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
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

Abstract
An increasing area of study for economists and sociologists is the varying organizational structures between business networks. The use of network science makes it possible to identify the determinants of the performance of these business networks. In this work we look for the determinants of inter-firm performance. On one hand, a new method of supervised clustering with attributed networks is proposed, SUWAN, with the aim at obtaining class-uniform clusters of the turnover, while minimizing the number of clusters. This method deals with representative-based supervised clustering, where a set of initial representatives is randomly chosen. One of the innovative aspects of SUWAN is that we use a supervised clustering algorithm to attributed networks that can be accomplished through a combination of weights between the matrix of distances of nodes and their attributes when defining the clusters. As a benchmark, we use Subgroup Discovery on attributed network data. Subgroup Discovery focuses on detecting subgroups described by specific patterns that are interesting with respect to some target concept and a set of explaining features. On the other hand, in order to analyze the impact of the network's topology on the group's performance, some network topology measures, and the group total turnover were exploited. The proposed methodologies are applied to an inter-organizational network, the EuroGroups Register, a central register that contains statistical information on business networks from European countries.

Abstract
PurposeThis study analyzes the impact of packaging-free practices in food retail stores, particularly supermarkets, on customer loyalty.Design/methodology/approachBased on the literature on the impacts of sustainable practices and corporate social responsibility (CSR) policies on consumer behavior, this study defined a set of seven hypotheses that were tested using data collected from 447 consumers that regularly buy food products at supermarkets. The data were subjected to structural equation modeling using SmartPLS.FindingsThis study confirmed that packaging-free practices positively influence brand image, brand trust, satisfaction and customer loyalty. The expected positive impacts of brand image and satisfaction on customer loyalty were also confirmed. However, the expected impact of brand trust on customer loyalty was not confirmed.Practical implicationsThis article demonstrates how a competitive sector can reap benefits from implementing sustainable practices in the operational domain, particularly by offering packaging-free products at the point of purchase. Thus, as recommended, general retail stores (e.g. supermarkets) gradually increase the stores' offering of packaging-free food products, as this practice has been shown to have positive impacts not only on brand image, but also on customer satisfaction and loyalty.Originality/valueThis study extends the literature on the effects of sustainable practices on customer loyalty, by focusing on a specific practice. Furthermore, this study contributes to the advancement of research on packaging-free practices in retail by developing a research framework and providing evidence on the direct and indirect effects of this specific practice on customer loyalty.

Abstract
Wine tourism (WT) is an important area of special-interest tourism in Portugal, and represents an increasingly significant component of regional development. In a more conservative approach, WT has been described as visiting vineyards, wineries, and engaging in wine-related activities. However, this perspective has been broadened, taking advantage of all the potential of the specific destination's terroir (nature/landscape, tangible and intangible cultural heritage, etc.). Wine routes make the connection between wine and tourism in a specific region and intend to boost wine tourism by promoting collaboration between different stakeholders. Different routes present distinct approaches to WT, within diverse regional contexts, and different ways of collaboration within the wine route. This study compares three wine routes at different stages of development, located in a rural periphery - in the central region of Portugal - Bairrada, Dao, and Beira Interior, considering both context data and information collected in 113 interviews conducted with diverse wine tourism agents from these routes. Besides a brief characterization of the three routes, the main results indicate supplier's preference for terroir routes instead of wine routes and show the importance of gender, age, and education level for the collaborative work between stakeholders. These aspects and their contribution to the development of WT routes are discussed. Some questions that additional studies may help answering are also reflected.

Abstract
The assessment of differences between skeletal age and chronological age in childhood is often based on the comparison of the patient's left hand x-ray with a reference atlas, performed by a experienced professional. This procedure involves a manual image analysis, that can be subject to inter rater variability posing several problems for clinical applications. In this paper a new methodology for skeleton maturation estimation based on automatic hand X-ray assessment for pediatric applications on a low resource devices (e.g. mobile device) is proposed. The pipeline covers hand-area estimation and bone-area estimation to achieve maturation scores which are then indexed with references images, separately for male and female. The proposed approach is based on simple image processing functions always bearing in mind the application on a mobile context. The involved steps are thoroughly presented and all the used functions are explained. The performance of the system was then evaluated using the complete pipeline. The obtained results pointed to an average error rate of 15,38 +/- 3,31%, which is subject to improvements. In particular, contrast enhancement in some lower quality images still offers some challenges.