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The brake pipe system was an essential braking component of the railway freight trains, but the existing E-type sealing rings had problems such as insufficient low-temperature resistance, poor heat stability and short service life. To address these issues, low-phenyl silicone rubber was prepared and tested, and the finite element analysis and experimental studies on the sealing performance of its sealing rings were carried out.

The low-temperature resistance and thermal stability of the prepared low-phenyl silicone rubber were studied using low-temperature tensile testing, differential scanning calorimetry, dynamic thermomechanical analysis and thermogravimetric analysis. The sealing performance of the low-phenyl silicone rubber sealing ring was studied by using finite element analysis software abaqus and experiments.

The prepared low-phenyl silicone rubber sealing ring possessed excellent low-temperature resistance and thermal stability. According to the finite element analysis results, the finish of the flange sealing surface and groove outer edge should be ensured, and extrusion damage should be avoided. The sealing rings were more susceptible to damage in high compression ratio and/or low-temperature environments. When the sealing effect was ensured, a small compression ratio should be selected, and rubbers with hardness and elasticity less affected by temperature should be selected. The prepared low-phenyl silicone rubber sealing ring had zero leakage at both room temperature (RT) and −50 °C.

The innovation of this study is that it provides valuable data and experience for the future development of the sealing rings used in the brake pipe flange joints of the railway freight cars in China.

The multiple goals make social enterprises vulnerable to mission drift, which hurts the sustainability of these hybrid organizations. As initiators, the relationship between social entrepreneurs and the mission drift of social enterprises needs to be further explored. This study aims to explore how entrepreneurs’ education and age impact social enterprise mission drift and examine the potential moderating effects of social enterprise legitimacy.

This study uses the Global Entrepreneurship Monitor data set to obtain the required samples, and further regression analysis is used to test the hypotheses.

The results showed that the more educated the entrepreneur, the lower the social enterprise mission drift. There is an inverted U-shaped relationship between social entrepreneurs’ age and mission drift. Finally, the inverted U-shaped relationship between age and mission drift was more pronounced in contexts with lower social enterprise legitimacy than high legitimacy levels.

This study highlights the significance of founders’ characteristics on the mission robustness of the social enterprises they create. At the same time, the role of social enterprise legitimacy is demonstrated in the context of this study. The findings of this research have implications for social entrepreneurs, social enterprises and policymakers.

This study will conduct an in-depth investigation into the development paths and factors influencing the patterns of social-emotional competence enhancement and social self-concept construction among higher education students.

This study used a longitudinal research methodology to perform a one-year follow-up study with the Class of 2022 freshmen from College C of the University of Macau to investigate the causal mechanisms and mediating pathways involved.

This study revealed that students’ open-mindedness was a full mediator between self-cognition and engagement in Residential College system’s activities, that engagement was a complementary mediator between an open-mindedness and social-emotional competence, and that social-emotional competence was a competitive mediator between the engagement and social self-concept. The variety of activities offered in Residential College system fosters social behavior acquisition, which supports the growth of social-emotional competence and the development of social self-concept through emotional competence.

This study enriches the theoretical connotation of whole-person education in the Residential College system and emphasizes the significance of social emotional learning and social self-concept in higher education.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of diamond nitrogen vacancy (NV) centers sensors.

In the field of magnetic detection, NV centers have corresponding advantages due to their unique long coherence property at room temperature. The important indicators for NV centers magnetometers are the magnetic detection sensitivity of the NV centers and the integration of the magnetometer. To solve this problem, the authors propose a multi-frequency modulated magnetic detection technique, using an integrated probe as well as a lock-in amplifier for the double enhancement of sensitivity as well as integration.

The following results can be obtained by processing and calculating the experimental data with an integrated lock-in amplifier circuit with an area of 27.50 cm² and a probe volume of 3.12 cm³. The multi-frequency modulation technique was used to increase the magnetic detection sensitivity of the NV centers from 8.59 nT/Hz1 / 2–2.42 nT/Hz1 / 2.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. This technique provides new research solution for the subsequent improvement of the magnetic detection sensitivity.

Focusing on the resource crowding-out effect, this study aims to examine the relationship between an enterprise’s digital transformation and the internalisation of environmental costs.

This paper manually collects environmental cost data and measures corporate digital transformation constructed through a machine learning word vector (Word2Vec) technology approach based on the text information of annual reports (MD&A) for heavily polluting firms.

Corporate digital transformation has a significant inhibitory effect on the internalisation of corporate environmental costs. This is because low-level digital transformation has crowded out cash flows, preventing China’s heavily polluting firms from having the extra capacity needed to internalise environmental costs. This crowding-out effect emerges when companies face problems such as capital shortages, short-term profit pressure and intense market competition. These results have the following important implications.

The research highlights the need for enterprises to align digital transformation and sustainability strategies by strengthening resource endowment and optimising internal resource allocation. This requires effective use of digital technology and a long-term sustainability vision for heavily polluting firms facing environmental policy pressures.

Enterprises should assume more social responsibility and achieve sustainable socioeconomic development. It will also help mitigate the adverse environmental externalities stemming from their operations.

To the best of the authors’ knowledge, this study considers the impact of enterprise digital transformation on the internalisation level of enterprise environmental costs for the first time and uses enterprises’ financial, management, market characteristics and ownership characteristics to analyse the impact mechanism.

Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.

This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.

The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.

The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.

This study aims to explore the underlying mechanism and boundary conditions of consumer’s preference on a multiple-percentage discount over an economically equivalent single discount.

The authors tested their predictions using two large-scale online experiments accompanied by a fully controlled laboratory experiment.

This study shows that the preference for a multiple-percentage discounts over an economically equivalent single-percentage discount is because consumers directly add the raw values of multiple-percentage discounts. The preference is less prominent if the percentage discounts are presented using round (vs non-round) numbers and is strengthened by experiential-thinking style.

This study examined consumer preferences between a single-percentage discount and an economic equivalent multiple-percentage discount. A promising research topic is exploring consumer preferences between multiple-percentage discounts.

Understanding how different presentation formats of the same price discount can lead to different evaluations enables managers and marketers to ensure a more effective promotional strategy.

This study extends literature on price-based promotions by demonstrating how consumers’ preferences for multiple discounts can be strengthened or weakened.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

The question of “what works?” for organizational diversity and inclusion (D&I) has captivated practitioners and researchers for some time, but there is a lack of unifying knowledge on what interventions are effective. The purpose of this study was to investigate the impact, outcomes and quality of evidence relating to interventions used by organizations for workplace D&I to inform what works in practice and where there are still evidence gaps.

This multidisciplinary meta-review – a systematic review of reviews – summarized the effects of various practices, policies, programs and initiatives across dimensions of diversity and disciplines.

From 37 reviews over 13 years, we identified 12 categories of interventions (e.g. diversity training, de-biasing selection procedures and workplace accommodations) mapped to 22 outcomes. Workplace accommodations and job training were linked with positive outcomes in the age and disability dimensions of diversity. Diversity training was associated with comparatively higher-quality evidence, albeit its effects were largely limited to awareness and learning outcomes. The literature on recruitment, leave and compensation policies reported some mixed effects on D&I.

This meta-review provides a synthesis of the current state of evidence across multiple disciplines and diversity dimensions to guide future research and practical D&I actions. It has implications for research (e.g. greater attention needed for interventions to improve inclusion) and practice (e.g. setting realistic expectations about what change organizations can expect to achieve).