Linking and publishing data in the Linked Open Data format increases the interoperability and discoverability of resources over the Web. To accomplish this, the process comprises several design decisions, based on the Linked Data principles that, on one hand, recommend to use standards for the representation and the access to data on the Web, and on the other hand to set hyperlinks between data from different sources. Despite the efforts of the World Wide Web Consortium (W3C), being the main international standards organization for the World Wide Web, there is no one tailored formula for publishing data as Linked Data. In addition, the quality of the published Linked Open Data (LOD) is a fundamental issue, and it is yet to be thoroughly managed and considered. In this doctoral thesis, the main objective is to design and implement a novel framework for selecting, analyzing, converting, interlinking, and publishing data from diverse sources, simultaneously paying great attention to quality assessment throughout all steps and modules of the framework. The goal is to examine whether and to what extent are the Semantic Web technologies applicable for merging data from different sources and enabling end-users to obtain additional information that was not available in individual datasets, in addition to the integration into the Semantic Web community space. Additionally, the Ph.D. thesis intends to validate the applicability of the process in the specific and demanding use case, i.e. for creating and publishing an Arabic Linked Drug Dataset, based on open drug datasets from selected Arabic countries and to discuss the quality issues observed in the linked data life-cycle. To that end, in this doctoral thesis, a Semantic Data Lake was established in the pharmaceutical domain that allows further integration and developing different business services on top of the integrated data sources. Through data representation in an open machine-readable format, the approach offers an optimum solution for information and data dissemination for building domain-specific applications, and to enrich and gain value from the original dataset. This thesis showcases how the pharmaceutical domain benefits from the evolving research trends for building competitive advantages. However, as it is elaborated in this thesis, a better understanding of the specifics of the Arabic language is required to extend linked data technologies utilization in targeted Arabic organizations.

Background:Coffee and Tea are very popular beverages in Libyan society. They contain many bioactive substances such as polyphenols and catchiness that could have some effects for instance; antioxidant activity. The way of preparation of tea and coffee drinks are different among society. For example, in Libya tea drink is prepared by boiling the crude of fresh tea for 10 min. Therefore, the method of extraction could influence the type and quality of extracted substances. Aims: The goal of the current study was to examine the neuroprotective properties of coffee and tea beverages made by using traditional Libyan techniqueson Parkinson's disease like symptoms induced by haloperidol.Methods: Different tea and coffee beverages (treatment) were prepared and given to rats in a concentration 10%w/v for 3 consecutive weeks.Tea and coffee beverages were made as Libyans do. At day 21, rats were injected IP with 1mg/kg of haloperidol, afterward, the behavioral and motor parameters for Parkinson’s disease were tested.Results:Coffee and Tea treated groups showed significant improvement (p > 0.05)inthe behavioral activity, and in muscle coordination. Also there was a decrease in oxidation markers as treatment resulted in an elevation of glutathione reductase and decreasing in Malondialdehyde levels. In addition, the histopathological investigation showed a reduction in haloperidol induced damage in substantia nigra.Conclusion:The results showeda possible neuroprotective effect of Coffee and Tea against PD.The mechanism of protection might be due to an antioxidant activity.

Short Tandem Repeats (STRs) are one of the utmost mutable provinces in the human genome. They comprise tandem repeating DNA sequences ranging in length from two to six base pairs. Owing to their significant mutation rate, they exhibit considerable variation in pattern among populations and the capacity to be passed on from generation to generation. These loci are broadly employed in medicine, biology, and criminal investigation. They are pivotal in the genesis of a variety of genetic illnesses and have been intensively investigated in forensics, population genetics, and genetic genealogy. Although many implementations that manage STR loci are offered, the overwhelming majority of them rely primarily on the Command-Line Interface (CLI) inputs, which frequently necessitate the implementation of tools carried out in various scripting languages. Installing and launching programs through the Command Line (CL) is timeconsuming and/or unprofitable for many students and scholars. The fundamental intention of this project is to develop a cross-platform Graphical User Interface (GUI) package directed to the Combined DNA Index System (CODIS) STR analysis. Zenobia is a Java-based application considered as a step in consistently making CL-only programs available to more apprentices and researchers. In general, Zenobia's application outcomes satisfy the evaluation metrics for efficiency and time consumption. However, more genetic markers should be introduced to increase productivity of the application.

This paper attempts to create an artificial neural networks (ANNs) technique for solving well-known fractal-fractional differential equations (FFDEs). FFDEs have the advantage of being able to help explain a variety of real-world physical problems. The technique implemented in this paper converts the original differential equation into a minimization problem using a suggested truncated power series of the solution function. Next, answer to the problem is obtained via computing the parameters with highly precise neural network model. We can get a good approximate solution of FFDEs by combining the initial conditions with the ANNs performance. Examples are provided to portray the efficiency and applicability of this method. Comparison with similar existing approaches are also conducted to demonstrate the accuracy of the proposed approach.