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The decision to become international is a highlighted organisational decision that affects all dimensions at all firm levels. Human resources are also among the parts of the organisation affected by this decision. Paying attention to employees can speed up and facilitate this process. Organisational integrity is one of the most significant issues that must be considered. In this regard, identifying, investigating and planning to deal with the destructive effects that may influence the employees of small and medium-sized enterprise (SMEs) in internationalisation, are among the subjects that have so far received less attention and should be studied more. The present study explores the destructive influences of internationalisation on the employees of SMEs by a hybrid multi-layer decision-making model-psychological solution. First, by reviewing the literature, the destructive impacts of internationalisation on employees are extracted. In the next stage, these factors are screened according to the condition of the SMEs in an emerging economy by interval-valued intuitionistic hesitant fuzzy Delphi (IVIHF-Delphi). The impact of these factors on each other is then evaluated applying interval-valued intuitionistic hesitant fuzzy DEMATEL-based ANP (IVIHF-DANP). Consequently, the highlighted destructive impacts are determined and the psychological solutions to face them are provided.

This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics.

A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately.

A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature.

The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

Plastic ball grid array packages were aged for up to 2000 hours. Various solder ball pad metallurgies were studied and solder ball shear tests were conducted at a range of ageing times. The solder ball shear strength was found to decrease after an initial hardening stage. The deterioration of solder ball shear strength was found to be mainly caused by the formation of intermetallic compound layers, together with microstructural coarsening and diffusion related porosity at the interface. For the ball pad metallurgy, two distinct intermetallic compound layer structures were observed to have formed after ageing. Once two continuous intermetallic compound layers formed fracture tended to occur at their interface. For the ball pad metallurgies which do not form two continuous intermetallic compound layers, the shear strength still decreased, due to the coarsening of the microstructure, intermetallic particle formation and diffusion related porosity at the surface of the Ni₃Sn₄.

Current US tax laws provide investors an incentive to time the sales of their bonds to minimize tax liability. This gives rise to a tax-timing option that affects bond value. In reality, corporate bond investors’ tax-timing strategy is complicated by risk of default. Existing term structure models have ignored the effect of the tax-timing option, and how much corporate bond value is due to the tax-timing option is unknown. In this chapter, we assess the effects of taxes and stochastic interest rates on the timing option value and equilibrium price of corporate bonds by considering discount and premium amortization, multiple trading dates, transaction costs, and changes in the level and volatility of interest rates. We find that the value of the tax-timing option accounts for a substantial proportion of corporate bond price even when interest rate volatility is low. Ignoring the timing option value results in overestimation of credit spread, and underestimation of default probability and the marginal investor’s income tax rate. These estimation biases generally increase with bond maturity and credit risk.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

Light Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.

To find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.

Thorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.

All results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

Using data from securities analysts, who are awarded status by the third-party organization Institutional Investor magazine, we examine the emergence of competition and articulate a model of competitive response among actors aware of the importance of status and some of the dimensions on which it may be gained. We predict analysts’ initiating or ceasing coverage of stocks in response to other analysts initiating coverage on stocks they cover. We find that competition can emerge because of status seeking rather than as a response to own capabilities or market needs, with compelling, and potentially negative, market implications for overt status seeking.

The fusion of aerial imagery and LiDAR point clouds are considered as one of the most promising approaches for many fields, such as 3D city reconstruction and tree detection. The purpose of this paper is to achieve reliable registering LiDAR data and aerial images without orientation parameters based on a progressive optimizing process.

First, combination of edges and their corners is extracted and considered as registration primitives; then search conjugate primitives globally with a suitable buffer of each edge; after that, a progressive algorithm is adopted to optimize the registration; finally, error analysis and data fusion are carried out.

After a progressive optimum algorithm, the number and the distribution of the matched pairs are sufficient for generation of reliable and accurate orientation parameters. The results show RMS of residual errors gets close to one DSM cell, which is equal to or even better than that in other literatures.

The method proposed in the paper is feasible and effective to generate reliable and accurate registering results.

The purpose of this study is to investigate the effects of the Perceptions of coronavirus 2019 (COVID-19) questionnaire and the Information System Success Questionnaire on students when using the school's COVID-19 epidemic prevention website. The study is aimed toward analyzing both questionnaires as well as evaluating an epidemic prevention website.

The school's COVID-19 prevention website and two questionnaires (Perceptions of COVID-19 and the Information System Success Questionnaire) are examined in order to investigate 73 students' COVID-19 perceptions. An open-ended question was used as the qualitative data to support quantitative data and evaluate a university's COVID-19 epidemic prevention website from a southern university in Taiwan.

The findings indicated that most students evaluated the school's COVID-19 website positively and were satisfied. In the open-ended questions, the majority of students rated the quality of the system positively and the need to fix some defects. Students have different COVID-19 perceptions and social distance compliance based on their current situations.

This study provides researchers and website developers a broader understanding of the construction of the school's COVID-19 prevention website and a better understanding of student's COVID-19 perceptions.

To the best of the authors' knowledge, this is the first study examining a school's epidemic prevention website, which is measured by the Information Success Questionnaire and the Perceptions of COVID-19 Questionnaire for college students.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.