IP2P K-means: an efficient method for data clustering on sensor networks

Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2) and the preliminary results show that the algorithm works effectively and relatively more precisely

Adaptive sliding neural network-based vibration control of a nonlinear quarter car active suspension system with unknown dynamics

This study investigates adaptive sliding neural network (NN) control for quarter active suspension system with dynamic uncertainties and road disturbances. A Multilayer Perceptron (MLP) neural network is adopted to estimate the unknown dynamics of the system. In addition, sliding mode controller is introduced to compensate the function of estimation error for improving the performance of the system. Furthermore, the uniformly and bounded of closed-loop signals is guaranteed by Lyapunov analysis; the adaptation laws for training of MLP are derived from stability analysis. The simulation results demonstrate that the proposed controller can effectively provide a good ride and makes great trade-off between passenger comfort and handling despite the dynamic uncertainties

The relationship between fear of hypoglycemia and sleep quality among type 2 diabetic patients

Background. Fear of hypoglycemia can result in anxiety, stress, anger, depression and severe avoidance behaviors that it affects the sleep quality of diabetic patients. Therefore, the present study was conducted with the aim of investigating the relationship between fear of hypoglycemia and sleep quality among type 2 diabetic patients. Methods. The present cross-sectional study was conducted on 400 type 2 diabetic patients referred to endocrinology clinic of Velayat Hospital and Boali Hospital in Qazvin, in 2019. Data were collected using a checklist for demographic variables, the Fear of Hypoglycemia Survey (FHS-W), and the Pittsburgh sleep quality index (PSQI). Descriptive statistics and Spearman correlation test were performed for data analysis using SPSS v24. Results. In this study, the mean age of diabetic patients was 55.75 ± 10.31. The majority of the participants were female (n = 299, 74.8%) and were treated with oral anti-diabetic drugs (n = 174, 43.5%). The mean score of sleep quality in patients was 8.98 ± 3.64 and the fear of hypoglycemia was 21.27 ± 11.92. The results of this study showed that there was a signifcant relationship between the fear of hypoglycemia and the poor sleep quality among patients (P < 0.001, r = 0.305). Conclusion. The fear of hypoglycemia has a direct and signifcant relationship with poor sleep quality in diabetic patients; so that this fear reduces the quality of sleep in diabetic patients. Therefore, in order to provide adequate sleep to prevent inappropriate sleep complications, great attention should be paid to the issue of fear of hypoglycemia, and consider some actions to reduce this fear. (Clin Diabetol 2020; 10, 1: 149–154) Key words: fear of hypoglycemia, sleep quality, type 2 diabete

The relationship between fear of hypoglycemia and sleep quality among type 2 diabetic patients

Background. Fear of hypoglycemia can result in anxiety, stress, anger, depression and severe avoidance behaviors that it affects the sleep quality of diabetic patients. Therefore, the present study was conducted with the aim of investigating the relationship between fear of hypoglycemia and sleep quality among type 2 diabetic patients. Methods. The present cross-sectional study was conducted on 400 type 2 diabetic patients referred to endocrinology clinic of Velayat Hospital and Boali Hospital in Qazvin, in 2019. Data were collected using a checklist for demographic variables, the Fear of Hypoglycemia Survey (FHS-W), and the Pittsburgh sleep quality index (PSQI). Descriptive statistics and Spearman correlation test were performed for data analysis using SPSS v24. Results. In this study, the mean age of diabetic patients was 55.75 ± 10.31. The majority of the participants were female (n = 299, 74.8%) and were treated with oral anti-diabetic drugs (n = 174, 43.5%). The mean score of sleep quality in patients was 8.98 ± 3.64 and the fear of hypoglycemia was 21.27 ± 11.92. The results of this study showed that there was a significant relationship between the fear of hypoglycemia and the poor sleep quality among patients (P &lt; 0.001, r = 0.305). Conclusion. The fear of hypoglycemia has a direct and significant relationship with poor sleep quality in diabetic patients; so that this fear reduces the quality of sleep in diabetic patients. Therefore, in order to provide adequate sleep to prevent inappropriate sleep complications, great attention should be paid to the issue of fear of hypoglycemia, and consider some actions to reduce this fear

Predicting the status of COVID-19 active cases using a neural network time series

The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model

Epidemiology of Hereditary Coagulation Bleeding Disorders: A 15-Year Experience From Southern Iran

Background: Data on the frequency of hereditary bleeding disorders (HBDs) and associated mortality and morbidities during a long-term follow-up from Iran are scarce. Objective: This study evaluated the epidemiologic features among patients with HBD in one of the largest referral centers in southern Iran. Methods: In this cross-sectional study, 619 patients with HBD were evaluated during the period 1996 to 2011. Aside from baseline characteristics and type of factor deficiency, associated morbidities including viral infections, neurological disorders, asthma, thalassemia, glucose-6-phosphate dehydrogenase (G6PD) deficiency, diabetes, hypertension, cardiac and renal diseases were evaluated. Furthermore, among patients who died, the underlying disease and etiology of death were also evaluated. Results: Patients’ mean age was 24.4 ± 13.5 years. Factor VIII deficiency was the most prevalent type (50.4%) of HBD, and combined Von–Willebrand and factor XIII deficiency (2.3%) was the most prevalent type of combined factor deficiency. A total of 0.5% had hepatitis B and 11.5% had hepatitis C. Cardiac disease was seen in 1.5%, hypertension in 0.2%, renal disease in 0.2%, and diabetes in 1.3% of patients. Overall, 5.2% had intracranial hemorrhage, 2.1% had epilepsy, and 0.8% had mental retardation. During the 15-year follow-up, 22 patients died; car accident was the leading cause of death in this population. Conclusion: Associated morbidities were seen in 24.3% of patients with HBD. Most prevalent morbidities were HCV infections (11.5%) and neurological disease (7.3%). The mortality rate among patients with HBD was 3.4%, and the most common cause of death was accident, which is similar to that of normal Iranian populations

Comparison of coronary artery disease guidelines with extracted knowledge from data mining

Introduction: Coronary artery disease (CAD) is one of the major causes of disability and death in the world. Accordingly utilizing from a national and update guideline in heart-related disease are essential. Finding interesting rules from CAD data and comparison with guidelines was the objectives of this study. Methods: In this study 1993 valid and completed records related to patients (from 2009 to 2014) who had suffered from CAD were recruited and analyzed. Total of 25 variable including a target variable (CAD) and 24 inputs or predictor variables were used for knowledge discovery. To perform comparison between extracted knowledge and well trusted guidelines, Canadian Cardiovascular Society (CCS) guideline and US National Institute of Health (NIH) guideline were selected. Results of valid datamining rules were compared with guidelines and then were ranked based on their importance. Results: The most significant factor influencing CAD was chest pain. Elderly males (age >54) have a high probability to be diagnosed with CAD. Diagnostic methods that are listed in guidelines were confirmed and ranked based on analyzing of local CAD patients data. Knowledge discovery revealed that blood test has more diagnostic value among other medical tests that were recommended in guidelines. Conclusion: Guidelines confirm the achieved results from data mining (DM) techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients