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Hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) has been widely reported and poses a global threat. However, the comprehensive genetic structure of ST11-KL64 hv-CRKP and the possible evolutionary mechanisms from a genetic structure perspective of this high-risk clone remain unclear. Here, a bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP isolate was obtained from a human bloodstream infection (BSI). Whole-genome sequencing and bioinformatics analyses revealed that it contained a fusion plasmid, pKPTCM2–1. pKPTCM2–1 is a conjugative plasmid composed of an oriT -positive pLVPK-like virulence plasmid and a type IV secretion system-produced bla NDM-1 -bearing IncX3 plasmid mediated by IS 26 -based co-integration. This progress generated 8-bp target site duplications (TGAAAACC) on both sides. The fusion plasmid possessed self-transferability and could be transferred to bla KPC-2 -harboring ST11-KL64 CRKP to form the ST11-KL64 hv-CRKP clone. The pLVPK-like-positive ST11-KL64 strain exhibited virulence levels similar to those of the typical hypervirulent K. pneumoniae NTUH-2044. The mutation, Tet(A) (A276S), which was believed to lead to tigecycline resistance was observed. Overall, this high-risk clone has emerged as a tremendous threat in fatal BSIs and thus, targeted surveillance is an urgent need to contain the hv-CRKP clones. • One bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP in human bloodstream infection. • oriT -positive pLVPK-like virulence plasmid and bla NDM-1 -bearing IncX3 plasmid fusion. • One mutation of Tet(A) (A276S) maybe lead to tigecycline resistance. [ABSTRACT FROM AUTHOR]

An efficient rhodium-catalyzed direct C-H activation for the synthesis of isoquinolone with 1,4,2-dioxazol-5-ones as oxidizing directing groups have been developed, which featured mild reaction conditions, short reaction time and satisfactory yield. An efficient approach to isoquinolone have been developed, which featured in completing expeditiously under mild conditions with satisfactory yield. Image 1 [ABSTRACT FROM AUTHOR]

Accurate prediction of recoverable train delay can support the train dispatchers' decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections' influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively. [ABSTRACT FROM AUTHOR]

Bus rapid transit system is designed to provide high-quality and cost-efficient passenger transportation services. In order to achieve this design objective, effective scheduling strategies are required. This research aims at improving the operation efficiency and service quality of a BRT system through integrated optimization of its service headways and stop-skipping strategy. Based on cost analysis for both passengers and operation agencies, an optimization model is established. A genetic algorithms based algorithm and an application-oriented solution method are developed. Beijing BRT Line 2 has been chosen as a case study, and the effectiveness of the optimal headways with stop-skipping services under different demand levels has been analyzed. The results has shown that, at a certain demand level, the proposed operating strategy can be most advantageous for passengers with an accepted increase of operating costs, under which the optimum headway is between 3.5 and 5.5 min for stop-skipping services during the morning peak hour depending on the demand with the provision of stop-skipping services. The effectiveness of the optimal headways with stop-skipping services is compared with those of existing headways and optimal headways without stop-skipping services. The results show that operating strategies under the optimal headways with stop-skipping services outperforms the other two operating strategies with respect to total costs and in-vehicle time for passengers. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present the cluster mass-richness scaling relation calibrated by a weak lensing analysis of ≿ 18 000 galaxy cluster candidates in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). Detected using the 3D-Matched-Filter (MF) cluster-finder of Milkeraitis et al., these cluster candidates span a wide range of masses, from the small group scale up to ~1015M⊙, andredshifts 0.2 ≲ z ≲ <0.9. The total significance of the stacked shear measurement amounts to 54σ. We compare cluster masses determined using weak lensing shear and magnification, finding the measurements in individual richness bins to yield 1σ compatibility, but with magnification estimates biased low. This first direct mass comparison yields important insights for improving the systematics handling of future lensing magnification work. In addition, we confirm analyses that suggest cluster miscentring has an important effect on the observed 3D-MF halo profiles, and we quantify this by fitting for projected cluster centroid offsets, which are typically ~0.4 arcmin. We bin the cluster candidates as a function of redshift, finding similar cluster masses and richness across the full range up to z ~ 0.9. We measure the 3D-MF mass-richness scaling relation M200 = M0 (N200/20)β. We find a normalization M0 ~ (2.7-0.4+0.5) x 1013M⊙, and a logarithmic slope of β ~ 1.4 ± 0.1, both of which are in 1σ agreement with results from the magnification analysis. We find no evidence for a redshift dependence of the normalization. The CFHTLenS 3D-MF cluster catalogue is now available at cfhtlens.org. [ABSTRACT FROM AUTHOR]

A microwave-assisted one-pot synthesis of fluoren-9-one derivatives is reported which tolerates a variety of additional functionalities within the starting materials. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a methodology to estimate and/or update SPF parameters under limited data conditions. [•] An extensive simulation exercise is developed to demonstrate the robustness of the suggested method. [•] Results clearly show the appropriateness of the presented technique. [•] This study contributes to unification of SPF updating process and understanding of comparative aspects of a large number of priors. [ABSTRACT FROM AUTHOR]

Abstract: Problem: This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method: A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. Results: This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions: As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry: The results show the benefits of incorporating prior information when working with limited crash data in road safety studies. [Copyright &y& Elsevier]

• An integrated framework is proposed to predict lane-changing risk. • Driving intentions are recognized using LSTM neural network. • LGBM algorithm achieves a higher lane-changing risk prediction accuracy. • Feature importance analysis is conducted using LGBM classifier. Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to ensure driving safety. However, most previous studies on LC risk prediction did not consider the driver's intention recognition, which made it difficult to guarantee the timeliness and practicability of LC risk prediction. Moreover, the difference in driving risks and its influencing factors between LC to left lane (LCL) and LC to right lane (LCR) have rarely been investigated. To bridge the above research gaps, this study proposes a proactive LC risk prediction framework which integrates the LC intention recognition module and LC risk prediction module. The Long Short-term Memory (LSTM) neural network with time-series input was employed to recognize the driver's LC intention. The Light Gradient Boosting Machine (LGBM) algorithm was then applied to predict the LC risk. Feature importance analysis was lastly conducted to obtain the key features that affect the LC risk. The highD trajectory dataset was used for framework validation. Results show that the recognition accuracy of the driver's LCL, LCR and lane-keeping (LK) intentions based on the proposed LSTM model are 97%, 96% and 97%, respectively. Meanwhile, the LGBM algorithm outperforms other machine learning algorithms in LC risk prediction. The results from feature importance analysis show that the interaction characteristics of the LC vehicle and its preceding vehicle in the current lane have the greatest impact on the LC risk. The proposed framework could potentially be implemented in advanced driver-assistance system (ADAS) or autonomous driving system for improved driving safety. [ABSTRACT FROM AUTHOR]

We use weak gravitational lensing to analyse the dark matter haloes around satellite galaxies in galaxy groups in the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) data set. This data set is derived from the Canada–France–Hawaii Telescope Legacy Survey Wide survey, and encompasses 154 deg2 of high-quality shape data. Using the photometric redshifts, we divide the sample of lens galaxies with stellar masses in the range 109–1010.5 M⊙ into those likely to lie in high-density environments (HDE) and those likely to lie in low-density environments (LDE). Through comparison with galaxy catalogues extracted from the Millennium Simulation, we show that the sample of HDE galaxies should primarily (∼61 per cent) consist of satellite galaxies in groups, while the sample of LDE galaxies should consist of mostly (∼87 per cent) non-satellite (field and central) galaxies. Comparing the lensing signals around samples of HDE and LDE galaxies matched in stellar mass, the lensing signal around HDE galaxies clearly shows a positive contribution from their host groups on their lensing signals at radii of ∼500–1000 kpc, the typical separation between satellites and group centres. More importantly, the subhaloes of HDE galaxies are less massive than those around LDE galaxies by a factor of 0.65 ± 0.12, significant at the 2.9σ level. A natural explanation is that the haloes of satellite galaxies are stripped through tidal effects in the group environment. Our results are consistent with a typical tidal truncation radius of ∼40 kpc. [ABSTRACT FROM AUTHOR]

The Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) comprises deep multicolour (u*g′r′i′z′) photometry spanning 154 deg2, with accurate photometric redshifts and shape measurements. We demonstrate that the redshift probability distribution function summed over galaxies provides an accurate representation of the galaxy redshift distribution accounting for random and catastrophic errors for galaxies with best-fitting photometric redshifts zp < 1.3.We present cosmological constraints using tomographic weak gravitational lensing by large-scale structure. We use two broad redshift bins 0.5 < zp ≤ 0.85 and 0.85 < zp ≤ 1.3 free of intrinsic alignment contamination, and measure the shear correlation function on angular scales in the range ∼1–40 arcmin. We show that the problematic redshift scaling of the shear signal, found in previous Canada–France–Hawaii Telescope Legacy Survey data analyses, does not affect the CFHTLenS data. For a flat Λ cold dark matter model and a fixed matter density Ωm = 0.27, we find the normalization of the matter power spectrum σ8 = 0.771 ± 0.041. When combined with cosmic microwave background data (Wilkinson Microwave Anisotropy Probe 7-year results), baryon acoustic oscillation data (BOSS) and a prior on the Hubble constant from the Hubble Space Telescope distance ladder, we find that CFHTLenS improves the precision of the fully marginalized parameter estimates by an average factor of 1.5-2. Combining our results with the above cosmological probes, we find Ωm = 0.2762 ± 0.0074 and σ8 = 0.802 ± 0.013. [ABSTRACT FROM AUTHOR]

Dark energy may be the first sign of new fundamental physics in the Universe, taking either a physical form or revealing a correction to Einsteinian gravity. Weak gravitational lensing and galaxy peculiar velocities provide complementary probes of general relativity, and in combination allow us to test modified theories of gravity in a unique way. We perform such an analysis by combining measurements of cosmic shear tomography from the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) with the growth of structure from the WiggleZ Dark Energy Survey and the Six-degree-Field Galaxy Survey, producing the strongest existing joint constraints on the metric potentials that describe general theories of gravity. For scale-independent modifications to the metric potentials which evolve linearly with the effective dark energy density, we find present-day cosmological deviations in the Newtonian potential and curvature potential from the prediction of general relativity to be ΔΨ/Ψ = 0.05 ± 0.25 and ΔΦ/Φ = −0.05 ± 0.3, respectively (68 per cent confidence limits). [ABSTRACT FROM PUBLISHER]

ABSTRACT We present the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) that accurately determines a weak gravitational lensing signal from the full 154 deg2 of deep multicolour data obtained by the CFHT Legacy Survey. Weak gravitational lensing by large-scale structure is widely recognized as one of the most powerful but technically challenging probes of cosmology. We outline the CFHTLenS analysis pipeline, describing how and why every step of the chain from the raw pixel data to the lensing shear and photometric redshift measurement has been revised and improved compared to previous analyses of a subset of the same data. We present a novel method to identify data which contributes a non-negligible contamination to our sample and quantify the required level of calibration for the survey. Through a series of cosmology-insensitive tests we demonstrate the robustness of the resulting cosmic shear signal, presenting a science-ready shear and photometric redshift catalogue for future exploitation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision at highway-railway grade crossings. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model that assumes the effect of various exogenous factors on injury severity to be constant across all accidents. The current research effort attempts to address this issue by applying an innovative latent segmentation based ordered logit model to evaluate the effects of various factors on the injury severity of vehicle drivers. In this model, the highway-railway crossings are assigned probabilistically to different segments based on their attributes with a separate injury severity component for each segment. The validity and strength of the formulated collision consequence model is tested using the US Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these highway railway crossings from 1997 to 2006. The model estimation results clearly highlight the existence of risk segmentation within the affected grade crossing population by the presence of active warning devices, presence of permanent structure near the crossing and roadway type. The key factors influencing injury severity include driver age, time of the accident, presence of snow and/or rain, vehicle role in the crash and motorist action prior to the crash. [Copyright &y& Elsevier]

Abstract: Background: Buprenorphine/naloxone (BUP/NX) is not licenced for use in China or Thailand and there was little clinical experience with this drug combination in these countries at the inception of HIV Prevention Trial Network (HPTN) 058, a randomized trial comparing risk reduction counselling combined with either short-term or long-term medication assisted treatment with BUP/NX to prevent HIV infection and death amongst opioid-dependent injectors. Methods: We conducted a safety phase that included the first 50 subjects enrolled at each of the three initial study sites (N =150). Clinical and laboratory assessments were conducted at baseline and weekly for the first 4 weeks. Changes in laboratory parameters were estimated with random effects models. Results: BUP/NX was well tolerated by study subjects and opioid withdrawal scores decreased substantially during the 3-day induction. Two participants experienced grade 3 clinical adverse events, which were categorized as probably not related to the study drug. Grade 2 or 3 increases in alanine aminotransferase (ALT) occurred in 25 (17%) subjects. The magnitude of ALT increase over 4-week follow-up was strongly associated with baseline ALT elevation. Conclusions: In Chinese and Thai opioid-dependent injectors, we found BUP/NX to be effective in reducing opioid withdrawal symptoms and safe during short-term use. ALT increases were observed over 4-week-follow-up, which are consistent with reports from Western populations. Long-term safety and efficacy evaluations are indicated. [Copyright &y& Elsevier]

Unlabelled: Introduction: Rosai-Dorfman disease, also known as sinus histiocytosis with massive lymphadenopathy, is a rare benign disorder characterized histologically by lymphatic sinus dilatation due to histiocyte proliferation. Rosai-Dorfman disease accompanied by IgG4+ plasma cell infiltration is an even rarer situation. To the best of our knowledge, no imaging report of fluoro-deoxyglucose positron emission tomography/computed tomography findings of Rosai-Dorfman disease with IgG4+ plasma cell infiltration has been published, although a series of pathological research has focused on this phenomenon.Case Presentation: We reviewed the 18F-fluoro-deoxyglucose positron emission tomography/computed tomography scan of a 78-year-old Chinese woman with a solid mass that was found in her right breast during a health checkup. 18F-fluoro-deoxyglucose positron emission tomography/computed tomography showed a hypermetabolic nodule in her right breast and slightly heterogeneous increased fluoro-deoxyglucose uptake of the pulmonary nodules, which were histologically proven to be mammary Rosai-Dorfman disease with IgG4+ plasma cell infiltration and pulmonary amyloidosis, respectively. A literature review was performed to gather information on this rare disease process.Conclusions: Although distinguishing benign lymphoplasmacytic proliferation from malignancy may be difficult with 18F-fluoro-deoxyglucose positron emission tomography/computed tomography in light of the pattern and intensity of fluoro-deoxyglucose uptake, our case highlights that whole-body positron emission tomography/computed tomography imaging not only can display the extent of the disease to help complete staging but also can provide functional information about disease activity to guide biopsy. [ABSTRACT FROM AUTHOR]

The first 18 months of data from the FY-3A Total Ozone Unit (FY-3A/TOU) was collected to analyse instrument performance. Retrieval tests were carried out using post-launch calibration coefficients. Validation was analysed at 74 ground-based stations of total ozone network by inter-comparison with AURA/Ozone Monitoring Instrument (OMI) Total Ozone Mapping Spectrometer (TOMS) V8 data and comparison with ground-based measurements. The validation results show that the TOU total column ozone has an average root mean square (rms) error of less than 3.1% compared with OMI products, and less than 4.4% compared with ground-based measurements. The solar irradiance is measured continuously, and the post-launch calibration coefficients will be updated only if the change of instrument sensitivity has noticeable influence on the retrieval. The instrument is currently stable. [ABSTRACT FROM PUBLISHER]

Abstract: Train derailments are important safety concerns, and they become increasingly so when dangerous goods (DG) are involved. One way to reduce the risk of DG derailments is through effective DG railway car placement along the train consist. This paper investigates the relationship between DG railway car placement and derailment for different route attributes and DG shipments. A model is presented for estimating the probability of derailment by position, based on the estimated point of derailment (POD) and the number of cars derailing. A DG placement model that considers in-transit derailment risk is shown to provide a sound scientific basis for effective DG marshalling in conventional rail hump yard operations. [Copyright &y& Elsevier]

Abstract: Impulsivity is a pathological hallmark of drug addiction. However, little is known about the neuropsychological underpinnings of this impaired impulsive control network on drug addiction. Twenty two abstinent heroin dependent (HD) subjects and 15 cognitively normal (CN) subjects participated in this study. Resting-state functional connectivity MRI was employed to measure abnormalities in the intrinsic amygdala functional connectivity (iAFC) network activity and the Barratt Impulsive Scale, 11th version was used to measure impulsivity. Linear regression analysis was applied to detect the neural constructs underlying impulsivity by correlating iAFC network activity with impulsive scores. In the HD group, higher impulsivity scores and significantly enhanced iAFC network activity were found, especially in bilateral thalamus, right insula, and inferior frontal gyrus. Markedly decreased anticorrelated iAFC network activity was seen in the left precuneus, and even switched to positive correlation pattern in right precuneus, relative to the CN group. The iAFC network strengths in the HD group were positively correlated with impulsivity in the right subcallosal gyrus, insula, thalamus and posterior cingulate cortex, and negatively correlated in left fusiform area. In the CN group, the left pre-somamotor area-amygdala connectivity was positively correlated, and right orbital frontal cortex-amygdala and precuneus-amygdala connectivity were negatively correlated with impulsivity scores. Our study demonstrates different constructs of the impulsive network in HD and CN subjects. Altered iAFC network connectivity in HD subjects may contribute to the loss of impulsive control. This further facilitates our understanding of the neural underpinnings of behavior dysfunction in addiction. [Copyright &y& Elsevier]

Retrieval experiment was made for global total column ozone using the first year measurements of Total Ozone Unit (TOU) on board the second generation polar orbiting meteorological satellite of China, FY-3/A. The retrieval results were analyzed and validated by comparison with AURA/OMI, Meteop/GOME-2 global ozone products and ground-based ozone measurement data. The qualititative comparisons over the globe especially over Antarctica and the Tibetan Plateau show that the spatial and temporal distribution characteristics are consistent with OMI and GOME-2 products. The quantitative comparisons with ground-based measurements and AURA/OMI ozone product were made over 74 stations, the TOU total ozone retrieval has a 3% rms relative error compared with AURA/OMI ozone product and 4.2% rms relative error with ground-based measurements. The maximum difference between satellite retrieval and ground-based measurements was found in the Antarctica ozone hole. The TOU global ozone product is operational and distributed to all users. [ABSTRACT FROM AUTHOR]

FY-3 satellites are Chinese second-generation polar orbit meteorological satellite series. Ultraviolet Total Ozone Unit (TOU) is one of the main payloads on FY-3 satellite and the first instrument for daily global coverage of total ozone monitoring in China. The main purpose of TOU is to measure the Earth backscatter ultraviolet radiation for retrieving daily global map of atmospheric ozone. TOU will provide the important parameters for environmental monitoring, climate forecasting and global climate changing research. At present, the in-orbit testing of TOU has accomplished and won a consummation, and it will be delivered to National Satellite Meteorological Center for the operational phase of the project. In this paper we introduce the instrument of TOU and its measuring principle, in general. We also analyze the recent working status of the instrument, including the sensitivity, measuring precision of solar irradiance, diffuser degradation and wavelength drift. The inversion results show that TOU can provide good global ozone maps, and a comparison with the OMI total ozone product shows that their RMS deviation is about 5%. It is indicated that the satisfied global distribution of total ozone can be obtained through TOU observational data and self-developed inversion method. [ABSTRACT FROM AUTHOR]

Abstract: In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%. [Copyright &y& Elsevier]

• A hybrid deep learning model was developed for delay propagation patterns. • Train timetables were modeled as images. • A cost-sensitive technique was used to address the data imbalance challenge. • The proposed model shows satisfactory performance on different situations. As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]

Predicting the population density of key areas of the city is crucial. It helps reduce the spread risk of Covid-19 and predict individuals' travel needs. Although current researches focus on using the method of clustering to predict the population density, there is almost no discussion about using spatial-temporal models to predict the population density of key areas in a city without using actual regional images. We abstract 997 key areas and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the DataFountain platform, we evaluate the model and compare it with some typical models. Experimental results show that WE-STGCN has 53.97% improved to baselines on average and can commendably predicting the population density of key areas. [ABSTRACT FROM AUTHOR]

• Traffic flow states are classified with smartphone sensor data. • The statistical features of vehicle accelerations of only one car are used. • Acceleration and angular acceleration help improve the classification accuracy. • Optimization of the hyper-parameters further reduces the classification error. This study proposes a framework to classify traffic flow states. The framework is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of vehicle acceleration, angular acceleration, and GPS speed data, recorded by smartphone software, are analyzed, and then used as input for traffic flow state classification. Data collected by a five-day experiment is used to train and test the proposed model. A total of 747,856 sets of data are generated and used for both traffic flow state classification and sensitivity analysis of input variables. After applying various algorithms to the proposed framework, the study found that acceleration and angular acceleration data can increase the accuracy of traffic flow classification significantly. When the hyper-parameters of the Deep Belief Network model are optimized by the Differential Evolution Grey Wolf Optimizer algorithm, the classification accuracy is further improved. The results have demonstrated the effectiveness of using smartphone sensor data to estimate the traffic flow states and shown that our proposed model outperforms some traditional machine learning methods in traffic flow state classification accuracy. [ABSTRACT FROM AUTHOR]

• This research developed a segment‐level derailment prediction model using Canadian derailment inventory data. • The research used a negative binomial modelling technique to predict the number of derailment for each railway segment. • The maximum daily train traffic, maximum train speed, segment length, and number of stations are used to estimate the derailment frequency. Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or when they involve hazardous materials that might result in environmental impacts. Whereas numerous road safety studies have suggested appropriate approaches to predicting vehicle-to-vehicle collisions, very few railway safety studies have considered predicting the number of derailments on rail tracks in North America. In addition, the existing few rail safety assessment and derailment prediction models have often been constrained by aggregated data limiting the safety assessments by, for example, failing to consider segment-level characteristics. This paper focused on the development of an integrated database for the development of a segment-level derailment prediction model for Canada's rail network. The primary objective of this paper is to report how challenges in the data integration process were overcome and also to develop a network screening tool to identify segments with high derailment risk in Canada's rail network. Negative binomial regression and the Empirical Bayes technique were used to estimate the predicted number of derailments on Canada's rail network at the segment level. A network screening process was then successfully applied to identify key segments of safety concern: the top ten segments of concern accounted for approximately 1% of the rail network allowing decision makers to focus their derailment mitigation efforts on a manageable part of Canada's vast rail network. The data processing approach and analysis in this study have strong implications for advancing research on rail safety in North America. [ABSTRACT FROM AUTHOR]

The Ionosphere Photometer (IPM) is a far ultraviolet nadir-viewing photometer that flew aboard the second-generation, polar-orbiting Chinese meteorological satellite FY-3D, which was launched on November 25th, 2017. The F 2 layer peak electron density (N m F 2) and O/N 2 the column density ratio can be deduced by far ultraviolet measurements of night-time OI 135.6 nm emissions, which are produced by the recombination of O+ ions and electrons, and the day-time OI 135.6 nm and N 2 LBH wavelength emissions produced by the photoelectron impact excitation of atomic oxygen O and molecular nitrogen N 2. Analysis of the IPM data indicated the stray light at 135.6 nm wavelength is under deducted. Based on comparison with the emissive intensity calculation of AURIC model, the day-time data was conducted to make a revision of stray light. The reliable factor (1.13) of stray light subtraction was obtained by fitting the median values of the observed data points per each latitude bin to the model data. According to the O/N 2 error estimation from the estimated factor of the stray light, when the factor does not change by more than 0.03, the error of O/N 2 ratio from the factor will not exceed 20%. The O/N 2 changes retrieved from revised IPM data during storms in April and August 2018 are well correlated with Dst indices. The research results show that the IPM data could provide a good monitoring of O/N 2 changes during magnetic storms. [ABSTRACT FROM AUTHOR]

• Channelized right turns have been widely implemented to reduce traffic delay. • The impact channelized right turns on pedestrian safety has not been addressed. • The performance of different right-turn designs on pedestrian safety is compared. • Surrogate safety and behavioral measures from multiple facets of safety were used. • Channelized right turns increase pedestrian risks from different safety dimensions. Channelized right turns or slip lanes have been widely implemented as an effective countermeasure of reducing traffic delay and number of conflicts between vehicles at signalized intersections. However, only a few studies have investigated the impact of channelized right turns (in left-band driving countries) on pedestrian safety. Channelized right turns may increase the risks for pedestrians since they bring pedestrian-vehicle interactions in a fully non-signalized environment. Furthermore, the increased turning radius at channelized lanes can lead to higher vehicle speeds. This paper investigates the impact of channelized right turns on pedestrian safety based on surrogate safety and behavior measures. Video data were collected from twelve signalized intersections in the city of Zunyi, China, involving three main types of right-turn designs: 1) non-channelized right-only lanes, 2) non-channelized right-through lanes, and 3) channelized right-turn lanes. Different measures are used, including interaction and behavior measures based on a recent-proposed Distance-Velocity model, the PET measurement, speed measurements, and observations of failures in interactions (pedestrian retreats and evasive maneuvers from pedestrians or vehicles). Results indicate that the design of channelized right-turn lane increases pedestrian risks at signalized intersections from different dimensions of safety. The impact of the nighttime condition on pedestrian safety was also compared. Pedestrians are safer at nighttime at non-channelized locations, while the impact of nighttime conditions on pedestrian safety at channelized intersections was not ascertained. Consequently, cities should be cautious to install channelized intersections as a safety countermeasure. Treatments are needed to improve pedestrian safety if channelized right turns are implemented. [ABSTRACT FROM AUTHOR]

Background: Public health emergencies have challenged the public health emergency management systems (PHEMSs) of many countries critically and frequently since this century. As the world's most populated country and the second biggest economy in the world, China used to have a fragile PHEMS; however, the government took forceful actions to build PHEMS after the 2003 SARS outbreak. After more than one decade's efforts, we tried to assess the improvements and problems of China's PHEMS between 2002 and 2012.Methods: We conducted two rounds of national surveys and collected the data of the year 2002 and 2012, including all 32 provincial, 139 municipal, and 489 county CDCs. The municipal and county CDCs were selected by systematic random sampling. Twenty-one indicators of four stages (preparation, readiness, response and recovery) from the National Assessment Criteria for CDC Performance were chosen to assess the ten-year trends.Results: At the preparation stage, organization, mechanisms, workforce, and stockpile across all levels and regions were significantly improved after one decade's efforts. At the readiness stage, the capability for formulating an emergency plan was also significantly improved during the same period. At the response stage, internet-based direct reporting was 98.8%, and coping scores were nearly full points of ten in 2012. At the recovery stage, the capabilities were generally lower than expected.Conclusions: Due to forceful leadership, sounder regulations, and intensive resources, China's PHEMS has been improved at the preparation, readiness, and response stages; however, the recovery stage was still weak and could not meet the requirements of crisis management and preventive governance. In addition, CDCs in the Western region and counties lagged behind in performance on most indicators. Future priorities should include developing the recovery stage, establishing a closed feedback loop, and strengthening the capabilities of CDCs in Western region and counties. [ABSTRACT FROM AUTHOR]