Abstract
Retailers depend on accurate sales forecasts to effectively plan operations and manage supply chains. These forecasts are needed across various levels of aggregation, making hierarchical forecasting methods essential for the retail industry. As competition intensifies, the use of promotions has become a widespread strategy, significantly impacting consumer purchasing behavior. This study seeks to improve forecast accuracy by incorporating promotional data into hierarchical forecasting models. Using a sales dataset from a major Portuguese retailer, base forecasts are generated for different hierarchical levels using ARIMA models and Multi-Layer Perceptron (MLP) neural networks. Reconciliation methods including bottom-up, top-down, and optimal reconciliation with OLS and WLS (struct) estimators are employed. The results show that MLPs outperform ARIMA models for forecast horizons longer than one day. While the addition of regressors enhances ARIMA's accuracy, it does not yield similar improvements for MLP. MLPs present a compelling balance of simplicity and efficiency, outperforming ARIMA in flexibility while offering faster training times and lower computational demands compared to more complex deep learning models, making them highly suitable for practical retail forecasting applications.

Abstract
For decades, the collaborative networks community has studied supply chains, focusing on trust, visibility, collaboration, and innovation, with emergent technologies being a key area of research. The rise of digital technologies has led to extensive studies on supply chain digital transformation. With the surge of AI-based technologies, there is an increasing body of research on AI's human and social impact on Supply Chain Management (SCM). However, while Socio-Technical Systems (STS) thinking has been applied to digital transformations, it has not yet addressed AI-induced changes in supply chains. This paper synthesises recent research on AI integration in SCM and the use of STS thinking in AI systems design. We propose a mapping approach for profiling AI-induced supply chain transformations for strategic design. We also present the Supply Chain Socio-Technical AI (SC-STAI) profiling tool in practice, demonstrating how it maps supply chain participants' current and desired states regarding AI integration.

Abstract
This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.

Abstract
IntroductionWilson's disease (WD) is associated with a variety of movement disorders and progressive neurological dysfunction. The aim of this study was to correlate baseline brain magnetic resonance imaging (MRI) features with clinical phenotype and long-term outcomes in chronically treated WD patients.MethodsPatients were retrospectively selected from an institutional database. Two experienced neuroradiologists reviewed baseline brain MRI. Functional assessment was performed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale, and disease severity was classified using the Global Assessment Scale for Wilson's Disease (GASWD).ResultsOf 27 patients selected, 14 were female (51.9%), with a mean (standard deviation [SD]) age at onset of 19.5 (7.1) years. Neurological symptoms developed in 22 patients (81.5%), with hyperkinetic symptoms being the most common (70.4%). Baseline brain MRI showed abnormal findings in 18 cases (66.7%), including T2 hyperintensities in 59.3% and atrophy in 29.6%. After a mean (SD) follow-up of 20.9 (11.0) years, WD patients had a mean score of 19.2 (10.2) on WHODAS 2.0 and 6.4 (5.7) on GASWD. The presence of hyperkinetic symptoms correlated with putaminal T2 hyperintensities (p = 0.003), putaminal T2 hypointensities (p = 0.009), and mesencephalic T2 hyperintensities (p = 0.009). Increased functional disability was associated with brain atrophy (p = 0.007), diffusion abnormalities (p = 0.013), and burden of T2 hyperintensities (p = 0.002). A stepwise regression model identified atrophy as a predictor of increased WHODAS 2.0 (p = 0.023) and GASWD (p = 0.007) scores.ConclusionsAtrophy and, to a lesser extent, deep T2 hyperintensity are associated with functional disability and disease severity in long-term follow-up of WD patients.

Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.

Abstract
Continuous monitoring of hydrogen (H2) concentration is critical for safer use, which can be done using optical sensors. Palladium (Pd) is the most commonly used transducer material for this monitoring. This material absorbs H2 leading to an isotropic expansion. This process is reversible but is affected by the interaction with interferents, and the lifetime of Pd thin films is a recurring issue. Fiber Bragg Grating (FBG) sensors are used to follow the strain induced by H2 on Pd thin films. In this work, it is studied the stability of Pd-coated FBGs, protected with a thin Polytetrafluoroethylene (PTFE) layer, 10 years after their deposition to assess their viability to be used as H2 sensors for long periods of time. It was found that Pd coatings that were PTFE-protected after deposition had a longer lifetime than unprotected films, with the same sensitivities that they had immediately after their deposition, namely 23 and 10 pm/vol% for the sensors with 150 and 100 nm of Pd, respectively, and a saturation point around 2 kPa. Furthermore, the Pd expansion was analyzed in the presence of H2, nitrogen (N2), carbon dioxide (CO2), methane (CH4) and water vapor (H2O), finding that H2O is the main interferent. Finally, an exhaustive test for 90 h is also done to analyze the long-term stability of Pd films in dry and humid environments, with only the protected sensor maintaining the long-term response. As a result, this study emphasizes the importance of using protective polymeric layers in Pd films to achieve the five-year lifetime required for a real H2 monitoring application.