An open-source software framework for the integrated simulation of structures in fire

The traditional methods to understand the development of elevated temperature in a structure, and also the associated structural response, are not representative of realistic fire scenarios. To provide a more accurate and realistic reflection of the fire development, the current paper develops a generic middleware which interfaces between the computational fluid dynamics (CFD) software Fire Dynamics Simulator (FDS) and the finite element (FE) analysis software OpenSees. This framework enables a fully integrated simulation of a realistic fire scenario including the heat transfer through the structure and the resulting thermo-mechanical response. The proposed framework is open-source and freely available and therefore can be used and further developed by researchers and practicing engineers and customised to their requirements. This paper shows validation against two sets of experimental results and one real fire incident. A number of different types of thermal boundary conditions such as gas temperatures and heat fluxes, are obtained from the CFD analysis and are then used in the subsequent heat transfer and thermo-mechanical analysis. The primary advantage of this computational tool is that it provides consultants and designers with the means to undertake large-scale projects requiring performance-based fire engineering solutions

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Fire modelling framework for investigating tall building fire: A case study of the Plasco Building

Fire can damage structures severely and even cause the building collapse. Structural and fire engineers must carry out a comprehensive forensic investigation of major structural failures in the same rigorous and meticulous manner the airline industry investigates air crashes. The forensic assessment should identify the cause, fire spread scenario, fire behaviour patterns from its growth to decay, de-compartmentation, the performance of fire protection systems, and firefighting management. Using the available tools and data, the current paper proposes a methodology to reconstruct the fire for the forensic assessment of tall buildings. This is done by first organising observed data into a coherent timeline and presenting the actual fire spread obtained from the visual evidence. The total fire spread within the building is estimated based on fire dynamics principles and observed fire scenes that can be verified with a calibrated CFD model. The collapse of the Plasco Building is assessed by employing the proposed framework. The rise in construction of the tall buildings increases the risk of the occupants’ safety from the fire induced structural failure or collapse. The framework presented in this paper can guide engineers to improve the building resilience designs and reduce the fire accidents related risks

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained R0≈1.50. Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics

Prevalence of Multidrug-Resistant and ESBL-Producing Bacterial Pathogens in Patients with Chronic Wound Infections and Spinal Cord Injury Admitted to a Tertiary Care Rehabilitation Hospital

A pressure ulcer is defined as a skin lesion of ischemic origin, a condition that contributes to morbidity and mortality in patients with spinal cord injuries. The most common complication of ulcers is a bacterial infection. Antimicrobial therapy should be selected with caution for spinal cord injury patients since they have a high risk of developing multidrug-resistant (MDR) infections. The aim of this study was to determine the prevalence of different bacterial pathogens in patients with pressure ulcers admitted with spinal cord injuries. This was a retrospective single-center study that included adult patients aged 18 years and above, admitted with chronic pressure wounds after a spinal cord injury requiring hospitalization between 2015 and 2021. A total of 203 spinal cord injury patients with pressure ulcers were included in the study. Ulcers were commonly infected by Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, and they were mostly located in the sacral and gluteal areas. More than half of the bacteria isolated from patients were sensitive to commonly tested antibiotics, while 10% were either MDR- or pan-drug-resistant organisms. Of the MDR bacterial isolates, 25.61% were methicillin-resistant S. aureus, and 17.73% were extended-spectrum beta-lactamase Enterobacteriaceae. The most prevalent bacteria in pressure ulcers of spinal cord injury patients were S. aureus. Other antibiotic-resistant organisms were also isolated from the wounds

Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly

Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly