Abstract
Tampered digital multimedia content has been increasingly used in a wide set of cyberattacks, chal-lenging criminal investigations and law enforcement authorities. The motivations are immense and range from the attempt to manipulate public opinion by disseminating fake news to digital kidnapping and ransomware, to mention a few cybercrimes that use this medium as a means of propagation.Digital forensics has recently incorporated a set of computational learning-based tools to automatically detect manipulations in digital multimedia content. Despite the promising results attained by machine learning and deep learning methods, these techniques require demanding computational resources and make digital forensic analysis and investigation expensive. Applied statistics techniques have also been applied to automatically detect anomalies and manipulations in digital multimedia content by statisti-cally analysing the patterns and features. These techniques are computationally faster and have been applied isolated or as a member of a classifier committee to boost the overall artefact classification.This paper describes a statistical model based on Benford's Law and the results obtained with a dataset of 18000 photos, being 9000 authentic and the remaining manipulated.Benford's Law dates from the 18th century and has been successfully adopted in digital forensics, namely in fraud detection. In the present investigation, Benford's law was applied to a set of features (colours, textures) extracted from digital images. After extracting the first digits, the frequency with which they occurred in the set of values obtained from that extraction was calculated. This process allowed focusing the investigation on the behaviour with which the frequency of each digit occurred in comparison with the frequency expected by Benford's law.The method proposed in this paper for applying Benford's Law uses Pearson's and Spearman's corre-lations and Cramer-Von Mises (CVM) fitting model, applied to the first digit of a number consisting of several digits, obtained by extracting digital photos features through Fast Fourier Transform (FFT) method.The overall results obtained, although not exceeding those attained by machine learning approaches, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), are promising, reaching an average F1-score of 90.47% when using Pearson correlation. With non-parametric approaches, namely Spearman correlation and CVM fitting model, an F1-Score of 56.55% and 76.61% were obtained respec-tively. Furthermore, the Pearson's model showed the highest homogeneity compared to the Spearman's and CVM models in detecting manipulated images, 8526, and authentic ones, 7662, due to the strong correlation between the frequencies of each digit and the frequency expected by Benford's law.The results were obtained with different feature sets length, ranging from 3000 features to the totality of the features available in the digital image. However, the investigation focused on extracting 1000 features since it was concluded that increasing the features did not imply an improvement in the results.The results obtained with the model based on Benford's Law compete with those obtained from the models based on CNN and SVM, generating confidence regarding its application as decision support in a criminal investigation for the identification of manipulated images.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
Entrepreneurial activity has been an element of economic and social enhancement. However, managing a startup is a difficult and risky activity that strongly depends on the entrepreneur's characteristics and skills. While much attention has been given recently to entrepreneurial learning, less has been studied about the learning dynamics in different industries. This study aims to understand and explore types of learning in different industries and find their similarities and differences. For this purpose, an exploratory comparative case study composed of four cases has been considered. Findings reveal that the main types of entrepreneurial learning are similar in all the industries analysed and are linked to social and experiential learning. The main dissimilarities are related to searching for customer information, employees' feedback, and solving issues. Lastly, some entrepreneurs reflect on the actions or decisions taken, while others do not reflect as much as they would like to. © 2023 Inderscience Enterprises Ltd.

Abstract
This study used a tomography-like analysis to reconstruct the hyperspectral data from different tissues of the grapes: skin, pulp, and seeds. The dataset included 216 grapes of Loureiro (VIVC 25085) and 205 Vinhão (VIVC 13100) at various dates from the veraison until the harvest. A more comprehensive spectral data analysis identified how the internal tissues are related to the total grape spectra. Each tissue was reconstructed separately by decomposing the whole grapevine hyperspectral information. The results showed that the spectral reconstruction was more successful for Loureiro than Vinhão, with a mean absolute error of 6.08% and 33.32%, respectively. Partial least squares (PLS) regression models were developed for both cultivars using the reconstructed spectral data, enabling the modelling of ºBrix, puncture force (N), chlorophyll (a.u.), and anthocyanin content (a.u.). These models exhibited strong performance, with R2 > 0.8 and mean absolute percentage errors (MAPE) below 37%. This study emphasises the critical role of considering the grape's internal tissue in assessing its maturation process. The findings introduce an innovative methodology for efficiently evaluating grape maturation dynamics and inner tissue characteristics. By highlighting the importance of internal tissue analysis, this research paves the way for expedited and accurate monitoring of grape maturation, offering valuable insights into physiological-based viticultural practices and grape quality assessment. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

Abstract
The list of Adaptive Optics (AO) simulators in the community has constantly been growing, guided by different needs and purposes (Compass, HCIPY, OOMAO, SOAPY, YAO. . .). In this paper, we present OOPAO (Object Oriented Python Adaptive Optics), a simulation tool based on the Matlab distribution OOMAO to adapt its philosophy to the Python language. This code was initially intended for internal use but the choice was made to make it public as it can benefit the community since it is fully developed in Python. The OOPAO repository is available in free access on GitHub (https://github.com/cheritier/OOPAO) with several tutorials. The tool consists of a full end-to-end simulator designed for AO analysis purposes. The principle is that the light from a given light source can be propagated through multiple objects (Atmosphere, Telescope, Deformable Mirror, Wave-Front Sensors. . .) among which experimental features can be input, in the spirit of OOMAO. This paper provides an overview of the main capabilities of the code and can be used as a user manual for interested users.

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