The Effects of Side-Weir Height on the Free Surface Turbulent Flow

Sharareh Mahmodinia,Mitra Javan,Afshin Eghbalzadeh 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.7

Side-weirs are flow diversion devices used widely in irrigation as a head regulator of distributaries and escapes, land drainage and urban sewage systems. In this study, a numerical analysis for predicting free surface turbulent flow over the sharp crest side-weir is performed. The Volume of Fluid (VOF) scheme and Reynolds Stress Model (RSM) turbulence model are used to numerical simulation of the turbulence free surface flow. Comparison between numerical results and laboratory measurements show that the model had predicted experimental trends with reasonable accuracy. In next steps of this study, the side-weir height effects on the flow pattern are investigated. The parametric study results show that under the low and high overflow rate conditions, the flow characteristics in low side-weir situation is virtually the same as the side-weir of zero height while in high weir situation, significant changes occurred in the shear stress and the water head above the weir crest.

Statistical index (SI) as spatial modeling to evaluate the potential for greenbelt development in medium-sized Iranian cities

Sharareh Madanian,Carlos Smaniotto Costa 대한공간정보학회 2020 Spatial Information Research Vol.28 No.5

The present paper addresses spatial modeling for an easy and effective way to evaluate the greenbelt potentials in medium-sized cities in Iran. Data were collected from the cities of Babol, Kashan, Neyshabour, and Qazvin, and analyzed through geographical information system (GIS). All key variables were systematically set in an evaluation matrix called a statistical index (SI). The SI values were used to assess the current landscape pattern potentials for implementing greenbelts in these four cities. The results revealed moderate spatial suitability for greenbelt development for two cases (Neyshabour and Kashan) with SI values of 0.406 and 0.333, respectively. On the other side, the model showed high spatial suitability for the two other cases (Qazvin and Babol) with SI values of 0.654 and 0.523, respectively. Overall, the case of Qazvin revealed better chances for the development of a greenbelt, due to existing orchards as a sub-natural protection ring surrounding the city. This spatial model as pioneer research in quantifying greenbelt development potentials can be a successful practice for urban planning in medium-sized cities; this not only for Iranian cities, but it can be a new method to investigate greenbelt potential in other cities in Middle Eastern countries.

Investigation of the Effects of Acupuncture on Post-Operative Chest Pain after Open Heart Surgery

Roshanzamir Sharareh,Haririan Yas,Ghaderpanah Rezvan,Jahromi Leila Sadat Mohamadi,Dabbaghmanesh Alireza 사단법인약침학회 2023 Journal of Acupuncture & Meridian Studies Vol.16 No.4

Background: Coronary artery bypass grafting (CABG) accounts for more than half of all adult cardiac surgeries worldwide. Post-operative chest pain is a common CABG complication and can cause significant discomfort. Objectives: Because taking large amounts of analgesics can have many side effects, we evaluated whether acupuncture effectively reduces pain and the use of analgesics by CABG patients. Methods: In this clinical trial, 30 patients who had recently undergone CABG were randomly allocated to two groups. For both groups, exercise therapy and routine analgesics were recommended. The intervention group underwent bilateral acupuncture in distinct acupoints, including the HT3, HT4, HT5, HT6, HT7, PC3, PC5, PC6, and PC7 for 10 daily sessions constantly. Visual analog scale (VAS) and analgesic use were evaluated in both groups at baseline and after completing the 10-day treatment. Results: Our analysis revealed significant decreases in the mean VAS scores in both the intervention and the control group, and that the reduction was more significant in the acupuncture group (p < 0.001). Moreover, analgesic use was significantly lower in the acupuncture group when compared with the control group (p < 0.001). Conclusion: Our findings highlight acupuncture as an alternative method of controlling CABG-associated post-operative chest pain and reducing the use of analgesics, which might have many side effects.

Investigating the Frequency of the ERCC1 Gene C8092A Polymorphism in Iranian Patients with Advanced Gastric Cancer

Mokmeli, Sharareh,Tehrani, Golnaz Asaadi,Zamiri, Reza Eghdam,Bahrami, Tayyeb Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.3

Background: Platinum compounds are the main drugs for treatment of advanced gastric cancer. Previous studies have shown that clinical outcome with platinum-based compounds depends on ERCC1 polymorphisms. The aim of this study was to investigate the frequency of a common polymorphism of ERCC1 gene (C8092A) in Iranian patients with advanced gastric cancer receiving platinum chemotherapy. Materials and Methods: Genetic analysis of the ERCC1 C8092A polymorphism was performed by the PCR - RFLP method using 50 paraffin-embedded tissue specimens. Results: Of the 50 cases, 32% of individuals showed CC genotype, 24% of them had CA genotype and 44% of patients had AA genotype. Conclusions: Based on the results, using of platinum-based chemotherapy would be expected to be specifically beneficial in only 32% of patients.

Bioengineering Approaches for Corneal Regenerative Medicine

Mahdavi S. Sharareh,Abdekhodaie Mohammad J.,Mashayekhan Shohreh,Baradaran-Rafii Alireza,Djalilian Ali R. 한국조직공학과 재생의학회 2020 조직공학과 재생의학 Vol.17 No.5

Background: Since the cornea is responsible for transmitting and focusing light into the eye, injury or pathology affecting any layer of the cornea can cause a detrimental effect on visual acuity. Aging is also a reason for corneal degeneration. Depending on the level of the injury, conservative therapies and donor tissue transplantation are the most common treatments for corneal diseases. Not only is there a lack of donor tissue and risk of infection/rejection, but the inherent ability of corneal cells and layers to regenerate has led to research in regenerative approaches and treatments. Methods: In this review, we first discussed the anatomy of the cornea and the required properties for reconstructing layers of the cornea. Regenerative approaches are divided into two main categories; using direct cell/growth factor delivery or using scaffold-based cell delivery. It is expected delivered cells migrate and integrate into the host tissue and restore its structure and function to restore vision. Growth factor delivery also has shown promising results for corneal surface regeneration. Scaffold-based approaches are categorized based on the type of scaffold, since it has a significant impact on the efficiency of regeneration, into the hydrogel and non-hydrogel based scaffolds. Various types of cells, biomaterials, and techniques are well covered. Results: The most important characteristics to be considered for biomaterials in corneal regeneration are suitable mechanical properties, biocompatibility, biodegradability, and transparency. Moreover, a curved shape structure and spatial arrangement of the fibrils have been shown to mimic the corneal extracellular matrix for cells and enhance cell differentiation. Conclusion: Tissue engineering and regenerative medicine approaches showed to have promising outcomes for corneal regeneration. However, besides proper mechanical and optical properties, other factors such as appropriate sterilization method, storage, shelf life and etc. should be taken into account in order to develop an engineered cornea for clinical trials.

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Mina Fallah,Sharareh R. Niakan Kalhori 대한의료정보학회 2017 Healthcare Informatics Research Vol.23 No.4

Objectives: Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Results: Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management. Conclusions: Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System

Hamidreza Maharlou,Sharareh R. Niakan Kalhori,Shahrbanoo Shahbazi,Ramin Ravangard 대한의료정보학회 2018 Healthcare Informatics Research Vol.24 No.2

Objectives: Accurate prediction of patients’ length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients’ length of stay in intensive care units (ICU) after cardiac surgery. Methods: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. Results: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). Conclusions: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts’ knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

The effects of repetitive transcranial magnetic stimulation on proliferation and differentiation of neural stem cells

Ali Ghanbari,Sharareh Sharififar,Mehrnaz Abedian,Amir Ghanbari,Keramatollah Abbasnia 대한해부학회 2015 Anatomy & Cell Biology Vol.48 No.2

Repetitive transcranial magnetic stimulation (rTMS) is a new method for treating many neurological conditions; however, the exact therapeutic mechanisms behind rTMS-induced plasticity are still unknown. Neural stem and progenitor cells (NS/PCs) are active players in brain regeneration and plasticity but their behavior in the context of rTMS therapy needs further elucidation. We aimed to evaluate the effects of rTMS on proliferation and differentiation of NS/PCs in the subventricular zone (SVZ) of adult mouse brain. Adult male mice (n=30) were divided into rTMS (1-Hz and 30-Hz) and sham groups and treated for 7 or 14 consecutive days. Harvested NS/PCs from the SVZ were cultured in the neurosphere assay for 8 days and the number and size of the resulting neurospheres as well as their in vitro differentiation capacity were evaluated. After one week of rTMS treatment at 1-Hz and 30-Hz compared with sham stimulation, the mean neurosphere forming frequency per brain was not different while this measure significantly increased after two weeks (P

Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran

Ahmad Naserpor,Sharareh R. Niakan Kalhori,Marjan Ghazisaeedi,Rasoul Azizi,Mohammad Hosseini Ravandi,Sajad Sharafie 대한의료정보학회 2019 Healthcare Informatics Research Vol.25 No.1

Objectives: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model. Methods: The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons. Results: The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of R(t) in the framework of the standard and modified SIR models are also compared. Conclusions: Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest.

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

Shahabeddin Abhari,Sharareh R. Niakan Kalhori,Mehdi Ebrahimi,Hajar Hasannejadasl,Ali Garavand 대한의료정보학회 2019 Healthcare Informatics Research Vol.25 No.4

Objectives: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. Methods: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. Results: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. Conclusions: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.