At the end of 2019, a variation of a coronavirus, named SARS-CoV-2, has been identified as being responsible for a respiratory illness disease (COVID-19). Since ventilation is an important factor that influences airborne transmission, we proposed to study the impact of heating, ventilation and air-conditioning (HVAC) with a variable air volume (VAV) primary air system, on the dispersion of infectious aerosols, in a cardiac intensive care unit, using a transient simulation with computational fluid dynamics (CFD), based on the finite element method (FEM). We analyzed three scenarios that followed the dispersion of pathogen carrying expiratory droplets particles from coughing, from patients possibly infected with COVID-19, depending on the location of the patients in the intensive care unit. Our study provides the mechanism for spread of infectious aerosols, and possibly of COVID-19 infection, by air conditioning systems and also highlights important recommendations for disease control and optimization of ventilation in intensive care units, by increasing the use of outdoor air and the rate of air change, decreasing the recirculation of air and using high-efficiency particulate air (HEPA) filters. The CFD-FEM simulation approach that was applied in our study could also be extended to other targets, such as public transport, theaters, philharmonics and amphitheaters from educational units.
The paper presents a numerical analysis of the operation of photovoltaic (PV) panels integrated in fixed position on the roofs or facades of the buildings. Knowing that the efficiency of photovoltaic panels is temperature-dependent, and due to fixed PV panel position, the possibility of the improving the conversion is analysed from the point of view of the temperature of the PV cells. The model is simulated using TRNSYS software and the main functioning parameters assessed are the operating temperature of the cells, open circuit voltage, maximum power generated and conversion efficiency. The solution proposed for cooling consists in using water heat exchangers attached to the backside of the photovoltaic panel. The results highlight the direct dependence of the photovoltaic efficiency with the temperature of the panel for different positions in the same geographical location. The energy gain during the cooling interval is about 26.9 Wh/m2 (vertical), 81.9 Wh/m2 (inclined) and 81.7 Wh/m2 (horizontal), which represents an increase of 5.8%, 9.3% and 9.2% respectively, compared to the normal operating conditions.
The increased usage of cyber-physical systems (CPS) has gained the focus of cybercriminals, particularly with the involvement of the internet, provoking an increased attack surface. The increased usage of these systems generates heavy data flows, which must be analyzed to ensure security. In particular, machine learning (ML) and deep learning (DL) algorithms have shown feasibility and promising results to fulfill the security requirement through the adoption of intelligence. However, the performance of these models strongly depends on the model structure, hyper-parameters, dataset, and application. So, the developers only possess control over defining the model structure and its hyper-parameters for diversified applications. Generally, not all models perform well in default hyper-parameter settings. Their specification is a challenging and complex task and requires significant expertise. This problem can be mitigated by utilizing hyper-parameter optimization (HPO) techniques, which intend to automatically find efficient learning model hyper-parameters in specific applications or datasets. This paper proposes an enhanced intelligent security mechanism for CPS by utilizing HPO. Specifically, exhaustive HPO techniques have been considered for performance evaluation and evaluation of computational requirements to analyze their capabilities to build an effective intelligent security model to cope with security infringements in CPS. Moreover, we analyze the capabilities of various HPO techniques, normalization, and feature selection. To ensure the HPO, we evaluated the effectiveness of a DL-based artificial neural network (ANN) on a standard CPS dataset under manual hyper-parameter settings and exhaustive HPO techniques, such as random search, directed grid search, and Bayesian optimization. We utilized the min-max algorithm for normalization and SelectKBest for feature selection. The HPO techniques performed better than the manual hyper-parameter settings. They achieved an accuracy, precision, recall, and F1 score of more than 98%. The results highlight the importance of HPO for performance enhancement and reduction of computational requirements, human efforts, and expertise.
The paper describes the behaviour of a heating system with radiators in a cult building. There has commonly used in many churches with many shortcomings. The temperature distribution in the analysed space is simulated in 2D. The simulation is based on an example, the Cathedral of the Assumption of the Virgin Mary in Jassy. The heating system with radiators simulated with the FLUENT program, the results being edifying for the factual state of the building. An important aspect is the impact of these heating systems on the works of art, the church being the 18th — century edifice. Current environmental issues lead to the continuous development of technologies used to reduce primary energy consumption. Churches are an invaluable wealth, sheltering heritage elements preserved in museums and historic buildings. Unheated churches have been used for centuries. Then, after installing one or more different heating systems, signs of rapid degradation appeared.
The paper presents a numerical study of the operation of photovoltaic panels integrated in ventilated facades of the buildings. In these circumstances, the position of the panels is fixed all the time and the possibility of the raising of the conversion efficiency is analysed from the point of view of the operating temperature of the photovoltaic cells. The model and the functioning parameters are obtained using TRNSYS software. The solution proposed for cooling the panels consistsin using waterheat exchangers attached to the backside of the photovoltaic panel.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.