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
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

Abstract
It is presented a study of the dependence between the free spectral range (FSR) and the cavity length in Fabry-Perot interferometers. Furthermore, the effect of frequency modulation on the FSR is studied when an optical spectrum analyser (OSA) is used as an interrogator. For low frequency range it is possible to observe this behaviour in the OSA and using an appropriate processing signal it is possible to use the white light interferometry technique.

Abstract
A strategy for the detection of H2O2 as a milk adulterant using a single shot membrane sensor, is presented. Direct quantitative evaluation of H2O2 in raw, skimmed, semi-skimmed and whole milk was carried out based on a chemiluminescence reaction with luminol. For H2O2 water solutions a linear response was attained from 0.0001% to 0.007 %w/w, with a limit of detection of 3x10(-5) %w/w. A coefficient of determination, R-2, greater than 0.97 was achieved, with a relative standard deviation (RSD) not exceeding 10%. In the analyzed milk samples, the lowest H2O2 concentration detected was 0.001% w/w for raw and for skim milk and 0.002%w/w for, semi-skimmed and whole milk. The presented method is original, sensitive, rapid, and cost-effective. Due to the achieved sensitivity the method has great potential to be used for H2O2 detection in diverse areas, such as environmental monitoring and food quality.