An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification

The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods

Forecasting Oxygen Demand in Treatment Plant Using Artificial Neural Networks

Modeling the wastewater treatment plant is difficult due to nonlinear properties of most of its different processes. Due to the increasing concerns over environmental effects of treatment plants considering the poor operation, fluctuations in process variables and problems of linear analyses, algorithms developed using artificial intelligence methods such as artificial neural networks have attracted a great deal of attention. In this research, first using regression analysis, the parameters of biological oxygen demand, chemical oxygen demand, and pH of the input wastewater were chosen as input parameter among other different parameters. Next, using error analysis, the best topology of neural networks was chosen for prediction. The results revealed that multilayer perception network with the sigmoid tangent training function, with one hidden layer in the input and output as well as 10 training nodes with regression coefficient of 0.92 is the best choice. The regression coefficients obtained from the predictions indicate that neural networked are well able to predict the performance of the wastewater treatment plant in Yazd

STR-925: BONDING BEHAVIOR IN BRIDGE STEEL-REINFORCED ELASTOMERIC ISOLATORS

Steel-reinforced elastomeric isolators (SREIs) have been shown to be efficient devices to protect structures against moderate and severe earthquakes by isolating them from ground motions. Bridge elastomeric isolators, however, deteriorate when undergone repetitive loading cycles due to either earthquakes or traffic loadings. One major damage type observed dominantly in these devices is delamination or de-bonding between rubber and supporting plates and steel reinforcements, if cold-bonded. This paper investigates potential damage scenarios likely to occur in cold-bonded bridge SREIs. It also looks into bonding properties of rubber and steel in tension and shear, the two important functional characteristics of elastomeric isolators. In this study, experimental tests are employed in order to observe the bonding behavior between rubber and steel. Damage states have been organized and it is observed that the adhesive properties and level of shear deformations govern bonding characteristics

Government Responsibility for Health Right Service in field of Obstetrics and Gynecology at the Hospital

The quality of the hospital is very much determined by two main factors, namely the service by hospital staff and the building and infrastructure of the hospital itself. In contrast to numerous studies and case reports on the physical complications of genital mutilation, little scientific research is available on the sexual and psychological effects of the practice. The impact that can be caused when both of these factors are not met with good is the poor hospital services, both in normal circumstances and during a disaster. Likewise, if there is an incident of malpractice in a hospital of the same type, then the law enforcement officers will easily to examine by simply checking the service standards set by the government by looking the type of hospital. It recommended that the government should be responsible establish the National Service Standards in each hospital of the same type in which it should have the same standard operating procedures in its care. In this context, the Provincial Government of South Sulawesi established a legal institution that houses hospitals or patients so that not only patients have the right to complain about their disappointment with hospital services, but hospitals can complain about patients violating the rules set by the government so that the principle of justice is achieved. Keywords: Obstetrics and Gynecology; Responsibility; Health Right; Local Governmen

Inhibition of the mitochondrial calcium uniporter (MCU) rescues dopaminergic neurons in pink1-/- zebrafish

Mutations in PTEN-induced putative kinase 1 (PINK1) are a cause of early onset Parkinson's disease (PD). Loss of PINK1 function causes dysregulation of mitochondrial calcium homeostasis, resulting in mitochondrial dysfunction and neuronal cell death. We report that both genetic and pharmacological inactivation of the mitochondrial calcium uniporter (MCU), located in the inner mitochondrial membrane, prevents dopaminergic neuronal cell loss in pink1Y431* mutant zebrafish (Danio rerio) via rescue of mitochondrial respiratory chain function. In contrast, genetic inactivation of the voltage dependent anion channel 1 (VDAC1), located in the outer mitochondrial membrane, did not rescue dopaminergic neurons in PINK1 deficient Danio rerio. Subsequent gene expression studies revealed specific upregulation of the mcu regulator micu1 in pink1Y431* mutant zebrafish larvae and inactivation of micu1 also results in rescue of dopaminergic neurons. The functional consequences of PINK1 deficiency and modified MCU activity were confirmed using a dynamic in silico model of Ca2+ triggered mitochondrial activity. Our data suggest modulation of MCU-mediated mitochondrial calcium homeostasis as a possible neuroprotective strategy in PINK1 mutant PD

Beyond Strong Coupling in a Massively Multimode Cavity

The study of light-matter interaction has seen a resurgence in recent years, stimulated by highly controllable, precise, and modular experiments in cavity quantum electrodynamics (QED). The achievement of strong coupling, where the coupling between a single atom and fundamental cavity mode exceeds the decay rates, was a major milestone that opened the doors to a multitude of new investigations. Here we introduce multimode strong coupling (MMSC), where the coupling is comparable to the free spectral range (FSR) of the cavity, i.e. the rate at which a qubit can absorb a photon from the cavity is comparable to the round trip transit rate of a photon in the cavity. We realize, via the circuit QED architecture, the first experiment accessing the MMSC regime, and report remarkably widespread and structured resonance fluorescence, whose origin extends beyond cavity enhancement of sidebands. Our results capture complex multimode, multiphoton processes, and the emergence of ultranarrow linewidths. Beyond the novel phenomena presented here, MMSC opens a major new direction in the exploration of light-matter interactions.Comment: 14 pages, 11 figures. References added, typos correcte

A Blender-based channel simulator for FMCW Radar

Radar simulation is a promising way to provide data-cube with effectiveness and accuracy for AI-based approaches to radar applications. This paper develops a channel simulator to generate frequency-modulated continuous-wave (FMCW) waveform multiple inputs multiple outputs (MIMO) radar signals. In the proposed simulation framework, an open-source animation tool called Blender is utilized to model the scenarios and render animations. The ray tracing (RT) engine embedded can trace the radar propagation paths, i.e., the distance and signal strength of each path. The beat signal models of time division multiplexing (TDM)-MIMO are adapted to RT outputs. Finally, the environment-based models are simulated to show the validation.Comment: Presented in ISCS2

Modeling Flame Propagation of Coal Char Particles in Heterogeneous Media

In the present research, combustion of a quiescent coal char particle cloud has been studied in the media with spatially discrete sources by means of numerical approach. A thermal model based on diffusion-controlled regime of coal char particles has been generated in order to estimate the characteristics of flame propagation in heterogeneous media. The model uses discrete heat sources to analyze dust combustion of particles with the diameter of 50 μm. Oxygen and Nitrogen have been considered as the main oxidizer and the inert gas, respectively. Flame propagation speed in various dust and oxygen concentrations has been studied. Flame speed as a function of particle size has been investigated and comparison between cases with and without consideration of radiation effect has been made. Furthermore, minimum ignition energy as a function of dust concentration for different particle sizes has been studied. Results show a reasonable compatibility with the existing experimental data