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scale. Moreover, the dataset displayed good agreement with (R2 = 0.85, RMSE = 0.74 μg/m3, and MAE = 0.55 μg/m3) some unlearning data collected from previous studies. The spatiotemporal variations of the developed product were also shown. The estimated NO3− concentration showed the highest value in North China Plain (NCP) (3.55 ± 1.25 μg/m3), followed by Yangtze River Delta (YRD (2.56 ± 1.12  g/m3)), Pearl River Delta (PRD (1.68 ± 0.81 μg/m3)), Sichuan Basin (1.53 ± 0.63 μg/m3), and the lowest one in Tibetan Plateau (0.42 ± 0.25 μg/m3). The higher ambient NO3− concentrations in NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3− pollution in Sichuan Basin. The lowest ambient NO3− concentration in Tibetan Plateau was contributed by the scarce anthropogenic emission and favorable meteorological factors (e.g., high wind speed). In addition, the ambient NO3− concentration showed marked increasing tendency of 0.10 μg/m3/year during 2005–2014 (p 3/year (p 3− levels in Beijing-Tianjin-Hebei (BTH), YRD, and PRD displayed gradual increases at the speed of 0.13, 0.08, and 0.03 μg/m3/year (p 3− concentrations in these regions from 2005 to 2014 were due to that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase of energy consumption. Afterwards, the government further strengthened these emission reduction measures, and thus caused the dramatic decreases of NO3− concentrations in these regions from 2014 to 2015 (p 3− dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3− levels over China during 2005–2015 are open access in https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c).

We developed a two-stage model called the random-forest–generalised additive model (RF–GAM), based on satellite data, meteorological factors, and other geographical covariates, to predict the surface 8 h O3 concentrations across the remote Tibetan Plateau. The 10-fold cross-validation result suggested that RF–GAM showed excellent performance, with the highest R2 value (0.76) and lowest root-mean-square error (RMSE) (14.41 µg m−3), compared with other seven machine-learning models. The predictive performance of RF–GAM showed significant seasonal discrepancy, with the highest R2 value observed in summer (0.74), followed by winter (0.69) and autumn (0.67), and the lowest one in spring (0.64). Additionally, the unlearning ground-observed O3 data collected from open-access websites were applied to test the transferring ability of the novel model and confirmed that the model was robust in predicting the surface 8 h O3 concentration during other periods (R2=0.67, RMSE = 25.68 µg m−3). RF–GAM was then used to predict the daily 8 h O3 level over the Tibetan Plateau during 2005–2018 for the first time. It was found that the estimated O3 concentration displayed a slow increase, from 64.74±8.30 µg m−3 to 66.45±8.67 µg m−3 from 2005 to 2015, whereas it decreased from the peak to 65.87±8.52 µg m−3 during 2015–2018. Besides this, the estimated 8 h O3 concentrations exhibited notable spatial variation, with the highest values in some cities of the northern Tibetan Plateau, such as Huangnan (73.48±4.53 µg m−3) and Hainan (72.24±5.34 µg m−3), followed by the cities in the central region, including Lhasa (65.99±7.24 µg m−3) and Shigatse (65.15±6.14 µg m−3), and the lowest O3 concentration occurred in a city of the southeastern Tibetan Plateau called Aba (55.17±12.77 µg m−3). Based on the 8 h O3 critical value (100 µg m−3) provided by the World Health Organization (WHO), we further estimated the annual mean nonattainment days over the Tibetan Plateau. It should be noted that most of the cities on the Tibetan Plateau had excellent air quality, while several cities (e.g. Huangnan, Haidong, and Guoluo) still suffered from more than 40 nonattainment days each year, which should be given more attention in order to alleviate local O3 pollution. The results shown herein confirm that the novel hybrid model improves the prediction accuracy and can be applied to assess the potential health risk, particularly in remote regions with few monitoring sites.

We developed a two-stage model named random forest-generalized additive model (RF-GAM) based on satellite data, meteorological factors, and other geographical covariates to predict the surface 8-h O3 concentration across the remote Tibetan Plateau. The 10-fold cross-validation result suggested that RF-GAM showed the excellent performance with the highest R2 value (0.76) and lowest root mean square error (RMSE) (14.41 μg/m3) compared with other seven machine learning models. The predictive performance of RF-GAM model showed significantly seasonal discrepency with the highest R2 value observed in summer (0.74), followed by winter (0.69) and autumn (0.67), and the lowest one in spring (0.64). Additionally, the unlearning ground-observed O3 data collected from open websites were applied to test the transferring ability of the novel model, and confirmed that the model was robust to predict the surface 8-h O3 concentration during other periods (R2 = 0.67, RMSE = 25.68 μg/m3). RF-GAM was then used to predict the daily 8-h O3 level over Tibetan Plateau during 2005–2018 for the first time. It was found that the estimated O3 concentration displayed a slow increase from 64.74 ± 8.30 μg/m3 to 66.45 ± 8.67 μg/m3 2005 through 2015, whereas it decreased from the peak to 65.87 ± 8.52 μg/m3 during 2015–2018. Besides, the estimated 8-h O3 concentrations exhibited notably spatial variation with the highest values in some cities of North Tibetan Plateau such as Huangnan (73.48 ± 4.53 μg/m3) and Hainan (72.24 ± 5.34 μg/m3), followed by the cities in the central region including Lhasa (65.99 ± 7.24 μg/m3) and Shigatse (65.15 ± 6.14 μg/m3), and the lowest one in some cities of Southeast Tibetan Plateau such as Aba (55.17 ± 12.77 μg/m3). Based on the 8-h O3 critical value (100 μg/m3) scheduled by World Health Organization (WHO), we further estimated the annually mean nonattainment days over Tibetan Plateau this period. It should be noted that most of the cities in Tibetan Plateau shared with the excellent air quality, while several cities (e.g., Huangnan, Haidong, and Guoluo) still suffered from more than 40 nonattainment days each year, which should be paid more attention to alleviate local O3 pollution. The result shown herein confirms the novel hybrid model improves the prediction accuracy and can be applied to assess the potential health risk, particularly in the remote regions with sparse monitoring sites.

Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubik's cube++, to pre-train 3D neural networks. Different from the existing context-restoration-based approaches, we adopt a volume-wise transformation for context permutation, which encourages network to better exploit the inherent 3D anatomical information of organs. Compared to the strategy of training from scratch, fine-tuning from the Rubik's cube++ pre-trained weight can achieve better performance in various tasks such as pancreas segmentation and brain tissue segmentation. The experimental results show that our self-supervised learning method can significantly improve the accuracy of 3D deep learning networks on volumetric medical datasets without the use of extra data., Comment: Accepted by MICCAI 2020

The acid deposition has been considered to be a severe environmental issue in China. The pH, electrical conductivity (EC), and the concentrations of the water soluble ions (NO3−, Cl−, Ca2+, K+, F−, NH4+, Mg2+, SO42−, and Na+) in the precipitation samples collected from the 320 cities during 2011–2016 across the whole China were measured. The mean concentrations of F−, NO3− and SO42− were in the order of winter (6.10, 19.44 and 45.74 μeq/L) > spring (3.45, 13.83, and 42.61 μeq/L) > autumn (2.67, 9.73, and 28.85 μeq/L) > summer (2.04, 7.66, and 19.26 μeq/L). The secondary ions (SO42−, NO3− and NH4+), and F− peaked in Yangtze River Delta (YRD) and Sichuan basin (SB). The crustal ions (i.e., Ca2+, Mg2+), Na+, and Cl− showed the highest concentrations in the semi-arid regions and the coastal cities, respectively. The statistical methods confirmed that the mean anthropogenic contribution ratios to SO42−, F−, NO3−, and NH4+ at a national scale were 46.12 %, 71.02 %, 79.10 %, and 82.40 %, respectively. However, Mg2+ (70.51 %), K+ (77.44 %), and Ca2+ (82.17 %) were mostly originated from the crustal source. Both Na+ (70.54 %) and Cl− (60.42 %) were closely linked to the sea-salt aerosols. On the basis of the stepwise regression (SR) analysis, it was proposed that most of the secondary ions and F− were closely related to gross industrial production (GIP), total energy consumption (TEC), vehicle ownership, and N fertilizer use, but the crustal ions (Ca2+ and K+) were mainly controlled by the dust events. The influence of dust days, air temperature, and wind speed on ions increased from Southeast China (SEC) to Central China, and then to Northwest China (NWC), whereas the influence of socioeconomic factors on acid ions (SO42− and NO3−) displayed the higher value in East China.

Taking an asymmetric single-tower cable-stayed bridge as the research object, this paper investigates the effects of seismic mitigation measures for the bridge under the earthquake. Three kinds of damping measures including lead-rubber bearings, viscous dampers and a hybrid seismic mitigation system are suggested. Firstly, parametric analyses have been performed so as to obtain the optimal parameters of the lead-rubber bearings and the damping coefficients of the viscous dampers for the bridge. Then, the hybrid seismic mitigation system combined by lead-rubber bearings and viscous dampers are considered. The effects of these seismic mitigation measures on the bridge have been compared and the results show that these three measures can effectively reduce the seismic responses of the bridge, but the hybrid seismic mitigation system has a better performance than the other two.

In order to further improve the efficiency of flapping wings, inspired by bird flight, a new compliant joint structure is proposed, which is easy to manufacture. The joint has the capacity of passive bending, twisting and sweeping under the aerodynamic effect of the flapping wing. A multi-objective optimization program for the compliant joint is developed, which is combined with the measured joint load. The optimized compliant joint has the effect of asymmetric passive deformation in a flapping cycle. The model wings with compliant joints are studied in the windless flapping experiment, wind tunnel experiment and free flight test. The experimental results show that the model wings have a significant advantage of power consumption compared with the original wings, and also have an advantage in the aerodynamic performance at low flight velocity and low angle of attack.

By reviewing a series of achievements that professional degree education has obtained in China, the author think that China's professional degree education has entered into a new period of strategic transition for comprehensively improving the quality as the core task. By the end of 2012 to early 2013,the Research Center for Graduate Education of Beijing Institute of Technology did an investigation on students’ satisfaction with postgraduate education. Results revealed that the quality of the professional degree education needs to move forward a single step. Improving the curriculum rationality, Changing teaching methods, Adding practice base construction and strengthening the construction of the tutor team are the new horizon for future reform.

Cdc42-interacting protein-4 (CIP4) is an F-BAR (Fer/CIP4 and Bin, amphiphysin, Rvs) family member that regulates membrane deformation and endocytosis, playing a key role in extracellular matrix (ECM) deposition and invasion of cancer cells. These processes are analogous to those observed during the initial epithelial-mesenchymal transition (EMT) of renal tubular epithelial cells. The role of CIP4 in renal tubular EMT and renal tubulointerstitial fibrosis was investigated over the course of the current study, demonstrating that the expression of CIP4 increased in the tubular epithelia of 5/6-nephrectomized rats and TGF-β1 treated HK-2 cells. Endogenous CIP4 evidenced punctate localization throughout the cytosol, with elevated levels observed in the perinuclear region of HK-2 cells. Subsequent to TGF-β1 treatment, CIP4 expression increased, forming clusters at the cell periphery that gradually redistributed into the cytoplasm. Simultaneously, EMT induction in cells was confirmed by the prevalence of morphological changes, loss of E-cadherin, increase in α-SMA expression, and secretion of fibronectin. Overexpression of CIP4 promoted characteristics similar to those commonly observed in EMT, and small interfering RNA (siRNA) molecules capable of CIP4 knockdown were used to demonstrate reversed EMT. Cumulatively, results of the current study suggest that CIP4 promotes TGF-β1-induced EMT in tubular epithelial cells. Through this mechanism, CIP4 is capable of inducing ECM deposition and exacerbating progressive fibrosis in chronic renal failure.

According to statistics from 2011 to 2013, in China, approximately 60,000 people were killed in road traffic accidents every year. And about 4% of these accidents have vehicle safety issues, such as brake failure, tire burst. To investigate association between vehicle safety performance and traffic accident casualty risk, 1302 traffic accident samples with detailed information were collected and transformed into data. Base on logistic regression analysis, the quantitative function relationship between vehicle status and driver injury severity was built. This research method represents an important step in the methodological evaluation of vehicle data with respect to traffic safety.

This study investigates the relationships among product/process modularity, organizational learning practices, and mass customization (MC) capability. Drawing on organizational learning theory, we propose that organizational learning practices help to embed the knowledge gained from modular design practices into organizational processes, enhancing MC capability. We empirically test the mediating effects of two organizational learning practices - customization knowledge utilization and business process improvement - on the relationship between product/process modularity and MC capability, using data collected from Chinese manufacturers. We find strong support for the mediating role of organizational learning practices in the relationship between modularity and MC capability. Our findings have implications for management strategies and point to directions for further research in this area.

Research on WLAN-3GPP interworking is regarded as a case of implementation of heterogeneous networks which are the tendency of future mobile communication systems. The paper describes a WLAN-3GPP interworking system, describes the issues existing in current networking selection procedures and analyses the disadvantages and advantages of the current solutions in detail. The efficiency of network selection determines the performance of system accessing, especially handover. Finally, two schemes are proposed to improve the procedure to meet the requirements of seamless handover when performing vertical handover between WLAN and UMTS.

Summary Organogenesis and tissue development occur through sequential stepwise processes leading to increased lineage restriction and loss of pluripotency. An exception to this appears in the adult human breast, where rare variant epithelial cells exhibit pluripotency and multilineage differentiation potential when removed from the signals of their native microenvironment. This phenomenon provides a unique opportunity to study mechanisms that lead to cellular reprogramming and lineage plasticity in real time. Here, we show that primary human mammary epithelial cells (HMECs) lose expression of differentiated mammary epithelial markers in a manner dependent on paracrine factors and epigenetic regulation. Furthermore, we demonstrate that HMEC reprogramming is dependent on gene silencing by the DNA methyltransferase DNMT3A and loss of histone transcriptional marks following downregulation of the methyltransferase DOT1L. These results demonstrate that lineage commitment in adult tissues is context dependent and highlight the plasticity of somatic cells when removed from their native tissue microenvironment., Graphical Abstract, Highlights • vHMECs arise through epigenetic modification of pre-existing human breast cells • DNA methylation by DNMT3a is required for vHMEC formation • Loss of DOT1L and active histone methylation marks accelerates vHMEC formation • Loss of mammary identity requires changes in both DNA and histone methylation, In this article, Kuperwasser and colleagues show that, upon removal from their native microenvironment, rare pre-existing human mammary epithelial cells undergo stochastic epigenetic reprogramming and lose mammary lineage identity. This dedifferentiation occurs through addition of repressive promoter methylation marks by DNMT3a and removal of active histone methylation marks through DOT1L loss, which together suppress mammary lineage commitment gene expression.