Proposing a model for evaluation of effective electronic learning strategies on students' achievement: A case study in virtual universities

The aim of the present study was to evaluate the effective electronic learning strategies on virtual students' academic improvement. The research method of the present study is correlational-descriptive. The population of the study includes all the virtual universities' students of Iran in academic year of 2011-2012. According to Morgan's Table, finally 363 participants were selected for the study. There are three data collection instruments including: a researcher-made questionnaire about electronic learning strategies (α=.94), a researcher-made questionnaire of educational interest (α=.92), and a comparison of students' mean scores during two successive terms in achievement tests (tests of the taken courses). Validity of the mentioned questionnaires was provided in terms of content. The results of the study indicated that there has been a significant relationship between electronic learning strategies based on cognitive presence, social presence, and teaching presence with virtual students' achievements

Proposing a model for evaluation of effective electronic learning strategies on students' achievement: A case study in virtual universities

The aim of the present study was to evaluate the effective electronic learning strategies on virtual students' academic improvement. The research method of the present study is correlational-descriptive. The population of the study includes all the virtual universities' students of Iran in academic year of 2011-2012. According to Morgan's Table, finally 363 participants were selected for the study. There are three data collection instruments including: a researcher-made questionnaire about electronic learning strategies (α=.94), a researcher-made questionnaire of educational interest (α=.92), and a comparison of students' mean scores during two successive terms in achievement tests (tests of the taken courses). Validity of the mentioned questionnaires was provided in terms of content. The results of the study indicated that there has been a significant relationship between electronic learning strategies based on cognitive presence, social presence, and teaching presence with virtual students' achievements

Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals

Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user's skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracyComment: This is the accepted version of an article published in the proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC

Comparison of gait phase detection using traditional machine learning and deep learning techniques

Human walking is a complex activity with a high level of cooperation and interaction between different systems in the body. Accurate detection of the phases of the gait in real-time is crucial to control lower-limb assistive devices like exoskeletons and prostheses. There are several ways to detect the walking gait phase, ranging from cameras and depth sensors to the sensors attached to the device itself or the human body. Electromyography (EMG) is one of the input methods that has captured lots of attention due to its precision and time delay between neuromuscular activity and muscle movement. This study proposes a few Machine Learning (ML) based models on lower-limb EMG data for human walking. The proposed models are based on Gaussian Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA) and Deep Convolutional Neural Networks (DCNN). The traditional ML models are trained on hand-crafted features or their reduced components using Principal Component Analysis (PCA). On the contrary, the DCNN model utilises convolutional layers to extract features from raw data. The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model. The highest achieved accuracy in 50 trials of the training DL model is 89.5%.Comment: Copyright \c{opyright} This is the accepted version of an article published in the proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC

CuFe2O4 nanoparticles-based electrochemical sensor for sensitive determination of the anticancer drug 5-fluorouracil

A fast and facile electrochemical sensor for the detection of an important anticancer drug, 5-fluorouracil, is fabricated using CuFe2O4 nanoparticles modified screen printed graphite electrode (CuFe2O4 NPs/SPGE). The electrochemical activity of the modified electrode was characterized by chronoamperometry, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) experiments. The CuFe2O4 NPs improved the electrochemical properties of the electrodes and enhanced their electroanalytical performance. Electrochemical measurements using differential pulse voltammetry showed a wide linear relationship between 5-fluorouracil concentration and peak height within the range 0.1 to 270.0 µM with a low detection limit (0.03 µM). Further, the sensor was testified with a urine sample and 5-fluorouracil injection sample, and the observed remarkable recovery results replicate its practical applicability

Effect of Irrigation Management on Water Productivity Indicators of Alfalfa

Introduction Over the last years, long-term average rainfall has experienced a meaningful decrease (from 250 to 206 mm per year) leading to continuous drought in Iran. In addition, population growth and increasing demand for food put more pressure on the limited available water resources. Thus, the quantitative and qualitative improvement of agricultural products become a necessity. There is 640,000 hectares of alfalfa cultivated land, standing for 5.4% of the total cultivated area. One of the most basic obstacles in these farms is the unsuitable model of water consumption management. Previous studies were conducted with the aim of evaluating the mutual effects of different treatments in controlled plots. Nonetheless, there is a need for large-scale investigations to monitor and improve water productivity in agricultural systems. In this research, the focus was on irrigation management and optimizing irrigation timing as a potential solution to enhance water productivity, considering the fixed irrigation cycles and traditional use of available water resources. The study began by assessing the current water productivity in 11 alfalfa farms located across four regions in Zanjan province, ensuring a suitable spatial distribution. Subsequently, the impact of irrigation management, particularly the adjustment of irrigation timing, was evaluated to determine its effectiveness in improving water productivity in these farms. Materials and Methods Eleven alfalfa farms, covering a total area of 28 hectares, were initially selected in the agricultural lands of Zanjan province. The majority of these farms were equipped with sprinkler irrigation systems. From these 11 farms, two specific farms were chosen to implement the proposed methods aimed at improving water productivity. These selected farms served as experimental sites where the irrigation management techniques were applied and evaluated. Improvement solutions were mainly focused on irrigation management. Each farm was divided into two parts; one part with real conditions (farmers' management) and the second one with controlled conditions. In the controlled treatments, irrigation management was implemented through optimization of irrigation time. A nutritional program was also prepared according to the soil quality of the fields and applied in the controlled treatments. In each farm, basic information such as area, physical and chemical properties of soil and water quality were determined. Irrigation information (such as inflow discharge and irrigation schedule) was measured and determined at least three times during the cropping season. Soil moisture were measured before and after irrigation in order to calculate the water application efficiency. The amount of harvested product and production costs were obtained at the end of the cropping season through measurements and interviews with farmers. In this research, the indicators including the volume of irrigation water, the water use efficiency, and the physical and economic efficiency of water have been calculated to analyze the water productivity. Results and Discussion The volume of irrigation water in alfalfa farms was measured as 14250 m3/ha on average (with the lowest and highest consumption values of 9849 and 20576 m3/ha, respectively). The average of irrigation water in farms with surface irrigation systems equals to 17,806 and in farms equipped with sprinkle irrigation systems is about 13,460 m3/ha. While the net water requirement of alfalfa in study area was 7160 to 7290 m3/ha. The minimum and maximum values of water application efficiency were 38.3 and 82%, respectively, with average of 64%. The average of application efficiency in surface and sprinkle irrigation systems were obtained 50 and 67%, respectively. The measured alfalfa yield ranged from a minimum of 6.5 ton/ha to a maximum of 14.1 ton/ha, with an average yield of 10.4 ton/ha. After implementing the revised irrigation program in the controlled plots, the harvested water decreased by an average of 49.5%. It was observed that the irrigation schedule in most farms followed a traditional and estimated pattern, with the depth of irrigation water in the middle of the growing season exceeding the net irrigation requirement. The water use efficiency (WUE) values varied between 0.42 and 1.28 kg/m3, with a minimum value of 0.42 kg/m3 and a maximum value of 1.28 kg/m3. The average WUE was calculated as 0.79 kg/m3. Analyzing the correlation between water consumption and the water use efficiency index revealed a decreasing trend. As the volume of irrigation water increased, the water use efficiency index experienced a decline. Specifically, an increase of 1000 m3 in irrigation water resulted in a decrease of 0.04 kg/m3 in the water use efficiency index. The implementation of the corrected irrigation program and appropriate to the water demand led to an increase of the mentioned index by 72%. Conclusion The lack of proper irrigation programs that consider climatic conditions and the actual needs of the alfalfa plant was identified as a key factor contributing to high water consumption in the farms. Additionally, the inefficient selection and design of the irrigation system led to lower irrigation efficiency than expected. Despite the majority of farms being equipped with sprinkle irrigation systems, the harvested water did not decrease significantly due to inadequate water management practices. These factors ultimately resulted in a decline in both physical and economic productivity indicators in the alfalfa farms. However, the results of the study highlighted that implementing corrected irrigation management, particularly through modifications to the irrigation timing, can lead to a significant decrease in volume of irrigation water and an improvement in both physical and economic productivity

Overexpression of miR-490-5p/miR-490-3p Potentially Induces IL-17-Producing T Cells in Patients With Breast Cancer

Objective: Breast cancer (BC) is the most prevalent female cancer globally and this is also true in Iranian women. Alteration in circulating microRNAs affects the fate of immune cells, affecting immunological response to neoplasia. Materials and Methods: We investigated the expression of miR-490-5p and miR-490-3p in peripheral blood mononuclear cells (PBMCs) and plasma of patients with BC. Moreover, the correlation of these microRNAs with the expression levels of CD3d, interleukin 2 (IL-2), IL-2 receptor chain alpha (IL-2RA), forkhead box O1 (FOXO1) and nuclear factor of activated T cells 5 (NFAT5) were investigated. Results: Two groups, including 42 patients with BC, aged 22-75 years with stage I, II, III disease without administration of immunosuppressive chemotherapy regimens/radiotherapy and 40 healthy controls aged 27-70 years, participated. Overexpression and higher circulation levels of miR-490-5p and miR-490-3p were found in the patients with consequent down-regulation of all targets investigated in PBMCs. Furthermore, there was a significant negative correlation between the overexpression of these microRNAs and a reduction in levels of CD3d, IL-2, and IL-2RA in patients with BC. Conclusion: These results suggest that down-regulation of the target genes by miR-490 may predispose and facilitate the production of Th17 lymphocytes and IL-17-producing Tregs. The variation in miR-490-5p/-3p and the investigated targets in the PBMCs of BC patients may be used as non-invasive diagnostic markers

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin