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The internationalization of Chinese higher education glimmers the hope for globalization and opened the doors for countries to exchange academics treasures and cultural exchange. The euphonious and mellifluous agenda behind the internationalization led the nations towards the silver lining of collaboration, interaction, and human resources exchange. However, a large volume of literature claimed the poor academic performance of international students and weak academic communication between international students and Chinese faculty members. So the study sought to explore the phenomena through doctoral. Student experience regarding their research self-efficacy development in a Chinese university. Thus, we took one Chinese university as a case due to its high accessibility of international students with broad and deep experience of being an international student in China. Our participants were international doctoral students from Asian and African countries with significant financial desperation. The findings shed light on the intention of international to pursue a doctoral degree in Chinese higher education and how it significantly delayed their research self-efficacy development.

ObjectiveThis study aimed to assess the risk of dietary exposure and study the residues of β-receptor agonists in livestock meat and liver marketed in Tianjin City.MethodsA total of 583 samples of pig， beef， mutton， and liver were randomly collected from supermarkets， online stores， and farmers’ markets in Tianjin City from 2016 to 2021. The β-receptor agonists clenbuterol， ractopamine， salbutamol， and terbutaline were tested by the relevant standard operating procedures of Manual China National Food Contamination and Harmful Factors Risk Monitoring， combined with residents’ food consumption data， using the index of food safety （IFS） to assess the health risk.ResultsThe overall detection rate of the 583 samples was 13.89% （81/583）. The main β-receptor agonist detected was clenbuterol， which accounted for 98.77% （80/81） of all the detected samples. Only 1 sample was detected with salbutamol， and the other 2 β-receptor agonists were not detected. The top 3 food categories in terms of detection rate were beef liver， 33.96% （36/106）； mutton， 15.79% （18/114）； and beef， 13.22% （16/121）， while the detection rate of pork and pig liver was 1.19% （1/84） and 2.00% （1/50）， respectively； the difference between the detection rates of the different categories was statistically significant （χ2=56.11， P < 0.05）. The detection rate of β-receptor agonists in samples collected from farmers’ markets was significantly higher than that collected from supermarkets and online shops （χ2= 10.91， P < 0.05）. The exposure risk assessment showed that the average exposure of pork， beef， and mutton ranged from 0.000 1 to 0.000 4 μg/kg·BW. The IFS ranged from 0.022 0 to 0.095 0， and the IFS values were less than 1.ConclusionA certain detection degree of β-receptor agonists was observed in livestock meat and liver marketed in Tianjin City， but the average exposure risk of β-receptor agonists was low from the dietary exposure of pig， beef， and mutton.

Although the attractiveness of voices plays an important role in social interactions, it is unclear how voice attractiveness and social interest influence social decision‐making. Here, we combined the ultimatum game with recording event‐related brain potentials (ERPs) and examined the effect of attractive versus unattractive voices of the proposers, expressing positive versus negative social interest ("I like you" vs. "I don't like you"), on the acceptance of the proposal. Overall, fair offers were accepted at significantly higher rates than unfair offers, and high voice attractiveness increased acceptance rates for all proposals. In ERPs in response to the voices, their attractiveness and expressed social interests yielded early additive effects in the N1 component, followed by interactions in the subsequent P2, P3 and N400 components. More importantly, unfair offers elicited a larger Medial Frontal Negativity (MFN) than fair offers but only when the proposer's voice was unattractive or when the voice carried positive social interest. These results suggest that both voice attractiveness and social interest moderate social decision‐making and there is a similar "beauty premium" for voices as for faces. [ABSTRACT FROM AUTHOR]

For decades, Pakistan's government has continuously provided its citizens with accessible, universal, basic education; however, these efforts have not yielded any gains for the citizens. Consequently, Pakistan has a lower literacy rate, one of the lowest in South Asia and the rest of the world. Although such a substantial issue is daunting and weakens the country's educational system, there needs to be more evidence that proposes potential implications or interventions in identifying the most important in‐school factors of primary school enrollment in Pakistan. Therefore, the present study derived a literature review‐driven hypothesis and administered a rigour analysis using the 2018 school census from the Punjab government education commission, showing detailed information on 24,305 public primary schools. The finding of this study asserts that the medium of instruction has shown a considerable impact on primary school enrollment and the availability of qualified teachers. Single‐sex schools, WASH facilities, and the security of schools have significant and diverse effects in determining student enrollment, which leads to forwarding possible implications to scholars, educators, and policymakers. [ABSTRACT FROM AUTHOR]

COVID-19 has affected the various aspects of social service delivery worldwide. Explicitly, pediatric service was affected mainly due to the travel restrictions, and children were at heightened risk due to these circumstances. Therefore, this study aims to analyze the Pediatric Clinical Practice in a Chinese children's hospital to describe and acknowledge the impact of COVID-19 on the pediatric healthcare system. For this purpose, the hospital's outpatient registration database was the primary information source, which comprises demographic data, including age, gender, city of residence, and hospital departments visited. Data on patients admitted from January 25, 2019, to April 16, 2019, were collected and used as a baseline. The results revealed that the number of pediatric visits to the case study hospital decreased during the COVID-19 pandemic, accounting for 40% of that in 2019, especially during the early stage of the pandemic. The proportion of pediatric patients from outside Chongqing decreased by approximately 14% in the early stages and increased by 24% in the second and third stages, corresponding to an overall decrease of 8.5%. Accordingly, the hospital responds to COVID-19 by providing valuable information and a guiding framework to the rest of the world on how to respond to a crisis. [ABSTRACT FROM AUTHOR]

Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving URT operation. However, due to the problems such as the unavailability of the OD matrix of the current day, high dimension and long-range spatio-temporal dependencies, it is difficult to further improve the prediction accuracy of an OD matrix. In this paper, a novel spatio-temporal self-attention network (SSNet) for OD matrix prediction in URT is proposed to further improve the prediction accuracy. In the proposed SSNet, a lightweight yet effective spatio-temporal self-attention module (STSM) is proposed to capture complex long-range spatio-temporal dependencies, thus helping improve the prediction accuracy of the proposed SSNet. Additionally, the finished OD matrices on previous days are used as the only data source without the passenger flow data on the current day in the proposed SSNet, which makes it possible to predict the OD matrices of all time intervals on the current day before the operation of the current day. It is demonstrated by experiments that the proposed SSNet outperforms three advanced deep learning methods for short-term OD prediction in URT, and the proposed STSM plays an important role in improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations. [ABSTRACT FROM AUTHOR]

Unfortunately, the role of school leadership in fostering school improvement in non‐Western countries, especially African countries, is relatively little discussed. The present study sheds light on the contextual role of instructional leadership (IL) and transformational leadership (TL) in promoting the Ethiopian School Improvement Program (SIP), which is intended to enhance student academic performance. Hence, this study aimed to extend the integrated leadership model by conceptualizing the moderating mediation effect of IL on student academic achievement in public primary schools. A cross‐sectional study was performed by distributing a survey to 1280 public primary school teachers in Addis Ababa. Surprisingly, the study's findings affirmed that unlike previous studies, IL and TL significantly directly affect student academic performance, and TL plays a significant role as a moderator of the indirect effect of IL on student academic achievement. Practitioner Points: The application of school leadership in fostering school improvement in non‐Western countries is less known and understood.School principals with academic qualifications in school leadership exhibited an exceeding knowledge and skill in practicing leadership to enrich school improvement.Instructional and transformational leadership models have a substantial influence on student academic performance. [ABSTRACT FROM AUTHOR]

Short-term origin–destination (termed as OD) prediction is crucial to improve the operation of urban rail transit (termed as URT). The latest research results show that deep learning can effectively improve the performance of short-term OD prediction and meet the real-time requirements. However, many advanced neural network design ideas have not been fully applied in the field of short-term OD prediction in URT. In this paper, a novel parallel interactive attention network (termed as PIANet) for short-term OD prediction in URT is proposed to further improve the short-term OD prediction accuracy. In the proposed PIANet, a novel omnidirectional attention module (termed as OAM) is proposed to improve the representational power of the network by calculating the feature weights in the channel–spatial dimension. Moreover, a simple yet effective feature interaction is proposed to improve the feature utilization. Based on the two real-world datasets from the Beijing subway, the comparative experiments demonstrate that the proposed PIANet outperforms the state-of-the-art deep learning methods for short-term OD prediction in URT, and the ablation studies demonstrate that the proposed OAMs and feature interaction play an important role in improving the short-term OD prediction accuracy. [ABSTRACT FROM AUTHOR]

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. [ABSTRACT FROM AUTHOR]

Parental migration from rural to cities in China is causing millions of children to be left behind or to live without parental care, support, and guidance, which violates the Convention on the Rights of the Child. This national phenomenon has consequences for the household registration system, known as the hukou system, which is meant to restrain internal migration. These consequences may result in economic and social imbalances. However, a noticeable number of children have been completely or partially left behind by their parents in villages, and as a result, their relationship with their parents and their academic attainment are weak. In particular, this study examines whether migrant parents can improve the academic performance of their Left-Behind Children (LBC) by strengthening their relationship with their children or by sending adequate remittances to the village household. Astonishingly, the structural equation model (SEM) results indicate that LBCs from both parents migrating households are the more privileged groups, although the study underlines that still, all LBC are disadvantaged compared to non-LBC. [ABSTRACT FROM AUTHOR]

With the increasing scale of the urban subway, the total energy consumption of the subway has increased dramatically and poses a great challenge to the comfort of passengers and the punctuality of train operation. In order to ensure on-time train operation and passenger comfort, and at the same time reduce the energy consumption of subway operation, this paper proposes a Proximal Policy Optimization (PPO)-based optimization algorithm for the optimal control of subway train operation. Firstly, a reinforcement learning architecture for optimal control of subway train operation is constructed with the position and speed of train operation as the reinforcement learning state, energy consumption and comfort as the optimization objectives, and train operation time as the constraint. The proposed reinforcement learning model is trained by the PPO algorithm, and the reward scaling is added to the training process to accelerate the training speed and improve the efficiency of the algorithm. The experimental results show that the proposed PPO with reward scaling algorithm can effectively reduce train energy consumption and improve passenger comfort while ensuring on-time train operation. [ABSTRACT FROM AUTHOR]