Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text-mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNN_classify (risk or no risk), DNN_reg1 (loss time), DNN_reg2 (body injury), DNN_reg3 (plant and fleet), DNN_reg4 (equipment), and DNN_reg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R² value for the five regression models was 0.92, with mean absolute error between 0.91 and 0.94. The presented results, in addition to the developed user-interface module, will help practitioners obtain a better understanding of H&S challenges, minimize project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.     

How do sustainability reports from the Nordic and the Mediterranean European countries compare

This study investigates the sustainability reporting practices of companies based in the Nordic and the Mediterranean European countries for the period 2013–2015. Its purpose is to analyse to what extent, if any, are there differences in these practices. It seeks to capture the influence of national institutions and firm specific characteristics in sustainability reporting. Non-parametric statistics are used to analyse some factors which influence disclosure, namely country, industry affiliation, type of property, listing status and size. In accordance with the theoretical frame used, that of the varieties of capitalism approach, findings suggest that in general companies from Mediterranean European countries present higher levels of engagement with the Global Reporting Initiative.     

Audit committee and internal audit and the quality of earnings: empirical evidence from Spanish companies

Corporate scandals and failures such as Enron, WorldCom, etc. were highly catastrophic and had a terrible effect on stakeholders. The pressure of globalisation and the intense competition have lead to organisations encouraging good corporate governance. Organisations have to promote transparency and accountability of financial information. As Gramling et al. (J Account Lit 23:194–244, 2004) argued there are four important mechanisms of corporate governance include boards of directors, audit committees (among others) and internal and external audit functions. However, this paper will focus on the relationship between two corporate governance mechanisms (audit committee and internal audit function). Thus, our purpose is to analyse the relation between characteristics of the audit committee and internal audit function and earnings management measured as abnormal accruals to test the quality of financial statements. We hypothesise that the association between the effectiveness of the audit committee and the presence of an internal audit function and its relationship with this committee would indicate less opportunity for management to manipulate earnings. Using a sample of 108 non-financial Spanish companies that traded on the Madrid Stock Exchange between 2003 and 2006 (432 observations), we have found that the size and number of meetings of the audit committee had a significant negative association with earnings manipulations. Also, our results suggest that a negative relation between having an internal audit function and earnings management.     

A test for randomness based on a complexity measure

A test for randomness based on a statistic related to the complexity of finite sequences is presented. Simulation of binary sequences under different stochastic models provides estimates of the power of the test. The results show that the test is sensitive to a variety of alternatives to randomness and suggest that the proposed test statistic is a reasonable measure of the stochastic complexity of a finite sequence of discrete random variables.     

MOMENT ESTIMATION IN THE CLASS OF BISEXUAL BRANCHING PROCESSES WITH POPULATION–SIZE DEPENDENT MATING

This paper concerns the estimation of the offspring mean vector, the covariance matrix and the growth rate in the class of bisexual branching processes with population‐size dependent mating. For the proposed estimators, some unconditional moments and some conditioned to non‐extinction are determined and asymptotic properties are established. Confidence intervals are obtained and, as illustration, a simulation example is given.     

A note on closure of the ILR and DLR classes under formation of coherent systems

In this paper, we give the closure of the class of increasing likelihood ratio (ILR) life distributions under the formation of certain coherent systems with independent and identically distributed components, and particularly in k-out-of-n systems. Some possible definitions of its dual class (DLR) are discussed and results about the closure are given. Furthermore, we include examples which show that these two aging classes are not closed under formation of special coherent systems. This work has been supported by Ministerio de Ciencia y Tecnologia, Grants PB96-1105 and BFM2000-0362.     

Influence Assessment in an Heteroscedastic Errors-in-Variables Model

The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.     

The Diffusion of Innovations: A Methodological Reappraisal

Studies of diffusion have traditionally relied on specific distributions, primarily the logistic, to characterize and estimate those processes. We argue that such an approach gives rise to serious problems of comparability and interpretation and may result in large biases in the estimates of the parameters of interest. We propose instead the Gini's expected mean difference as a measure of diffusion speed. We discuss its advantages over the traditional approach, present a nonparametric estimation procedure, and tackle with it the problems of truncated processes and of intergroup comparisons. We also elaborate on the use of the hazard rate and suggest various extensions. The diffusion of computed tomography scanners is presented as an illustration.