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This paper aims to study the effect of different Ni content on the microstructure and properties of Sn-0.7Cu alloy. Then, the spreading area, wetting angle, interface layer thickness and microstructure of the soldering interface was observed and analyzed at different soldering temperatures and times.

Sn-0.7Cu-xNi solder alloy was prepared by a high-frequency induction melting furnace. Then Sn-0.7Cu-xNi alloy was soldered on a Cu substrate at different soldering temperatures and times.

It was found that Ni made the intermetallic compounds in the Sn-0.7Cu solder alloy gradually aggregate and coarsen, and the microstructure was refined. The phase compositions of the solder alloy are mainly composed of the ß-Sn phase and a few intermetallic compounds, Cu₆Sn₅ + (Cu, Ni)₆Sn₅. The maximum value of 12.1 HV is reached when the Ni content is 0.1 Wt.%. When the Ni content is 0.5 Wt.%, the wettability of the solder alloy increases by about 15%, the interface thickness increases by about 8.9% and the scallop-like structure is the most refined. When the soldering time is 10 min and the soldering temperature is 280 °C, the wettability of Sn-0.7Cu-0.2Ni is the best.

It is groundbreaking to combine the change in soldering interface with the soldering industry. The effects of different soldering temperatures and times on the Sn-0.7Cu-xNi alloy were studied. Under the same conditions, Sn-0.7Cu-0.2Ni exhibits better wettability and more stable solder joint stability.

The motivation of value co-creation among the multi-actor in sharing economy was an important topic in interactive marketing communication research. This study investigated how customer gratitude expression leads to value co-creation of PSPs in the sharing economy, and also investigates the moderating effect of platform benevolent climate.

A three-wave field survey (Study 1) and two experiments (Studies 2 and 3) were given to respondents with sharing economy practitioners.

First, customer gratitude expression positively influenced PSP's perceived meaningful work, which in turn enhanced their value co-creation intention. Second, PSP's perceived platform benevolent climate moderated the relationship between customer gratitude expression and PSP's perceived meaningful work.

Prior research discussed PSPs' value co-creation intention mainly from the perspective of platforms and PSPs, but few considered customer-PSP interaction perspective. This study revealed how customer gratitude expression influences PSP's value co-creation intention in highly interactive digital business context, examined the boundary condition of gratitude expression, and extended the application scenarios of social information processing theory.

Digital healthcare manages to grab considerable attention from people and practitioners to avoid severity and provide quick access to healthcare. Entrepreneurs also adopt the digital healthcare segment as an opportunity; nevertheless, their intentions to participate and encourage innovation in this growing sector are unexplored. Drawing upon the social capital theory and health belief model, the study examines the factors that drive entrepreneurship. A novel model is proposed to comprehend entrepreneurial intentions and behavior entrenched in social capital and other encouraging and dissuading perceptive elements with the moderation of trust in digitalization and entrepreneurial efficacy.

The cross-sectional method is used to collect data through a questionnaire from experienced respondents in China. The valid data comprises 280 respondents, analyzed by partial least square structural equation modeling.

Social capital significantly influences monetary attitude, and perceived risk and holds an inconsequential association with perceived usefulness, whereas monetary attitude and perceived usefulness meaningfully explain entrepreneurial activities. Perceived risk has a trivial impact on entrepreneurial intention. Entrepreneurial efficacy and trust in digitalization significantly explain entrepreneurial behavior and moderate the positive relationship between intention and behavior.

The present research proposes a novel research model in the context of entrepreneurship rooted in a digitalized world and offering new correlates. It provides valuable insights by exploring entrepreneurial motivation and deterring factors to get involved in startup activities entrenched in social capital, providing guidelines for policymakers and practitioners to promote entrepreneurship.

The paper's goal is to examine and illustrate the useful uses of submodeling in finite element modeling for topology optimization and stress analysis. The goal of the study is to demonstrate how submodeling – more especially, a 1D approach – can reliably and effectively produce ideal solutions for challenging structural issues. The paper aims to demonstrate the usefulness of submodeling in obtaining converged solutions for stress analysis and optimized geometry for improved fatigue life by studying a cantilever beam case and using beam formulations. In order to guarantee the precision and dependability of the optimization process, the developed approach will also be validated through experimental testing, such as 3-point bending tests and 3D printing. Using 3D finite element models, the 1D submodeling approach is further validated in the final step, showing a strong correlation with experimental data for deflection calculations.

The authors conducted a literature review to understand the existing research on submodeling and its practical applications in finite element modeling. They selected a cantilever beam case as a test subject to demonstrate stress analysis and topology optimization through submodeling. They developed a 1D submodeling approach to streamline the optimization process and ensure result validity. The authors utilized beam formulations to optimize and validate the outcomes of the submodeling approach. They 3D-printed the optimized models and subjected them to a 3-point bending test to confirm the accuracy of the developed approach. They employed 3D finite element models for submodeling to validate the 1D approach, focusing on specific finite elements for deflection calculations and analyzed the results to demonstrate a strong correlation between the theoretical models and experimental data, showcasing the effectiveness of the submodeling methodology in achieving optimal solutions efficiently and accurately.

The findings of the paper are as follows: 1. The use of submodeling, specifically a 1D submodeling approach, proved to be effective in achieving optimal solutions more efficiently and accurately in finite element modeling. 2. The study conducted on a cantilever beam case demonstrated successful stress analysis and topology optimization through submodeling, resulting in optimized geometry for enhanced fatigue life. 3. Beam formulations were utilized to optimize and validate the outcomes of the submodeling approach, leading to the successful 3D printing and testing of the optimized models through a 3-point bending test. 4. Experimental results confirmed the accuracy and validity of the developed submodeling approach in streamlining the optimization process. 5. The use of 3D finite element models for submodeling further validated the 1D approach, with specific finite elements showing a strong correlation with experimental data in deflection calculations. Overall, the findings highlight the effectiveness of submodeling techniques in achieving optimal solutions and validating results in finite element modeling, stress analysis and optimization processes.

The originality and value of the paper lie in its innovative approach to utilizing submodeling techniques in finite element modeling for structural analysis and optimization. By focusing on the reduction of finite element models and the creation of smaller, more manageable models through submodeling, the paper offers designers a more efficient and accurate way to achieve optimal solutions for complex problems. The study's use of a cantilever beam case to demonstrate stress analysis and topology optimization showcases the practical applications of submodeling in real-world scenarios. The development of a 1D submodeling approach, along with the utilization of beam formulations and 3D printing for experimental validation, adds a novel dimension to the research. Furthermore, the paper's integration of 1D and 3D submodeling techniques for deflection calculations and validation highlights the thoroughness and rigor of the study. The strong correlation between the finite element models and experimental data underscores the reliability and accuracy of the developed approach. Overall, the originality and value of this paper lie in its comprehensive exploration of submodeling techniques, its practical applications in structural analysis and optimization and its successful validation through experimental testing.

This Friction stir welding study aims to weld thick AA8011 aluminium plates, and the interface joints created with a variety of tool pin profiles were examined for their effects on the welding process.

Scanning electron microscopy and optical microscopy and X-ray diffraction were used to examine the macro and micro-structural characteristics, as well as the fracture surfaces, of tensile specimens. The mechanical properties (tensile, hardness tests) of the base metal and the welded specimens under a variety of situations being tested. Additionally, a fracture toughness test was used to analyse the resilience of the base metal and the best weldments to crack formation. Using a response surface methodology with a Box–Behnken design, the optimum values for the three key parameters (rotational speed, welding speed and tool pin profile) positively affecting the weld quality were established.

The results demonstrate that a defect-free junction can be obtained by using a cylindrical tool pin profile, increasing the rotational speed while decreasing the welding speeds. The high temperature and compressive residual stress generated during welding leads to the increase in grain size. The grain size of the welded zone for optimal conditions is significantly smaller and the hardness of the stir zone is higher than the other experimental run parameters.

The work focuses on the careful examination of microstructures behaviour under various tool pin profile responsible for the change in mechanical properties. The mathematical model generated using Taguchi approach and parameters was optimized by using multi-objectives response surface methodology techniques.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

The objective of this study is to investigate how the implementation of an Emission Trading Scheme (ETS) influences an ETS-regulated firm’s level of earnings smoothness.

Using a staggered difference-in-differences model based on China’s ETS pilots commencing in 2013, this study investigates how the implementation of ETS pilots affects regulated firms’ earnings smoothing relative to non-regulated firms. The sample period spans from 2008 to 2019. This model incorporates time-invariant firm-specific heterogeneity, time-specific heterogeneity, and a series of firm characteristics to establish causality. Robustness tests justify findings.

The results show that after implementing an ETS pilot, regulated firms increase their earnings smoothness relative to non-regulated firms. Regulated firms strategically smooth their earnings to obtain additional financial resources and meet compliance costs arising from an ETS. Further analysis reveals that regulated firms’ earnings smoothing activity is a function of environmental regulations, managerial integrity, and capital market incentives.

This study deviates from past research focusing on the environmental consequences of ETS by indicating that an ETS affects regulated firms’ financial reporting decisions. Specifically, regulated firms resort to earnings smoothing as a short-term exit strategy from financing concerns arising from environmental regulations. This finding expands prior literature primarily focusing on the effect of tax and financial reporting regulations on earnings smoothness. This study also indicates that firms utilize earning smoothing to lower their short-term cost of capital, which enables them to access additional financing at a lower cost and reconfigure their operations to meet stakeholder environmental demands.

Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution.

Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function.

Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved.

This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application.

This study aims to propose a moderated mediation model to investigate the moderating effects of environmental, social and governance (ESG) performance on the relationship between inefficient investment and firm performance and the mediating effect of firms that participate in institutional research on the relationship between investment efficiency and performance. This study also analyses the heterogeneity of the corporate nature, intensity of industrial research and development (R&D), industrial competition and regional marketization.

This study uses a panel data fixed-effects model to conduct a regression analysis of 1,918 Chinese listed firms from 2016 to 2020. A Fisher’s permutation test is used to examine the differences between state-owned and nonstate-owned firms.

Inefficient investment negatively impacts corporate performance and higher ESG performance exacerbates this effect by attracting more institutional research which reveals more problems. State-owned enterprises perform significantly better than nonstate-owned enterprises in terms of ESG transformation. Industrial R&D intensity, competition and regional marketization also mitigate the negative effects of inefficient investment on corporate performance.

This study suggests that companies should consider inefficient investments that arise from agency issues in corporate ESG transformation. In addition, state-owned enterprises in ESG transformation should take the lead to achieve sustainable development more efficiently. China should balance regional marketization, encourage enterprises to increase R&D intensity, reduce industry concentration, encourage healthy competition and prevent market monopolies.

This study combines the agency and stakeholder theories to reveal how inefficient investments that arise from agency issues inhibit value creation in ESG initiatives.

Guided by the cognitive-affective system theory of personality (CAPS), this study aims to investigate the parallel mediating effects of cognitive and affective cynicism on the relationship between illegitimate tasks and employees’ adaptive performance. It also proposes growth need strength as a moderating variable for relationships between illegitimate tasks and employees’ adaptive performance.

Using a time-lagged design, data were gathered from 330 frontline hotel employees in China.

The authors found that the presence of illegitimate tasks is negatively associated with employees’ adaptive performance, this relationship being mediated by cognitive and affective cynicism. Growth need strength weakens the negative impacts of cognitive and affective cynicism on employees’ adaptive performance. In addition, the indirect effect of illegitimate tasks on employees’ adaptive performance via cognitive and affective cynicism is stronger for employees with lower levels of growth need strength.

Hotel managers must heed the negative impact of illegitimate tasks. Furthermore, they should underscore the importance of promoting a harmonious and positive organizational culture and atmosphere. Naturally, hotel managers must also establish effective communication with employees, assisting them in fostering a desire for excellence in their work.

This study provides valuable insights for the hospitality industry by investigating how illegitimate tasks hold sway over hotel employees’ adaptive performance. The study uses a moderated dual-path model to uncover the mechanisms behind this impact and the influence of boundary conditions, thereby expanding the understanding of the topic.