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The article examines the Xylella fastidiosa crisis in Southern Salento as an emblematic case of knowledge conflict, in which the legitimacy of scientific expertise comes into tension with local knowledge and alternative epistemic practices. Drawing on a qualitative approach based on autoethnographic reflection and in-depth interviews with activists from the Popolo degli ulivi movement, the study explores the epistemic and political dynamics emerging at the intersection of science, media, and governance of emergency. The analysis shows how the technocratic management of the crisis – grounded in a reductionist paradigm and in the performative use of the category of “emergency” – produced the marginalization of local knowledges, while fostering a counter-narrative centered on the demand for “open” and participatory research. Through the analytical categories of cultura dell’avidità and cultura meridiana, the article interprets the conflict not merely as a scientific dispute, but as a political and symbolic confrontation between models of development, epistemic regimes, and forms of environmental justice. In this perspective, the Xylella crisis becomes an arena in which broader tensions emerge between agricultural modernization, extractivism, and the claim for a plural and territorially rooted knowledge.

In this paper are investigated the re-engineering approaches for the optimization of pasta production processes. A preliminary study of technologies and information systems architectures to be applied to the entire pasta supply chain has been carried out. In the first part of the paper is presented an overview concerning Industry 4.0 enabling technologies, besides, in the second part are discussed the engineered processes improving production quality. Concerning simulation process, is designed by means of modelling workflows the wheat storage process, by simulating the automatism of silos controlled and managed by volume sensors. Finally, following Industry 5.0 facilities have been applied image vision and artificial intelligence methodologies suitable for auto-adaptive quality check of pasta. The paper discusses different models about production processes, efficiency, risks, costs and benefits.

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

With this article, we attempted to analyze the radicalization process of the Italian environmental movement with a particular focus on the birth and the practices of the group called Ultima Generazione (Last Generation). Using a qualitative-quantitative approach that integrated PEA (Protest Event Analysis) and participant observation, we tried to understand how a new insurgent consciousness emerged from the experience of the mobilizations promoted by Extinction Rebellion. In this sense, Last Generation is analyzed as a specific case of spin-off movement.

The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.

Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%.

The concept of ‘convergence’ represents an innovative perspective for analysing collective mobilisations and contemporary conflicts. This article develops a theorisation of convergence as a dynamic process that integrates actors, resources and repertoires of action into collective configurations capable of transcending immediate contingencies. Through the case of the Gkn dispute, one of the most significant workers’ mobilisations in Italy, the study analyses how workers built a network of solidarity and resistance, transforming a local crisis into a national symbol. Using methodological tools such as Protest Event Analysis (PEA) and a mechanism-process approach, the research highlights how convergence functions as a catalyst for the construction of collective identities, the articulation of innovative strategies and the redefinition of power relations. The work thus proposes a critical reinterpretation of contemporary mobilisations, suggesting that convergence, in addition to explaining the duration and impact of conflicts, can be an explanatory model of contemporary socio-political transformations.

The proposed paper is related to a case of study of an e-health telemedicine system oriented on homecare assistance and suitable for de-hospitalization processes. The proposed platform is able to transfer efficiently the patient analyses from home to a control room of a clinic, thus potentially reducing costs and providing high-quality assistance services. The goal is to propose an innovative resources management platform (RMP) integrating an innovative homecare decision support system (DSS) based on a multilayer perceptron (MLP) artificial neural network (ANN). The study is oriented in predictive diagnostics by proposing an RMP integrating a KNIME (Konstanz Information Miner) MLP-ANN workflow experimented on blood pressure systolic values. The workflow elaborates real data transmitted via the cloud by medical smart sensors and provides a prediction of the patient status. The innovative RMP-DSS is then structured to enable three main control levels. The first one is a real-time alerting condition triggered when real-time values exceed a threshold. The second one concerns preventative action based on the analysis of historical patient data, and the third one involves alerting due to patient status prediction. The proposed study combines the management of processes with DSS outputs, thus optimizing the homecare assistance activities.

The present paper discusses a comparative application of image processing techniques, i.e., Discrete Fourier Transform, K-Means clustering and Artificial Neural Network, for the detection of defects in the industrial context of assembled tires. The used Artificial Neural Network technique is based on Long Short-Term Memory and Fully Connected neural networks. The investigations focus on the monitoring and quality control of defects, which may appear on the external surface of tires after being assembled. Those defects are caused from tires which are not properly assembled to their respective metallic wheel rim, generating deformations and scrapes which are not desired. The proposed image processing techniques are applied on raw high-resolution images, which are acquired by in-line imaging and optical instruments. All the described techniques, i.e., Discrete Fourier Transform, K-Means clustering and Long Short-Term Memory, were able to determine defected and acceptable external tire surfaces. The proposed research is taken in the context of an industrial project which focuses on the development of automated quality control and monitoring methodologies, within the field of Industry 4.0 facilities. The image processing techniques are thus meant to be adopted into production processes, giving a strong support to the in-line quality control phase.

The characterization of a quarry site which is suitable for railway ballast aggregate production represents a big challenge for the mining industry. The knowledge of structural discontinuities within local geological materials is fundamental to guide mining operations, optimize investments, and guarantee quarry security. This research work presents an innovative methodology for the subsurface investigation of a quarry excavation area down to a depth of about 50 m in Falconara Albanese, Calabria, Italy. The proposed methodological approach incorporates photogrammetry, drone technology, and GPR data acquisition and processing. Photogrammetry represents the first step for obtaining a 3D topographical model reconstruction of the whole quarry, helping to detail the acquisition approach and properly plan the subsequent drone survey. In particular, two 120 MHz antennas have been mounted on the drone and two profiles have been acquired above and across the quarry. Results show the presence of fractured material and demonstrate the applicability of the method for identification of areas that are more suitable for railway ballast production. The presented method is therefore capable of detecting subsurficial fractures at a quarry site by means of a relatively fast and cost-effective procedure. Results are achieved within the framework of an industry project.