Traffic accident duration prediction using multi-mode data and ensemble deep learning

Predicting the duration of traffic accidents is a critical component of traffic management and emergency response on expressways. Traffic accident information is inherently multi-mode data in terms of data types. However, most existing studies focus on single-mode data, and the influence of multi-mode data on the prediction performances of models has been the subject of only very limited quantitative analysis. The present work addresses these issues by proposing a heterogeneous deep learning architecture employing multi-modal features to improve the accuracy of predictions for traffic accident durations on expressways. Firstly, six unique data modes are obtained based on the structured data and the text data. Secondly, a hybrid deep learning approach is applied to build classification models with reduced prediction error. Finally, a rigorous analysis of the influence for multi-mode data on the accident duration prediction performances is conducted using a variety of deep learning models. The proposed method is evaluated using survey data collected from an expressway monitoring system in Shaanxi Province, China. The experimental results show that Word2Vec-BiGRU-CNN is a suitable and better model using text features for traffic accident duration prediction, as the F1-score is 0.3648. This study confirms that the newly established structured features extracted from text data substantially enhance the prediction effects of deep learning algorithms. However, these new features were a detriment to the prediction effects of conventional machine learning algorithms. Accordingly, these results demonstrate that the processing and extraction of text features is a complex issue in the field of traffic accident duration prediction

A Universal Method for Extracting and Quantitatively Analyzing Bias‐Dependent Contact Resistance in Carbon‐Nanotube Thin‐Film Transistors

Abstract A single‐device method is reported for extracting gate‐ and/or drain‐voltage‐dependent contact resistance of thin‐film transistors (TFTs). An extended transition‐voltage method is proposed and verified by experiments of all‐carbon‐nanotube thin‐film transistors (ACNT‐TFTs), which can extract gate‐ and/or drain‐voltage‐dependent contact resistance at source and drain independently. By measuring the output and transfer characteristics of a single‐device and extracting the basic parameters with the aid of mature Y‐function method, the contact resistance can be calculated directly. The results show that although a slight Schottky contact behavior is exhibited at very small drain voltages, good electrical contact characteristics can still be obtained in ACNT‐TFTs, exhibiting quasi‐Ohmic contacts. Compared with the existing single‐device methods, this method is suitable for both Ohmic and Schottky contact scenarios without requiring a complex iteration process, which greatly improves the universality and efficiency of the contact resistance extraction. Besides, this method reveals the physical essence of the complex interface contacts and enables researchers to quantitatively analyze the contact performance, not only for network carbon nanotube TFTs but also for the other emerging transistors

Evaluation of acellular pertussis vaccine: comparisons among different strains of mice

AbstractThe current study was designed to comparatively analyze the reactions of different mouse strains in response to acellular pertussis(aP) vaccine, with attempt to further provide a reference for aP vaccine evaluation. NIH mice, ICR mice, and BALB/c mice adopted from different pharmacopoeias and studies were utilized to measure the immune protection and immunogenicity of the same batch of aP vaccine according to the MICA from some Asian pharmacopoeias and the pertussis serological potency test (PTST) method from European Pharmacopoeia. Based on our results, the aP vaccine detected by NIH mice had the best potency. So the NIH mice were more suitable for detecting the immune protection of aP vaccine by the Modified intracerebral challenge assay (MICA)method. Given that the levels of PT-IgG and FHA-IgG antibodies in ICR mice were the highest, and the levels of Th1 and Th2 cells were significantly increased (P < 0.01), it was more suitable for the detection of immunogenicity of aP vaccine by PSPT method. Spleen lymphocytes were stimulated by PT and FHA. And the levels of IL-4 in ICR mice and NIH mice were significantly increased, so were the levels of IL-17, IL-23, IL-27, and TNF-α in BALB/c mice. NIH mice have stronger adaptive immunity and the weakest inflammatory response, and ICR mice have enhanced adaptive immunity and inflammatory responses, both of which can be thereby used for evaluation by different pharmacopoeia methods. NIH was more suitable for the MICA method of Chinese Pharmacopoeia, and ICR for the PSPT method of European Pharmacopoeia

Oil generation threshold depth of Songliao Basin: Revision and its significance

A systematic analysis was carried out on the relation curve of hydrocarbon transformation ratio vs. buried depth and that of vitrinite reflectance (Ro) vs. buried depth in Binbei area, major source rock area (Qijia-Gulong Sag and Sanzhao Sag), and major source rock layer (Cretaceous Qing-1 Member, Qing-2 – Qing-3 Members) in Songliao Basin, to re-determine the oil generation threshold depth of the basin. The oil generation threshold depth of the major source rocks is likely to range from 1400 m to 1700 m, rather than 1 200 m in the previous estimation. The change of oil generation threshold has different effects on the resources potential and exploration orientation of various areas: For the major source rock areas, increase of oil generation threshold depth will cause decrease of oil generation and expulsion quantity, with no evident effect on the resources potential of major source rock area but with certain effect on the exploration orientation and favorable target evaluation. For the Binbei area with shallower buried depth of source rocks, it will have great effect on the exploration potential, exploration orientation, and favorable target evaluation. For the southeastern uplift area with great denudation in the basin, the oil generation threshold depth is reduced to about 700 m, which will improve the exploration potential of Chaoyanggou terrace, Changchunling anticline, and Binxian-Wangfu area. Key words: Songliao Basin, oil generation threshold depth, oil generation peak, major source rock area, major source rock layer, exploration potentia

Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease

Chronic kidney disease is one of the most important causes of mortality worldwide, but a shortage of nephrology pathologists has led to delays or errors in its diagnosis and treatment. Immunofluorescence (IF) images of patients with IgA nephropathy (IgAN), membranous nephropathy (MN), diabetic nephropathy (DN), and lupus nephritis (LN) were obtained from the General Hospital of Chinese PLA. The data were divided into training and test data. To simulate the inaccurate focus of the fluorescence microscope, the Gaussian method was employed to blur the IF images. We proposed a novel multi-task learning (MTL) method for image quality assessment, de-blurring, and disease classification tasks. A total of 1608 patients’ IF images were included—1289 in the training set and 319 in the test set. For non-blurred IF images, the classification accuracy of the test set was 0.97, with an AUC of 1.000. For blurred IF images, the proposed MTL method had a higher accuracy (0.94 vs. 0.93, p &lt; 0.01) and higher AUC (0.993 vs. 0.986) than the common MTL method. The novel MTL method not only diagnosed four types of kidney diseases through blurred IF images but also showed good performance in two auxiliary tasks: image quality assessment and de-blurring

Ganab Haploinsufficiency Does Not Cause Polycystic Kidney Disease or Polycystic Liver Disease in Mice

Background. Heterozygous GANAB mutations that can cause autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD) have been described previously, but their roles in ADPKD and PLD are largely unknown. With the increase in polycystic kidney disease caused by GANAB gene mutations in recent years, a suitable animal model is still needed to further explore the pathogenic role of this gene. Methods. To construct a mouse model of Ganab gene deletion, we analyzed the Ganab gene structure and designed two CRISPR-/Cas9-based targeting strategies. The Cas9/sgRNA we constructed was microinjected into fertilized mouse eggs to obtain chimeric F0 mice. Mice with stable genotypes were selected from offspring born after mating F0 mice with wild-type mice. Results. We found that homozygous mutation of the Ganab gene in C57BL/6 mice resulted in early embryonic lethality, and there were no cysts in the kidneys or livers of Ganab+/- mice. Additionally, Ganab protein expression was reduced by at least 50%, while the expression of ADPKD proteins (PC1 and PC2) and acetylated tubulin was not affected in the Ganab+/- kidney. However, the Ganab+/- mice did not show any abnormal clinical phenotypes after birth and failed to reveal renal tubule dilatation or any abnormalities of the glomeruli in the Ganab+/- kidney. Conclusions. Homozygous Ganab mutations are lethal in the fetal stage, and Ganab haploinsufficiency does not cause kidney or liver cysts in mice, suggesting that it may not be the causative gene in polycystic kidney disease

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing

Self-Powered Electrospinning System Driven by a Triboelectric Nanogenerator

Broadening the application area of the triboelectric nanogenerators (TENGs) is one of the research emphases in the study of the TENGs, whose output characteristic is high voltage with low current. Here we design a self-powered electrospinning system, which is composed of a rotating-disk TENG (R-TENG), a voltage-doubling rectifying circuit (VDRC), and a simple spinneret. The R-TENG can generate an alternating voltage up to 1400 V. By using a voltage-doubling rectifying circuit, a maximum constant direct voltage of 8.0 kV can be obtained under the optimal configuration and is able to power the electrospinning system for fabricating various polymer nanofibers, such as polyethylene terephthalate (PET), polyamide-6 (PA6), polyacrylonitrile (PAN), polyvinylidene difluoride (PVDF), and thermoplastic polyurethanes (TPU). The system demonstrates the capability of a TENG for high-voltage applications, such as manufacturing nanofibers by electrospinning

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human–Machine Interaction

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human–machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human–machine interfacing