In critical energy infrastructures, Insulated Gate Bipolar Transistors (IGBTs) serve as essential components but are prone to unexpected failures. Precise estimation of the Remaining Useful Lifetime (RUL) of IGBTs is imperative for implementing predictive maintenance and assuring system reliability. This paper presents an innovative GPU-based approach for real-time health monitoring and lifetime prediction of IGBTs. The study explores a range of machine learning algorithms to determine the most effective one for precise lifetime prediction. Contrary to prior studies that concentrated on singular sensor data to minimize complexity and resource expenditure, this research leverages the capabilities of modern, economical, and robust GPUs to facilitate a data-driven, multi-sensor monitoring framework. The application of this approach has the potential to substantially bolster the reliability of energy infrastructure, notably in hydrogen plant. The paper conducts an exhaustive analysis of both single-variable and multivariate machine learning models, including Random Forest (RF), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNNs), operating in real-time on edge GPUs. It also assesses the performance of two distinct GPU architectures - the NVIDIA Jetson Nano and Jetson Orin - in executing these machine learning algorithms.

In the context of critical energy infrastructures (e.g., hydrogen infrastructure) that extensively utilize power converters, the need for reliable and accurate monitoring is of paramount importance. Addressing this necessity, this paper presents a novel GPU-based multivariate approach to Insulated Gate Bipolar Transistor (IGBT) lifetime prediction. Despite the substantial technological advances in the field, accurately predicting the lifetime of IGBTs remains a significant challenge. Current methods often rely on single precursor variable models, which can lack the precision required in demanding power electronic applications. In contrast, this study utilizes multiple precursor variables (V CE(ON) and case temperature) to achieve more accurate results. Initial results using NASA's open-source dataset, and Gaussian Process Regression (GPR) reveal that our multivariate model outperforms its single-variable counterparts in prediction accuracy. Furthermore, the paper elaborates on the development of a small-scale experimental setup to enable real-time health monitoring of the IGBT. It uses an NVIDIA Jetson Nano GPU to handle real-time data from V CE(ON) and Case Temperature. A pre-trained random forest model based on V CE(ON) and Case Temperature profile data is deployed in NVIDIA Jetson Nano GPU for real-time prediction of the remaining useful life (RUL) of the IGBT, laying the groundwork for future implementation of the real-time multivariate model. The findings from this research highlight the significant potential for enhancing IGBT lifetime prediction accuracy through GPU-based multivariate models

Buildings are responsible for 39% of global energy-related carbon emissions; 11% come from materials and construction, and 28% come from operational emissions (the energy required to heat, cool, and power). Embodied carbon will contribute an increasing amount to the emissions from new buildings in the future as carbon emissions from building operations continue to decline. This study examined several construction methods and materials to evaluate alternatives for remodeling a residential villa. A villa in the Al-Qassim Region, Buraidah, served as a case study for this research. The methodology employed provided a meticulous appraisal of the building materials and techniques currently in use. The structural integrity and cost comparison between the recommended scenarios and the existing situation were examined in this study. Based on this assessment, alternative building materials and techniques are found, and their potential use in retrofitting the villa is investigated. Six options have been investigated: (1) Insulating Precast System, (2) Autoclaved Aerated Concrete, (3) Tunnel Form, (4) Light Gauge Steel, (5) Cladding Materials, and (6) Nanotechnology materials. The construction project can achieve cost savings, enhanced thermal comfort, and long-term environmental sustainability by implementing these suggested technologies. In conclusion, even though new construction technology systems could be more expensive initially, they can save money over time by increasing building lifespan, lowering maintenance requirements, and improving energy efficiency. In the Saudi Arabian market, selecting between innovative construction technology systems and conventional construction methods is also influenced by several other factors, including client preferences, local restrictions, sustainability goals, and project requirements.

The paint industry significantly contributes to soil, water, and air pollution. Since it contains a variety of substances, such as heavy metals, solvents, and volatile organic compounds (VOCs), the environment and human health may be negatively impacted by these pollutants. Paint can include dangerous compounds that can lead to cancer, skin irritation, respiratory issues, and environmental damage.To compare different wall painting types utilized in the Ibny Baitak Project, a new city development in Egypt, using the life cycle assessment (LCA) approach. The environmental impact of 12 different painting types is determined by the raw materials utilized, production, and transportation. The cut-off of the paint application and end-of-life disposal steps are applied. This article compared several wall paint types, including water-based, solvent-based, and low-VOC paints. By the single score results, the alkyd paint type recorded the highest impact by (1.57 pt), the ceramic-based paint came in the second rank by (1.19 pt), then the acrylic paint by (0.27 pt), finally the Gypsum Plasterboard by (0.25 pt). However, by the weighting result, the Alkyd paint type has recorded the highest of the three environmental impacts by 4.75 [kg co]_2 eq, 65.61 Mj primary, and 3.30E-06 DALY, respectively. In contrast, the Gypsum Plasterboard recorded the lowest numbers by 0.45 [kg co]_2 eq, 5.19 Mj primary, and 1.13E-06 DALY, respectively. The article's findings show that solvent-based paints have the most significant environmental impact, whereas water-based and low-VOC paints (such as acrylic paint) have the lowest.