A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

Human beings are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solutions to these problems. Nature acts as an optimized source for solving the complex problems.  Decomposition is a basic strategy in traditional multi-objective optimization. However, it has not yet been widely used in multi-objective evolutionary optimization.   

Although computational strategies for taking care of Multi-objective Optimization Problems (MOPs) h

The primary objective of this paper is to introduce a new concept of fibrewise topological spaces on D is named fibrewise multi- topological spaces on D. Also, we entroduce the concepts of multi-proper, fibrewise multi-compact, fibrewise locally multi-compact spaces, Moreover, we study relationships between fibrewise multi-compact (resp., locally multi-compac) space and some fibrewise multi-separation axioms.

Neurolinguistics is a new science, which studies the close relationship between language and neuroscience, and this new interdisciplinary field confirms the functional integration between language and the nervous system, that is, the movement of linguistic information in the brain in receiving, acquiring and producing to achieve linguistic communication; Because language is in fact a mental process that takes place only through the nervous system, and this research shows the benefit of each of these two fields to the other, and this science includes important topics, including: language acquisition, the linguistic abilities of the two hemispheres of the brain, the linguistic responsibility of the brain centers, and the time limit for langua

     In this work, the characterization and application of thin films composite incorporated titanium dioxide (TiO₂) (0.8)% volume ratio for (Rutile) nanostructure with poly [2- methoxy-5-(2’-ethylhexoxy-p-phenylene vinylene] (MEH-PPV) were deposited by a spin-coating technique. The optical properties for deposited (MEH-PPV/TiO₂) nanocomposite thin films have two peaks which are the Q-band in the visible region and B-band in ultraviolet. This study shows that the absorption spectrum of organic polymer mixing with TiO₂ increased with increasing the volume ratios TiO₂. The I-V characteristic of nanocomposite thin films shows that the current at dark and light condition

When scheduling rules become incapable to tackle the presence of a variety of unexpected disruptions frequently occurred in manufacturing systems,  it is necessary to develop a reactive schedule which can absorb the effects of such disruptions. Such responding requires efficient strategies, policies, and methods to controlling production & maintaining high shop performance. This can be achieved through rescheduling task which defined as an essential operating function to efficiently tackle and response to uncertainties and unexpected events. The framework proposed in this study consists of rescheduling approaches, strategies, policies, and techniques, which represents a guideline for most manufacturing companies operatin

An excellent reputation earned by initiating and practicing sustainable business practices has additional benefits, of which are reducing environmental incidents and an improvement in operational efficiency as this has the potential to help firms improve on productivity and bring down operating costs. Taken further, with ever-increasing socially and environmentally-conscious investors and the public alike, this act of natural resources management could have a significant implication on market value and income of the practicing firms.

The above proposition has been supported by sustainable business practices literature that is continuously conversing and deliberating upon the impact of efficient resource d

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

To maintain a sustained competitive position in the contemporary environment of  knowledge  economy,  organizations  as an open social systems must have an ability to learn and know  how to adapt to rapid changes  in a proper fashion so that organizational objectives will be achieved efficiently and effectively.  A multilevel approach is adopted proposing that organizational learning suffers from the lack of interest about the strategic competitive performance of the organization. This remains implicit almost in all models of organizational learning and there is little focus on how learning organizations achieve sustainable competitive advantage . A dynamic model that captures t