Introduction: The management performance of hospitals in emergency departments in coping with clinical hazards is necessary for their success in providing quality services. The purpose of this study is to provide an optimization procedure and applying management performance in health and medical emergency on the rate of emergency preparedness of the suburb city hospitals. Research method: A semi-experimental and interventional descriptive study in the emergency department of Shahid Motahari Hospital in 2015-2016. Data was collected through the standard checklist of hospital emergency responsiveness test to WHO disasters with 90 questions in 9 domains which is codified by the World Health Organization, in the form of interviews and observing evidences , and if necessary a numerical analysis. Initially, the situation was measured according to this checklist. In the next stage, a set of solutions was developed and then the solutions were implemented according to the obtained information. Data analysis before and after proposing the procedure and its application in order to improve the quality of health management performance and emergency preparedness was performed using central indices, and for describing and presenting the survey results, tables and graphs were used. Results: The highest and lowest emergency preparedness rates for disasters and emergencies before the proposed procedure were the Communication Factor (%77/8) and Human resource factor (%2/8) respectively. The degree of preparedness of the medical center was measured in the management performance according to the model (WHO) before proposing the procedure (%77.98). The researcher's procedure provided and applied based on the information obtained from survey environment, location and type of hospital structure. After providing the procedure, the maximum and minimum rates were the continuity of service factor (%58.3) and post-accident rebuilding factor (%100), respectively. Generally, the rate of emergency preparedness of studied medical center in management performance for coping with disasters and medical emergencies was measured (%97.2) according to hospital emergency responsiveness test for accidents and (WHO). The significance level is less than 0.05, so we can say that the effectiveness with confidence (95%) was effective. Conclusion: According to the statistics of accidents and disasters in the suburb cities, the results of the research and the importance of the role of emergency departments in interurban hospitals in controlling accidents, the need for planning and implementation of practical measures such as conducting training courses on health management in disasters and emergencies, having an estimate of critical and essential resources, changing the structure, providing manpower (emergency management) in order to more precise control and ease of service, maintenance and repair of equipment, timely triage and retrofitting of hospitals were effective in improving their preparedness.

Selecting the optimal activation function for training deep neural networks has always been challenging because it significantly impacts the neural network’s performance and training speed. At this point, researchers are more likely to employ RELU than other well-known activation functions. After RELU, researchers have proposed many activation functions to improve RELU. None of them was capable of surpassing RELU as their most significant rival. SWISH outperformed RELU in several challenging experiments like classification, object detection, and tracking. Replacing RELU units with SWISH, which improves performance in many tasks. This paper proposed a new activation function surpassing Google’s brain’s SWISH function, Which we named AIF. Experiments indicate that our proposed activation function outperforms SWISH in various tasks.

Alzheimer's disease is a neurological illness that worsens gradually. It is known as the most frequent cause of dementia. Moreover, Alzheimer's disease is a general term for memory loss and other cognitive impairments severe enough to disrupt daily life. The disease often grows in middle-aged and elderly people with the gradual loss of cognitive ability. Since the diagnosis of the illness is time consuming, a novel machine learning approach is proposed in this research to diagnose Alzheimer's levels based on Magnetic Resonance Imaging (MRI). More specifically, to provide distinct information for classification we have implemented the proposed method on a native MRI scans dataset containing three MRI channels; namely T1, T2, and, T2 tirm, which are shown in red, blue and green, respectively. The five middle layers of these three types of MRI scans are combined to form five RGB layers. In addition, we use a Convolutional Neural Network to classify each composed layer, and the final disease type is predicted using majority voting. finally, the F1-Score has been used to assess method performance, indicated that 92% of predictions of the proposed model were accurate.

When training deep neural networks tasks, the most popular choices are cross-entropy loss. On the other hand, in general speaking, a decent loss function can take on shapes that are considerably more flexible and ought to be adapted for different activities and datasets. In most of the classification tasks, generally if the true class is not correctly recognized by the network (top1), that class is placed among the five classes with the highest probability (top5). This shows that the network does not necessarily recognize the correct class with a low probability, but a class similar to it (such as 3 vs. 8 in mnist) assigns a higher probability and this causes a mistake in that task. Accordingly, we proposed a loss function deals with the error of class that the neural network incorrectly recognized as correct, in addition to the correct class error. We call our proposed loss as False Positive Loss (FPL), with the intention of viewing and designing loss functions not only through the utilization of true class but also through the utilization of the value of false positive classes. One of the core properties of our proposed loss is full adaptability, which makes False Positive Loss be fully capable of getting reformulated by using other widely used loss functions formulas based on the task or the need of the users. Extensive experimental results demonstrate that our suggested loss function outperforms other well-known losses on a variety of tasks and datasets. As can be observed, the performance of our False Positive Loss is superior to that of the cross-entropy loss when it comes to tasks involving 2D picture classification. We have compared our loss with cross entropy as the most common classification loss function on some models (such as ResNet-18, ResNet-50 and Efficientnet-V2) through classification known as a basic computer vision task. with both random or pre-trained initial weights. As a result, in some cases the models with our loss outperform the same tasks with cross entropy from the viewpoint of some metric (i.e. accuracy and FP). For example, the resnet-50 on cifar-10 dataset with random initialization indicated a top1 accuracy of 94.93 with cross entropy and 95.25 with our loss, while for top5 accuracy the results are 99.86 and 99.87, respectively.

Heat stress affects the yield of medicinal plants and can reduce biomass and/or metabolite production. In order to evaluate the effect of heat-induced stress on the essential oil production in Mentha x piperita L. var. Mitcham (Mitcham mint) and Mentha x arvensis var. piperascens Malinv. ex L. H. Bailey (Japanese mint), we studied the chemical composition of the oils of the two mint species under different heat shock stress in growth chambers. The antibacterial activity of the essential oils was also evaluated; microscopic observation (fluorescence and electron transmission) was used to assess the effect of the tested samples on bacterial growth. The results obtained shed light on the mint essential oils composition and biological activity in relation to heat stress.