Search results for: Shahrzad Jalali

Authors: Sdiqeh Jalali, M. R. Khazdair

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Liver X receptors (LXR) have an essential role in the regulation of cholesterol metabolism, and their activation increase ABCG5 and ABCG8 genes expression for the improvement of cholesterol excretion from the body during reverse cholesterol transport (RCT). The aim of this study was to investigate the effects of high-intensity interval (HIT) and low intensity continuous (LIT) trainings on gene expression of these substances after a high-fat diet in Wistar rats. Materials and Methods: Fifteen male Wistar rats were divided into 3 groups: control group (n = 5), HIT exercise group (n = 5) and LIT exercise group (n = 5). All groups used a high-fat diet for 13 weeks, and the HIT and LIT groups performed the specific training program. The expression of LXRβ, ABCG5, and ABCG8 genes was measured after the training period. Findings: Data analysis showed significantly higher levels of LXRβ, ABCG5, and ABCG8 gene expression in HIT and LIT groups compared to the control group (P ≤ 0.05). Conclusion: HIT and LIT trainings after a high-fat diet have beneficial effects on RCT that prevent heart attack. Also, HIT training may have a greater effect on cholesterol excretion during the reverse cholesterol transport mechanism than LIT.

Keywords: liver X receptor, atherosclerosis, interval training, endurance training

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Authors: Mahnaz Motamedi, Mohammad Shahbazi, Shahrzad Rahimi-Naghani, Mehrdad Salehi

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Introduction and Aim: This study assessed sexual behaviours among men and women aged 15 to 49 years in Tehran. Material and Methods: This was a cross-sectional study conducted on 755 men and women aged 15 to 49 years who were residents of Tehran. To select the participants, a multistage, cluster, random sampling method was used and included different regions of Tehran. The data were collected using the WHO-endorsed Questionnaire of Sexual and Reproductive Health. Descriptive, bivariate, and multivariate analyses were conducted using SPSS version 20. Sexual and reproductive health (SRH) behaviours was a scale variable that was constructed from items of six sections: sexual experiences, characteristics of the first sexual partner, characteristics of the first intercourse, next sexual contact and the consequences of the first sexual contact, homosexual experiences and the causes of sexual abstinence. Results: The mean age at the time of sexual intercourse with penetration (vaginal, anal) was 19.88 in men and 21.82 in women. Multivariate analysis using linear regression showed that by controlling for other variables, gender had a significant relationship with having sexual experience, mean age of first sexual intercourse, and being multi-partner. Thus, women with sexual experience were 0.158 units less than men. The mean age of first intercourse in women was 1.57 units higher than men and being a multi-partner in women was 0.247 less than men (P < 0.001). Sexual experience in very religious and relatively religious individuals was 0.332 and 0.218 units less than those for whom religion did not matter (P < 0.001). 25.6% of men and 40.7% of women who did not have sexual experience at the time of the study stated that their reason for abstinence was their unwillingness to have sex (P < 0.05). 35.9% of men and 16.5% of women stated that the reason for abstinence was not providing a suitable opportunity (P < 0.001). 4.7% of men and 1.7% of women had sexual attraction to the same sex. The difference between men and women was significant (P < 0.001). Conclusion: Sexual relation is also present in singles and younger groups and is not limited to married or final marriage candidates. Therefore, more evaluation should be done in national research and interventions for sexual and reproductive health services should be done at the macro level of policy making.

Keywords: sexual behaviours, Iranian men and women, Iran, cross-sectional study

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Houda Jalali, Hassan Abbassi

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In this numerical work, natural convection and entropy generation of Al₂O₃–water nanofluid in square cavity have been studied. A two-dimensional steady laminar natural convection in a differentially heated square cavity of length L, filled with a nanofluid is investigated numerically. The horizontal walls are considered adiabatic. Vertical walls corresponding to x=0 and x=L are respectively maintained at hot temperature, T_h and cold temperature, T_c. The resolution is performed by the CFD code "FLUENT" in combination with GAMBIT as mesh generator. These simulations are performed by maintaining the Rayleigh numbers varied as 10³ ≤ Ra ≤ 10⁶, while the solid volume fraction varied from 1% to 5%, the particle size is fixed at dp=33 nm and a range of the temperature from 20 to 70 °C. We used models of thermophysical nanofluids properties based on experimental measurements for studying the effect of adding solid particle into water in natural convection heat transfer and entropy generation of nanofluid. Such as models of thermal conductivity and dynamic viscosity which are dependent on solid volume fraction, particle size and temperature. The average Nusselt number is calculated at the hot wall of the cavity in a different solid volume fraction. The most important results is that at low temperatures (less than 40 °C), the addition of nanosolids Al₂O₃ into water leads to a decrease in heat transfer and entropy generation instead of the expected increase, whereas at high temperature, heat transfer and entropy generation increase with the addition of nanosolids. This behavior is due to the contradictory effects of viscosity and thermal conductivity of the nanofluid. These effects are discussed in this work.

Keywords: entropy generation, heat transfer, nanofluid, natural convection

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Authors: Catherine Thompson, Maryam Jalali, Peter Hills

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The allocation of visual attention is critical when driving and anything that limits attention will have a detrimental impact on safety. Engaging in a secondary task reduces the amount of attention directed to the road because drivers allocate resources towards this task, leaving fewer resources to process driving-relevant information. Yet the dangers associated with a secondary task do not end when the driver returns their attention to the road. Instead, the attentional settings adopted to complete a secondary task may persist to the road, affecting attention, and therefore affecting driver performance. This 'attentional inertia' effect was investigated in the current work. Forty drivers searched for hazards in driving video clips while their eye-movements were recorded. At varying intervals they were instructed to attend to a secondary task displayed on a tablet situated to their left-hand side. The secondary task consisted of three separate computer games that induced horizontal, vertical, and random eye movements. Visual search and hazard detection in the driving clips were compared across the three conditions of the secondary task. Results showed that the layout of information in the secondary task, and therefore the allocation of attention in this task, had an impact on subsequent search in the driving clips. Vertically presented information reduced the wide horizontal spread of search usually associated with accurate driving and had a negative influence on the detection of hazards. The findings show the additional dangers of engaging in a secondary task while driving. The attentional inertia effect has significant implications for semi-autonomous and autonomous vehicles in which drivers have greater opportunity to direct their attention away from the driving task.

Keywords: attention, eye-movements, hazard perception, visual search

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Authors: Jahede Tekeykhah, Seyed Mohsen Hossini, Gholamali Jalali

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Industrial and traffic activities cause large amounts of heavy metals enter into the atmosphere and the use of plant species can be effective in assessing and reducing air pollution by metals. This study aimed to investigate the adsorption level of heavy metals in leaves of Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica trees in Abidar forest park. For this purpose, samples leaves of the trees were prepared from the contaminated and control areas in each region in 3 stations with 3 replicates in mid-August and finally 90 samples were sent to the laboratory. Then, the concentrations of heavy metals were measured by graphite furnace. To do this, factorial experiment based on a completely randomized design with two factors of location on two levels (contaminated area and control area) and the factor of species on five levels (Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica) with three replications was used. The analysis of collected data was performed by SPSS software and Duncan's multiple range test was used to compare the means. The results showed that the accumulation of all metals in the leaves of most species in the infected area with a significant difference at 95% level was higher than the control area. In the contaminated area, with a significant difference at 5% level, the highest accumulations of metals were observed as the following: lead, cadmium, zinc and manganese in Platanus orientalis, nickel in Fraxinus rotundifolia and copper in Platycladus orientalis.

Keywords: airborne, tree species, heavy metals, absorption, Abidar Forest Park

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Maryam Tabrizi, Shahrzad Aarup

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Objectives- Understanding the close association between oral health and overall health for older adults at the right time and right place, a person, focus treatment through Project ECHO telementoring. Methodology- Data from monthly ECHO telementoring sessions were provided for three years. Sessions including case presentations, overall health conditions, considering medications, organ functions limitations, including the level of cognition. Contributions- Providing the specialist level of providing care to all elderly regardless of their location and other health conditions and decreasing oral health inequity by increasing workforce via Project ECHO telementoring program worldwide. By 2030, the number of adults in the USA over the age of 65 will increase more than 60% (approx.46 million) and over 22 million (30%) of 74 million older Americans will need specialized geriatrician care. In 2025, a national shortage of medical geriatricians will be close to 27,000. Most individuals 65 and older do not receive oral health care due to lack of access, availability, or affordability. One of the main reasons is a significant shortage of Oral Health (OH) education and resources for the elderly, particularly in rural areas. Poor OH is a social stigma, a thread to quality and safety of overall health of the elderly with physical and cognitive decline. Poor OH conditions may be costly and sometimes life-threatening. Non-traumatic dental-related emergency department use in Texas alone was over $250 M in 2016. Most elderly over the age of 65 present with at least one or multiple chronic diseases such as arthritis, diabetes, heart diseases, and chronic obstructive pulmonary disease (COPD) are at higher risk to develop gum (periodontal) disease, yet they are less likely to get dental care. In addition, most older adults take both prescription and over-the-counter drugs; according to scientific studies, many of these medications cause dry mouth. Reduced saliva flow due to aging and medications may increase the risk of cavities and other oral conditions. Most dental schools have already increased geriatrics OH in their educational curriculums, but the aging population growth worldwide is faster than growing geriatrics dentists. However, without the use of advanced technology and creating a network between specialists and primary care providers, it is impossible to increase the workforce, provide equitable oral health to the elderly. Project ECHO is a guided practice model that revolutionizes health education and increases the workforce to provide best-practice specialty care and reduce health disparities. Training oral health providers for utilizing the Project ECHO model is a logical response to the shortage and increases oral health access to the elderly. Project ECHO trains general dentists & hygienists to provide specialty care services. This means more elderly can get the care they need, in the right place, at the right time, with better treatment outcomes and reduces costs.

Keywords: geriatric, oral health, project echo, chronic disease, oral health

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Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

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Authors: Mohammad R. Jalali

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Movement of liquid with a free surface in a container is known as slosh. For instance, slosh occurs when water in a closed tank is set in motion by a free surface displacement, or when liquid natural gas in a container is vibrated by an external driving force, such as an earthquake or movement induced by transport. Slosh is also derived from resonant switching of a natural basin. During sloshing, different types of motion are produced by energy exchange between the liquid and its container. In present study, a numerical model is developed to simulate the nonlinear even harmonic oscillations of free surface sloshing of an initial disturbance to the free surface of a liquid in a closed square basin. The response of the liquid free surface is affected by amplitude and motion frequencies of its container; therefore, sloshing involves complex fluid-structure interactions. In the present study, nonlinear interaction of free surface sloshing of an initial Gaussian hump with its uneven container is predicted numerically. For this purpose, Green-Naghdi (GN) equations are applied as governing equation of fluid field to produce nonlinear second-order and higher-order wave interactions. These equations reduce the dimensions from three to two, yielding equations that can be solved efficiently. The GN approach assumes a particular flow kinematic structure in the vertical direction for shallow and deep-water problems. The fluid velocity profile is finite sum of coefficients depending on space and time multiplied by a weighting function. It should be noted that in GN theory, the flow is rotational. In this study, GN numerical simulations of initial Gaussian hump are compared with Fourier series semi-analytical solutions of the linearized shallow water equations. The comparison reveals that satisfactory agreement exists between the numerical simulation and the analytical solution of the overall free surface sloshing patterns. The resonant free surface motions driven by an initial Gaussian disturbance are obtained by Fast Fourier Transform (FFT) of the free surface elevation time history components. Numerically predicted velocity vectors and magnitude contours for the free surface patterns indicate that interaction of Gaussian hump with its container has localized effect. The result of this sloshing is applicable to the design of stable liquefied oil containers in tankers and offshore platforms.

Keywords: fluid-structure interactions, free surface sloshing, Gaussian hump, Green-Naghdi equations, numerical predictions

Procedia PDF Downloads 364