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The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.

Through establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors, the principle of grounding current monitoring is proposed. Furthermore, the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments. Finally, through practical application in the traction substation of the railway bureau on site, a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.

The experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status. The system performs excellently in terms of data collection accuracy, real-time performance and reliability of alarm functions. In addition, the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications, providing strong technical support for the safe operation of high-speed railway traction power supply systems.

This study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system, which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current. The design, experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance, contributing innovative solutions to the field of railway power supply safety monitoring.

This study aims to investigate the effect of social media use on healthcare workers’ psychological safety and task performance and the moderating role of perceived respect from patients during public health crises.

To test the proposed moderated mediation model, a survey was conducted in 12 Chinese medical institutions. A total of 637 valid questionnaires were collected for data analysis.

The results revealed that psychological safety mediated the relationships between task-related social media (TSM) use and social-related social media (SSM) use and task performance. In addition, perceived respect from patients moderated the relationship between TSM use and psychological safety, as well as the indirect relationship between TSM use and task performance through psychological safety.

This study sheds new light on understanding how different types of social media use influence task performance in the context of public health crises. Furthermore, this study considers the interactions of healthcare workers with colleagues and patients and examines the potential synergistic effects of these interactions on healthcare workers’ psychological state and task performance.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

This study aims to explore the essential factors of the reason for (RF) and reason against (RA) and their impact on tourist attitudes and intentions to use e-bike sharing services by using a novel approach based on behavioral reasoning theory (BRT).

A quantitative method was used to collect data from 292 tourists and analyze the data using the partial least squares structural equation modeling (PLS-SEM) technique.

The outcomes indicate that both “RF” and “RA” are crucial in affecting tourist attitudes and intentions to use e-bike-sharing services. In addition, factors such as convenience, price saving and time saving were identified as “RF” while “RA” weather conditions, safety concerns and image barriers were identified that affect tourist attitudes and intention to use e-bike sharing services. These findings also indicate that tourists’ values of openness to change significantly influence “RF” and have no impact on “RA” and attitude toward e-bike-sharing services.

This study provides valuable insights that complement the existing literature on electric bike sharing and BRT in the context of promoting sustainable and eco-friendly consumption.

This research takes a significant step forward in investigating the tourist attitudes and intentions toward using e-bike sharing services in a tourist destination growing economy by using the BRT theory and proposing a model that considers both the “RF” and “RA” using e-bikes for urban transportation.

本研究旨在利用基于 BRT 的新方法, 探讨使用电动自行车共享服务的支持原因（RF）和反对原因（RA）的基本因素及其对游客使用电动自行车共享服务的态度和意图的影响。

采用定量方法收集 292 名游客的数据, 并使用 PLS-SEM 技术分析数据。

结果表明“RF”和“RA”对于影响游客使用电动自行车共享服务的态度和意图至关重要。此外, 方便、节省价格和节省时间等因素被确定为“RF”, 而“RA”则被确定为影响游客态度和使用电动自行车共享服务意愿的天气条件、安全问题和形象障碍。这些发现还表明, 游客对变革的开放价值观显着影响“RF”, 但对“RA”和对电动自行车共享服务的态度没有影响。

这项研究提供了宝贵的见解, 在促进可持续和环保消费的背景下补充了有关电动自行车共享和 BRT 的现有文献。

本研究利用 BRT 理论, 在调查经济增长的旅游目的地中游客对使用电动自行车共享服务的态度和意图方面迈出了重要一步, 并提出了一个同时考虑“RF”和“RA”的模型用于城市交通的电动自行车。”

Este estudio pretende explorar los factores esenciales de las razones a favor (RF) y en contra (RA) y su impacto en las actitudes e intenciones de los turistas de utilizar los servicios de bicicletas eléctricas compartidas (e-bike sharing) utilizando un enfoque novedoso basado en la BRT.

Se utilizó un método cuantitativo para recopilar datos de 292 turistas y analizarlos mediante la técnica PLS-SEM.

Los resultados indican que tanto la “RF” como la “RA” son cruciales para afectar a las actitudes e intenciones de los turistas de utilizar los servicios de e-bike-sharing. Además, se identificaron como “RF” factores como la comodidad, el ahorro de precio y el ahorro de tiempo, mientras que como “RA” se identificaron las condiciones meteorológicas, las preocupaciones por la seguridad y las barreras de imagen que afectan a las actitudes de los turistas y a su intención de utilizar los servicios de e-bike sharing. Estos resultados también indican que los valores de apertura al cambio de los turistas influyen significativamente en “RF” y no tienen ningún impacto en “RA” ni en la actitud hacia los servicios de e-bike sharing.

Este estudio aporta valiosas ideas que complementan la bibliografía existente sobre el uso compartido de bicicletas eléctricas (e-bike sharing) y el BRT en el contexto de la promoción de un consumo sostenible y respetuoso con el medio ambiente.”

Esta investigación da un importante paso adelante en la investigación de las actitudes e intenciones de los turistas hacia la utilización de los servicios de e-bike sharing en un destino turístico de economía en crecimiento utilizando la teoría del BRT y proponiendo un modelo que tiene en cuenta tanto la “RF” como la “RA” que utilizan las e-bikes para el transporte urbano.”

The objective of this paper is to quantitatively assess shear band evolution by using two-dimensional discrete element method (DEM).

The DEM model was first calibrated by retrospectively modelling existing triaxial tests. A series of DEM analyses was then conducted with the focus on the particle rotation during loading. An approach based on particle rotation was developed to precisely identify the shear band region from the surrounding. In this approach, a threshold rotation angle ω₀ was defined to distinguish the potential particles inside and outside the shear band and an index g(ω₀) was introduced to assess the discrepancy between the rotation response inside and outside shear band. The most distinct shear band region can be determined by the ω₀ corresponding to the peak g(ω₀). By using the proposed approach, the shear band development of two computational cases with different typical localised failure patterns were successfully examined by quantitatively measuring the inclination angle and thickness of shear band, as well as the microscopic quantities.

The results show that the shear band formation is stress-dependent, transiting from conjugated double shear bands to single shear band with confining stress increasing. The shear band evolution of two typical localised failure modes exhibits opposite trends with increasing strain level, both in inclination angle and thickness. Shear band featured a larger volumetric dilatancy and a lower coordination number than the surrounding. The shear band also significantly disturbs the induced anisotropy of soil.

This paper proposed an approach to quantitatively assess shear band evolution based on the result of two-dimensional DEM modelling.

The commercial sharing service (CSS) represents an emerging business model in which users pay a minor fee to rent a product for a short period of time. Fashion CSSs enable individuals to rent various garments and accessories with the goal of enhancing one’s public image while saving money. Marketers have strived to popularize fashion CSSs, but concerns related to contamination have thwarted their efforts. Based on face consciousness theory, this research examines how consumers’ desire to enhance their public image (i.e. to “gain face”) can attenuate the negative impacts of contamination concerns and thus facilitate fashion CSS usage.

Two scenario-based studies were conducted to collect data. Participants were recruited via online survey platforms in mainland China. The hypotheses were tested by partial least squares (PLS) path modeling and linear regression analysis.

The analysis results revealed a two-stage mediation model. Contamination concerns were found to inhibit consumers’ participation in fashion-sharing by increasing their perceived risk, which further decreased the perceived value of the CSS. However, consumers’ desire to gain face can mitigate the negative (direct and indirect) effects of contamination concerns on CSS usage, facilitating CSS adoption.

Our findings suggest that eliciting consumers’ desire to gain face can promote fashion CSS usage and attenuate the negative impacts of contamination concerns. Moreover, consumers are less risk-averse and less concerned about shared pieces being contaminated when they seek to enhance their face through fashion products. Practical implications for fashion marketers are discussed.

Augmented reality (AR) technology presents novel opportunities for marketers to captivate consumers, enhancing their experiences with brands. Although recent research underscores consumers' favorable responses to AR within retail contexts, there remains a paucity of studies examining the specific factors motivating these responses, i.e. reasons for, such as the appeal of entertainment, trendiness, customization, interaction and augmentation quality. Conversely, reasons against AR acceptance, such as perceived lack of realism, skepticism or perceived deception, are also underexplored.

Guided by the behavioral reasoning theory, the current study delves into consumers' personal values and beliefs, their reasons for and reasons against using AR apps and how these factors influence their engagement and relationship performance with AR apps. The data were collected from 343 AR app users to test the developed theoretical framework.

Results reveal that while perceived value compatibility does not influence reasons for using AR apps, it negatively influences reasons against using AR apps and positively influences connectedness to AR apps. Interestingly, openness to change does not significantly influence users’ connection to AR apps, and no association was found between reasons-against and connectedness to AR apps and relationship performance. However, a positive relationship was observed between users’ connectedness to AR apps and their relationship performance.

By offering these insights, this study contributes to a foundational understanding of the behavioral intricacies and dynamic interaction patterns within the rapidly emerging AR user base.

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable. The cloth absorbs sweat and then releases it, allowing the body to chill down. By capillary action, moisture is driven away from fabric pores or sucked out of yarns. Convectional air movement improves sweat drainage, which may aid in body temperature reduction. Clothing reduces the skin's ability to transport heat and moisture to the outside. Excessive moisture makes clothing stick to the skin, whereas excessive heat induces heat stress, making the user uncomfortable. Wet heat loss is significantly more difficult to understand than dry heat loss. The purpose of this study is to provided a good compilation of complete information on wet thermal comfort of textile and technological elements to be consider while constructing protective apparel.

This paper aims to critically review studies on the thermal comfort of textiles in wet conditions and assess the results to guide future research.

Several recent studies focused on wet textiles' impact on comfort. Moisture reduces the fabric's thermal insulation value while also altering its moisture characteristics. Moisture and heat conductivity were linked. Sweat and other factors impact fabric comfort. So, while evaluating a fabric's comfort, consider both external and inside moisture.

The systematic literature review in this research focuses on wet thermal comfort and technological elements to consider while constructing protective apparel.

Companies utilize increasingly capable Artificial Intelligence (AI) technologies to deliver modern services across a range of consumer service industries. AI autonomy, however, sparks skepticism among consumers leading to a decrease in their willingness to adopt AI services. This raises the question as to whether consumer trust in companies can overcome consumer reluctance in their decisions to adopt high (vs low) autonomy AI services.

Using a representative survey (N = 503 consumers corresponding to N = 3,690 observations), this article investigated the link between consumer trust in a company and consumers' intentions to adopt high (vs low) autonomy AI services from the company across 23 consumer service companies accounting for six distinct service industries.

The results confirm a significant and positive relationship between consumer trust in a company and consumers' intentions to adopt AI services from the same company. AI autonomy, however, moderates this relationship, such that high (vs low) AI autonomy weakens the positive link between trust in a company and AI service adoption. This finding replicates across all 23 companies and the associated six industries and is robust to the inclusion of several theoretically important control variables.

The current research contributes to the recent stream of AI research by drawing attention to the interplay between trust in companies and adoption of high autonomy AI services, with implications for the successful deployment and marketing of AI services.