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The paper aims to study the effect of sintering temperature on the microstructure, shear strength and ratcheting fatigue life of nanosilver sintered lap shear joint. In addition, the Gerber model is used to predict the ratcheting fatigue lives of nanosilver sintered lap shear joints at different sintering temperatures.

In this paper, the nanosilver sintered lap shear joints were prepared at three sintering temperatures of 250 °C, 280 °C and 310 °C. The bonding quality was characterized by scanning electron microscopy, X-ray diffraction, transmission electron microscope and shear tests, and the long-term reliability was studied by conducting ratcheting fatigue tests. In addition, three modified models based on Basquin equation were used to predict the ratcheting fatigue life of nanosilver sintered lap shear joint and their accuracies were evaluated.

When the sintering temperature is 250°C, the nanosilver sintered lap shear joint shows the porosity of 22.9 ± 1.6 %, and the shear strength of 22.3 ± 2.4 MPa. Raising the sintering temperature enhances silver crystallite size, strengthens sintering necks, thus improves shear strength and ratcheting fatigue life in joints. In addition, the ratcheting fatigue lives of the joints sintered at different temperatures are effectively predicted by three equivalent force models, and the Gerber model shows the highest life prediction accuracy.

The sintered silver bondline is suffering a complex stress state. The study only takes the shear stress into consideration. The tensile stress and the combination of shear stress and tensile stress can to be considered in the future study.

The paper provides the experimental and theoretical support for robust bonding and long-term reliability of sintered silver structure.

The introduced model can predict the ratcheting fatigue lives of the joints sintered at different temperatures, which shows a potential in engineering applications.

The study revealed the relationship between the sintering temperature and the microstructure, the shear strength and the ratcheting fatigue life of the joint. In addition, the Gerber model can predict the ratcheting fatigue life accurately at different sintering temperatures.

The design of cantilever pile walls (CPWs) presents several common challenges. These challenges include soil variability, groundwater conditions, complex loading conditions, construction considerations, structural integrity, uncertainties in design parameters and construction and monitoring costs. Accordingly, this paper is to provide a detailed literature review on the design criteria of CPWs, specifically in cohesionless soil. This study aims to present a comprehensive overview of the current state of knowledge in this area.

The paper uses a literature review approach to gather information on the design criteria of CPWs in cohesionless soil. It covers various aspects such as excavation support systems (ESSs), deformation behavior, design criteria, lateral earth pressure calculation theories, load distribution methods and conventional design approaches.

The review identifies and discusses common challenges associated with the design of CPWs in cohesionless soil. It highlights the uncertainties in determining load distribution and the potential for excessive wall deformations. The paper presents various approaches and methodologies proposed by researchers to address these challenges.

The paper contributes to the field of geotechnical engineering by providing a valuable resource for geotechnical engineers and researchers involved in the design and analysis of CPWs in cohesionless soil. It offers insights into the design criteria, challenges and potential solutions specific to CPWs in cohesionless soil, filling a gap in the existing knowledge base. The paper draws attention to the limitations of existing analytical methods that neglect the serviceability limit state and assume rigid plastic soil behavior, highlighting the need for improved design approaches in this context.

The emergence of Construction 4.0 technologies provides an impetus for radical change and rejuvenates the interest of stakeholders in addressing long-standing performance issues in the construction sector. However, construction firms struggle to implement Construction 4.0 technologies for performance measurement and improvement. Therefore, the purpose of this research is to develop a conceptual model of innovation management for implementing Construction 4.0 that guides and facilitates the strategic transformation of construction firms.

A conceptual model of innovation management is presented, and the findings are synthesised based on a literature review, 20 semi-structured interviews, two focus group discussions, three workshops, expert consultation and observations on three digitally-enabled projects. Data were inductively analysed using thematic analysis.

The analysis of empirical data revealed: (i) Four scenarios that could lead the industry to different futures, based on the extent of research and development, and the extent of integration/collaboration; (ii) Construction 4.0 capability stages for a sustained implementation route; (iii) Possible business model configurations derived from servitisation strategies; and (iv) Skills management challenges for organisations.

First, the empirical data was only collected in the UK with its unique industry context, which may limit the applicability of the results. Second, most of the research data comes from the private sector, without the views of public sector organisations. Third, the model needs to be further validated with specific data-driven use cases to address productivity and sustainability issues.

Successful Construction 4.0 transformation requires a concerted effort of stakeholders, including those in the supply chain, technology companies, innovation networks and government. Although a stakeholder’s action would depend on others’ actions, each stakeholder should undertake action that can influence the factors within their control (such as the extent of collaboration and investment) and the outcomes.

The conceptual model brings together and establishes the relationships between the scenarios, Construction 4.0 capability stages, business models and skills management. It provides the first step that guides the fuzzy front-end of Construction 4.0 implementation, underpins the transformation to the desired future and builds long-term innovation capabilities.

Conventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.

The proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).

The comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.

Traditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

Intestinal mucosa functions as a specialized permeable barrier, facilitating nutrient absorption and safeguarding against external influences. Gut barrier serves as a channel of communication between gastrointestinal and bodily processes. Research investigations have postulated that enhancing gut barrier through microbiota could potentially enhance overall well-being. Hence, this intervention study was designed to assess whether intervention of microbiota modulators delivers intestinal and extra-intestinal benefits.

A randomized, double-blind, placebo-controlled trial was devised to examine the impact of two doses (1.5 and 3 g) of intervention, comprising prebiotics and vitamins on gut barrier indicators (faecal IgA and calprotectin) and microbiota markers (lactobacilli and bifidobacteria) of healthy human subjects. In addition, cholera vaccine challenge test was conducted to assess the influence of the barrier improvement on the mucosal response to a stressor.

After two weeks of intervention, prebiotics and vitamins resulted in a significant (p = 0.04) enhancement of faecal IgA levels compared to placebo. This enhancement coincided with an increase in levels of faecal lactobacilli and bifidobacteria. A significant (p < 0.001) reduction in faecal calprotectin levels was observed in both intervention groups at the end of intervention, compared to placebo. Following cholera vaccine challenge, a markedly (p < 0.03) heightened response was documented in intervention groups.

This study illustrated that combination of prebiotics and vitamins could effectively modulate gut barrier and microbiota markers in healthy individuals, which contribute to a good gut health. These findings establish a foundation for delivering optimal bodily functions dwelling from a healthy gut.

This is one of its kind study which has probed into the physiological response with improvement in gut health markers.

The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

A question remains unresolved in existing cross-disciplinary research at the marketing/entrepreneurship interface (MEI). This features circumstances when employing a combination of market-oriented and entrepreneurially-oriented activities, known as entrepreneurial marketing (EM) behavior, is likely to lead to positive performance outcomes. Earlier mixed findings provide the need to unpack the nuances of EM practices, in terms of their boundary conditions, regarding circumstances where this behavior does or does not lead to performance-enhancing outcomes. Consequently, the purpose of this study is to examine the complexities of the association between EM activities and small firm performance by assessing quadratic and moderating effects.

This study was underpinned by resource-based theory (RBT). Survey responses were collected from 214 smaller-sized companies in the United Kingdom. The statistical data passed all major checks for reliability, different forms of validity, common method variance and endogeneity bias.

EM activities had a quadratic connection with small firm performance, with this relationship being enhanced (in terms of a positive two-way interaction effect) by market dynamism (a counter-intuitive result regarding environmental conditions). Surprisingly, through a post-hoc test, coopetition (cooperation among competitors to leverage assets and overcome resource constraints) did not play any influential part in helping owner-managers to overcome the potential downsides of EM practices, like the time and cost implications of identifying and exploiting opportunities (i.e., a non-significant three-way interaction effect).

Unique insights outline how decision-makers in smaller-sized organizations can harness the potential benefits, and minimise the likely drawbacks, of employing EM activities. However, owner-managers should be cautious when implementing these organization-wide practices, since they are likely to enhance performance, but only up to a fixed point. Indeed, excessive forms of EM activities can weaken small firms’ performance. A counter-intuitive positive moderation effect regarding market dynamism challenges certain earlier findings. Specifically, in some dynamic market conditions, EM activities could be performance-enhancing, since certain environmental-level forces might assist owner-managers to amplify the merits of behavior at the MEI when implemented effectively.

Due to the environmental concerns in our society, governments are moving towards green purchasing. However, public sector organizations have substantial internal problems. By using a theory of natural resource-based view, this study aimed to analyse the influence of leadership styles and innovation capabilities on green purchasing in the public sector organizations of a developing economy.

Data was collected from 199 purchasing professionals of public sector organizations, and hypotheses were tested through a quantitative method using partial least square-structural equation modelling with the help of SmartPLS version 4 to validate the measurement model.

This study identified that transformational leadership and delegation style of leadership have a significant and positive effect on innovation capability and green purchasing. Similarly, innovation capability mediates the relationship between leadership styles and green purchasing. However, transformational leadership found a more substantial effect on innovation capability and green purchasing than the delegation leadership style.

Results provide insight into the role of leadership styles in green purchasing and innovation capabilities in public sector organizations. Thus, addressing the important issues of how leadership styles and innovative capabilities (IC) can improve green culture, specifically green purchasing, to enhance ecological sustainability.

Results provide insight into the role of leadership styles in green purchasing and innovation capabilities in public sector organizations. Thus, it addresses how leadership styles and IC can improve green culture, specifically green purchasing, to enhance ecological sustainability.