bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maternal organophosphate flame retardant exposure alters the developing mesencephalic dopamine system in fetal rat
Authors: Newell Andrew J , Kapps Victoria A , Cai Yuheng , Rai Mani Ratnam , St. Armour Genevieve , Horman Brian M , Rock Kylie D , Witchey Shannah K , Greenbaum Alon , Patisaul Heather B 
Published: December 2022

AbstractOrganophosphate flame retardants (OPFRs) have become the predominant substitution for legacy brominated flame retardants but there is concern about their potential developmental neurotoxicity (DNT). OPFRs readily dissociate from the fireproofed substrate to the environment, and they (or their metabolites) have been detected in diverse matrices including air, water, soil, and biota, including human urine and breastmilk. Given this ubiquitous contamination, it becomes increasingly important to understand the potential effects of OPFRs on the developing nervous system. We have previously shown that maternal exposure to OPFRs results in neuroendocrine disruption, alterations to developmental metabolism of serotonin (5-HT) and axonal extension in male fetal rats, and potentiates adult anxiety-like behaviors. The development of the serotonin and dopamine systems occur in parallel and interact, therefore, we first sought to enhance our prior 5-HT work by first examining the ascending 5-HT system on embryonic day 14 using whole mount clearing of fetal heads and 3-dimensional (3D) brain imaging. We also investigated the effects of maternal OPFR exposure on the development of the mesocortical dopamine system in the same animals through 2-dimensional and 3D analysis following immunohistochemistry for tyrosine hydroxylase (TH). Maternal OPFR exposure induced morphological changes to the putative ventral tegmental area and substantia nigra in both sexes and reduced the overall volume of this structure in males, whereas 5-HT nuclei were unchanged. Additionally, dopaminergic axogenesis was disrupted in OPFR exposed animals, as the dorsoventral spread of ventral telencephalic TH afferents were greater at embryonic day 14, while sparing 5-HT fibers. These results indicate maternal exposure to OPFRs alters the development trajectory of the embryonic dopaminergic system and adds to growing evidence of OPFR DNT.
Bus passengers’ anger scale and expression in anger-provoking situations
Authors: Yuan Zhili , Chen Qun , Ye Shi 
Published: December 2022

Abstract
This paper investigates the scale and expression of passengers’ anger arising from disputes between drivers and passengers, and between passengers, by surveying a sample of 757 undergraduate students. The bus passengers’ anger scale and expression inventory (BPASX), a newly-designed questionnaire battery, is used to analyse the anger levels and resulting behaviour expression of passengers in 6-item anger-provoking situations. The analysis shows that a positive correlation exists between the frequency or duration of bus rides and anger levels/external anger expression, while self-adaptive expression is only correlated with frequency. Gender differs only in the violent external expression, where males display a greater preference. Meanwhile, the correlations of anger levels between anger-provoking situations are significantly positive. Moreover, anger expression patterns gradually shift from self-adaptation to external expression as anger levels grow in general, but the variation rules of expression frequency are different. Overall, this study provides a newly effective tool to explore characteristics of the anger expression of bus passengers in different anger-provoking scenarios and demonstrates its variation features when anger levels change.
Establishment of a metric to characterize retroreflector properties using a consumer-grade colour camera
Authors: He Huayang , Su Wenying , Cheng Qiutong 
Published: December 2022

Abstract
The coefficients of retroreflection and chromaticity coordinates are critical metrics for determining the quality of a retroreflector. However, conventional retroreflection measurement techniques rely heavily on the night-time colour, size and characteristics of the sample being measured. This complicates the measurement process and leads to deviations in the test results. In addition, chromaticity cannot be determined using this approach. Therefore, it is necessary to improve the measurement accuracy, simplify the measurement process and achieve the measurement of the traffic sign RA and chromaticity coordinates simultaneously. In this study, an improved method for retroreflector characterization using imaging was proposed. This allowed for the measurement of the coefficients of retroreflection and the chromaticity coordinates simultaneously, and only a white standard sample was required for calibration. The primary components of the proposed system included a lighting projector, a receiver, two motorized rotational stages, a retroreflective sample holder and customized software that using scaling factors to convert a digital signal into sample retroreflection coefficients and retroreflective chromaticity coordinates. The experimental results indicated that the raw data output from the camera exhibited a positive correlation relationship with the luminous flux from the surface of the retroreflector. The maximum measurement errors for the retroreflection and chromaticity were −12.2 cd/(lx·m2) and −2.09%, respectively. This method was inexpensive and convenient, used a commercially available digital camera, could help to identify defects in retroreflection and chromaticity for retroreflective sheeting and led to increased accessibility for the quality control of retroreflective sheeting.
Hazard-based duration modelling of merging time interval on freeway on-ramps
Authors: Li Ye , Zhu Jichen , Haque Md Mazharul , Lee Jaeyoung 
Published: December 2022

Abstract
Freeway on-ramps suffer high crash risks due to frequent merging behaviours. This study developed hazard-based duration models to investigate the merging time interval on freeway on-ramps based on microscopic trajectory data. Fixed effect, random effect and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables. The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of individual drivers was captured. Modelling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables. Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane, whereas the influence of surrounding vehicles is marginal for those with an auxiliary lane. The findings of this study identify potential countermeasures for improving safety during the merging process.
Modelling medium- and long-term purchasing plans for environment-orientated container trucks: a case study of Yangtze River port
Authors: Li Shuai , Wu Weijia , Ma Xiaofeng , Zhong Ming , Safdar Muhammad 
Published: December 2022

Abstract
The transportation sector is the most significant contributor to anthropogenic greenhouse gas (GHG) emissions. Particularly, maritime transportation, which is predominantly powered by fossil-fuel engines, accounts for more than 90% of world freight movement and emits 3% of global carbon dioxide (CO2) emissions. China is the world's largest emitter of CO2 and plays a key role in mitigating global climate change. In order to tackle this pressing concern, this study analyses the port's throughput, the current number of trucks and their emissions during the container truck purchasing process. Previous studies about container truck purchasing plans mostly focused on the trucks' price and port needs. The objective of this study is to minimize the total cost of a port's inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs. The study considers the port of Yangtze as a case study. The study has designed two scenarios. (i) The base scenario (business-as-usual, BAU) is used to quantify the relationship between pollutant emissions and system cost. In the base scenario, no environmental control facilities are used during the planning period, and there is no need to purchase new energy container trucks. (ii) The expected scenario (Scenario A) is for three planning periods. In Scenario A, the emissions levels are required to remain at the same level as the first planning period during the whole planning period. By solving the above model, the number of all truck types, system cost, container throughput and truck emissions in the port area were analysed. The results showed that if no emission reduction control measures are implemented in the next 9 years, the growth rate of pollutants in the port area could reach 20%. In addition, the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel (diesel) trucks. This study could also help to minimize system costs associated with port planning and management.
Impacts of mobile phone use while driving on the performance of petroleum product drivers in Cameroon
Authors: Tuekam-Bopda Elisée-Enselme , Tamba Jean Gaston , Fopah-Lele Armand 
Published: December 2022

Abstract
The objective of this article is to show the influence of mobile phone use while driving on the performance of petroleum product drivers in Cameroon. The topical questioning centred on the series of accidents and near misses in general and specifically in the petroleum products sector and showed the need to look for other causes. To do so, we conceptually proposed a model derived from the literature review and adjusted by practical research, which shows that our approach is the hypothetico-deductive type. Methodologically, the observations from the experience of 90 questionnaires were operationalized using multivariate logistic regression analyses. The results indicate that making or receiving calls while driving significantly influences the occurrence of reckless driving situations. Reading and sending short message service (SMS or text messages) while driving negatively influences drivers' performance by increasing delivery times. The type of phone used has an effect on the risk of accidents or near misses. Indeed, drivers who use smartphones are 2.14 times more likely to experience crash-like events compared to those who use conventional mobile phones. In addition, with a view to reducing near misses and accidents, it is important to organize regular appropriate road safety campaigns, to install on-board cameras in all trucks, to provide rest areas and encourage drivers to stop at these areas to check their phones and catch up on missed calls. Additionally to introduce training for drivers in defensive driving with a renewable licence after a maximum of 2 years.
Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals
Authors: Sun Yongkui , Cao Yuan , Liu Haitao , Yang Weifeng , Su Shuai 
Published: December 2022

Abstract
Condition monitoring of railway point machines is important for train operation safety and effectiveness. Referring to the fields of mechanical equipment fault detection, this paper proposes a fault detection and identification strategy of railway point machines via vibration signals. A comprehensive feature distilling approach by combining variational mode decomposition (VMD) energy entropy and time- and frequency-domain statistical features is presented, which is more effective than single type of feature. The optimal set of features was selected with ReliefF, which helps improve the diagnosis accuracy. Support vector machine (SVM), which is suitable for a small sample, is adopted to realize diagnosis. The diagnosis accuracy of the proposed method reaches 100%, and its effectiveness is verified by experiment comparisons. In this paper, vibration signals are creatively adopted for fault diagnosis of railway point machines. The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.
A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy
Authors: Yan Guangxi , Liu Hui , Yu Chengqing , Yu Chengming , Li Ye , Duan Zhu 
Published: December 2022

Abstract
This paper proposes a hybrid deep reinforcement learning framework for locomotive axle temperature by combining the wavelet packet decomposition (WPD), long short-term memory (LSTM), gated recurrent unit (GRU) reinforcement learning and generalized autoregressive conditional heteroskedasticity (GARCH) algorithms. The WPD is utilized to decompose the raw nonlinear series into subseries. Then the deep learning predictors LSTM and GRU are established to predict the future axle temperatures in each subseries. The Q-learning could generate optimal ensemble weights to integrate the predictors to finish the deterministic forecasting and GARCH is used to conduct the deterministic forecasting based on the deterministic forecasting residual. These parts of the hybrid ensemble structure contributed to optimal modelling accuracy and provided effective support in the real-time monitoring and fault diagnosis of transportation.
Short-term traffic forecasting model: prevailing trends and guidelines
Authors: Soon Kian Lun , Chan Robin Kuok Cheong , Lim Joanne Mun-Yee , Parthiban Rajendran 
Published: December 2022

Abstract
The design parameters serve as an integral part of developing a robust short-term traffic forecasting model. These parameters include scope determination, input data preparation, output parameters and modelling techniques. This paper takes a further leap to analyse the recent trend of design parameters through a systematic literature review based on peer-reviewed articles up to 2021. The key important findings are summarized along with the challenges of performing short-term traffic forecasting. Intuitively, this paper offers insights into the next wave of research that contributes significantly to industries.
Research on numerical simulation of transient pressure for high-speed train passing through the most unfavourable length tunnel
Authors: Liu Zhao , Liu Feng , Yao Shuanbao , Chen Dawei , Yang Mingzhi 
Published: December 2022

Abstract
The length of high-speed railway tunnel is an important factor affecting transient pressure of high-speed train. When the tunnel length is the most unfavourable, the transient pressure changes in the tunnel and on the surface of the train are the most severe, which may affect the safe operation of the train or damage the structure in the tunnel. Based on the three-dimensional, compressible, unsteady N-S equation and finite volume method, this paper uses the CFD numerical simulation method to study the change and amplitude distribution of the transient pressure on the train surface and the tunnel when a high-speed train passes through the most unfavourable length tunnel. A fast calculation method is proposed to save the cost of calculation; it has great applicability of pressure amplitude. The results show that the pressure distribution in the tunnel and on the surface of the train is affected by the train speed, the length of the train and the position of the measuring point. The minimum negative peak value in the tunnel appears at the position where the superposition phenomenon is the most severe, and the position will change with the speed of the train. There are two negative peak waveforms of the train surface pressure, and the first waveform is greatly affected by the train speed. It improves a reference for studying the strength requirement of the most unfavourable length tunnels and trains, and ensures the safe operation of trains in tunnels of different lengths.
Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China
Authors: Liu Bin , Pan Xinmin , Yang Rui , Duan Zhu , Li Ye , Yin Shi , Nikitas Nikolaos , Liu Hui 
Published: December 2022

Abstract
Considering the application of wind-forecasting technology along the railway, it becomes an effective means to reduce the risk of train derailment and overturning. Accurate prediction of crosswinds can provide scientific guidance for safe train operation. To obtain more reliable wind-speed prediction results, this study proposes an intelligent ensemble forecasting method for strong winds along the high-speed railway. The method consists of three parts: the data preprocessing module, the hybrid prediction module and the reinforcement learning ensemble module. First, fast ensemble empirical model decomposition (FEEMD) is used to process the original wind speed data. Then, Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, non-linear autoregressive network with exogenous inputs (NARX) and deep belief network (DBN), three benchmark predictors with different characteristics are employed to build prediction models for all the sublayers of decomposition. Finally, Q-learning is utilized to iteratively calculate the combined weights of the three models, and the prediction results of each sublayer are superimposed to obtain the model output. The real wind speed data of two railway stations in Xinjiang are used for experimental comparison. Experiments show that compared with the single benchmark model, the hybrid ensemble model has better accuracy and robustness for wind speed prediction along the railway. The 1-step forecasting results mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of Q-learning-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s, 0.6509%, 0.1146 m/s, and 0.0458 m/s, 0.2709%, 0.0616 m/s, respectively. The proposed ensemble model is a promising method for railway wind speed prediction.
Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines
Authors: Liu Junqi , Wen Tao , Xie Guo , Cao Yuan 
Published: December 2022

Abstract
Railway point machine (RPM) condition monitoring has attracted engineers’ attention for safe train operation and accident prevention. To realize the fast and accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement and broad learning system (BLS). Firstly, the modified multi-scale symbolic dynamic entropy (MMSDE) module extracts dynamic characteristics from the collected acoustic signals as entropy features. Then, the fuzzy BLS takes the above entropy features as input to complete model training. Fuzzy BLS introduces the Takagi-Sugeno fuzzy system into BLS, which improves the model’s classification performance while considering computational speed. Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.
Study on risk assessment and factors ranking of the LTE-M communication system
Authors: Wu Xiaochun , Gao Yu , Zheng Weichao 
Published: December 2022

Abstract
To assess the operational safety risk of long-term evolution for the metro (LTE-M) communication system more accurately, the guide maintenance strategy, the improved evidence theory and the multi-attribute ideal reality comparative analysis (MAIRCA) approaches are proposed. According to the features of LTE-M system, the risk evaluation system is established. The enhanced structural entropy weight method is used to obtain the weight. Furthermore, it is combined with nine-element fuzzy mathematics to transform the degree of membership, modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory. Then, the system risk grade assessment result is obtained. For the purpose of forming the ranking of indicator importance, the MAIRCA is introduced and the ranking is three-dimensional. The operational state of the metro line is used as the data source in various ways based on the test and calculation. The results show that the method is effective; compared with others, the confidence degree of the obtained risk grade increased by 7.12%. It is verified that MAIRCA can be applied to the field of urban rail transit because it has excellent stability and the ranking result of risk factors is reasonable. The influencing indicator with the highest importance is the ‘equipment failure rate’.
Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure
Authors: Wen Jiakun , Liu Zhen , Ding Huan 
Published: December 2022

Abstract
With the increasing computing demand of train operation control systems, the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years. How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems. Because the cloud computing platform is in an open network environment, it can face many security loopholes and malicious network attacks. Therefore, it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform, thereby enhancing its safety and reliability. Firstly, a private cloud safety computer platform architecture based on dynamic heterogeneous redundant (DHR) structure is proposed, and a dynamic migration mechanism for heterogeneous executives is designed. Then, a generalized stochastic Petri net (GSPN) model of a private cloud safety computer platform based on DHR is established, and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model (CTMC) to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance. Finally, through experimental verification, the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform, thereby improving its safety and reliability.
Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine
Authors: Zheng Yunting , Chen Shaohua , Tan Zhiyong , Sun Yongkui 
Published: December 2022

Abstract
A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines. A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet entropy. Firstly, empirical mode decomposition is performed on sound signals to obtain modal components with different time scales. Then, multi-scale permutation entropy is extracted from these components. Meanwhile, the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer nodes. Since the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal, the subtle features of the original signal can be obtained as the feature vector of the ZDJ9 railway point machine in different states. To reduce the redundant information among the high-dimensional features, ReliefF is utilized. Finally, a support vector machine (SVM) is used to judge the fault type of a ZDJ9 railway point machine.
Drug Approved to Prevent Cisplatin-Associated Ototoxicity in Children
Authors: Aschenbrenner Diane S. 
Published: December 2022

Sodium thiosulfate injection (Pedmark) has been approved to decrease the risk of ototoxicity associated with cisplatin use in children who are at least one month old and have localized nonmetastatic solid tumors.Nurses should regularly weigh children taking this drug, as the dose is based on weight. Patients should be premedicated with antiemetics to prevent nausea and vomiting and monitored closely for evidence of electrolyte imbalances.