dictates the need to study the cultural aspects of the context and the consequent relations between the person and the objective environment surrounding him, as the philosophical understanding of the role of culture has led to the emergence of new theoretical interpretations of design that are organically linked with the development of society, especially that the development of the human environment philosophically and culturally is linked to the philosophical perception of its role in Culture as a precondition for new theoretical interpretations of design.
From the above, this problem can be studied by defining the following question (What are the implications of the cultural context in graphic design)?
The research included

In this paper, an Anti-Disturbance Compensator is suggested for the stabilization of a 6-DoF quadrotor Unmanned Aerial vehicle (UAV) system, namely, the Improved Active Disturbance Rejection Control (IADRC). The proposed Control Scheme rejects the disturbances subjected to this system and eliminates the effect of the uncertainties that the quadrotor system exhibits. The complete nonlinear mathematical model of the 6-DoF quadrotor UAV system has been used to design the four ADRCs units for the attitude and altitude stabilization. Stability analysis has been demonstrated for the Linear Extended State Observer (LESO) of each IADRC unit and the overall closed-loop system using Hurwitz stability criterion. A minimization to a

Over the last few years, the interior designer has been given the ability to access many innovative tools for new forms of unprecedented diversity and efficiency. Some design experts have described the new parametric procedures they are introducing to create new interior projects as a radical transformation that carries all the elements of a qualitative shift in interior design. The best of these parametric procedures is the technical capabilities offered by us to create new forms that are different from what has been discussed in everything that has been produced by designers and architects since modernity and even before it to the present time, which returns our design products through a series of computer programs that perform the pro

     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med