The video game industry faces significant challenges in content creation, with AAA games requiring extensive time and resources. This research addresses these challenges through the development of a Unity module for automatic 3D kitchen model generation using a Machine Learning-based Procedural Kitchen Generation (PKG) model. The module significantly reduces the time needed for game-level design, achieving designs over five times faster than traditional methods. A comparative study shows that the module produces results comparable to industry-standard tools in terms of user preference. This Unity module offers promising potential for commercial use, streamlining the design process and enhancing efficiency in game development.
This article investigates the different challenges and phases of building an ecosystem to utilise data centre waste heat. Data centres consume an increasing amount of energy, and currently, in many cases, excess heat is not utilised beyond heating the data centre property. The research examines how a circular economy can be integrated into the operations of a data centre’s waste heat business ecosystem and how a business ecosystem based on utilising waste heat from a data centre should be built. The research is based on a literature review, interviews with experts and stakeholders in the case ecosystem and a survey. Based on the research, a concept for a business ecosystem based on the utilisation of waste heat from a pre-existing data centre perspective is modelled, and the concept outlines things to be considered when building such an ecosystem. Key findings were the challenges, opportunities and prerequisites of developing a data centre waste heat ecosystem and the roadmap of constructing one.
This study investigates how sustainable supply chain management (SCM) practices and information technology (IT) drive firm performance in small and medium enterprises (SMEs), with a focus on the mediating roles of supply chain agility (SCA) and sustainable supply chain integration (SSCI). Drawing on data from 219 managers and supply chain experts in Iranian SMEs, structural equation modeling (SEM) revealed that both sustainable SCM practices and IT adoption significantly enhance firm performance directly and indirectly through SCA and SSCI. Sustainable SCM practices demonstrated a robust influence on SSCI (β = 0.35, p < 0.001), SCA (β = 0.49, p < 0.001), and firm performance (β = 0.44, p < 0.001), while IT similarly impacted SSCI (β = 0.34, p < 0.001), SCA (β = 0.39, p < 0.001), and performance (β = 0.21, p < 0.001). Critically, SCA and SSCI emerged as pivotal mediators, explaining 48% of the variance in firm performance. The findings underscore that agility and integration transform sustainable and technological investments into competitive outcomes, enabling SMEs to navigate dynamic markets. Practical insights highlight the need for firms to prioritize cross-departmental coordination, IT-driven customer insights, and adaptive decision-making frameworks.
In the modern soccer world, also referred to as football, analytics has become integral IT serves a role akin to assistant coaches, contributing significantly to the analysis of team and individual player performance during both games and training sessions. Despite the abundance of advanced technologies, there is still a need for concrete measurements in soccer analytics. A prime example is Expected Goals (xG), a widely embraced metric that goes beyond mere scorelines to offer in-depth insights into player and team dynamics, and has proven its value in recent years. The identification of key features from a dataset to predict xG is a critical aspect of machine learning research applied to soccer analytics. However, prior investigations have either overlooked certain crucial features or failed to recognize their significance. This study proposes a novel approach by incorporating features related to coaches, coaching tenure, and tactics, which have the potential to improve predictive accuracy and provide actionable insights for teams and analysts. Using a dataset of 2,917 observations covering the top five European leagues, we applied regression-based machine learning models, employing a preprocessing pipeline and k-fold cross-validation to ensure robust evaluation. The findings reveal that the xG values for the English Premier League (EPL) surpass those of the other four leagues studied, with an average xG of approximately 1.93. This indicates that, on average, teams in the EPL tend to have a higher expected goal count per match than teams in other top European leagues. This superiority can be attributed to various factors, such as the higher average quality of teams and players, tactical nuances, and the overall competitiveness inherent within the EPL compared with the other leagues under scrutiny. In our study, we introduced new features such as coaches, coaching durations (years), and tactics. Incorporating these features enhanced the model performance across MSE, EVS, and R², thereby demonstrating the efficacy of our approach. Lasso and Ridge Regression models achieved improved predictive accuracy, with EVC and R² reaching up to 96%, while the Decision Tree model showed a nearly 6% reduction in the MSE.
Inefficient municipal solid waste management represents a significant obstacle on the transition towards a circular economy in densely populated urban environments, such as the district of San Juan de Lurigancho, Lima, Peru. This study presents the implementation of advanced technologies, specifically smart containers integrated with sensors and real-time monitoring systems, to optimize waste collection. This resulted in decrements in operating costs and unrecycled waste by 20% and 27%, respectively, whilst increasing citizen engagement by 18% regarding segregation and recycling activities, highlighting positive changes in community behavior. However, the present technological infrastructure, technical training and necessary funds are all insufficient, which presents limitations on the deployment and scalability of these measures. This study thusly indicates the necessity to elaborate public policies, environmental education and engagement strategies to guarantee system stability. These findings both provide a replicable model to address these issues and offer a roadmap for the integration of smart technologies in sustainable city planning, bridging technological innovation and sustainability.
