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The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects of 0.5%Sb and 0.07%Ce doping on microstructure, thermal properties and mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder were investigated.

According to the mass ratio, the solder alloys were prepared from tin ingot, antimony ingot, silver ingot and copper ingot with purity of 99.99% at 400°C. X-ray diffractometer was adopted for phase analysis of the alloys. Optical microscopy, scanning electron microscopy and energy dispersive spectrometer were used to study the effect of the Sb and Ce doping on the microstructure of the solder. Then, the thermal characteristics of alloys were characterized by a differential scanning calorimeter (DSC). Finally, the ultimate tensile strength (UTS), elongation (EL.%) and yield strength (YS) of solder alloys were measured by tensile testing machine.

With the addition of Sb and Ce, the ß-Sn and intermetallic compounds of solders were refined and distributed more evenly. With the addition of Sb, the UTS, EL.% and YS of Sn-1.0Ag-0.5Cu increased by 15.3%, 46.8% and 16.5%, respectively. The EL.% of Sn-1.0Ag-0.5Cu increased by 56.5% due to Ce doping. When both Sb and Ce elements are added, the EL.% of Sn-1.0Ag-0.5Cu increased by 93.3%.

The addition of 0.5% Sb and 0.07% Ce can obtain better comprehensive performance, which provides a helpful reference for the development of Sn-Ag-Cu lead-free solder.

Based on the conservation of resources (COR) theory, this study seeks to investigate how job embeddedness (JE) and job frustration (JF) as serial mediators linking abusive supervision (AS) to project performance (PP) in the construction industry.

Data were gathered from 297 respondents working in six organizations involved in large-scale construction projects. The respondents were project managers, field engineers, consultants and civil engineers. Partial least squares structural equation modeling was used for data analysis and hypothesis testing.

The study findings indicate that JE and JF mediate AS’s impact on PP. The findings further reveal that JE and JF serially mediated the linkage between AS and PP.

This manuscript contributes to the relevant knowledge by investigating the overlooked psychological mechanisms of JE and JF between the linkage of AS to PP. The results of this study hold significant implications for both theoretical research and management practices.

This paper aims to analyze the role and advantages of knowledge resources in the carbon emission reduction of the industrial chain, and how it can be used to promote the carbon emission reduction of the industrial chain, so that the industry can better achieve the saving of energy and the reduction of emission.

This paper argues that the traditional resource-plundering industrial chain production method can no longer meet the needs of sustainable development of the green and low-carbon industrial chain, and builds the coupling and coordination of knowledge technology innovation drive and industrial chain carbon emission reduction mechanism, in the four dimensions of industrial chain organization, government support, internet support and staff brainstorming, put forward suggestions for knowledge resources to drive carbon emission reduction in the industrial chain.

This paper holds that the use of knowledge resource advantages can better help industrial chain enterprises to carry out technological innovation, knowledge resource digital platform construction, knowledge resource overflow and transfer, application and management of network information technology, so as to reduce carbon emission in industrial chain.

This paper contributes to the discussion about the high-quality implementation of the revitalization strategy of the industrial chain and also deepens research on the knowledge resource-driven carbon emission reduction of the industrial chain. Further, this paper enriches the role of knowledge resources in the industrial industry, and the theoretical results support the advantages of knowledge resource in the field of chain carbon emission reduction.

This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration. Consequently, a low-cost, data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.

A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration. Further, field measurements were performed on a 51.95-km metro line. Data from 272 metro sections were tested as a case study, and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.

First, the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage, particularly at speeds of 15.4, 18.3, and 20.9 m/s. Second, resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance. Finally, combined with the traditional linear relationship between speed and acceleration, the statistical relationships between the speed and car body acceleration under different quantiles were determined. We concluded the lateral/vertical quantiles of 0.8989/0.9895, 0.9942/0.997, and 0.9998/0.993 as being excellent, good, and qualified control limits, respectively, for the lateral and vertical acceleration of the car body. In addition, regression lines for the speed-related acceleration limits at other quantiles (0.5, 0.75, 2s, and 3s) were obtained.

The proposed method is expected to serve as a reference for further studies on speed-related acceleration limits in rail transit systems.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

This paper aims to demonstrate that, in line with the emerging trend of multifunctional yarn development, cotton yarn can effectively harness renewable solar energy to achieve photothermal conversion and thermochromism. This innovation not only maintains the comfort associated with natural fiber cotton yarn but also enhances its ultraviolet (UV) light resistance.

In this work, 4% zirconium carbide (ZrC) and thermochromic powder were adhered to cotton yarn through polyurethane (PU) by sizing coating method. After sizing, the two cotton yarns are twisted by ring spinning to obtain composite yarns with photothermal conversion and thermochromic functions.

The yarn obtained by cotton/6%PU/8% thermochromic dye single yarn and cotton/6%PU/4% ZrC single yarn composite is the best match. After 5 min of infrared light, the temperature of the composite yarn rose to the maximum, increasing by 36.1°C. The ΔE* value before and after irradiation of infrared lamp is 26.565, which proves that the thermochromic function is good. The yarn dryness unevenness was significantly reduced by 27.2%. The composite yarn has a UPF value of up to 89.22, and its performance characteristics remain stable after 100 minutes of washing.

The composite yarn’s photothermal conversion and thermochromism functions are mutually reinforcing. Using sunlight can simultaneously achieve heating and discoloration effects without consuming additional energy. The cotton yarn used in this application is versatile, and suitable for a wide range of uses including clothing, temperature visualization detection and other scenarios.

This paper aims to address the pressing challenges in research data management within institutional repositories, focusing on the escalating volume, heterogeneity and multi-source nature of research data. The aim is to enhance the data services provided by institutional repositories and modernise their role in the research ecosystem.

The authors analyse the evolution of data management architectures through literature review, emphasising the advantages of data lakehouses. Using the design science research methodology, the authors develop an end-to-end data lakehouse architecture tailored to the needs of institutional repositories. This design is refined through interviews with data management professionals, institutional repository administrators and researchers.

The authors present a comprehensive framework for data lakehouse architecture, comprising five fundamental layers: data collection, data storage, data processing, data management and data services. Each layer articulates the implementation steps, delineates the dependencies between them and identifies potential obstacles with corresponding mitigation strategies.

The proposed data lakehouse architecture provides a practical and scalable solution for institutional repositories to manage research data. It offers a range of benefits, including enhanced data management capabilities, expanded data services, improved researcher experience and a modernised institutional repository ecosystem. The paper also identifies and addresses potential implementation obstacles and provides valuable guidance for institutions embarking on the adoption of this architecture. The implementation in a university library showcases how the architecture enhances data sharing among researchers and empowers institutional repository administrators with comprehensive oversight and control of the university’s research data landscape.

This paper enriches the theoretical knowledge and provides a comprehensive research framework and paradigm for scholars in research data management. It details a pioneering application of the data lakehouse architecture in an academic setting, highlighting its practical benefits and adaptability to meet the specific needs of institutional repositories.

This study investigates the multifaceted barriers and facilitators affecting research data sharing across the research data lifecycle. It aims to broaden the understanding of data sharing beyond the publication phase, emphasizing the continuous nature of data sharing from generation to reuse.

Employing a mixed-methods approach, the study integrates the Theory of Planned Behavior, the Technology Acceptance Model, and the Institutional Theory to hypothesize the influence of various factors on data sharing behaviors across the lifecycle. A questionnaire survey and structural equation modeling are utilized to empirically test these hypotheses.

This study identifies critical factors influencing data sharing at different lifecycle stages, including perceived behavioral control, perceived effort, journal and funding agency pressures, subjective norms, perceived risks, resource availability, and perceived benefits. The findings highlight the complex interplay of these factors and their varying impacts at different stages of data sharing.

This study illuminates the dynamics of research data sharing, offering insights while recognizing its scope might not capture all disciplinary and cultural nuances. It highlights pathways for stakeholders to bolster data sharing, suggesting a collaborative push towards open science, reflecting on how strategic interventions can bridge existing gaps in practice.

This study offers actionable recommendations for policymakers, journals, and institutions to foster a more conducive environment for data sharing, emphasizing the need for support mechanisms at various lifecycle stages.

This study contributes to the literature by offering a comprehensive model of the research data lifecycle, providing empirical evidence on the factors influencing data sharing across this continuum.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

In urban planning, sustainability is closely linked to the quality of urban public spaces (UPS). However, some UPS encounter issues of low attractiveness and underutilisation. Vitality serves as a crucial measure in this context. The research perspective on the vitality of UPS centres on the balance between human activities and the built environment. Therefore, this article aims to systematically review critical aspects of UPS vitality evaluation system, including research objects, vitality components and research methods, from the dimensions of crowd activity and the built environment.

A systematic literature review using PRISMA analysed English-language publications from 2008 to 2023 in Scopus and Web of Science (WOS) databases, employing keywords related to UPS and vitality, with defined inclusion and exclusion criteria.

(1) Research objects, including parks, squares, waterfronts, blocks and streets. (2) The factors contributing to crowd activity characteristics originate from five dimensions, namely spatial, temporal, visitor, activity and feedback. Environmental factors, both external (accessibility, surrounding function mix and population density) and internal (service facility mix and water presence), significantly impact vitality. (3) The study primarily relies on quantitative data, including traditional surveys and emerging significant data sources like dynamic location and traffic, social media, geospatial and point of interest (POI) data. Data analysis methods commonly used include correlation analysis and comprehensive evaluation techniques.

The findings contribute to a comprehensive understanding of the vitality evaluation system for UPS from multiple perspectives for urban planners, aiding in identifying key factors and research methods in the vitality evaluation of various types of UPS.