Abstract
Highlights What are the main findings? The pneumatic insole can monitor, stabilize and offload plantar pressure in real time. Over 91% of measurements are reliable, with up to about 42% pressure reduction. What is the implication of the main finding? Strong potential to support foot injury prevention strategies in at-risk populations.Highlights What are the main findings? The pneumatic insole can monitor, stabilize and offload plantar pressure in real time. Over 91% of measurements are reliable, with up to about 42% pressure reduction. What is the implication of the main finding? Strong potential to support foot injury prevention strategies in at-risk populations.Abstract Plantar pressure monitoring is decisive in injury prevention, especially in at-risk populations such as people with diabetic foot. In this context, innovative solutions such as pneumatic insoles can be essential in plantar pressure management. This study describes the development of a variable pressure system that promotes the monitoring, stabilization, and offloading of plantar pressure through a pneumatic insole. This research was also intended to evaluate its ability to redistribute plantar pressure, reduce peak pressure in both static and dynamic conditions, and validate its pressure measurements by comparing the results with those obtained from a pedar (R) insole. Tests were carried out under both static and dynamic conditions, before and after the pressure stabilization process by air cells and the subsequent pressure offloading. During the validation process, methods were used to evaluate the agreement between measurements obtained by the two systems. The results of the static test showed that pressure stabilization reduced pressure on the heel by 32.43%, distributing it to the metatarsals and toes. After heel pressure offloading, the reduction reached 42.72%. In the dynamic test, despite natural dispersion of the measurements, a trend to reduce the peak pressure in the heel, metatarsals, and toes was observed. Agreement analysis recorded 96.32% in the static test and 94.02% in the dynamic test. The pneumatic insole proved effective in redistributing and reducing plantar pressure, with more evident effects in the static test. Its agreement with the pedar (R) system reinforces its reliability as a tool for measuring and managing plantar pressure, representing a promising solution for preventing plantar lesions.

Abstract
This demo shows an infrastructure that allows for easy implementation of real remote labs. In this infrastructure, several nodes are remotely interconnected by publishing/subscribing MQTT messages. There are physical nodes capable of connecting to real circuits and/or sensors/actuators, and virtual nodes that implement simulated versions of circuits that interact remotely with signals from other nodes. The latencies that occur are low enough for groups of students located in different physical locations to benefit from a near real-time experience in interacting with the circuits thus implemented.

Abstract
Data intensive applications increasingly make use of multiple data stores in the cloud, providing a diversity of data and query models, as well as durability and scale trade-offs. However, this has a severe impact on reliability, as the key fault-tolerance mechanism for database systems, i.e. ACID transactions, is no longer available. Although it is possible to implement transactions without changes to the database servers, this either requires a proxy server, which compromises scale and availability, or a client-side layer that changes the data schema, excludes legacy applications, and adds significant overhead. We address this challenge with a proposal to delegate functionality from a client-side transactional layer to a server-side query engine such that compatibility with legacy applications is restored. We implemented a proof-of-concept and show that it significantly improves performance for analytical applications.

Abstract
Artificial Intelligence (AI), in particular Generative Artificial Intelligence (GenAI), is a quickly developing field capable of revolutionizing educational digital escape rooms. Traditionally reliant on static content, these immersive environments have faced limitations in adaptability, replayability, and personalization. However, recent advancements in AI and GenAI enable dynamic puzzle generation, adaptive storytelling, and AI-driven non-player characters (NPCs) with agentic AI, allowing for highly responsive and personalized experiences. This paper reviews the state-of-the-art in integrating AI (with the focus on GenAI) into educational digital escape rooms, integrating interdisciplinary insights from cognitive science, game design, and machine learning, and showing how AI can improve engagement, scalability, and content diversity, but also indicates challenges related to ethical AI use, bias in algorithmic decision-making, and the need for robust evaluation frameworks to assess player satisfaction and learning outcomes.

Abstract
The effectiveness of digital marketing relies on the seamless integration of intelligent technology, enabling encounters that closely resemble those experienced with physical vendors in the real world. Thus, the importance of scalable artificial intelligence (AI) systems guided by a multimodal approach cannot be overstated, as they can be used to gain a deeper understanding of user preferences and engagement behaviors. The investigation conducted concerning multimodal learning in this review uncovers a variety of benefits and limitations on the available data, presenting consistency in finding the relationship between modalities. The results suggest multimodality as a topic with a noticeable dearth of research, yet a promising path to reduce uncertainty and develop innovative perspectives on decision-making for Digital Marketing improvement tasks. The complexity inherent in data processes like analysis, processing, and granular modulation requires a lot of effort for researchers to build accurate multimodal representations while trying to suppress imprecision in these new elements. Therefore, our approach aims to explore how theoretical foundations are successfully applied to learning operational procedures, considering real-life case comprehension, the technical challenges of the learning process, and the importance given to each feature. Even so, comparing the restrictions found in the state-of-the-art made possible the reformulation of limitations to this particular type of technology and encouraged the search for more guidelines on the entire process.

Abstract
The Industry 4.0 paradigm (I4.0) supports the improvement of industrial processes through Information and Communication Technologies (ICT), with information systems providing real-time information to humans and machines, in order to make the production process more flexible and efficient. In this context, Virtual Assistants (VA) collect and process production data and provide contextualized and real-time information to the workers in the production environment. This paper presents a prototype of a VA developed to collect production data from heterogeneous sources in the factory, process them based on contextual information, and provide workers with useful information to assist them in taking informed decisions. In that context, VA can represent a valuable aid to improve overall productivity and efficiency in the I4.0 factories.