INTRODUCTION:The effects of pre-treatment with mangnesium on cardiovascular responses associated with intubation were studied previously.In this dose-response study we wanted to find optimal dose of magnesium that causes in a decreased responses after larngoscopy & endotracheal intubation . METHODS:After approval of ethics committee of Isfahan university. In a double-blind, randomized clinical trial,120 ASA-1 patient in ages between 15-50 years old, candidates of elective surgery were selected and classified in 6 groups(20 patients in each).The pulse rate and arterial blood pressure were measured and recorded at 5 min before took any drugs and then according to different groups magnesium sulphate (10,20,30,40,50mg/kg) and lidocaine(1.5mg/kg). The induction of anesthesia was same in all groups and the pulse rate and arterial blood pressure were measured and recorded at just before intubation and also at 1, 3, and 5min after intubation (before surgical incision). Statistical analysis was performed by use of ANOVA, post Hoc test (Duncan),Pearson correlation, and Chi square tests. RESULTS :The groups were well matched for demographic data and no statistically significant differences were found between groups in age , weight , sex and surgical position . After laryngoscopy and intubation statistically significant changes as percent base values were found between magnesium groups and lidocaine (p<0.05) but between magnesium groups was not significant (p>0.05) .In group of C ( 30 mg/kg mgso4) changes in heart and mean blood pressure was lower from another groups. TOF at minute 45 after induction of anesthesia in all groups has been no statistically significant differences (p>0.05) .Complications of magnesium , like hypotension , arrhythmia , nausea , sweating , flashing and hot sense has been no significant change between all groups (p>0.05) . DISCUSSION:There were no statistically significant differences in blood pressure,pulse rate,TOF and complications in and between groups who received magnesium but the significant differences were seen between magnesium and lidocaine in those parameters. We concluded that pretreatment with different doses of magnesium sulfate have a safe decreasing effect on cardiovascular responses, that were more effective then pretreatment. REFERECES:1-British J of Anesth . 89 ,:594-598.2-American J of cardiology. 67:1435-7.

The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.

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In 2018, the Indonesian fruit exports increased by 24% from the previous year. The surge in demand for tropical fruits from non-tropical countries is one of the contributing factors for this trend. Some of these countries have strict quality requirements – the poor level quality control of fruit is an obstacle in achieving greater export yield. This is because some exporters still use manual sorting processes performed by workers, hence the quality standard varies depending on the individual perception of the workers. Therefore, we need an intelligent system that is capable of automatic sorting according to the standard set. In this research, we propose a system that can classify fruit defects automatically. Faster R-CNN (FRCNN) architecture proposed as a solution to detect the level of defect on the surface of the fruit. There are three types of fruit that we research, its mangoes (sweet fragrant), lime, and pitaya fruit. Each fruit divided into three categories (i) Super, (ii) middle, (iii) and fruit defects. We exploit join detection and video tracking to calculate and determine the quality fruit in real-time. The datasets are taken in the field, then trained using the FRCNN Framework using the Tensorflow platform. We demonstrated that this system can classify fruit with an accuracy level of 88% (mango), 83% (lime), and 99% (pitaya), with an average computation cost of 0.0131 m/s. We can track and calculate fruit sequentially without using additional sensors and check the defect rate on fruit using the video streaming camera more accurately and with greater ease.