The paper presents the SEAWALL platform enabling heterogeneous data acquisition and low-latency processing for the Industry 4.0, developed within the homonymous project founded by the Italian BIREX industrial consortium. The architecture features cutting-edge technologies (such as Kubernetes, ISTIO, KubeEdge, W3C WoT), in order to support the seamless orchestration of workloads among the nodes of a cloud-edge continuum in QoS-aware scenarios where the latency requirement of the anomaly detection must be continuously assessed. The paper presents the industrial use-case from the SEAWALL project and the components of the cloud/edge architecture.

Background: Anxiety disorders are common in childhood, yet access to cognitive behavioral therapy (CBT) is often limited. Internet-delivered CBT (iCBT) can help overcome these barriers, but evidence in younger children remains scarce. This pilot study describes the development and preliminary evaluation of an Italian iCBT platform for children with mild to moderate anxiety. Methods: Five children aged 8–12 years and their caregivers were recruited through pediatricians. Eligibility was assessed using the MASC-2 and a psychiatrist interview. Each child completed a supervised session with the WebApp, which delivers CBT modules combining psychoeducation, cognitive restructuring, relaxation, and gamified activities. Usability was evaluated using the ita-MAUQ, observation, and interviews. Results: All participants completed the session without dropouts. Mean ita-MAUQ scores were consistently above the midpoint, with the highest ratings for interface design and satisfaction. Children appreciated the interactive, game-like features, while caregivers valued the clarity and practicality of content. Qualitative feedback indicated good comprehensibility and engagement, with suggestions for improving navigation flow and language adaptation. No adverse events occurred. Conclusions: This pilot study supports the feasibility, safety, and acceptability of the new iCBT platform and provides essential insights for its refinement and future large-scale clinical trials.

The use of big data has grown rapidly in recent years, finding its way into various fields of use, from medicine to industry, from traffic flow optimisation to environmental protection. In the field of waste management, the European REthinkWASTE ¹ project provided the opportunity for research and testing of new methods for intelligent waste management, leading to a better understanding of the problems in this area and solutions to solve them. This paper describes the results obtained in the REThinkWASTE project by exploiting MOMIS (Mediator EnvirOnment for Multiple Information Sources) [4], the open-source data integration system developed by UniMoRe and DataRiver. First, the architecture of the REThinkWASTE data integration and analysis platform and the choices made during the problem analysis phase are described. Next, the technologies used for Key Performance Indicator extraction are described and compared with the alternatives evaluated. The aim of the paper is to provide a valid example of the effectiveness of Big Data management and analysis technologies in a real-world scenario.