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Purpose: The aim of this study is to evaluate of the predictive ability of goodwill and other intangible assets on forecasting corporate profitability. Subsequently, this study compares the efficiency of deep learning model to that of other machine learning models such as random forest (RF) and support vector machine (SVM) as well as traditional statistical methods such as the linear regression model. Design/methodology/approach: Studies confirm that goodwill and intangibles are valuable assets that give companies a competitive advantage to increase profitability and shareholders' returns. Thus, by using as sample Greek-listed financial data, this study investigates whether or not the inclusion of goodwill and intangible assets as input variables in this modified deep learning models contribute to the corporate profitability prediction accuracy. Subsequently, this study compares the modified long-short-term model with other machine learning models such as SVMs and RF as well as the traditional panel regression model. Findings: The findings of this paper confirm that goodwill and intangible assets clearly improve the performance of a deep learning corporate profitability prediction model. Furthermore, this study provides evidence that the modified long short-term memory model outperforms other machine learning models such as SVMs and RF , as well as traditional statistical panel regression model, in predicting corporate profitability. Research limitations/implications: Limitation of this study includes the relatively small amount of data available. Furthermore, the aim is to challenge the authors' modified long short-term memory by using listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis. However, this study proposes that future research may apply deep learning corporate profitability models on a bigger pool of data such as STOXX Europe 600 companies. Practical implications: Subsequently, the authors believe that their paper is of interest to different professional groups, such as financial analysts and banks, which the authors' paper can support in their corporate profitability evaluation procedure. Furthermore, as well as shareholders are concerned, this paper could be of benefit in forecasting management's potential to create future returns. Finally, management may incorporate this model in the evaluation process of potential acquisitions of other companies. Originality/value: The contributions of this work can be summarized in the following aspects. This study provides evidence that by including goodwill and other intangible assets in the authors' input portfolio, prediction errors represented by root mean squared error are reduced. A modified long short-term memory model is proposed to predict the numerical value of the profitability (or the profitability ratio) in contrast to other studies which deal with trend predictions, i.e. the binomial output result of positive or negative earnings. Finally, posing an extra challenge to the authors' deep learning model, the authors' used financial statements according to International Financial Reporting Standard data of listed companies in Greece, a code-law country that suffered during the recent fiscal debt crisis, heavily influenced by tax legislation and characterized by its lower investors' protection compared to common-law countries. [ABSTRACT FROM AUTHOR]

The recognition of wood species is a laborious process, which is performed by experts, who attempt to distinguish the different species in different wood sections based on their macroscopic and microscopic characteristics. Most of these characteristics can be observed in the transverse or cross section of woods. According to experts, the next most important surface for wood species recognition is the tangential section while significant information can also be obtained from the radial section of woods. Based on the recent advances in the area of computer vision and pattern recognition, most researchers have proposed imagebased approaches attempting to address the problem either in microscopic or macroscopic scale. The main limitation is that in many cases there are some features that are not visible in each wood section. Firstly, we examine the contribution of each section in wood species recognition using two different computer-based texture analysis methods. Furthermore, we compare wood species recognition methods for both grayscale images and colorscale images. Finally, we propose a novel fusion method and we demonstrate that wood species recognition accuracy can be increased by fusing features from different wood sections. For the evaluation of the proposed method, a dataset, namely "WOOD-AUTH", consisting of more than 4272 wood images of twelve common wood species, was used. [ABSTRACT FROM AUTHOR]

Forest biomass originating from Mediterranean forest vegetation could be a potential source of renewable energy in the form of pellets, butdue to its diversity there is a need for better understanding and detailed examination of its main fuel characteristics. The aim of this work was the evaluation of the impact that bark percentage and barked stem diameter have on the ash content and heating value of thefollowing evergreen Mediterranean hardwood species: Arbutus unedo, Erica arborea, Quercus coccifera, Quercus ilex and Phillyrea latifolia. For all the above species, the barked diameter, bark thickness, bark percentage as well as the ash content and higher heating value of bark and wood have been determined. In all cases the ash content of bark was higher than that of wood and also higher than the requirement of the related EN standard. Therefore the bark of tested species could be used in the production of pellets provided that its ratio would be in a level corresponding to the maximum allowed ash content values. Taking into account the results of the determinations, the equations and graphs were used in order to calculate the minimum of the stem diameter requirements in order to meet with the ash content restrictions. The effect of stem diameter on the HHV (higher heating value) was also evaluated. Among the tested species, Erica arborea proved to be the most appropriate for the production of pellets showing the highest HHV and the lowest ash content. [ABSTRACT FROM AUTHOR]

In this study, Scots pine wood (Pinus sylvestris L.) was thermally treated at 200oC, for three different time periods of 4, 6 and 8 hours. The Bending strength (MOR) and Impact Bending strength of treated and untreated pine wood specimens were determined and measurements of wood colour were implemented before and after each thermal treatment, in order to evaluate the colour changes coming from the treatment processes. An attempt was made to correlate this colour change with the change of the mechanical properties of treated wood. The results indicated that MOR decreased as the intensity of the treatment increased, recording decrease percentages of between 0.34-25.9%, compared to untreated wood, while the impact bending strength values of treated specimens marked 0.69-22.34% lower strength. The noteworthy is that a strong and significant relationship was recorded between the colour change and the mechanical properties change. Based on the results of this study, it could be claimed that the mechanical strength values of treated pinewood could be sufficiently estimated by the corresponding colour change values that have been measured. Therefore, the measurement of colour coordinates and the calculation of the total colour change values offer the opportunity to estimate automatically, quite precisely and through a non-destructive way the mechanical properties of wood. [ABSTRACT FROM AUTHOR]