Abstract
The entrepreneurs responsible for establishing university spinoffs are incessantly looking for new ways to leverage existing technology or create an entirely new product or service market. The creation of disruptive innovative solutions has assumed a key role in enhancing the role of university spinoffs in the global marketplace. This study aims to characterize and explore the phenomenon of disruptive innovation in university spinoffs and identify how university spinoffs recognize and evaluate low-end and new-market disruptive technologies. The findings reveal that university spin-offs have essentially privileged new-market disruptive technologies. Participation in trade fairs, conferences and journals have been the main methods adopted by these organizations to identify disruptive technologies, while the evaluation of the potential of these disruptive technologies is essentially based on the experience and scientific knowledge of the founders of the university spin-offs. Furthermore, the size and number of years of activity of university spinoffs are two factors that allow us to understand the greater proximity of the younger and smaller spinoffs with universities and research centers, whereas the larger organizations prioritize mainly market analysis and product research techniques.

Abstract
This paper proposes an energy community management system for local energy sharing with grid flexibility services to solve the potential grid constraints of the local distribution network. A three-stage model is proposed. Stage 1 is the individual minimization of the energy bill of each prosumer by optimizing the schedules of its battery. The second stage optimizes the energy bill of the energy community by sharing internally the prosumers energy surplus and re-dispatching their batteries, while guaranteeing that each new individual prosumer energy bill is always equal or less than its stage 1 bill. The third stage is performed by the DSO to solve the grid constraints by re-dispatching the batteries, curtailing local generation or reducing consumption. Stage 3 minimizes the impact on stage 2 by minimizing the loss of profit or utility of every prosumer which is compensated accordingly.

Abstract
Medical devices are rapidly evolving and becoming more interconnected with healthcare networks, overcoming resource constraints, and increasingly focused on patient well-being and needs. This work intends to identify future research themes in the area of cybersecurity in health by surveying the articles being developed and identifying their current limitations and future work. The developed analysis was based on the publications with the highest number of citations, enabling us to find several challenges and restrictions such as integrating devices in systems. Innovations and the emergence of new technologies with inherent security vulnerabilities, will continue to evolve, escalating the attackers interest in exploiting unknown cybersecurity risks within healthcare. It is mandatory to consider cybersecurity risks since the conception of the devices to reduce security flaws, ensure the patients with a better quality of life, and guarantee information security properties. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist's attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

Abstract
After publishing an in-depth study that analyzed the ability of computerized methods to assist or replace human experts in obtaining carotid intima-media thickness (CIMT) measurements leading to correct therapeutic decisions, here the same consortium joined to present technical outlooks on computerized CIMT measurement systems and provide considerations for the community regarding the development and comparison of these methods, including considerations to encourage the standardization of computerized CIMT measurements and results presentation. A multi-center database of 500 images was collected, upon which three manual segmentations and seven computerized methods were employed to measure the CIMT, including traditional methods based on dynamic programming, deformable models, the first order absolute moment, anisotropic Gaussian derivative filters and deep learning-based image processing approaches based on U-Net convolutional neural networks. An inter- and intra-analyst variability analysis was conducted and segmentation results were analyzed by dividing the database based on carotid morphology, image signal-to-noise ratio, and research center. The computerized methods obtained CIMT absolute bias results that were comparable with studies in literature and they generally were similar and often better than the observed inter- and intra-analyst variability. Several computerized methods showed promising segmentation results, including one deep learning method (CIMT absolute bias = 106 +/- 89 mu m vs. 160 +/- 140 mu m intra-analyst variability) and three other traditional image processing methods (CIMT absolute bias = 139 +/- 119 mu m, 143 +/- 118 mu m and 139 +/- 136 mu m). The entire database used has been made publicly available for the community to facilitate future studies and to encourage an open comparison and technical analysis

Abstract
The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.