Estimation of cardiovascular and respiratory diseases attributed to PM10 using AirQ model in Urmia during 2011-2017

Background: Quantification of the attributed effects of air pollution determines the impact of air pollutants on the community and shows the critical condition of air quality. This study aimed to quantify and estimate the cardiovascular and respiratory diseases attributed to PM10 in Urmia during 2011-2016. Methods: In this descriptive-analytic study, at first, hourly data of pollutant PM10 concentrations were received from air pollutants station located in the Department of Environmental Protection. The data were evaluated using AirQ2.2.3 software after primary and secondary processes and filtering. Results: The results showed that the mean annual concentration of PM10 during 2011-2016 was 88.66, 92.45, 81.22, 78.38, 113.78, and 92.67 μg /m3, respectively. The number of hospitalized cases due to respiratory diseases attributed to PM10 in this period was 486, 525, 459, 453, 684, and 552, respectively, and the number of cases due to cardiovascular diseases was 188, 203, 177, 175, 263, and 213, respectively. Conclusion: Considering the attributed health effects of PM10, the necessary measures should be taken to identify the causative agents and to understand the mechanisms of these processes and correct them. © Iran University of Medical Sciences

Lunar soft landing trajectory optimization in a 6DoF dynamical model

An algorithm for the optimization of a lunar soft-landing trajectory is presented. A 6DOF modeling of the dynamics is adopted together with an accurate description of the Moon gravity field. The problem is faced as a direct optimization problem with the goal of obtaining a vertical landing whilst minimizing the overall fuel consumption. The descent trajectory is supposed to start from the periselenium of a low Moon orbit. Four optimization phases are considered. Each phase is characterized by a different set of optimization variables, constraints, and increasing level of complexity. In the first phase the thrust direction is optimized considering the translational motion of the lander only. Furthermore, no throttle capability is considered. In the second phase the thrust direction is fixed in the spacecraft body reference frame. The proper thrust orientation is obtained by optimizing the control torques supplied to the lander by the attitude sub-system. In the third phase the thrust magnitude is optimized too, and the constraint of landing on specific site is added. Furthermore, more restrictive constraints on the final velocities (linear and angular) are set. Finally, in the fourth phase a more accurate gravitational model of Moon that includes the main harmonics is considered. The algorithm is tested on two different landing scenarios. One describes a landing near the north pole area for a mission whose goal is to visit the craters where recently the presence of water has been discovered. The second one considers a landing in an area close to the Moon's equator, and it is inspired by Google Lunar X-prize

Lunar soft landing trajectory optimization in a 6DoF dynamical model

An algorithm for the optimization of a lunar soft-landing trajectory is presented. A 6DoF modeling of the dynamics is adopted together with an accurate description of the Moon gravity field. The problem is faced as a direct optimization problem with the goal of obtaining a vertical landing whilst minimizing the overall fuel consumption. The descent trajectory is supposed to start from the periselenium of a low Moon orbit. Four optimization phases are considered. Each phase is characterized by a different set of optimization variables, constraints, and increasing level of complexity. In the first phase the thrust direction is optimized considering the translational motion of the lander only. Furthermore, no throttle capability is considered. In the second phase the thrust direction is fixed in the spacecraft body reference frame. The proper thrust orientation is obtained by optimizing the control torques supplied to the lander by the attitude sub-system. In the third phase the thrust magnitude is optimized too, and the constraint of landing on specific site is added. Furthermore, more restrictive constraints on the final velocities (linear and angular) are set. Finally, in the fourth phase a more accurate gravitational model of Moon that includes the main harmonics is considered. The algorithm is tested on a lunar landing trajectory in an area close to the Moon's equator, with a candidate spacecraft for Google Lunar X-prize

Lunar soft landing trajectory optimization in a 6DoF dynamical model

An algorithm for the optimization of a lunar soft-landing trajectory is presented. A 6DoF modeling of the dynamics is adopted together with an accurate description of the Moon gravity field. The problem is faced as a direct optimization problem with the goal of obtaining a vertical landing whilst minimizing the overall fuel consumption. The descent trajectory is supposed to start from the periselenium of a low Moon orbit. Four optimization phases are considered. Each phase is characterized by a different set of optimization variables, constraints, and increasing level of complexity. In the first phase the thrust direction is optimized considering the translational motion of the lander only. Furthermore, no throttle capability is considered. In the second phase the thrust direction is fixed in the spacecraft body reference frame. The proper thrust orientation is obtained by optimizing the control torques supplied to the lander by the attitude sub-system. In the third phase the thrust magnitude is optimized too, and the constraint of landing on specific site is added. Furthermore, more restrictive constraints on the final velocities (linear and angular) are set. Finally, in the fourth phase a more accurate gravitational model of Moon that includes the main harmonics is considered. The algorithm is tested on a lunar landing trajectory in an area close to the Moon's equator, with a candidate spacecraft for Google Lunar X-prize

A Real-Time Anomaly Network Intrusion Detection System with High Accuracy

Reliance on Internet and online procedures increased the potential of attacks launched over the Internet. Therefore, network security needs to be concerned to provide secure information channels. Intrusion Detection System (IDS) is a valuable tool for the defense-in-depth of computer networks. However, building an efficient IDS faces a number of challenges. One of the important challenges is dealing with data containing high number of features. This paper is devoted to solve this challenge by proposing an effective PSO-Discritize-HNB intrusion detection system. The proposed PSO-Discritize-HNB IDS combines Particle Swarm Optimization (PSO) and Information Entropy Minimization (IEM) discritize method with the Hidden Naive Bayes (HNB) classifier. To evaluate the performance of the proposed network IDS several experiments are conducted on the NSL-KDD network intrusion detection dataset. A comparative study of applying Information Gain (IG) which is a well known feature selection algorithm with HNB classifier was accomplished. Also, to validate the proposed PSO-Discritize-HNB network intrusion detection; it is compared with different feature selection methods as Principal Component Analysis (PCA) and Gain Ratio. The results obtained showed the adequacy of the proposed network IDS by reducing the number of features from 41 to 11, which leads to high intrusion detection accuracy (98.2%) and improving the speed to 0.18 sec

A Real-Time Anomaly Network Intrusion Detection System with High Accuracy

Abstract: Reliance on Internet and online procedures increased the potential of attacks launched over the Internet. Therefore, network security needs to be concerned to provide secure information channels. Intrusion Detection System (IDS) is a valuable tool for the defense-in-depth of computer networks. However, building an efficient IDS faces a number of challenges. One of the important challenges is dealing with data containing high number of features. This paper is devoted to solve this challenge by proposing an effective PSO-Discritize-HNB intrusion detection system. The proposed PSO-Discritize-HNB IDS combines Particle Swarm Optimization (PSO) and Information Entropy Minimization (IEM) discritize method with the Hidden Naïve Bayes (HNB) classifier. To evaluate the performance of the proposed network IDS several experiments are conducted on the NSL-KDD network intrusion detection dataset. A comparative study of applying Information Gain (IG) which is a well known feature selection algorithm with HNB classifier was accomplished. Also, to validate the proposed PSO-Discritize-HNB network intrusion detection; it is compared with different feature selection methods as Principal Component Analysis (PCA) and Gain Ratio. The results obtained showed the adequacy of the proposed network IDS by reducing the number of features from 41 to 11, which leads to high intrusion detection accuracy (98.2%) and improving the speed to 0.18 sec