Abstract
In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The literature on the isothermal model gas flow is extensive, but the effect of temperature variation on the hydraulic characteristics has been rarely addressed. Additionally, the impact of hydrogen blending on the thermal condition of NG pipelines is also an emergent topic that requires new approaches to the gas flow problem formulation and resolution. In this paper, a model for the gas flow problem was developed to optimise the operation of natural gas distribution networks with hydrogen injection while maintaining pressure, gas flows, and gas quality indexes within admissible limits. The goal is to maximise the injection of hydrogen and investigate the influences of thermal variations in the gas blending. Also, this model enables the calculation of the maximum permitted volume of hydrogen in the network, quantifying the total savings in natural gas usage and carbon dioxide emissions in different temperature conditions.

Abstract
Biofeedback is a promising tool to complement conventional physical therapy by fostering active participation of neurologically impaired patients during treatment. This work aims at a user-centered design and usability assessment for different age groups of a novel wearable augmented reality application composed of a multimodal sensor network and corresponding control strategies for personalized biofeedback during gait training. The proposed solution includes wearable AR glasses that deliver visual cues controlled in real-time according to mediolateral center of mass position, sagittal ankle angle, or tibialis anterior muscle activity from inertial and EMG sensors. Control strategies include positive and negative reinforcement conditions and are based on the user's performance by comparing real-time sensor data with an automatically user-personalized threshold. The proposed solution allows ambulatory practice on daily scenarios, physiotherapists' involvement through a laptop screen, and contributes to further benchmark biofeedback regarding the type of sensor. Although old healthy adults with low academic degrees have a preference for guidance from an expert person, excellent usability scores (SUS scores: 81.25-96.87) were achieved with young and middle-aged healthy adults and one neurologically impaired patient.

Abstract
This paper introduces Labadain-30k+, a monolingual dataset comprising 33.6k documents in Tetun, a low-resource language spoken in Timor-Leste. The dataset was acquired through web crawling and augmented with Wikipedia documents released by Wikimedia. Both sets of documents underwent thorough manual audits at the document level by native Tetun speakers, resulting in the construction of a Tetun text dataset well-suited for a variety of natural language processing and information retrieval tasks. This dataset was employed to conduct a comprehensive content analysis aimed at providing a nuanced understanding of document composition and the evolution of Tetun documents on the web. The analysis revealed that news articles constitute the predominant documents within the dataset, accounting for 89.87% of the total, followed by Wikipedia documents at 4.34%, and legal and governmental documents at 3.65%, among others. Notably, there was a substantial increase in the number of documents in 2020, indicating 11.75 percentage points rise in document quantity, compared to an average of 4.76 percentage points per year from 2001 to 2023. Moreover, the year 2017, marked by the increased popularity of online news in Tetun, served as a threshold for analyzing the evolution of document writing on the web pre- and post-2017, specifically regarding vocabulary usage. Surprisingly, this analysis showed a significant increase of 6.12 percentage points in the Tetun written adhering to the Tetun official standard. Additionally, the persistence of Portuguese loanwords in that trajectory remained evident, reflecting an increase of 5.09 percentage points. © 2024 ELRA Language Resource Association.

Abstract
We propose a solution for generating dynamic heightmap data to simulate deformations for soft objects, with a focus on the human skin. The solution utilizes mesostructure-level wrinkles and procedural textures to add static microstructure details. It offers flexibility beyond human skin during animations to mimic other material deformations, such as leather and rubber. Various methods suffer from self-intersections and increased storage requirements during synthesizing wrinkles. Although manual intervention using wrinkles and tension maps offers control, it lacks information on principal deformation directions. Physics-based simulations can generate detailed wrinkle maps, but may limit artistic control. Our research presents a procedural method to enhance the generation of dynamic deformation patterns, including wrinkles, with better control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the proposed approach can be used in other application areas.

Abstract
This article presents a real-time trajectory optimizer for shore-to-ship operations using Unmanned Aerial Vehicles (UAVs). This concept aims to improve the efficiency of the transportation system by using UAVs to carry out parcel deliveries to offshore ships. During these operations, UAVs would fly relatively close to manned vessels, posing significant risks to the crew in the event of any incident. Additionally, in these areas, UAVs are exposed to meteorological phenomena such as wind gusts, which may compromise the stability of the flight and lead to potential collisions. Furthermore, this is a phenomenon difficult to predict, which poses a risk that must be considered in the operations. For these reasons, this work proposes a gust-aware multi-objective optimization solution for calculating fast and safe trajectories, considering the risk of flying in areas prone to the formation of intense gusts. Moreover, the system establishes a risk buffer with respect to all vessels to ensure compliance with EASA (European Union Aviation Safety Agency) regulations. For this purpose, Automatic Identification System (AIS) data are used to determine the position and velocity of the different vessels, and trajectory calculations are periodically updated based on their motion. The system computes the minimum-cost trajectory between the ground base and a moving destination ship while keeping these risk buffer constraints. The problem was solved through an Optimal Control formulation discretized on a dynamic graph with time-dependent costs and constraints. The solution was obtained using a Reaching Method that allowed efficient and real-time computations.