Abstract
Background: Aberrant or loss of cell adhesion drives invasion and metastasis, key hallmarks of cancer progression. In this work, we hypothesized that a gene signature related to cell adhesion could predict breast cancer prognosis. Methods: Highly variant genes were tested for association with overall survival using Cox regression. Adhesion-related genes were identified through gene ontology analysis and multivariate Cox regression, with AIC selection, defined the prognostic signature. The AdhesionScore was then calculated as a weighted sum of gene expression, with risk stratification assessed by Kaplan-Meier and log-rank tests. Results: We found that the AdhesionScore was a significant independent predictor of poor survival in three large independent datasets, as it provided a robust stratification of patient prognosis in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (HR: 2.65; 95% CI: 2.33-3.0, p = 2.34 x 10-51), The Cancer Genome Atlas (TCGA) (HR: 3.46; 95% CI: 2.35-5.09, p = 3.50 x 10-10), and the GSE96058 (HR: 2.83; 95% CI: 2.20-3.65, p = 6.29 x 10-16) datasets. The 5-year risk of death in the high-risk group was 32.41% for METABRIC, 27.8% for TCGA, and 17.54% for GSE96058 datasets. Consistently, HER2-enriched and triple-negative breast carcinomas (TNBC) cases showed higher AdhesionScores than luminal subtypes, indicating an association with aggressive tumor biology. Conclusions: We have developed, for the first time, a molecular signature based on cell adhesion, as well as an associated AdhesionScore that can predict patient prognosis in invasive breast cancer, with potential clinical application. We developed a novel adhesion-based molecular signature, the AdhesionScore, that robustly predicts prognosis in breast cancer across independent cohorts, highlighting its potential clinical utility for patient risk stratification.

Abstract
The increasing integration of renewable energy sources and decentralized generation requires demand-side flexibility to improve grid stability and balance local energy flows. Local Flexibility Markets (LFMs) provide a framework for optimizing flexibility transactions within energy communities. This paper presents a model for quantifying and pricing residential resources flexibility, enabling prosumers to submit bids in an LFM managed by the Community Manager. The methodology relies on a linear optimization problem, where a Home Energy Management System first determines optimal consumption baselines. Then an iterative sensitivity analysis estimates upward, and downward flexibility bands and sets offer prices per resource. The market operates as two asymmetric voluntary pools, clearing flexibility offers and requests. Results show that Battery Energy Storage Systems and Electric Vehicles provide the most effective flexibility, significantly reducing energy costs. Future research should improve pricing mechanisms and scalability to support LFM adoption in different residential settings.

Abstract
Neonatal seizures represent a critical medical issue that requires prompt diagnosis and treatment. Typically, at-risk newborns undergo a Magnetic Resonance Imaging (MRI) brain assessment followed by continuous seizure monitoring using multichannel EEG. Visual analysis of multichannel electroencephalogram (EEG) recordings remains the standard modality for seizure detection; however, it is limited by fatigue and delayed seizure identification. Advances in machine and deep learning have led to the development of powerful neonatal seizure detection algorithms that may help address these limitations. Nevertheless, their performance remains relatively low and often disregards the non-stationary attributes of EEG signals, especially when learned from weakly labeled EEG data. In this context, the present paper proposes a novel deep-learning approach for neonatal seizure detection. The method employs rigorous preprocessing to reduce noise and artifacts, along with a recently developed time-frequency distribution (TFD) derived from a separable compact support kernel to capture the fast spectral changes associated with neonatal seizures. The high-resolution TFD diagrams are then converted into RGB images and used as inputs to a pre-trained ResNet-18 model. This is followed by the training of an attention-based multiple-instance learning (MIL) mechanism. The purpose is to perform a spatial time-frequency analysis that can highlight which channels exhibit seizure activity, thereby reducing the time required for secondary evaluation by a doctor. Additionally, per-instance learning (PIL) is performed to further validate the robustness of our TFD and methodology. Tested on the Helsinki public dataset, the PIL model achieved an area under the curve (AUC) of 96.8%, while the MIL model attained an average AUC of 94.1%, surpassing similar attention-based methods.

Abstract
Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path planning being key. The Time Enhanced A* (TEA*) algorithm extends A* by adding time as a dimension to resolve temporal conflicts in multi-robot coordination. However, inconsistencies in edge lengths within the graph can hinder optimal path calculation. To address this, a Graph Decomposition submodule was developed to standardize edge lengths and temporal costs. Integrated into a ROS-based fleet coordination system, this approach significantly reduces execution time and improves coordination capacity.

Abstract
In today's highly competitive fashion retail market, it is crucial to have accurate demand forecasting systems, namely for new products. Many experts have used machine learning techniques to forecast product sales. However, sales that do not happen due to lack of product availability are often ignored, resulting in censored demand and service levels that are lower than expected. Motivated by the relevance of this issue, we developed a two-stage approach to forecast the demand for new products in the fashion retail industry. In the first stage, we compared four methods of transforming historical sales into historical demand for products already commercialized. Three methods used sales-weighted averages to estimate demand on the days with stock-outs, while the fourth method employed an Expectation-Maximization (EM) algorithm to account for potential substitute products affected by stock-outs of preferred products. We then evaluated the performance of these methods and selected the most accurate one for calculating the primary demand for these historical products. In the second stage, we predicted the demand for the products of the following collection using Random Forest, Deep Neural Networks, and Support Vector Regression algorithms. In addition, we applied a model that consisted of weighting the demands previously calculated for the products of past collections that were most similar to the new products. We validated the proposed methodology using a European fashion retailer case study. The results revealed that the method using the Expectation-Maximization algorithm had the highest potential, followed by the Random Forest algorithm. We believe that this approach will lead to more assertive and better-aligned decisions in production management.

Abstract