THE IMPACT OF E-LEARNING ON UNIVERSITY STUDENTS' ACADEMIC ACHIEVEMENT AND CREATIVITY

Research on the efficacy of ICT-based teaching methods in improving generic skills in addition to content skills among future workforce is increasing.  Accordingly, this study investigates the impact of e-learning on creativity and content knowledge of chemistry students at the Payame Noor University of Hamedan, Iran. The study used the pre-test/post-test experimental design with a control group. The statistical population of the study included was 100 pure chemistry students who were following two separate classes. Forty students were selected from this group who placed in the experimental group (n = 20) and the control group (n = 20). Two instruments were used for data collection; a specifically developed test on the Introduction to Chemistry course and the Abedi Inventory for assessing creativity. Results of data analysis using the independent t-test (aided by SPSS) demonstrated statistically significantly higher scores for the experimental group on measured variables, knowledge and creativity. Therefore, it is concluded that e-learning is effective for knowledge and creativity acquisitions among chemistry students and the greater e-learning opportunities should be provided for wider audiences

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated to potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50\%, 66\%, and 80\% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30\% by applying our approach, while keeping the transmission risk at the lowest level

Pneumocephalus after Tympanomastoidectomy: A Case Presentation

Introduction: Pneumocephalus is the presence of air or gas within the cranial cavity. It can occur following otorhinolaryngological procedures. A small pneumocephalus spontaneously heals without any treatment. In severe cases, conservative therapy includes a 30-degree head elevation, avoidance of the Valsalva maneuver, analgesics, osmotic diuretics, and oxygen therapy.   Case Report: A 56-year-old woman was referred to the emergency department due to a severe headache in the frontal area for 2 days before admission. The patient experienced nausea and vomiting in the morning and had no history of seizures or decreased consciousness. Examination of neurological symptoms was completely normal and showed no symptoms of meningeal irritation. In terms of past history, the patient had undergone tympanomastoidectomy surgery and resection of the cholesteatoma 1 week previously. The Mount Fuji sign was found on the brain computed tomography (CT) scan of the patient. Treatments such as CBR (complete bed rest), 30-degree head elevation, anti-fever, analgesics and oxygen therapy, along with anti-compulsive drug (phenytoin), were prescribed. At the end of 5 days, the patient's pneumocephalus was resolved completely.   Conclusion: Pneumocephalus should be considered a post-operative complication of tympanomastoidectomy. In most cases, pneumocephalus responds to conservative therapy. Supplemental oxygen increases the rate of absorption of pneumocephalus. Serial imaging is needed to ensure gradual reduction of the pneumocephalus

Comparison of Two Methods, Gradient Boosting and Extreme Gradient Boosting to Pre- dict Survival in Covid-19 Data

Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning algorithms, Gradient boosting and Extreme gradient boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed. Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient boosting and Extreme gradient boosting) in predicting survival were compared using the Concordance Index and calibration. Results: The Concordance Index values obtained for gradient boosting and Extreme gradient boosting models were 0.734 and 0.736, in the imbalanced and In the balanced data, the Concordance Index values were 0.893 for gradient boosting and 0.894 for Extreme gradient boosting. The surv.calib_beta index, the gradient boosting model had an estimated value of 0.59 in the imbalanced data and 0.66 in the balanced data. The Extreme gradient boosting model had an estimated value of 0.86 in the balanced data and 0.853 in the imbalanced data. The Extreme gradient boosting model was faster in the learning process compared to the gradient boosting model. Conclusion: The Gradient boosting and Extreme gradient boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient boosting model demonstrated relatively good calibration compare to Gradient boosting model

Prevalence of anti-HCV antibody and related risk factors among bleeding disorder patients in Yazd province of Iran

زمینه و هدف: مصرف جایگزین درمانی خون و فرآورده های خونی غربال نشده یا فاکتورهای انعقادی تغلیظ شده ویروس زدایی نشده در بیماران اختلال انعقادی خطر ابتلا به هپاتیت C را در آنها ایجاد می کند. مطالعه حاضر به منظور بررسی فراوانی آنتی بادی بر علیه ویروس هپاتیت C (anti-HCV Ab) و فاکتورهای خطر مربوطه در بیماران اختلال انعقادی استان یزد انجام شد. روش بررسی: در این مطالعه توصیفی-تحلیلی که به روش سرشماری در تابستان 1385 انجام شد، پس از جمع آوری اطلاعات پرسشنامه ای، از 77 بیمار نمونه خون گرفته شد. نمونه های پلاسما با کیت الیزا از نظر آنتی بادی بر علیه ویروس هپاتیت C و سپس نمونه های مثبت با روش تست RIBA (Recombinant Immonoblot Assay) تایید شدند. داده ها با استفاده از آزمون های آماری کای دو و آنالیز رگرسیون لجستیک مورد تجزیه و تحلیل قرار گرفتند. یافته ها: فراوانی آنتی بادی بر علیه ویروس هپاتیت ‍‍C معادل 4/49 (38 بیمار) بدست آمد. بین داشتن فرم شدید بیماری (از نظر نیاز به فرآورده های خونی) (01/0

The effects of curcumin on the prevention of atrial and ventricular arrhythmias and heart failure in patients with unstable angina: A randomized clinical trial

Objective: Inflammation along with oxidative stress has an important role in the pathophysiology of unstable angina which leads to acute myocardial infarction, arrhythmias and eventually heart failure. Curcumin has anti-inflammatory and anti-oxidant effects and thereby, it may reduce cardiovascular complications. This randomized controlled trial aimed to investigate the effects of curcumin on the prevention of atrial and ventricular arrhythmias and heart failure in patients with unstable angina. Materials and Methods: Forty patients with unstable angina who met the trial inclusion and exclusion criteria, participated in this double-blind randomized clinical trial. The patients were randomized into two groups: curcumin (80 mg/day for 5days) and placebo (80 mg/day for 5days). Cardiac function was evaluated by two-dimensional echocardiography devices at baseline (immediately after hospitalization) and 5 days after the onset of the trial. Atrial and ventricular arrhythmias were recorded by Holter monitors in cardiology ward, Ghaem academic hospital, Mashhad, Iran. Progression to heart failure, myocardial infarction, and pulmonary and cardiopulmonary resuscitation events as well as mortality were recorded daily throughout the study. Results: There were no significant differences between the two groups in atrial and ventricular arrhythmias (p=0.2), and other echocardiographic parameters (Ejection fraction, E, A, E/A ratio, Em, and pulmonary artery pressure) at baseline and five days after the start of the trial. Conclusion:  Nanocurcumin administered at the dose of 80 mg/day for five days had no effect in the incidence of cardiovascular complications in patients with unstable angina

The relationship between ultra processed food consumption and premature coronary artery disease: Iran premature coronary artery disease study (IPAD)

BackgroundUltra-processed foods (UPF) consumption may affect the risk of PCAD through affecting cardio metabolic risk factors. This study aimed to evaluate the association between UPFs consumption and premature coronary artery disease (PCAD).MethodsA case–control study was conducted on 2,354 Iranian adults (≥ 19 years). Dietary intake was assessed using a validated 110-item food frequency questionnaire (FFQ) and foods were classified based on the NOVA system, which groups all foods according to the nature, extent and purposes of the industrial processes they undergo. PCAD was defined as having an stenosis of at least single coronary artery equal and above 75% or left main coronary of equal or more than 50% in women less than 70 and men less than 60 years, determined by angiography. The odds of PCAD across the tertiles of UPFs consumption were assessed by binary logistic regression.ResultsAfter adjustment for potential confounders, participants in the top tertile of UPFs were twice as likely to have PCAD compared with those in the bottom tertile (OR: 2.52; 95% CI: 1.97–3.23). Moreover, those in the highest tertile of the UPFs consumption had more than two times higher risk for having severe PCAD than those in the first tertile (OR: 2.64; 95% CI: 2.16–3.22). In addition, there was a significant upward trend in PCAD risk and PCAD severity as tertiles increased (P-trend < 0.001 for all models).ConclusionHigher consumption of UPFs was related to increased risk of PCAD and higher chance of having severe PCAD in Iranian adults. Although, future cohort studies are needed to confirm the results of this study, these findings indicated the necessity of reducing UPFs intake

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95$7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95$43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained

Network sensor location problem for flow observability and Origin-Destination estimation with consideration of sensor failure

The network sensor location problem (NSLP) addresses the location of traffic sensors to observe/estimate the link, route or OD flows in a traffic network. While counting sensors such as loop detectors still have an extensive application for traffic monitoring purposes, they suffer from a considerable rate of failure. In this study, I focus on two well-known problems in the NSLP known as the full link flow observability problem and the origin-destination estimation problem while considering the failure of sensors. The full link flow observability problem is to identify the minimum set of traffic sensors to be installed in links in a road traffic network. The sensors are used to both monitor the flow of observed links and to provide flow information for the link flow inference of unobserved links. Unavoidably, the traffic sensors deployed in a traffic network are subject to failure which leads to missing the link flow observation of observed links as well as the inability to infer the link flow of unobserved links. This study aims to identify the minimum set of links in a traffic network to be instrumented with two different types of counting sensors (basic and advanced sensors) to reach full link flow observability while minimizing the effect of sensor failure on the link flow inference of unobserved links. Mathematically, I formulate two objective functions including min-max and min-sum functions. The first function attempts to minimize the maximum effect of sensor failure on the link flow inference of unobserved links while the second one minimizes the expected number of unobserved links where the flow cannot be inferred due to the failure of sensors. I select the genetic algorithm (GA) as a well-known heuristic to solve the proposed optimization model. The results recommend minimizing the number of sensors required for the link flow inference of each unobserved link as well as installing advanced sensors on links involved in the link flow inference of multiple unobserved links. I also develop a new objective function to reflect that links in a traffic network can be either minor or major roads with different levels of importance. The results suggest installing more advanced sensors on the major roads as well as minimizing the number of major roads included in the set of unobserved links. Concerning the availability of route flow information in a network, I consider the effect of this information on evaluating the sensor deployment in a network. To maintain full link flow observability of a traffic network if any sensor fails, I study the location and type of additional sensors introduced as redundant sensors, which are more than the minimum required for full link flow observability. Finally, I discuss the applicability of the proposed model for the partial observability problem in which the full link flow observability conditions are not satisfied. In addition to the link flow observability problem, this study also focuses on the OD estimation problem considering the failure of sensors. The OD estimation problem is to find the location of the minimum number of sensors to estimate the flow of OD pairs in a traffic network. Traffic sensors can observe the summation of OD demand flows traversing a link and through OD estimation techniques such as maximum entropy, I can estimate the OD demand flows. Contrary to the flow observability problem, the failure of a sensor, does not necessarily lead to missing the chance of estimating the OD demand of one or more OD pairs but can affect the OD demand flow information gain from OD demands. In this study, I identify the location of counting sensors aiming to minimize the possible adverse effect of sensor failure on the OD estimation process. The input data required for the OD estimation may consist of the prior information of the OD trips that can be used to make the OD trip estimation as close as possible to the actual vehicular trips generated between each OD in the road network. However, the sensors, similar to other measurement apparatus, are subject to failure and this failure can affect the reliability of the OD trip information especially under congested traffic conditions. In this paper, I address the sensor location problem (NSLP) to identify the most reliable location set of sensors in a road traffic network with consideration of the possibility of sensors failure. I introduced two objective functions including maximization of expected OD demand flow information gain on both observed link and each OD pair. I then employed the weighted sums method (WSM) and an ε-constraint to incorporate these two objective functions. With respect to the available budget constraint, different types of sensors are considered to identify different location sets of sensors with different levels of reliability for the OD estimation. The results applied to different road traffic networks indicate the improvement in the reliability of information obtained from the selected sensor location sets