A customer segmentation framework for targeted marketing in telecommunication

© 2017 IEEE. Telecommunication industry is highly competitive, and mass marketing is not applicable anymore. Moreover, Mobile customers have different behaviors that urge telecom industries to differentiate their strategies to meet customers' needs. At the same time, mobile operators have an enormous amount of customer records, and data-driven approaches can help them to draw insights from this huge amount of data. Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans. In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers. The results indicate that our method has an outstanding performance for customer segmentation. Moreover, we have recommended some marketing strategies based on each segment's behavior with the aim of increasing in Average Revenue Per User (ARPU) and decreasing in marketing expenses

Vortex lattices in dipolar two-component Bose-Einstein condensates

We consider a rapidly rotating two-component Bose-Einstein condensate with short-range s-wave interactions as well as dipolar coupling. We calculate the phase diagram of vortex lattice structures as a function of the intercomponent s-wave interaction and the strength of the dipolar interaction. We find that the long-range interactions cause new vortex lattice structures to be stable and lead to a richer phase diagram. Our results reduce to the previously found lattice structures for short-range interactions and single-component dipolar gases in the corresponding limits.Comment: 5 pages, 3 figure

Antidiabetic potential of salvianolic acid B in multiple low-dose streptozotocin-induced diabetes

Context: Salvianolic acids are the most abundant water-soluble compounds extracted from the herb Salvia miltiorrhiza L. (Lamiaceae) with antioxidant and protective effects. Objective: This study evaluates the antidiabetic effect of salvianolic acid B (Sal B) in multiple low-dose streptozotocin (MLDS)-induced diabetes in rat. Materials and methods: Rats were divided into control, Sal B40-treated control, diabetic, Sal B20-, and Sal B40-treated diabetic groups. Sal B was daily administered at doses of 20 or 40 mg/kg (i.p.), started on third day post-STZ injection for 3 weeks. Serum glucose and insulin level and some oxidative stress markers in pancreas were measured in addition to the oral glucose tolerance test (OGTT), histological assessment, and apoptosis determination. Results: After 3 weeks, treatment of diabetic rats with Sal B20 and Sal B40 caused a significant decrease of the serum glucose (p<0.05-0.01) and improvement of OGTT. Meanwhile, serum insulin was significantly higher in Sal B20- and Sal B40-treated diabetics (p<0.01) and treatment of diabetics with Sal B40 significantly lowered malondialdehyde (MDA) (p<0.05), raised glutathione (GSH) (p<0.05), and activity of catalase (p<0.01) with no significant change of nitrite. Furthermore, the number of pancreatic islets (p<0.05) and their area (p<0.01) was significantly higher and apoptosis reactivity was significantly lower (p<0.05) in the Sal B40-treated diabetic group versus diabetics. Discussion and conclusion: Three-week treatment of diabetic rats with Sal B exhibited antidiabetic activity which is partly exerted via attenuation of oxidative stress and apoptosis and augmentation of antioxidant system. © 2015 Informa Healthcare USA, Inc

Using Combined Descriptive and Predictive Methods of Data Mining for Coronary Artery Disease Prediction: a Case Study Approach

Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors coupled with data mining knowledge. This paper presents a model developed using combined descriptive and predictive techniques of data mining that aims to aid specialists in the healthcare system to effectively predict patients with Coronary Artery Disease (CAD). To achieve this objective, some clustering and classification techniques are used. First, the number of clusters are determined using clustering indexes. Next, some types of decision tree methods and Artificial Neural Network (ANN) are applied to each cluster in order to predict CAD patients. Finally, results obtained show that the C&RT decision tree method performs best on all data used in this study with 0.074 error. All data used in this study are real and are collected from a heart clinic database

Psychological Violence in the Health Care Settings in Iran: A Cross-Sectional Study

Background: Psychological violence is the most common form of workplace violence that can affect professional performance and job satisfaction of health care workers. Although several studies have been conducted in Iran, but there is no consensus regarding current status of such violence. Objectives: This study aimed to investigate the prevalence of psychological violence among healthcare workers employed at teaching hospitals in Iran. Patients and Methods: In this cross-sectional study, 5874 health professionals were selected using multistage random sampling. Data were collected using a self-administered questionnaire developed by the International Labor Organization, International Council of Nurses, World Health Organization, and Public Services International. Descriptive statistics were used to analyze the data. Results: It was found that 74.7% of the participants were subjected to psychological violence during the past 12 months. Totally, 64.5% of psychological violence was committed by patients’ families, but 50.9% of participants had not reported the violence, and 69.9% of them believed that reporting was useless. Conclusions: The results are indicative of high prevalence of psychological violence against healthcare workers. Considering non-reporting of violence in more than half of participants, use of an appropriate reporting system and providing training programs for health professionals in order to prevent and manage workplace violence are essential

Heat transfer enhancement and pressure drop for fin-and-tube compact heat exchangers with delta winglet-type vortex generators

Heat transfer rate, pressure loss and efficiency are considered as the most important parameters in designing compact heat exchangers. Despite different types of heat exchangers, fin-and-tube compact heat exchangers are still common device in different industries due to the diversity of usage and the low space installation need. The efficiency of the compact heat exchanger can be increased by introducing the fins and increasing the heat transfer rate between the surface and the surroundings. Numerous modifications can be applied to the fin surface to increase heat transfer. Delta-winglet vortex generators (VGs) are known to enhance the heat transfer between the energy carrying fluid and the heat transfer surfaces in plate-fin-and-tube banks, but they have drawbacks as well. They increase the pressure loss and this should be considered. In this paper, the thermal efficiency of compact heat exchanger with VGs is investigated in different variations. The angle of attack, the length and horizontal and vertical position of winglet are the main parameters to consider. Numerical analyses are carried out to examine finned tube heat exchanger with winglets at the fin surface in a relatively low Reynolds number flow for the inline tube arrangements. The results showed that the length of the winglet significantly affects the improvement of heat transfer performance of the fin-and-tube compact heat exchangers with a moderate pressure loss penalty. In addition, the results show that the optimization cannot be performed for one criterion only. More parameters should be considered at the same time to run the process properly and improve the heat exchanger efficiency

Investigation the integration of heliostat solar receiver to gas and combined cycles by energy, exergy, and economic point of views

Due to the high amount of natural gas resources in Iran, the gas cycle as one of the main important power production system is used to produce electricity. The gas cycle has some disadvantages such as power consumption of air compressors, which is a major part of gas turbine electrical production and a considerable reduction in electrical power production by increasing the environment temperature due to a reduction in air density and constant volumetric airflow through a gas cycle. To overcome these weaknesses, several methods are applied such as cooling the inlet air of the system by different methods and integration heat recovery steam generator (HRSG) with the gas cycle. In this paper, using a heliostat solar receiver (HSR) in gas and combined cycles are investigated by energy, exergy, and economic analyses in Tehran city. The heliostat solar receiver is used to heat the pressurized exhaust air from the air compressor in gas and combined cycles. The key parameter of the three mentioned analyses was calculated and compared by writing computer code in MATLAB software. Results showed the use of HSR in gas and combined cycles increase the annual average energy efficiency from 28.4% and 48.5% to 44% and 76.5%, respectively. Additionally, for exergy efficiency, these increases are from 29.2% and 49.8% to 45.2% and 78.5%, respectively. However, from an economic point of view, adding the HRSG increases the payback period (PP) and it decreases the net present value (NPV) and internal rate of return (IRR)