Abstract
Diabetes, a chronic condition affecting millions of people, requires ongoing medical care and treatment, which can place a significant financial burden on society, directly and indirectly. In this paper we propose a vision-robotics system for the automatic assessment of the diabetic foot, one the exams used for the disease management. We present and discuss various computer vision techniques that can support the core operation of the system. U-Net and Segnet, two popular convolutional network architectures for image segmentation are applied in the current case. Hardcoded and machine learning pipelines are explained and compared using different metrics and scenarios. The obtained results show the advantages of the machine learning approach but also point to the importance of hard coded rules, especially when well know areas, such as the human foot, are the systems' target. Overall, the system achieved very good results, paving the way to a fully automated clinical system.

Abstract
The energy storage batteries, employed in solar systems installed on lampposts, are usually placed in devices such as switchboards fixed at an elevation near the top of the column. However, this storage solution becomes inefficient, because it is not possible to guarantee the control of the working temperature of the batteries, due to the low thermal insulation capacity of these storage devices. In this sense, an underground compartment made of concrete, steel plate and rock wool were created, embedded in the foundation of the lamppost, with the purpose of using geothermal energy to maintain an adequate temperature inside the compartment. To verify the temperature inside the battery storage compartment, a thermal analysis was performed, where heat transfer by conduction, convection and radiation was considered. Analyses were performed in steady state, and later, transient state, considering the initial temperatures of the thermal study in the previous steady state. With a storage volume of 1m3 and the base of the compartment at a depth of 2m, it was verified that it is possible to use geothermal energy to cool or heat, depending on the season, a system through geothermal energy. Considering a typical day in July, with room temperature of 35oC, a reduction of approximately 8oC was obtained inside the storage compartment, compared to the ambient temperature.

Abstract
In this work, a method based on the use of explainable artificial intelligence techniques with multiscale and multidimensional fractal techniques is presented in order to investigate histological images stained with Hematoxylin-Eosin. The CNN GoogLeNet neural activation patterns were explored, obtained from the gradient-weighted class activation mapping and locally-interpretable model-agnostic explanation techniques. The feature vectors were generated with multiscale and multidimensional fractal techniques, specifically fractal dimension, lacunarity and percolation. The features were evaluated by ranking each entry, using the ReliefF algorithm. The discriminative power of each solution was defined via classifiers with different heuristics. The best results were obtained from LIME, with a significant increase in accuracy and AUC rates when compared to those provided by GoogLeNet. The details presented here can contribute to the development of models aimed at the classification of histological images. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
[No abstract available]

Abstract
Adopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology's immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.

Abstract
The gut microbiome is shaped early in life by dietary and lifestyle factors. Specific compounds in the gut affect the growth of different bacterial species and the production of beneficial or harmful byproducts. Dysbiosis of the gut microbiome has been linked to various diseases resulting from the presence of harmful bacteria and their byproducts. Existing methods for detecting microbial species, such as microscopic observation and molecular biological techniques, are costly, labor-intensive, and require skilled personnel. Biosensors, which integrate a recognition element, transducer, amplifier, signal processor, and display unit, can convert biological events into electronic signals. This review provides a comprehensive and systematic survey of scientific publications from 2018 to June 2024, obtained from ScienceDirect, PubMed, and Scopus databases. The aim was to evaluate the current state-of-the-art and identify knowledge gaps in the application of aptamer biosensors for the determination of gut microbiota. A total of 13 eligible publications were categorized based on the type of study: those using microbial bioreceptors (category 1) and those using aptamer bioreceptors (category 2) for the determination of gut microbiota. Point-of-care biosensors are being developed to monitor changes in metabolites that may lead to disease. They are well-suited for use in the healthcare system and offer an excellent alternative to traditional methods. Aptamers are gaining attention due to their stability, specificity, scalability, reproducibility, low production cost, and low immunogenicity. While there is limited research on using aptamers to detect human gut microbiota, they show promise for providing accurate, robust, and cost-effective diagnostic methods for monitoring the gut microbiome.