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This paper aims to investigate the microstructural evolution rules of the intermetallic compound (IMC) layers in high‐density solder interconnects with reduced stand‐off heights (SOH).

Cu/Sn/Cu solder joints with 100, 50, 20 and 10 μm SOH were prepared by the same reflow process and isothermally aged at 150°C. The IMC microstructural evolution was observed using scanning electron microscopy.

The whole IMC layer (Cu₃Sn + Cu₆Sn₅) grew faster in the solder joints with lower SOH because of the thinner IMC layer before aging. Also, the IMC proportion increased more rapidly in solder joints with the lower SOH. In all solder joints with different SOH, the growth rates of the Cu₃Sn (ϵ) layers were similar, and slowed down with increasing aging time. The Cu₆Sn₅ (η) was consumed by the Cu₃Sn (ϵ) growth at the beginning of the aging stage; while it turned to thickening after a period of aging. Finally, the Cu₆Sn₅ thickness was similar in all the solder joints. It is inferred that the thickness ratio of Cu₃Sn to Cu₆Sn₅ would maintain a dynamic balance in the subsequent aging. Based on the diffusion flux ratio of Cu to Sn at the ϵ/η interface, a model has been established to explain the microstructural evolution of IMC layers in high‐density solder interconnects with reduced SOH. In the model, interfacial reactions are mainly supposed to occur at the ϵ/η interface.

The findings provide electronic packaging reliability engineers with an insight into IMC microstructural evolution in high‐density solder interconnects with reduced SOH.

The purpose of this paper is to develop an field programmable gate array (FPGA)-based neuron circuit to mimic dynamical behaviors of tabu learning neuron model.

Numerical investigations for the tabu learning neuron model show the coexisting behaviors of bi-stability. To reproduce the numerical results by hardware experiments, a digitally FPGA-based neuron circuit is constructed by pure floating-point operations to guarantee high computational accuracy. Based on the common floating-point operators provided by Xilinx Vivado software, the specific functions used in the neuron model are designed in hardware description language programs. Thus, by using the fourth-order Runge-Kutta algorithm and loading the specific functions orderly, the tabu learning neuron model is implemented on the Xilinx FPGA board.

With the variation of the activation gradient, the initial-related coexisting attractors with bi-stability are found in the tabu learning neuron model, which are experimentally demonstrated by a digitally FPGA-based neuron circuit.

Without any piecewise linear approximations, a digitally FPGA-based neuron circuit is implemented using pure floating-point operations, from which the initial conditions-related coexisting behaviors are experimentally demonstrated in the tabu learning neuron model.

Environmental Accounting (EA) practices have developed rapidly in some countries and have a positive impact on their organizations. Sustainability report (SR) as an indicator of EA practices helps company gain a better reputation and it is set by management. However, some ASEAN countries including Indonesia do not have relevant accounting standards on the environment. EA practice is still not widely known in Indonesia, although, internationally there have been standards that provide guidelines for aspect of the environment such as the Global Reporting Initiative (GRI). Another aspect in GRI is remuneration. Remuneration is part of personnel cost which is a motivation about the positive effects of EA practices to disclose management concern. This research introduces a tool to evaluate a remuneration structure and the consistency of EA practices in the Sustainability Report.

Outlines previous research on the relationship between stock returns and earnings and investigates the effects of divergence between security analysts’ earnings forecasts on this relationship. Uses cross‐sectional analysis, time series and variable parameter models on 1981‐88 US data to see whether the stock returns‐earnings coefficient decreases with increased disagreement between analysts and shows that it does. Considers the implications for policy makers and accounting research design; and consistency with other research.

From a roundup of recent literature on the effectiveness of the current accounting curriculum in the USA, identifies a consensus that additional education is needed to better prepare students for their careers in the accounting profession and seeks to determine which areas need to be addressed if substantial improvement is to be achieved within the curriculum; analyses data collected from the results of students’ tests undertaken over three consecutive semesters at a US Midwest university to assess students’ quantitative, verbal and problem‐solving skills, and finds that performance in the latter two areas was consistently less impressive than in the former. As each of these areas is seen as important to accounting careers, suggests that more attention should be paid to communication and problem‐solving skills as part of the curriculum, although not at the expense of technical skills; tabulates and briefly discusses a number of other findings from the research, for example with regard to gender differences across the skill areas.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study.

Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data.

The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E).

This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses.

This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies.

This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

This study was based on the perceptions of lecturers and black CTA students at Unisa, a South African distance education university regarding on factors that contribute to black students’ academic success and failure. The main purposes of the study were to help black CTA students to understand the reasons for success and failure better, and to improve lecturers’ teaching approach(es). The research shows that students and lecturers have divergent views on what factors contribute to academic success or failure and the relative importance of the various factors.

The purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience.

By integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed.

By fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components.

The process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.