Optical Coherence Tomography (OCT) is one of the most vital tools for diagnosing and tracking progress of medication of various retinal disorders. Many methods have been proposed to aid with the analysis of retinal images due to the intricacy of retinal structures, the tediousness of manual segmentation and variation from different specialists. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. In this paper we present an automatic retinal layer segmentation method, which comprises of fuzzy histogram hyperbolization and graph cut methods. We impose hard constraints to limit search region to sequentially segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and center of foveal regions. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistence of the retinal structures in all regions.

A new record for Abutilon theophrasti is recorded for the first time in the flora of Libya. This widespread agricultural weed was collected from El-Hamra region (El-Qabel) in the Nafusa Mountain south of Tripoli. To facilitate further identification and future detection, a full description, habitat information, distribution map and distribution data are provided. A brief discussion about the most important threats posed by this species is presented.

This study investigate the removal efficiency of chromium (VI) from water by using (R-AgCl) Silver chloride resin (SCR) as selective sorbent material for removal chromium (VI) from aqueous solution; the study was carried out by sorption batch experiments at room temperature(25°C). Efficiency of Cr(VI) removal was determined by measuring the chromium concentration before and after the sorption process. Atomic absorption spectroscopy (AAS) method has been applied as analytical method for determination of Cr concentration in water. Governing factors for removal efficiency were proposed, analyzed and compared. It was conducted that the process is mostly affected by pH value of solution, mass of sorbent and concentration of Cr(VI) , which was the most efficient sorbent showed maximal efficiency and maximal sorption capacity, 1.5g of SCR after 60 min at 25 °C and pH 6.5 recovered 98% of Cr(VI) from 100 mL of water polluted with 20 mg of Cr(VI).

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.