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Cigarette smoking causes multiple cancers by directly influencing mutation burden of driver mutations. However, the mechanism between somatic mutation caused by cigarette smoking and bladder tumorigenesis remains elusive. Smoking‐related mutation profile of bladder cancer was characterized by The Cancer Genome Atlas cohort. Integraticve OncoGenomics database was utilized to detect the smoking‐related driver genes, and its biological mechanism predictions were interpreted based on bulk transcriptome and single‐cell transcriptome, as well as cell experiments. Cigarette smoking was associated with an increased tumor mutational burden under 65 years old (p = 0.031), and generated specific mutational signatures in smokers. RB1 was identified as a differentially mutated driver gene between smokers and nonsmokers, and the mutation rate of RB1 increased twofold after smoking (p = 0.008). RB1 mutations and the 4‐aminobiphenyl interference could significantly decrease the RB1 expression level and thus promote the proliferation, invasion, and migration ability of bladder cancer cells. Enrichment analysis and real‐time quantitative PCR (RT‐qPCR) data showed that RB1 mutations inhibited cytochrome P450 pathway by reducing expression levels of UGT1A6 and AKR1C2. In addition, we also observed that the component of immunological cells was regulated by RB1 mutations through the stronger cell‐to‐cell interactions between epithelial scissor+ cells and immune cells in smokers. This study highlighted that RB1 mutations could drive smoking‐related bladder tumorigenesis through inhibiting cytochrome P450 pathway and regulating tumor immune microenvironment. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff's hands are considered a major source of hospital infection. Effective hand-washing can prevent up to 30% of diarrhea-related illnesses, which is crucial in preventing nosocomial infections (Tartari et al. in Clin Microbiol Infect 23(9):596–598, 2017). This paper proposes an electronic-based real-time hand-washing identification framework called Alpha Hand Washing (ALPHA HW). The system uses camera-based identification, edge computing, and deep learning to automatically identify correct hand-washing behaviors, thereby facilitating effective hand-washing (Bertasius et al. in: Computer vision and pattern recognition, 2015). We achieved an accuracy of 78.0% mAP and a speed of 52 FPS in detecting scenes using specific monitoring datasets in hospitals by constructing the complex recognition system into a grid computing problem. Leveraging edge computing, our system achieves real-time identification with low memory consumption and high-efficiency computation. Alpha HW presents scientific and financial values in epidemic prevention and control that can facilitate popularization to reduce virus spread (Bewley et al. in 2016 IEEE international conference on image processing, 2016). [ABSTRACT FROM AUTHOR]

Carsharing is regarded as an efficient way to reduce parking difficulty and road congestion, and the sub-leasing type of round-trip carsharing is an innovative way to enhance the efficiency of carsharing. Meanwhile, with the help of sub-leasing, the turnover rate of a parking lot increases, and the parking operator can earn a certain profit when the parking lot acts as a sub-leasing station. This study aims at extending the application of sub-leasing carsharing by suggesting an integrated framework of parking and sub-leasing based on the concept of autonomous transportation systems (ATSs), which are believed to be able to correctly sense demand, give proper reactions, and reduce operator workloads. A detection approach based on this ATS-based integrated framework is proposed to identify potential sub-leasing customers from the parking users, which is the main contribution of this study. Furthermore, this detection approach was tested using Shenyang parking data, and the parking lots with the greatest potential were analyzed in detail. The results show that 48.4% of parking activities had the potential to be transformed into sub-leasing activities. Of these potential activities, 52.7% were made by people who used a monthly payment scheme, and the promotion of sub-leasing should focus on these people. [ABSTRACT FROM AUTHOR]

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions. [ABSTRACT FROM AUTHOR]

Different human-machine collaboration modes and driving simulation tests with the orthogonal method considered are designed for a series of typical intelligent highway landscapes. The feedback of drivers under different interaction modes is evaluated through NASA-LTX questionnaire, driving simulator, eye tracker, and electroencephalograph (EEG). This optimal interaction mode (including voice form, broadcasting timing, and frequency) of each driving assistance scene in CVI (Cooperative Vehicle Infrastructure) environment under the conditions of high and low traffic is determined from subjective and objective perspectives. In accordance with feedback of these subjects on each set scene, the voice information structure of each assistance mode plays the most important role on drivers followed by the broadcasting timing and frequency. These broadcasts which provide good effects include scenarios such as various assistance scenes at curves and an early warning timing at a long-distance trip as well as a high early warning frequency; in addition, as for an exit-tip assistance scenario, a voice mode assistance is preferred; and for various speed assistance scenes, the beep mode is better. Furthermore, it is found that, at a higher traffic level but a short-distance trip, an early warning timing is favored generally for various scenes while under a low traffic level, a long-distance early warning timing is better. [ABSTRACT FROM AUTHOR]

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS). [ABSTRACT FROM AUTHOR]

The combination of electric vehicle (EV) and carsharing is expected to provide social and environmental benefits, like encouraging sustainable travel behaviors (reducing car ownership and vehicle kilometres of travel) and improving the accessibility and flexibility of urban transport. Thus, electric carsharing is encouraged to be adopted for daily trips, and the operators propose the friend-invitation promotion scheme for the membership expansion. This study explores the effectiveness of this scheme and the characteristics of the scheme participants and their invited friends (e.g., age, friend-invitation pattern, and EV rental pattern). The analysis found that 28.4% of these invited friends would make at least one EV rental after registration, whereas 30.4% of the other members who registered in the same period would do so, indicating that these invited friends were less active. Therefore, suggestions are given based on the EV rental pattern of these invited friends (preferring a longer journey using a smaller but cheaper EV) to enhance the effectiveness of the friend-invitation promotion scheme. [ABSTRACT FROM AUTHOR]

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable. [ABSTRACT FROM AUTHOR]

About 90% of traffic crashes are caused by human factors, within which traffic violations are one of the most typical and common causes. In order to investigate the relationship between traffic violations and traffic crashes, this research targets signalized intersections in two Chinese cities: Yinchuan and Suqian. Thirty-one intersections are selected as the research sites, and additionally, the traffic volume, traffic violation, and traffic crash data of each intersection are collected for one year. A White's test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. The results show the following: (1) although the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) There is a significant multiple linear regression relationship (R2 = 0.782, adjusted R2 = 0.716) between the total number of traffic crashes and traffic violations. Among the chosen 7 independent variables, four are significantly related to the dependent variable, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation. (3) With the increase of annual average daily traffic (AADT), the number of total crashes goes up; however, the injury-or-fatality rate decreases, which means that intersections with smaller traffic volumes tend to have higher traffic crash severity. Based on the above conclusions, it is possible to conduct more targeted enforcement to improve the safety of intersections. [ABSTRACT FROM AUTHOR]

In an earlier study micro-LED micro display (< 1 inch) on silicon CMOS backplane was demonstrated for augmented reality (AR) applications. Here we report the feasibility of wafer-level monolithic integration of micro-LEDs on glass substrate/backplane. Such issues as the cracking of GaN epitaxial layer, the deviation of alignment, and the peeling of insulator are discussed. SU-8 is proposed as the insulator material in vertical micro-LEDs, resulting into improved light extraction efficiency and allowing for the reduced light crosstalk between sub pixels if the integrated reflective mirrors are used. A directly driven micro-LED parallel array with a resolution of ${320}\times {720}$ with individual LED size ranging from 5 $\mu \text{m}$ to 28 $\mu \text{m}$ is demonstrated. It is believed that this monolithic technology on glass will play an important role in future high performance and low cost wearable and/or phone displays. [ABSTRACT FROM AUTHOR]

In recent years, the transparent waveguide liquid crystal display (WLCD) technology attracted a lot of attention for the high transmittance. To further improve the display performance, the contrast ratio was systematically optimized in this work. A coarse‐grained model based on Mie scattering was established to perform the limiting theoretical analysis of the contrast ratio of polymer‐stabilized liquid crystal devices. To improve the contrast ratio, we tried different material formulations and optimized the UV polymerization process, and also applied light shielding layer to suppress metal lines reflection. Finally, the contrast ratio can reach to 18:1. With these improvements and the advantage on transmittance, it is believed that the WLCD technology will be applied in various intelligent IoT systems in the near future. [ABSTRACT FROM AUTHOR]

Waveguide liquid crystal display (WLCD) is a newly developed transparent display technology. Since polarizers and color filters are not necessary for the WLCD, high transparency is easily reached. Meanwhile, owing to the fast response time, field sequential color (FSC) can be applied to realize full color display. In this work, by optimizing materials and process conditions, we obtained lower driving voltage and higher contrast ratio than previously reported. These enhanced characteristics provide the possibility for designing larger size panel. Finally, we have developed an 8 inch full color transparent WLCD prototype and discussed the upgrading methods to improve brightness uniformity. [ABSTRACT FROM AUTHOR]

Residence location and workplace are the two most important urban land-use types, and there exist strong interdependences between them. Existing researches often assume that one choice dimension is correlated to the other. Using the mixed logit framework, three groups of choice models are developed to illustrate such choice dependencies. First, for all households, this paper presents a basic methodology of the residence location and workplace choice without decision sequence based on the assumption that the two choice behaviors are independent of each other. Second, the paper clusters all households into two groups, choosing residence or workplace first, and formulates the residence location and workplace choice models under the constraint of decision sequence. Third, this paper combines the residence location and workplace together as the choice alternative and puts forward the joint choice model. A questionnaire survey is implemented in Beijing city to collect the data of 1994 households. Estimation results indicate that the joint choice model fits the data significantly better, and the elasticity effects analyses show that the joint choice model reflects the influences of relevant factors to the choice probability well and leads to the job-housing balance. [ABSTRACT FROM AUTHOR]

A novel hot embossing method is developed to fabricate polymer nanochannels. The pattern on the silicon nanomould is transferred to polymethylmethacrylate (PMMA) plates, and then polyethylene terephthalate (PET) nanochannels are embossed by using the PMMA mould. The use of the PMMA intermediate mould can extremely increase the device yield of the expensive silicon nanomould. To avoid the use of nanolithography, a method based on UV-lithography techniques for fabricating silicon nanomoulds with sub-micrometer width was put forward. 1 PMMA mould can be used to repeatedly emboss at least 30 PET substrates without damage and obvious deformation. Good pattern fidelity of PET nanochannels was obtained at the optimized embossing temperature of 90 °C. For an 808 nm-wide and 195 nm-deep nanochannel, the variations in width and depth between PET nanochannels and PMMA moulds were 1.8 and 2.5 %, respectively. The reproducibility was also evaluated, and the relative standard deviations in width and depth of 5 PET nanochannels were 5.1 and 7.3 %, respectively. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate the relationships between three components of organizational commitment and word-of-mouth intentions among the staff employed in two hotels located in mainland China. A total of 120 hotel employees participated in a questionnaire survey. The results indicate that there are direct correlations between the three components of organizational commitment and word-of-mouth intentions. The effects of normative commitment in predicting the amount of word-of-mouth intentions is especially significant. Implications for the industry and directions for future research are presented. [ABSTRACT FROM AUTHOR]

The detailed numerical simulation has been carried out to investigate the effect of synthetic jet excitation on the secondary flow at 5° incidence in a compressor cascade, in which the synthetic jet actuation is equipped on the suction surface. The influence of excitation position, one fixed near the trailing edge and the other fixed a little far from the trailing edge, has also been studied. The results show that unsteady disturbance of desirable synthetic jet effectively enhances the mixing of the fluid inside the separation region, which reduces the vortex intensity and the energy loss, improves the flow status in the cascade, and also suppresses velocity fluctuation near the trailing edge. Additionally, the actuation fixed near the separation region proves to be more effective and exit load distribution is more uniform due to the employment of the synthetic jet. [ABSTRACT FROM AUTHOR]

In this manuscript, n-type CdSxSe1−xnanobelts (NBs) with the entire composition range were synthesized viaa simple Cd-riched chemical vapor deposition method. Moreover, using the n-CdSxSe1−xalloy NBs and p+-Si NBs as representative building blocks, novel high-efficiency color tunable parallel-nanobelts heterojunction light-emitting diodes (PNBs-HLEDs) were fabricated and studied for the first time, where the two nanobelts forming the heterojunction were face-contacted and parallel to each other. Compared with previously reported nano-LEDs, where the active region was near point-shaped, the PNBs-HLEDs here have the advantages of larger active region, smaller series resistance, higher electron and hole injective current, and lower turn-on voltage. Intense room-temperature electroluminescence with a wide range of color (from 510–708 nm) was observed from the n-CdSxSe1−x/p+-Si PNBs-HLEDs. The novel device structure together with the well-developed CdSxSe1−xNB growth and doping method promises a bright future for the CdSxSe1−xbased PNBs-HLEDs in various nano-device applications, such as electrically driven lasers, multi-color displays, white light illumination etc.Besides, the approach to fabricating PNBs-HLEDs should be easy to extend to other material system. [ABSTRACT FROM AUTHOR]

Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GAN) by combining bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN) as the generative model and discriminative model, respectively, to keep learning with continuous feedback. BiLSTM constantly generates temporal candidate flows based on valuable memory units, and CNN screens out the best spatial prediction by returning the feedback gradient to BiLSTM. Multi-dimensional indicators are selected to map the multi-view fusion local trend for accurate prediction. To balance computing efficiency and accuracy, different batch sizes are pre-tested and allocated to different lanes. The models are trained with rectified adaptive moment estimation (RAdam) by dividing the dataset into the training and testing sets with a rolling time-domain scheme. In comparison with the autoregressive integrated moving average (ARIMA), BiLSTM, generating adversarial network for traffic flow (GAN-TF), and generating adversarial network for non-signal traffic (GAN-NST), the proposed improved generating adversarial network for traffic flow (IGAN-TF) successfully generates more accurate and stable flows and performs better. [ABSTRACT FROM AUTHOR]

Objective To investigate the effects of root canal preparation treated by endodontists of different levels in different difficulty associated with the root canal anatomy factors. Methods Totally 422 teeth with 901 root canals were randomly selected from the Conservative Dentistry Department, West China Stomatology Hospital, and these teeth were respectively treated by junior specialist postgraduates, senior specialist postgraduates and endodontic specialists. Grading criteria of root canal risk factor and root canal therapy difficulty assessment were found based on 6 items: Tooth type, working length, root curvature, calcification, number of canals, and the previous endodontic treatment. The effects of root canal preparation with different difficulty in different groups were analyzed with RxC Chi-square test. Results Success rates of root canal preparation in junior group and senior group during three difficulty groups were statistically different (P<0.05), and the root canal therapy difficulty group I >group II > group HI. The operator factors in root canal therapy difficulty group I and group II were not significantly different (P>0.05). But in root canal therapy difficulty group DI, the endodontic specialist group have highest success rate in root canal preparation. Conclusion The cases of root canal treatment should be treated by endodontist of corres-ponding level according to the difficulty. In difficult cases, endodontic specialist can provide better root canal prepa-ration result. [ABSTRACT FROM AUTHOR]