One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

 Infertility is an increasingly prevalent global issue, affecting approximately %15 of couples, with male factors accounting for half of these cases. The most severe form of male infertility is non-obstructive azoospermia (NOA), in which sperm production is drastically reduced. Sperm retrieval from testicular tissue samples in these patients is a time-consuming process that relies heavily on the operator’s expertise. The presence of background cells and tissue debris further complicates the identification of sperm. In this study, a two-stage passive microfluidic system was designed and simulated, integrating an inertial spiral module with a deterministic lateral displacement (DLD) module. In the first stage, the spiral module utilizes inertial and Dean vortex forces to remove larger particles from the main flow, thereby preventing channel blockage in the second stage. In the subsequent stage, the high-resolution DLD module removes the remaining background cells, completing the sperm separation process. Simulation results demonstrate that particles smaller than 4.7 μm in diameter, corresponding to sperm cells and residual red blood cells, are efficiently separated from larger background cells, effectively preventing clogging in downstream microfluidic channels. Given the DLD module’s high resolution, the system can isolate sperm cells from residual red blood cells with more than %95 efficiency. 

Positioning on Earth has consistently been a crucial and complex challenge. Inertial Navigation Systems (INS) serve as essential tools for determining the position of moving platforms but face two primary limitations: the accumulation of navigation errors over time and the dependence on accurate initial conditions. While GNSS satellites have revolutionized positioning on and near Earth, their effectiveness diminishes with distance from the planet, necessitating alternative methods. In such cases, celestial observation and star-based positioning become vital solutions. Star trackers—high-precision sensors capable of identifying and locating celestial star patterns—play a pivotal role in this context. When integrated with inclinometers and chronometers, they form a positioning system capable of calculating geographic latitude and longitude through sensor data processing. This study investigates the underlying mathematics of this approach, models various error sources (including star tracker accuracy, inclinometer and chronometer precision, sensor alignment, and gravitational modeling), and analytically evaluates their impact on positioning accuracy. The findings provide valuable insights for optimizing system configurations and error budgeting in the design of advanced positioning systems

Background: Air pollution and weather are just two of many environmental factors contributing to traffic accidents (RTA).Objectives: This study assessed the effects of these factors on traffic accidents and related mortalities in Ahvaz, Iran.Methods: In this ecological study, data about RTA, traffic-related mortalities, air pollution (including NO, CO, NO2, NOx PM10, SO2, and O3 rates) and climate data from March 2008 until March 2015 was acquired from the Khuzestan State Police Force, the Environmental Protection Agency and the State Meteorological Department. Statistical analysis was performed with STATA 12 through both crude and adjusted negative binomial regression methods.Results: There was a significant positive correlation between increase in the monthly average temperature, the number of rainy days, and the number of frost days with the number of RTA (P<0.05). Increased monthly average relative humidity, evaporation, and number of sunny days were negatively correlated with the frequency of RTA (P<0.05). We also observed an inverse significant correlation between monthly average relative humidity, evaporation, and wind speed with traffic accident mortality (P<0.05). Some air pollutants were negatively associated with the incidence rate of RTA.Conclusions: It appears that some weather variables were significantly associated with increased RTA. However, increased levels of air pollutants were not associated with increased rates of RTA and/or related mortalities. Additional studies are recommended to explore this topic in more detail.

Analyzing motion patterns in traffic videos can be employed directly to generate high-level descriptions of their content. For traffic videos captured from intersections, usually, we can easily provide additional information about traffic phases. Such information can be obtained directly from the traffic lights or through traffic lights controllers. In this paper, we focus on incorporating additional information to analyze the traffic videos more efficiently. Using side information on traffic phases, the semantic of motion patterns from traffic intersection scenes can be learned more effectively. The learning is performed based on optical flow features extracted from training video clips, and applying them to supervised topic models such as MedLDA and MedSTC. Based on such models, any video clip can be represented based on the learned patterns. Such representations can be further exploited in scene analysis, rule mining, abnormal event detection, etc. Our experiments show that employing side information in intersection video analysis leads to improvement in discovering scene pattern. Moreover, supervised topic models achieve about 4% improvement in abnormal event detection, compared to the unsupervised ones, in terms of area under ROC.

BACKGROUND AND OBJECTIVES: One of the short-term strategies to manage the traffic and make a balance between travel supply and demand for the near future is the short-term prediction of traffic parameters and informing the passengers. Therefore passengers are more likely to avoid traveling during traffic peak hours. In this study, hourly average traffic speed and hourly traffic volume as two traffic parameters that indicate traffic state are predicted for Karaj-Chaloos road in Iran. METHODS: Since traffic data have large volume, machine learning-based models have more suitable performance than traditional models. However, it is not merely possible to discover the cause and effect relationships and the importance of features. In this study, after using the artificial neural network and K-nearest neighbor models to predict traffic parameters, to analyze the sensitivity of the results, the importance of used features is investigated. Also, the effect of passing the time over the accuracy of predictions has been examined. FINDINGS: According to the results, the highest accuracy of predicting hourly traffic volume and hourly average traffic speed is achieved by the K-nearest neighbor that is equal to 61% and 91%, respectively. CONCLUSION: Compared to the historical average as a benchmark model, a significant improvement in the accuracy of predictions has been obtained by the artificial neural network and K-nearest neighbor models.

Background: Application of vehicles in urban areas is one of the necessities of social lives and such needs certainly lead to greater use of vehicles. Traffic collision, road injuries and deaths are some consequence of traffic violations. The major element in traffic accidents is human factor. Personality type influences their behavior while driving. The objective of this research is to investigate the frequency of human errors while driving in urban areas.Materials and Methods: Eight locations on streets of urban area of Shiraz was selected by multistage sampling and a camera recorded 5-minute period in each point. Ten moving violations checked for every driver or pedestrian.Results: The results showed that 26% of the travelers commit traffic violations. 92% of the motorbike drivers disregarded their safety and did not use helmet. The pedestrians also ignored the risk of accident and passed illegal points.Conclusion: According to the findings of the research, it can be said that in a short period of time, one in every four people commit a traffic offense. The reason is lack of proper training of traffic rules, lack of understanding the risk and non-standard passages.

Today no one can deny the role of automobiles in human life. Increasing number of automobiles, weak road structures, crowded cities, misused misuse of vehicles, and defying traffic rules have all created a large number of problems for the governments. The average 25000 death rate and 250/000 casualties are among such problems. The present study aims at investigating the training methods of traffic rules and regulations for students of the following countries: Netherland, Germany, the UK, Switzerland, Sweden, and Norway.

Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a method to identify network traffics. In this method, for cleaning and preparing data, we perform effective preprocessing approach. Then effective features are extracted using the behavioral analysis of application. Using the effective preprocessing and feature extraction techniques, this method can effectively and accurately identify network traffics. For this purpose, two network traffic databases namely UNIBS and the collected database on router are analyzed. In order to evaluate the results, the accuracy of network traffic identification using proposed method is analyzed using machine learning techniques. Experimental results show that the proposed method obtains an accuracy of 97% in network traffic identification.