Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions

Serum Anti-Hbs-Ag in Stable Hemodialysis Patients and its Relationship with Various Demographic and Biochemical Data

Introduction: To evaluate the relationship between various biochemical, nutritional and demographic factors with immune response to hepatitis B vaccine in maintenance hemodialysis (HD) patients. Material and Methods: A retro-prospective study was carried out on 68 patients undergoing maintenance hemodialysis .Patients were vaccinated against hepatitis B virus with an intramuscular hepatitis B vaccination schedule, 40 micrograms at 0, 1, and 6 months. We also selected 32 age matched normal healthy persons who had vaccinated against hepatitis B previously to compare the antibody production with HD patients. Results: The value of serum antibody level against hepatitis B surface antigen ( HBs) in hemodialisis patients and healthy persons were 35±55(median=5.5) and 135±71 (median=175) mIU/ml respectively. There was a significant deference between mean serum antibody level against HBs antigen of hemodialysis patients and normal subjects (p<0.001), there were not any significant differences of antibody production against HBs antigen between males and females or diabetic and non diabetics. There were no correlation between serum antibody level against HBs-Ag and serum albumin and also with body mass index. There were not significant correlation between anti-HBs antibody level and age, amounts of hemodialysis, duration of dialysis, dialysis adequacy, serum ferritin level and serum lipids. There were not also significant correlation between anti-HBs antibody level and serum parathormone, calcium, phosphorus, serum hemoglobin and hematocrit level. Conclusion: In this study, there was not significant correlation between serum antibody level against hepatitis B surface antigen and various nutritional and demographic factors of patients under regular hemodialysis

IoT-Based Smart Management of Healthcare Services in Hospital Buildings during COVID-19 and Future Pandemics

The paper aims to design and develop an innovative solution in the Smart Building context that increases guests' hospitality level during the COVID-19 and future pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end-users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created

Causes of academic failure of medical and medical sciences students in Iran : A systematic review

Peer reviewedPublisher PD

The effect of soy protein containing soy isoflavones on serum concentration of cell adhesion molecules : A systematic review and meta-analysis of randomized controlled trials

Funding Information: This study was financially supported by Neyshabur University of Medical Sciences (Grant number: 98-01-149 ; Ethical code: IR.NUMS.REC.1399.006). Acknowledgement We are extremely grateful to the data collection team at the Ney- shabur University of Medical Sciences.Peer reviewedPublisher PD

The Diabetes Mellitus-Related Problems among Diabetic Elderly

Diabetes mellitus (DM) is one of the main cause of death and disability in the elderly. We aimed to evaluate the diabetes mellitus-related problems in elderly participants attended the diabetes clinic of Ardabil. In a cross-sectional analytical study, 91 elderly participants were randomly enrolled. Standard Problem Area in Diabetes scale was used. Data were obtained through interview and analyzed using SPSS version 22 by descriptive and analytical methods. The mean and standard deviation of the Total Problems Related to Diabetes Mellitus was 31.51 ± 15.37. There were significant relationships between Total Problems Related to DM and marital status (P=0.02), the score of depression-related problems and gender (P=0.04) and the score of treatment barriers with the family composition (P=0.009) and marital status (P=0.005). Elderly with further chronic illness have had additional treatment barriers (P=0.02). Most of the elderly are suffering from various DM-associated problems. They needed to be taken into account in promotion planning sex, family composition, and numbers of chronic diseases

Ethnic Differences in Disability Prevalence and Their Determinants Studied over a 20-Year Period: A Cohort Study.

BACKGROUND: To compare disability prevalence rates in the major ethnic groups in the UK and understand the risk factors contributing to differences identified. It was hypothesised that Indian Asian and African Caribbean people would experience higher rates of disability compared with Europeans. METHODS: Data was collected from 888 European, 636 Indian Asian and 265 African Caribbean men and women, aged 58-88 years at 20-year follow-up of community-based cohort study, based in West London. Disability was measured using a performance-based locomotor function test and self-reported questionnaires on functional limitation, and instrumental (IADL) and basic activities of daily living (ADL). RESULTS: The mean (SD) age of participants at follow-up was 69.6 (6.2) years. Compared with Europeans, Indian Asian people were significantly more likely to experience all of the disability outcomes than Europeans; this persisted after adjustment for socioeconomic, behavioural, adiposity and chronic disease risk factors measured at baseline (locomotor dysfunction: adjusted odds ratio (OR) 2.20, 95% CI 1.56-3.11; functional limitation: OR 2.77, 2.01-3.81; IADL impairment: OR 3.12, 2.20-4.41; ADL impairment: OR 1.58, 1.11-2.24). In contrast, a modest excess risk of disability was observed in African Caribbeans, which was abolished after adjustment (e.g. locomotor dysfunction: OR 1.37, 0.90-1.91); indeed a reduced risk of ADL impairment appeared after multivariable adjustment (OR from 0.99, 0.68-1.45 to 0.59, 0.38-0.93), compared with Europeans. CONCLUSIONS: Substantially elevated risk of disability was observed among Indian Asian participants, unexplained by known factors. A greater understanding of determinants of disability and normative functional beliefs of healthy aging is required in this population to inform intervention efforts to prevent disability

Antioxidant and kidney protection; differential impacts of single and whole natural antioxidants.

Implication for health policy/practice/research/medical education: Oxidative stress is caused by an imbalance in production of reactive oxygen and the biological ability to detoxify the reactive intermediates or repair the resulting damage. Herbal medicines commonly fight these complications with their antioxidant properties. However, it should be noted that herbal drugs extracts are abundant sources of polyphenols and these compounds are unstable and might be subjected to polymerization. Thus, it is essential to check that the observed biological properties are not due to polymerization of phenolic compounds

Cesarean section rates and indications in sub-Saharan Africa: a multi-country study from Medecins sans Frontieres.

The World Health Organization considers Cesarean section rates of 5-15% to be the optimal range for targeted provision of this life saving intervention. However, access to safe Cesarean section in resource-limited settings is much lower, estimated at 1-2% reported in sub-Saharan Africa. This study reports Cesarean sections rates and indications in Democratic Republic of Congo, Burundi, and Sierra Leone, and describe the main parameters associated with maternal and early neonatal mortality

A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)