The indigenous peoples of the Amazon have experienced changes in cultural identity due to Western colonisation, contact with other cultures, migration, and pandemics. COVID-19, caused by the SARS-CoV-2 virus, affected all of humanity, including populations with limited contact with Western cultures, such as the Waorani indigenous people of the Amazon. Following the global pandemic, their culture may have undergone modifications. This study presents a comparative analysis of Waorani indigenous culture in the pre- and post-pandemic periods (2017–2022). In 2022, the same instrument designed to measure their culture in 2017 was applied, using the same methodology (participatory action in the territory) and in the same indigenous communities (88 individuals in 2017, 85 individuals in 2022). The results show that the cultural identity of the Waorani indigenous people has remained largely unchanged from the first measurement in 2017 to the second measurement after the pandemic in 2022 across most variables (economic, production, property, and land cultivation; family, reproduction, education, childcare, and medicine; organization, community politics, and justice; social, music, art, food, clothing, and housing). However, in the ideological, religious, beliefs, and spirituality domains, there was a significant decline in scores after COVID-19.

Video games have evolved into a key part of modern culture and a major economic force, with the global market projected to reach ${\$}$ 282.30 billion in 2024. As technology advances, video games increasingly demand high computing power, often requiring specialized hardware for optimal performance. Real-time strategy games, in particular, are computationally intensive, with complex artificial intelligence algorithms that simulate numerous units and behaviors in real-time. Specialized gaming PCs are use a dedicated GPU to run video games. Due to the usefulness of GPUs besides gaming, modern processors usually include an integrated GPU, specially in the laptop market. We propose a hybrid architecture that utilizes both the dedicated GPU and the integrated GPU simultaneously, to accelerate AI and physics simulations in video games. The hybrid approach aims to maximize the utilization of all available resources. The AI and physics computations are offloaded from the dedicated GPU to the integrated GPU. Therefore, the dedicated GPU can be used exclusively for rendering, resulting in improved performance. We implemented this architecture in a custom-built game engine using OpenGL for graphics rendering and OpenCL for general-purpose GPU computations. Experimental results highlight the performance characteristics of the hybrid architecture, including the challenges of working with the two devices and multi-tenant GPU interference.

In any industry, maximizing the use of raw materials is essential to reduce waste and costs, which also positively impacts the environment. In footwear production, components are typically derived from cutting processes, requiring optimized systems to maximize the use of different materials, minimize waste, and accelerate production. In this context, nesting is a technique that arranges shapes within a confined space to maximize area utilization and reduce unused space. As this problem is classified as NP-Hard, only algorithmic approximations can be employed. This paper focuses on optimizing the cutting of leather parts for shoe manufacturing. Footwear parts are cut from cattle hides, which are not only irregular in shape but also vary in resistance and quality across different areas of the same piece of leather. This study proposes automated nesting methods that aim to compete with current manual approaches, which are conducted exclusively by experts with deep knowledge of the characteristics of both the pieces and the leather, making the manual process time-intensive. This research reviews current methods and introduces hybrid ones, achieving up to 38.4× acceleration and up to 10.18% increase in nested pieces over manual methods.

Within various industrial settings, such as shipping, aeronautics, woodworking, and footwear, there exists a significant challenge: optimizing the extraction of sections from material sheets, a process known as “nesting”, to minimize wasted surface area. This paper investigates efficient solutions to complex nesting problems, emphasizing rapid computation over ultimate precision. We introduce a dual-approach methodology that couples both a greedy technique and a genetic algorithm. The genetic algorithm is instrumental in determining the optimal sequence for placing sections, ensuring each is located in its current best position. A specialized representation system is devised for both the sections and the material sheet, promoting streamlined computation and tangible results. By balancing speed and accuracy, this study offers robust solutions for real-world nesting challenges within a reduced computational timeframe.