Analysis on Characteristics of Traffic Demand about SuTong Bridge

AbstractThanks to her special geographical location, SuTong Changjiang Highway Bridge, which lies in the most economically developed area-Yangtze River Delta, becomes the vital transportation passageway to prosper the economic development between South of Jiangsu province and Zhejiang & Shanghai. This paper analyzed the characteristics of traffic demand of SuTong Bridge from different angles, such as hourly/daily/monthly/space/vehicle-types features and drew some significant conclusions, based on the particular data of traffic volume of SuTong Bridge in 2010. The conclusions comprises: the traffic demand of Sutong Bridge increased by nearly 18 thousand vehicles per month; Average monthly growth rate was 1.95%; MADT in October was the highest (1/k was up to 1.12); In addition, the spatial distribution and the motor-type proportion of river-crossing vehicles were analysed

MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners. In this work, we propose a novel task of Trans-Lingual Definition Generation (TLDG), which aims to generate definitions in another language, i.e., the native speaker's language. Initially, we explore the unsupervised manner of this task and build up a simple implementation of fine-tuning the multi-lingual machine translation model. Then, we develop two novel methods, Prompt Combination and Contrastive Prompt Learning, for further enhancing the quality of the generation. Our methods are evaluated against the baseline Pipeline method in both rich- and low-resource settings, and we empirically establish its superiority in generating higher-quality trans-lingual definitions.Comment: Accepted by ACL-BEA worksho

Investigation of bio-aerosol dispersion in a tunnel-ventilated poultry house

Bio-aerosol concentrations in poultry houses must be controlled to provide adequate air quality for both birds and workers. High concentrations of airborne bio-aerosols would affect the environmental sustainability of the production and create environmental hazards to the surroundings via the ventilation systems. Previous studies demonstrate that several factors including the age of the birds, the housing configuration, the humidity and temperature would strongly affect the indoor concentration of bio-aerosols. However, limited studies are performed in the literature to investigate the bio-aerosol dispersion pattern inside poultry buildings. In order to fill a gap of the understanding of the bio-aerosol dispersion behavior, experimental measurements of the indoor bio-aerosol distribution are performed in a tunnel-ventilated poultry house in this paper. Meanwhile a three-dimensional computational fluid dynamics (CFD) model is built and validated to further investigate the effect of flow pattern, turbulence and vortex on the dispersion and deposition of the bio-aerosols. Furthermore, bio-aerosols with various diameters are also examined in the CFD model. It is found that higher concentrations of bio-aerosols are detected at the rear part of the house and strong turbulent flow resulting from the ventilation inlets enhances the diffusion and dispersion of bio-aerosols. Local vortex or disturbed flow is responsible for higher local concentration due to the re-suspension of settled bio-aerosols, which suggests that careful attentions should be paid to these locations during cleaning and disinfection. Results from present study contribute to the optimization of design and operation of the poultry houses from the standing point of reducing airborne bio-aerosol concentrations

Effective noninvasive zygosity determination by maternal plasma target region sequencing

Background: Currently very few noninvasive molecular genetic approaches are available to determine zygosity for twin pregnancies in clinical laboratories. This study aimed to develop a novel method to determine zygosity by using maternal plasma target region sequencing. Methods: We constructed a statistic model to calculate the possibility of each zygosity type using likelihood ratios (Li) and empirical dynamic thresholds targeting at 4,524 single nucleotide polymorphisms (SNPs) loci on 22 autosomes. Then two dizygotic (DZ) twin pregnancies, two monozygotic (MZ) twin pregnancies and two singletons were recruited to evaluate the performance of our novel method. Finally we estimated the sensitivity and specificity of the model in silico under different cell-free fetal DNA (cff-DNA) concentration and sequence depth. Results/Conclusions: We obtained 8.90 Gbp sequencing data on average for six clinical samples. Two samples were classified as DZ with L values of 1.891 and 1.554, higher than the dynamic DZ cut-off values of 1.162 and 1.172, respectively. Another two samples were judged as MZ with 0.763 and 0.784 of L values, lower than the MZ cut-off values of 0.903 and 0.918. And the rest two singleton samples were regarded as MZ twins, with L values of 0.639 and 0.757, lower than the MZ cut-off values of 0.921 and 0.799. In silico, the estimated sensitivity of our noninvasive zygosity determination was 99.90% under 10% total cff-DNA concentration with 2 Gbp sequence data. As the cff-DNA concentration increased to 15%, the specificity was as high as 97% with 3.50 Gbp sequence data, much higher than 80% with 10% cff-DNA concentration. Significance: This study presents the feasibility to noninvasively determine zygosity of twin pregnancy using target region sequencing, and illustrates the sensitivity and specificity under various detecting condition. Our method can act as an alternative approach for zygosity determination of twin pregnancies in clinical practice.Multidisciplinary SciencesSCI(E)2ARTICLE6null

Developing a Traffic Management Framework for Coastal Expressway Bridges under Adverse Weather Conditions: Case Study of Rain Day in Shenzhen, China

Adverse weather can reduce visibility and road surface friction, lower vehicle maneuverability, and increase crash frequency and injury severity. The impacts of adverse weather and its interactions with drivers and roadway on the operation and management of expressway or expressway bridges have drawn the researchers’ and managers’ attention to develop traffic management frameworks to mitigate the negative influence. Considering the peculiar geographical location and meteorological conditions, the Guangshen Coast Expressway-Shenzhen Segment (GSCE-SS) was selected as a case in this study to illustrate the proposed traffic management framework on rain days. Conditions categorized by rainfall intensity and traffic flow were the main precondition to make the management decisions. CORSIM simulator was used to develop the alternate routes choice schemes, providing reference for other systems in the proposed traffic management framework. Maps of (a) entrance ramp control (ERC) strategies; (b) mainline control strategies; (c) alternate routes choice; (d) information release schemes, under scenarios of different volume and rainstorm warning grades (BLUE to RED), were drawn to present a reference or demonstration for managers of long-span expressway bridges not only in China, but even in the world

A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions