مقاله ISI خشونت محیط کار بیمارستانی

Introduction: Physicians, nurses, emergency workers, health professionals, and all health care personnel and hospital staff are more exposed to workplace violence (WPV) than any other occupational group. The purpose of this study was to evaluate the implementation of WPV management training program among all health care personnel over the past ten years (2009-2019).
Materials and methods: This study was conducted a review of the existing literature in the field of research through the databases of authoritative articles and journals and by searching for the relevant keywords. In this study, 86 articles related to the research topic were extracted from which 64 articles were excluded from the study due to not having any of the inclusion criteria and finally 22 articles were evaluated and content evaluated.
Results: The results of this study illustrate the methods used to train violence management among hospital staff in research conducted over the past ten years.
Conclusion: The health authority in Iran should have a raft of health policy and training programs specific to managing violence to equip its frontline healthcare workers to deal with aggressive episodes in clinical settings.

ادامه مطلب

مقاله ISI: Image classification optimization models using the convolutional neural network (CNN) approach

Deep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational units. When the target objects have rich meanings, the performance and ability to generalize complex classification problems will be quite inadequate. The convolutional neural network (CNN), which has been developed in recent years, widely used in image processing; because it has high skills in dealing with image classification and image recognition issues and it has led to great care in many machine learning tasks and it has become a powerful and universal model of deep learning. The combination of deep learning and embedded systems has created good technical dimensions. In this paper, several useful models in the field of image classification optimization, based on convolutional neural network and embedded systems, are discussed. Since this paper focuses on usable models on the FPGA board, models known for embedded systems such as MobileNet, ResNet, ResNeXt and ShuffNet have been studied.

ادامه مطلب

مقاله ISI: Design of MobileNet algorithm to optimize image classification in Convolutional Neural Network (CNN)

Deep learning has developed rapidly in recent years and has been applied in many areas that are major areas of artificial intelligence. The combination of deep learning and embedded systems has created good dimensions in the technical field. In this paper, a deep learning neural network algorithm can be designed that can be implemented on FPGA hardware. The PyTorch and CUDA were used as assistant methods. Convolution neural network (CNN) was also used for image classification. Three good CNN models such as ResNet, ResNeXt and MobileNet were reviewed in this article. Using these models in the design, an algorithm was eventually designed with the MobileNet model. Models were selected from different aspects such as floating operation point (FLOP), number of parameters and classification accuracy. In fact, the MobileNet-based algorithm was selected with a top-1 error of 5.5% in software with a 6-class data set. In addition, hardware simulation in MobileNet-based algorithms was presented. The parameters were converted from floating numbers to 8-bit integers. The output numbers of each layer were cut into integer fixed bits to fit the hardware constraint. A method based on working with numbers was designed to simulate number changes in hardware. The results of simulation show that, the top-1 error increased to 12.3%, which is acceptable.

ادامه مطلب

مقاله پلیمر ISI:Synthesis of sulfonated CS/PVA/ s-ZrO2

In this study, the morphological (DLS and FTIR tests), mechanical (tensile strength tests), structural (XRD, conductivity, WU, swelling ratio and IEC tests), and thermal properties (Thermogravimetric tests) of sulfonated Chitosan / Poly (vinyl alcohol) /sulfated zirconia (sulfonated CS / PVA /s-ZrO2) nanoparticles as fuel cell membranes were investigated.
Electrolyte membranes characterization was conducted using FTIR, XRD, and TG analysis. The DLS approach is utilized for the determination of the size of nanoparticles. Membranes were also evaluated for conductivity, water uptake, ion exchange capacity, swelling ratio, tensile strength and membrane stability. The results of the study indicate strong interactions between sulfonated chitosan, PVA and sulfated zirconia (s-ZrO2). The mixture of nanocomposite of sulfonated chitosan with PVA and s-ZrO2 increased the membrane hydrophobicity. The composite (sulfonated chitosan/PVA/s-ZrO2) consisting 7.5 wt.% s-ZrO2 demonstrated a considerable improvement in ultimate tensile strength as well as an increase in conductivity in comparison with the CS/PVA/s-ZrO2 membranes by the order of 10−2 S/cm under 25 oC temperature.

ادامه مطلب

مقاله سیستماتیک ریویو متاآنالیز: The effects of human actions on managed bee’s welfare

Background:
Managed bees for various reasons, including: over-medication, malnutrition, exposure to pesticides, global warming, electromagnetic waves, increased demand for pollination services and increased mobility and size of beekeeping, land use change, artificial nectar etc., are exposed to disease and hazard that many of these stressors lead to CDD and colony destruction. Materials and Methods:
In this systematic review, 65 articles related to the study entry criteria were included using PRISMA method. In each of articles, the type of destructive and effective human activity on managed bees and effect of these activities on bees was examined. Then, using the meta-analysis approach, the impact of each activities on the welfare of managed bees based on the five principles of animal freedom was assessed.
Results:
According to the findings of this systematic review, the use of pesticides had the greatest impact (55.38%) on managed bees and subsequent global warming (15.38%). The majority of studies (89.23%) have dealt with the third principle of animal welfare. The fourth principle of animal welfare considered as the second criterion (26.15%) in the welfare of managed bees in reviewed articles. The results of meta-analysis show that the use of pesticides has had the greatest impact on the third (f3=47.7%) and fourth (f4=15.38%) principles of freedom. Also, the influential factor of global warming has had the greatest impact on the third (f3=12.3%) principle of animal freedom.
Conclusion:
According to the results of this study, the use of pesticides as the most important destructive and effective factor of human activities has been identified on the welfare of managed bees and is seriously related to bee disease. Therefore, there is a need for stricter and seriously rules on the use of pesticides, also need to pay more attention to the use of pesticides for chemical control of pests, especially in the habitat of managed bees.

ادامه مطلب

مقاله ISI: An Optimal Model to Improve the Allocation of Computational and Radio Resources in IoT Fog Computing

Fog computing has been proposed as an emerging approach to the evolution of the cloud computing platform and the expansion of the IoT to the edge of the network. In this type of computing, service providers can control signals by assigning specific tasks to users, and tasks sensitive to offload users’ latency are transferred to distributed fog nodes (FNs) at the edge of the network. In this article, with the aim of optimizing system performance and user satisfaction, we will examine the issue of allocating joint radio and computing resources. In this paper, evaluate the impact of each of the cache parameters, CPU clock frequency performance, bandwidth, CPU cycle, aging and IPC (Instruction per cycle/clock) to Iot-Fog users, to provide a model for allocating computational and radio resources in Iot computing. This algorithm performs the process of selecting the optimal resources for data processing in the fog layer, taking into account both energy and latency criteria at the same time. In this paper, a matching game framework, called Student Project Allocation (SPA), has been used to distribute solutions to the issue of radio and computational resource allocation, instead of the usual centralized optimization. An efficient SPA- (S, P) algorithm has been implemented to solve the problem and the stable SPA results. In addition, external effects instability, or, in other words, independence between identical players, is eliminated by the proposed User-oriented cooperation strategy (UOC). The results showed that our proposed framework can provide a distributed and close to optimal performance both from the perspective of users and from the perspective of the system. Therefore, it can take advantage of the combined approach of SPA- (S, P) and UOC in order to allocate joint radio and computational resource allocation in fog computing for IoT environment. Also, the proposed method of this research can be used to allocate joint radio and computational resources in limited resource devices, delayed sensitive programs and conditions of bandwidth limitation in various types of fog computing in IoT environment.

ادامه مطلب

مقاله ISI پزشکی Frontal fibrosis alopecia (FFA)

A retrospective analysis of all cases of FFA presenting between November 2011 and November 2015 at the Razi hospital in Tehran was performed. In order to study the histopathologic findings of patients with a tabular design based on pathologic findings, all patients’ lambs were reviewed. In order to complete the study, a clinical-based questionnaire and other findings used for general evaluation and conclusion by the researchers.

ادامه مطلب