The Algorithmic Mashrabiya: Reimagining the Traditional Islamic Screen

Traditionally, a mashrabiya was an ornate wooden structure attached to the side of an Arabian building or house, with small, intricately patterned openings to provide both ventilation and privacy for the people inside. The patterns, following the geometric rules of Islamic ornament, lent a distinctive appearance to buildings in the region. A mashrabiya converted the house into a safe, private sanctuary, providing a magical scene inside, characterized by linear sun rays, filtered points of light and shadow. Over time, as building technology changed and the number of skilled craftsmen dwindled, the traditional mashrabiya has all but vanished. The aim of this thesis is to propose a new future for the mashrabiya, converting it from a stylistically preordained Islamic artifact to a culturally resonant architectural element expressive of Arabic culture and lifestyle. The research explores connections between culture and nature, using a generative design process powered by algorithms, to produce patterns inspired by nature reminiscent of formations found in the Arabian desert. A contemporary structure, The Algorithmic Mashrabiya is respectful of the Islamic principles embedded within the traditional mashrabiya but is newly aligned with the needs and capabilities of twenty-first century architecture

An Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data

In this thesis, we introduce a novel architecture called Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data (iARTEC ) . The proposed architecture integrates different terrain characterization and classification with other robotic system components. Within iARTEC , we consider the problem of having a legged robot autonomously learn to identify different terrains. Robust terrain identification can be used to enhance the capabilities of legged robot systems, both in terms of locomotion and navigation. For example, a robot that has learned to differentiate sand from gravel can autonomously modify (or even select a different) path in favor of traversing over a better terrain. The same knowledge of the terrain type can also be used to guide a robot in order to avoid specific terrains. To tackle this problem, we developed four approaches for terrain characterization, classification, path planning, and control for a mobile legged robot. We developed a particle system inspired approach to estimate the robot footâ ground contact interaction forces. The approach is derived from the well known Bekkerâ s theory to estimate the contact forces based on its point contact model concepts. It is realistically model real-time 3-dimensional contact behaviors between rigid body objects and the soil. For a real-time capable implementation of this approach, its reformulated to use a lookup table generated from simple contact experiments of the robot foot with the terrain. Also, we introduced a short-range terrain classifier using the robot embodied data. The classifier is based on a supervised machine learning approach to optimize the classifier parameters and terrain it using proprioceptive sensor measurements. The learning framework preprocesses sensor data through channel reduction and filtering such that the classifier is trained on the feature vectors that are closely associated with terrain class. For the long-range terrain type prediction using the robot exteroceptive data, we present an online visual terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs). In addition, we described a terrain dependent navigation and path planning approach that is based on E* planer and employs a proposed metric that specifies the navigation costs associated terrain types. This generated path naturally avoids obstacles and favors terrains with lower values of the metric. At the low level, a proportional input-scaling controller is designed and implemented to autonomously steer the robot to follow the desired path in a stable manner. iARTEC performance was tested and validated experimentally using several different sensing modalities (proprioceptive and exteroceptive) and on the six legged robotic platform CREX. The results show that the proposed architecture integrating the aforementioned approaches with the robotic system allowed the robot to learn both robot-terrain interaction and remote terrain perception models, as well as the relations linking those models. This learning mechanism is performed according to the robot own embodied data. Based on the knowledge available, the approach makes use of the detected remote terrain classes to predict the most probable navigation behavior. With the assigned metric, the performance of the robot on a given terrain is predicted. This allows the navigation of the robot to be influenced by the learned models. Finally, we believe that iARTEC and the methods proposed in this thesis can likely also be implemented on other robot types (such as wheeled robots), although we did not test this option in our work

STUDIES ON THE SUDANESE INDIGENOUS AFRICAN TOAD BUFO SPP. (Amphibia): PARTIAL CHARATERIZATION OF ANTIBACTERIAL PEPTIDES AND PROTEINS OF THE PARATOID GLAND

The parotoid gland secretion of Bufo spp., obtained by manual compression, was found to contain 23.1-41.2% (w/w) of total protein. Gel filtration chromatography showed the existence of four fractions of peptides and proteins responsible for antibacterial activity. Thin layer chromatography showed seven Ninhydrin-positive spots in addition to the origin, in the parotoid gland secretion of Bufo spp. Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis revealed the existence, in the crude secretion, of 2 bands of proteins (P1 and P2) and 2 bands of peptides (P3 and P4) with various molecular weights

The Consistitutionality of Acts of Sovereiginty and Immune Laws

There is No. doubt that the Administrative Decisions should be issued in conformity with the provisions of the law to be described as legitimate, but for certain circumstances some decisions failed to be subject to the  Judicial Review because of its nature, such as Acts of Sovereignty where some acts  are immunized and out of judicial control.Literature and the judicial system have cooperated on a specific position on these decisions, whether they are Acts of Sovereignty or Immune Decisions. Accordingly, the researchers saw the need to set terms and regulations over Administrative Acts. The decisions taken must adhere to the elements and bases of the right Administrative Decision. Especially that Administrative Decisions constitute a violation of the rights and freedoms of individuals. Keywords: Constitutional, Administrative Decision, Immunization, Appeal, Acts of Sovereignty, Laws, Legitimacy. DOI: 10.7176/JLPG/88-16 Publication date: August 31st 201