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This study aims to describe the behavior of blocks in the system under consideration using systems modeling language (SysML) state machine diagrams. In this paper, formalization and model checking for SysML state machine diagrams have been investigated.

The work by Zhang and Liu (2010) proposed a formalization of SysML state machine diagrams in which the diagrams were translated into CSP# processes that could be verified by the state-of-the-art model checker PAT. In this paper, several modifications have been made and new rules have been added to the translation described in that work.

First, three translation rules were modified, which apparently are inappropriately defined according to the SysML definition of state machine diagrams. Next, we add new translation rules for two components of the diagrams – junction and choice pseudostates – which have not been dealt with previously. Further, we are implementing the automatic translation system on a web-based model-driven development tool, which reflects on our translation rules.

As the contribution of this work, more reasonable verification results for more general SysML state machine diagrams can be achieved.

The purpose of this paper is to investigate whether audit opinions of listed firms in China vary systematically with the political connections of the firm’s chief executive officer (CEO). Prior literature only shows the importance of political influence to auditor choice and audit quality.

A politically connected firm is defined as a firm in which the CEO has a political background. The authors use a “difference-in-difference” model to control for self-selection problems.

The authors find that the likelihood of receiving a favourable opinion in the subsequent period is positively associated with a CEO’s political connections. This positive association is stronger with CEOs connected to local government within the same region. The authors further find that the CEO’s political connections have more influence on favourable audit opinions in non-state-owned enterprises (non-SOEs) in a less developed and lower investor protection region. The influence is also less significant in the regions where there are more non-state-owned or foreign banks and where there are greater penalties for political corruption and relationship-based contracting.

The study complements and extends the existing literature on the role of political connections in the economy by providing evidence on the effect of a CEO’s political connections on audit opinions. The authors extend the research on auditing in emerging markets by explicitly accounting for unique institutional and market factors in China. They explore audit quality by observing how audit opinions are directly shaped by political and institutional factors.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Hyperbranched poly(ester-amide)s (HPEAs) have been synthesized from diethanolamine and maleic anhydride with ethylene glycol as a core monomer by using a two-step method, which are marked as Hupea polymers, and dehydration was carried out in xylene under reflux.

In comparison with Hupea polymers was synthesized by one-pot method, Hupea polymers synthesized by two-step method has different structure and rheological properties. The intermediate monomer and the resulting polymer are characterized by FTIR and NMR spectroscopies.

All of Mw, Mn and Mw/Mn of the hyperbranch polymers decrease with the core/monomer molar ratio increasing. The intrinsic viscosity ([η]) of the polymers decreases with Mw increasing in the investigated range of Mw and scales as [η]∼Mw-0.82, which implies that the molecular weight grew faster with core/monomer molar ratio decreasing than the volume in the investigated range of core/monomer molar ratio.

The hydrodynamic radius was calculated by using Einstein’s equation and scales as Rh ∼ Mw0.061, and the lower exponent reveals the slow growth in the volume of Hupea molecule. In addition, the viscosity of Hupea polymer in concentrated aqueous solution is independent of shear rate and slightly dependent on molecular weight.

Hyperbranched poly(ester-amide)s (HPEAs) were synthesized by using a two-step method, which had different structure and rheological properties.

Hupea polymers show different features from Hupea polymers in structure and rheological properties, which revealed that the synthesis process of HPEA has effect on its performance.

The COVID-19 pandemic has hit different industries and firms with widely differing degrees of severity. The authors investigate whether ownership structure (family vs non-family) might represent a differentiating factor. The article's purpose is to conduct an initial, descriptive analysis of the impact of COVID-19 on different stock and operating performance measures of listed German companies.

The authors use a sample of 299 listed companies in Germany and gathered operating as well as stock market performance data following the outbreak of COVID-19. For the purpose of this paper, the authors solely focus on static and descriptive observations thus far. The intention of this paper is to describe potential implications for more differentiated, especially multivariate causal research, on family businesses in a post-COVID world.

The results indicate that, over the last five years, stock returns of family businesses have been higher than those of non-family firms. This effect seems to have been more pronounced during the first month following the COVID-19 outbreak. When applying operating measures, the outperformance becomes even more evident. The findings therefore seem to support the hypothesis proffered in the literature that family involvement enhances the potential for resilience in such firms.

Scholars on COVID-19 crisis performance have begun to explore firm-level factors related to financial and organizational factors, industry characteristics and country-level factors. The research extends this line of inquiry by probing the importance of family involvement in ownership.

This paper aims to examine the extent to which financial performance (FP) represents one of the main determinants for tone disclosure (TD) in Egyptian annual reports. The authors also measure the bidirectional relationship between TD and FP.

The manual content analysis is used to measure the levels of TD in annual reports for a sample of 105 firms listed on the Egyptian stock market. The sample covers a three-year period (2011-2013).

The descriptive analysis in this paper shows that Egyptian firms disclose more good news than bad news. Therefore, the net news disclosure, or net variances, between good/bad is positive. The empirical analysis shows a positive association between the narrative disclosure of good/bad news and FP based on return on assets. The authors also find a highly significant association between the auditor, profitability, leverage, firm growth and financial reporting of good/bad news information. Finally, the results of the ordinary least squares regression show that the causality between the two endogenous variables runs from FP to TD. Thus, TD is determined by FP.

This study offers a novel contribution to disclosure studies by being the first study to examine TD in one of the developing countries.