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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]

Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. [ABSTRACT FROM AUTHOR]

Wood recognition is a crucial task for wood sciences and industries, since it leads to the identification of the anatomical features and physical properties of wood. Traditionally, the recognition process relies almost exclusively on human experts, who are based on various characteristics of wood, such as color, structure and texture. However, there are numerous types of wood species in the nature that are difficult to be identified even by experienced scientists. Towards this end, in this paper we propose a novel approach for automated wood species recognition through multidimensional texture analysis. By taking advantage of the fact that static wood images contain periodic spatially-evolving characteristics, we introduce a new spatial descriptor considering each wood image as a collection of multidimensional signals. More specifically, the proposed methodology enables the representation of wood images as concatenated histograms of higher order linear dynamical systems produced by vertical and horizontal image patches. The final classification of images, i.e., histogram representations, into wood species, is performed using a Support Vector Machines (SVM) classifier. For the evaluation of the proposed method, a dataset, namely “WOOD-AUTH”, consisting of more than 4200 wood images (from cross, radial and tangential sections of normal wood structure) of twelve common wood species existing in Greek territory, was created. Experimental results presented in this paper show the great potential of the proposed methodology, which, despite a small number of misclassification cases with regards to both anatomically similar and different species, outperforms a number of state of the art approaches, yielding a classification rate of 91.47% in wood cross sections. [ABSTRACT FROM AUTHOR]

High-quality academic publishing is built on rigorous peer review. I Algorithms i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

Thanks to the efforts of our reviewers in 2022, the median time to first decision was 17 days and the median time to publication was 37 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Mathematics i : those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). I Mathematics i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

The editors would like to express their sincere gratitude to the following reviewers for their precious time and dedication, regardless of whether the papers were finally published: Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that I Applied Sciences i maintains its standards for the high quality of its published papers. [Extracted from the article]

Aim: In this paper, the main aim is to detect fire using a novel frame difference method and compare it with conventional method. This is based on video processing and computational methods to reduce the computational complexity. Materials and method: The method was performed over a sample size of 20. Same samples were applied for both the control group and experimental group. Improved accuracy detection was obtained using the proposed method. Results: The Accuracy and precision was found (94.03, 64.62) and (86.24,57.19) was obtained for the frame difference method and conventional method. It also shows a significance of 0.048 for accuracy and 0.018 for precision which is less than 0.05. Conclusion: It would be concluded that the frame difference method is producing high accuracy and precision when compared with the Vibe method. It is applicable for monitoring systems and home security. [ABSTRACT FROM AUTHOR]

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI. [ABSTRACT FROM AUTHOR]