IAQ in CCU units: an experimental and numerical investigation based on the outlet air height (case study: Namazi Hospital, Shiraz)

Indoor air quality (IAQ) is a significant concern that affects our health. Recent studies show how poor IAQ amplifies the effects of airborne viruses, which endanger the health of the population relative to the COVID-19. This study aims to find the relationship among IAQ, the location of the air outlet valve and the behavior of the IAQ indicators in the cardiac care unit (CCU) at Namazi Hospital, Shiraz, Iran. In this context, the condition of the air outlet valve can play an important part in preparing a better IAQ. To test the hypothesis, articles based on IAQ guidelines have been studied. Also, certain emissions (CO2, CO, PM2.5 and PM10) have been measured, and the relationship between IAQ, the location of the air outlet valve and the behavior of these emissions in the patient's room at Namazi Hospital. This room has been analyzed using computational fluid dynamics for the prediction of the specification of incoming air flow particles. Also, a Eulerian–Lagrangian model was used. In constant, the turbulence model (realizable k–ԑ) and discrete particle model were employed. The results show that when the outlet valve is placed on the wall at 20 cm, it decreased particle deposition in the room, and as a result, IAQ will be improved and at the same time, the chances of transmitting infectious diseases will be reduced. It is also indicated that a higher amount of particle deposition fraction (ca. 0.71) obtains when the outlet valve is located on the top of the wall. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

SIR Model-Based Experimental Investigations over Covid-19

In this chapter, simulations of real data have been conducted using the SIR model. The chapter also investigates the effect of lockdown for the four prominently Covid-19 hit areas of India. Afterwards, the estimated fraction of the population that will get infected is determined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A notched connection for clt-concrete composite slabs resisting to uplift without metallic connectors: Experimental investigation

In search for a solution of a sustainable construction with less impact on environment while maintaining a sufficient structural performance, CLT-concrete composite slabs/beams have been increasingly proposed for medium-to-large span structures. Different types of mechanical shear connectors have been studied in the literature for these composite elements. Among them, the notch type is the most preferable due to the high shear resistance contributed by the concrete. However, steel screw or bolt is needed in the connector to limit the uplift between the timber and the concrete. In this paper, a novel type of notched connectors with a particular shape that is able to limit the uplift without the need for steel bolts is proposed. The main objective of this paper is to determine the local and global behaviours of this new shear connector by experimental investigations. Two series of experimental tests were ordered by Thierry Soquet, an architect of Architecture Plurielle and an inventor of innovative construction systems directed by Horizon Bois. A series of three symmetrical push-out tests were performed on large-scale specimens in order to determine the shear resistance, the stiffness, the deformation capacity and the failure mode of the connector. The test results have shown high shear resistance and large stiffness of the connectors. However, the ductility of the connectors is still limited, as the failure mode was governed by the shear failure of the transverse layer of the CLT. In addition, the global behaviour of the CLT-concrete slab was assessed by a series of two full-scaled flexural tests on the slab specimens under a positive bending moment. It was shown in the test results that the design of the composite slab was not limited by the flexural bearing capacity as a high value of the maximum bending moment was obtained in the tests, but governed by the deflection of the composite slab. The delay in the tests caused by the Covid crisis has moreover set in evidence the importance of the shrinkage of concrete in the total deflection. © Fédération Internationale du Béton (fib) – International Federation for Structural Concrete.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation. © 2022 Tech Science Press. All rights reserved.

PPE kit to bio-oil: an experimental investigation

Covid 19 is a highly infectious disease caused by SARS-CoV-2 that can spread from an infected person’s nose, mouth during coughing & sneezing. One of the warriors against Covid-19 is personal protection equipment (PPE), which is indispensable for everyone working closely with Covid-19 patients. The kits are made of different types of plastics and their disposal is going to be a serious menace, if not taken care of properly. The sustainable alternative to handle cast-off PPE kit is to convert them into pyrolysis oil using a thermochemical process. In the present paper, pyrolysis of PPE kit is done in a lab scale reactor to get the pyrolysis oil that has the potential to be used as alternate fuel in the neat or in blended form with conventional fuels. GC-MS of the obtained oil shows the presence of (C-7 to C-32) hydrocarbon fraction. FTIR analysis of the oil is also done for functional group composition. The results obtained also support the application of pyrolyisis oil as an alternate fuel. Physico-chemical characteristics of the pyrolysis oil sample are also comparable to the commercially available fuel with exception of viscosity as the fuel obtained has not been fractionated and has both light and heavy naphtha. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Experimental Investigation and neural network modelling of diesel engine using hexanol blended ternary waste cooking oil biodiesel with moderate preheating

The depleting fossil fuel reserves, rising air pollution, technology transformation threat, and most recently, global economic slowdown by the COVID-19 pandemic, led the internal combustion engine-based automotive industries in a critical condition. The development of improved biofuels to meet stringent emission norms is a promising solution. Higher alcohols possess the fuel properties better than lower alcohols to blend with diesel and biodiesel. The miscibility and higher viscosity is the issue. Preheating can help the vaporization and atomization of fuel. The present study investigates the engine characteristics of moderately preheated ternary fuel using 20 to 40% blends of 1-hexanol, waste cooking oil biodiesel, and diesel. The study found that moderately preheated ternary fuel blends showed a drop in brake-specific fuel consumption, HC, CO, and smoke emissions with improvement in peak cylinder pressure, heat release rate, and brake thermal efficiency. A multi-layer neural network model is developed to prognosticate the engine characteristics. Backpropagation algorithm-based neural network with single hidden layers using Levenberg–Marquardt training function gave the best results. The mean square error of the network was 0.00028517 and the correlation coefficient was 0.99944, 0.99945, and 0.99923 for training, validation, and testing respectively. The mean absolute percentage error was found below 4%. © 2022 Elsevier Ltd

Analysis of SARS-CoV-2 protein interactome map

By calculating the centrality measures of the nodes of the SARS-CoV-2 protein interactome network, we have identified the viral proteins of potential greatest interest for further experimental investigation to understand the mechanisms by which SARS-CoV-2 attacks cells and to identify possible therapeutic targets. The proteins identified in this study including NSP13, NSP7, ORF3a, ORF8a, and ORF8b, were found to be involved in crucial processes of the viral life cycle, and some of them are currently suspected to be antiviral targets. These results thus demonstrate the importance - and the predictive power- of the in silico analysis of the viral interactome to guide and support experimental investigation, which could otherwise be too complex and time-consuming to carry out in clinical and experimental research, given the size and interaction density of the viral protein network and the current still partial knowledge of this new virus. © 2021 IEEE.

Investigation of recurrent networks LSTM in the problem of Covid-19 forecasting

in this paper problem of Covid-19 forecasting was considered and investigated. Review of different models and methods of pandemic forecasting are presented. For middle term forecasting indicators of Covid-19 the application of LSTM networks is suggested. The experimental investigations were carried out during which the optimal parameters LSTM network were found: sliding window size, forecasting interval and network architecture. The efficiency of LSTM in Covid-19 forecasting was estimated. © 2021 IEEE.