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The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.

The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.

In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.

It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.

Individual students have different learning styles, and lecturers can no longer afford to ignore this. Lecturers have a responsibility to accommodate students’ different learning styles by including learning style flexibility in the offered learning opportunities. The purpose of this study is to map a teaching case study against the Herrmann Whole Brain® model to determine whether learning style flexibility has been incorporated in the teaching case study.

A teaching case study was developed and delivered as part of an undergraduate level course at a South African residential university. The case study’s primary intention was to illustrate the practical evaluation of general controls in an information technology environment. The teaching case study was analysed in terms of the Herrmann Whole Brain® model to determine whether learning style flexibility had been accommodated in the learning opportunity.

Based on an analysis of the teaching case study against the Herrmann Whole Brain® model, it is evident that the teaching case study incorporated activities that addressed all four quadrants of the Whole Brain® model. It can therefore be concluded that the learning opportunity incorporated learning style flexibility.

This paper contributes to the literature in accounting education by focusing on learning style flexibility specifically using the Herrmann Whole Brain® model, as it appears that limited examples of the use of this model in accounting education have yet been published. Although this paper discusses the use of an auditing case study, the results may be of interest to lecturers in other subject areas across the academic spectrum.

The purpose of this paper is to focus on two welfare state regimes with differing degrees of de‐familialisation strategies, Germany and Sweden, to study whether and how women's career interruptions influence their labour market prospects. By comparing women with continuous careers to those with discontinuous careers due to: parental leave or homemaking; unemployment; or other reasons, the authors explore the support for the skill depreciation hypothesis and signalling theory. Depending on the type of welfare state regime, the authors expect women to be subject to varying degrees of career punishment for time spent out of the labour market.

Cox proportional hazard regression models of the transition rate of an upward or downward occupational move among women in the labour market were estimated.

Focusing on upward career moves, the results show no significant relationship between a career interruption and upward occupational moves in Germany. In Sweden, the longer the accumulated duration of family leave, the lower the transition rate to an upward move. Overall occupational mobility is higher in Sweden, and in a policy regime where almost all women work, extended leaves may have a more negative effect on career prospects than in Germany, where many mothers drop out of the labour force altogether. In Germany, on the other hand, the authors find traces of unemployment to be scarring, as the risk of downward moves increases with increased unemployment experience.

The paper explores the impact of policies in shaping women's career trajectories and critically examines the often‐cited skill depreciation hypothesis.