Traditionally, path selection within routing is formulated as a shortest path optimization problem. The objective function for optimization could be any one variety of parameters such as number of hops, delay, cost...etc. The problem of least cost delay constraint routing is studied in this paper since delay constraint is very common requirement of many multimedia applications and cost minimization captures the need to
distribute the network. So an iterative algorithm is proposed in this paper to solve this problem. It is appeared from the results of applying this algorithm that it gave the optimal path (optimal solution) from among multiple feasible paths (feasible solutions).

The current study examines the combined impacts of ultrasonic waves and nano silica (NS) on reducing the viscosity Sharqy Baghdad heavy crude oil with an API gravity of 20.32. NS of an average particle size of 59.93 nm and 563.23 m²/g surface area were produced utilizing the sol-gel technique from Iraqi sand. The XRD analysis indicates the existence of an amorphous silica, the SEM analysis showed that NS tends to agglomerate, and the FTIR spectra exhibited the presence of siloxane and silanol groups. In addition, the TGA analysis demonstrated a total weight loss of 15.62%, validating the thermal stability of the NS. The experiments included a study of the impact of ultrasonic power, exposure time, duty cycle, temperature, and the c

Evolutionary algorithms are better than heuristic algorithms at finding protein complexes in protein-protein interaction networks (PPINs). Many of these algorithms depend on their standard frameworks, which are based on topology. Further, many of these algorithms have been exclusively examined on networks with only reliable interaction data. The main objective of this paper is to extend the design of the canonical and topological-based evolutionary algorithms suggested in the literature to cope with noisy PPINs. The design of the evolutionary algorithm is extended based on the functional domain of the proteins rather than on the topological domain of the PPIN. The gene ontology annotation in each molecular function, biological proce

In this study the role of remote sensing and geographic information systems in the planning of some Iraqi cities, including the city of Aziziyah in Kut / Wasit Governorate, was considered. Certain appropriate models (sprawling growth patterns) have been adopted to plan this city, and tables and a future map have been prepared to arrange this city. Land uses for public services will be proposed for adopted case(Aziziyah in Kut / Wasit Governorate). This paper describes a specific application of GIS functionalities for spatial planning analysis, examines the breadth of GIS and attempts to address the analytical part’s limitations in planning studies. The study concluded, through high-resolution visuals, to the possibility of determining th

Issam al-Din al-Asfrani's footnote
On the interpretation of the oval
Imam
Issam al-Din Ibrahim Arbashah al-Asfrani
(Th 159 e)
  Surah Al-Baqarah (verse 55-911)

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.    

Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm³.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod