Determining the Frequency of Defensive Medicine Among General Practitioners in Southeast Iran

Background: Defensive medicine prompts physicians not to admit high-risk patients who need intensive care. This phenomenon not only decreases the quality of healthcare services, but also wastes scarce health resources. Defensive medicine occurs in negative and positive forms. Hence, the present study aimed to determine frequency of positive and negative defensive medicine behaviors and their underlying factors among general practitioners in Southeast Iran. Methods: The present cross-sectional study was performed among general practitioners in Southeast Iran. 423 subjects participated in the study on a census basis and a questionnaire was used for data collection. Data analysis was carried out using descriptive and analytical statistics through SPSS 20. Results: The majority of participants were male (58.2%). The mean age of physicians was 40 ± 8.5. The frequency of positive and negative defensive medicine among general practitioners in Southeast Iran was 99.8% and 79.2% respectively. A significant relationship was observed between working experience, being informed of law suits against their colleagues, and committing defensive medicine behavior (P< 0.001). Conclusion: The present study indicated high frequency of defensive medicine behavior in the Southeast Iran. So, it calls policy-makers special attention to improve the status quo

Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS)

The sustainable use of renewables will represent a key challenge in the near future, and relative energy management operations will play a crucial role in energy efficiency and savings for future generations. The operation and maintenance of energy systems are a very high valuable activity to prevent energy losses, and a correct monitoring can detect in advance equipment degradation guaranteeing good performance over time. Present research strives to find out possibility of unmanned aerial vehicle (UAV) use in monitoring applications for energy production sites and to investigate effects of this novel method on energy management procedures. Furthermore, investigation about novel approaches in cooperative inspection of real photovoltaic (PV) plants was carried out by light UAVs and utilize the global positioning system to find out the optimum route mapping during the solar PV modules monitoring. The purpose of this work is to propose a reliable, fast and cost effective method for PV plant planning and monitoring by means of UAS technolog

Determining the frequency of defensive medicine among general practitioners in Southeast Iran

Abstract Background: Defensive medicine prompts physicians not to admit high-risk patients who need intensive care. This phenomenon not only decreases the quality of healthcare services, but also wastes scarce health resources. Defensive medicine occurs in negative and positive forms. Hence, the present study aimed to determine frequency of positive and negative defensive medicine behaviors and their underlying factors among general practitioners in Southeast Iran. Methods: The present cross-sectional study was performed among general practitioners in Southeast Iran. 423 subjects participated in the study on a census basis and a questionnaire was used for data collection. Data analysis was carried out using descriptive and analytical statistics through SPSS 20. Results: The majority of participants were male (58.2%). The mean age of physicians was 40 ± 8.5. The frequency of positive and negative defensive medicine among general practitioners in Southeast Iran was 99.8% and 79.2% respectively. A significant relationship was observed between working experience, being informed of law suits against their colleagues, and committing defensive medicine behavior (P&lt; 0.001). Conclusion: The present study indicated high frequency of defensive medicine behavior in the Southeast Iran. So, it calls policy-makers special attention to improve the status quo

Bushehr Elderly Health (BEH) Programme, phase I (cardiovascular system)

Purpose: The main objective of the Bushehr Elderly Health Programme, in its first phase, is to investigate the prevalence of cardiovascular risk factors and their association with major adverse cardiovascular events. Participants: Between March 2013 and October 2014, a total of 3000 men and women aged ≥60 years, residing in Bushehr, Iran, participated in this prospective cohort study ( participation rate=90.2%). Findings to date: Baseline data on risk factors, including demographic and socioeconomic status, smoking and medical history, were collected through a modified WHO MONICA questionnaire. Vital signs and anthropometric measures, including systolic and diastolic blood pressure, weight, height, and waist and hip circumference, were also measured. 12-lead electrocardiography and echocardiography were conducted on all participants, and total of 10 cc venous blood was taken, and sera was separated and stored at –80°C for possible future use. Preliminary data analyses showed a noticeably higher prevalence of risk factors among older women compared to that in men. Future plans: Risk factor assessments will be repeated every 5 years, and the participantswill be followed during the study to measure the occurrence of major adverse cardiac events. Moreover, the second phase, which includes investigation of bone health and cognition in the elderly, was started in September 2015. Data are available at the Persian Gulf Biomedical Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran, for any collaboratio

Rising stars in energy research: 2022

Recognising the future leaders of Energy Research is fundamental to safeguarding tomorrow's driving force in innovation. This collection will showcase the high-quality work of internationally recognized researchers in the early stages of their careers. We aim to highlight research by leading scientists of the future across the entire breadth of Energy Research, and present advances in theory, experiment and methodology with applications to compelling problems

Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction

Autonomous Monitoring and Analysis of Photovoltaic Systems

At the beginning of 2022, photovoltaic (PV) installation exceeded 1 TWp which was an impressive milestone in the solar energy industry [...