Social personalized e-learning framework

This thesis discusses the topic of how to improve adaptive and personalized e-learning in order to provide novel learning experiences. A recent literature review revealed that adaptive and personalized e-learning systems are not widely used. There is a lack of interoperability between adaptive systems and learning management systems, in addition to limited collaborative and social features. First of all, this thesis investigates the interoperability issue via two case studies. The first case study focuses on how to achieve interoperability between adaptive systems and learning management systems using e-learning standards and the second case study focuses on how to augment e-learning standards with adaptive features. Secondly, this thesis proposes a new social framework for personalized e-learning, in order to provide adaptive and personalized e-learning platforms with new social features. This is not just about creating learning content, but also about developing new ways of learning. For instance, in the presented vision, adaptive learning does not refer to individuals only, but also to groups. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. Finally, a new social personalized prototype is introduced based on the new social framework for personalized e-learning in order to test and evaluate this framework. The implementation and evaluation of the new system were carried out through a number of case studies.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo

Rh alleles and phenotypes among Saudi women in Hail Region, Saudi Arabia

Background: The Rh system is considered as the most complex among the human blood group systems, with 61 antigens identified to date. This study aimed to provide preliminary data on the distribution of Rh alleles and phenotypes among Saudi women and compare them with other ethnic groups.Methods: This retrospective cross-sectional study was conducted among Saudi women who visited the Maternity and Children Hospital of Hail from November 2019 to March 2020. A fully automated blood bank analyzer was used in determining the Rh subgroups (D, C, c, E, e) and phenotypes. Inferential statistics and chi-square tests were used appropriately for comparisons.Results: The study included a total of 500 Saudi female patients. The most prevalent antigen found was the “e” antigen, while phenotype CcDee has shown to have the highest frequency. A significant difference exists in comparison with the other studies from various ethnic groups.Conclusions: The prevalence and distributions of Rh alleles and phenotypes among Saudi women were revealed in this study. The findings showed that Rh alleles and phenotypes are diverse across various races and regions globally.Keywords: Rh allele; Rh phenotype; Rh system; Saudi Arabi

Global Efforts to Address Severe Acute Malnutrition.

Detailed description on the two formats presented, proBAM (S1A) and proBed (S1B). (XLSX 46 kb

Galaxy Integrated Omics:Web-based standards-compliant workflows for proteomics informed by transcriptomics

With the recent advent of RNA-seq technology the proteomics community has begun to generate sample-specific protein databases for peptide and protein identification, an approach we call proteomics informed by transcriptomics (PIT). This approach has gained a lot of interest, particularly among researchers who work with nonmodel organisms or with particularly dynamic proteomes such as those observed in developmental biology and host-pathogen studies. PIT has been shown to improve coverage of known proteins, and to reveal potential novel gene products. However, many groups are impeded in their use of PIT by the complexity of the required data analysis. Necessarily, this analysis requires complex integration of a number of different software tools from at least two different communities, and because PIT has a range of biological applications a single software pipeline is not suitable for all use cases. To overcome these problems, we have created GIO, a software system that uses the well-established Galaxy platform to make PIT analysis available to the typical bench scientist via a simple web interface. Within GIO we provide workflows for four common use cases: a standard search against a reference proteome; PIT protein identification without a reference genome; PIT protein identification using a genome guide; and PIT genome annotation. These workflows comprise individual tools that can be reconfigured and rearranged within the web interface to create new workflows to support additional use cases

The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience.

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online

Social personalized e-learning framework

This thesis discusses the topic of how to improve adaptive and personalized e-learning in order to provide novel learning experiences. A recent literature review revealed that adaptive and personalized e-learning systems are not widely used. There is a lack of interoperability between adaptive systems and learning management systems, in addition to limited collaborative and social features. First of all, this thesis investigates the interoperability issue via two case studies. The first case study focuses on how to achieve interoperability between adaptive systems and learning management systems using e-learning standards and the second case study focuses on how to augment e-learning standards with adaptive features. Secondly, this thesis proposes a new social framework for personalized e-learning, in order to provide adaptive and personalized e-learning platforms with new social features. This is not just about creating learning content, but also about developing new ways of learning. For instance, in the presented vision, adaptive learning does not refer to individuals only, but also to groups. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. Finally, a new social personalized prototype is introduced based on the new social framework for personalized e-learning in order to test and evaluate this framework. The implementation and evaluation of the new system were carried out through a number of case studies

MOT 2.0 : a case study on the usefuleness of social modeling for personalized e-learning systems

In this paper, we report on our findings from the first evaluation of MOT 2.0, an Adaptive Web 2.0 e-learning tool, which supports: 1) collaborative authoring (i.e. editing content of other users, describing content using tags, rating, commenting on the content, etc); 2) authoring for collaboration (i.e., adding author activities, such as defining groups of authors, subscribing to other authors, communication between authors, etc); 3) group-based adaptive authoring via group-based privileges; 4) social annotation i.e., tagging, rating, and feedback on the content via group-based privileges; 5) adaptive authoring, by recommending related content and/or other authors; adaptive delivery based on users' activities. Our main contributions are: 1) defining a new social layer in LAOS, a five-layer model for generic adaptive hypermedia authoring; 2) removing the barrier between tutors, learners and authors, which all become authors, with different sets of privileges; 3) adding the power of group-based authoring to the course creating

Interoperability between MOT and learning management systems : converting CAF to IMS QTI and IMS CP

The chain of applying adaptivity to Learning Management Systems (LMS) is still deficient; there is a gap between authoring adaptive materials and delivering them in LMS. In this paper, we extend My Online Teacher (MOT), an adaptive authoring system, by adding compatibility with IMS Question & Test Interoperability (QTI) and IMS Content Packaging (CP). Thus, the authors can utilize the authored materials for learning process adaptation on any standards-compatible LMS. From a technical perspective, we initialize the creation of adaptive LMS by converting Common Adaptation Format (CAF), XML representation of MOT database, into IMS QTI and IMS CP, to ensure a wider uptake and use of adaptive learning systems. Finally, this work represents a significant step towards the little explored avenue of adaptive collaborative systems based on extant learning standards and popular LMS

Sistem Box Carton Product Quality Checker Machine Menggunakan Image Processing Dengan Metode Gray Level Co-occurrence Matrix (GLCM)

PT.X mengalami kendala terkait gagalnya proses sortir pada robotic palletizer system, sebelum proses sortir terdapat pengecekan kualitas box carton product oleh manusia. Cacatnya kondisi berat box atau isi produk dalam box carton tidak sesuai dengan semestinya dan cacatnya kondisi fisik box dapat mengakibatkan proses sortir pada robotic palletizer system menjadi terganggu dan terjadi maintenance pada sistem yang dapat mengakibatkan perusahaan mengalami kerugian. Maka dari itu dirancang sebuah alat yang dapat dapat mendeteksi kualitas box menggunakan image processing dengan metode Gray Level Co-occurrence Matrix (GLCM) sebagai pendeteksi tekstur dari objek dengan mengeluarkan nilai energy, correlation, homogeneity, dissimilarity, dan contrast.. Kemudian hasil pembacaan GLCM akan dilakukan klasifikasi menggunakan SVM (Support Vector Machine) untuk memilah kondisi goodbox dan badbox. Kemudian berat box akan ditimbang menggunakan loadcell. Kondisi kualitas box dari segi fisik dan berat keduanya harus dalam keadaan baik atau harus terpenuhi (menggunakan logika AND), jika salah satu saja tidak memenuhi maka dilakukan proses reject. Hasil dari alat pendeteksi kualitas box ini, ketika mendeteksi kualitas fisik box pada ruang terbuka yang dilakukan pengujian pada siang hari menghasilkan nilai accuracy sebesar 85%, Ketika dilakukan pengujian pada malam hari menghasilkan akurasi sebesar 80%, Ketika dilakukan pengujian didalam ruang tertutup menghasilkan akurasi sebesar 90% dan Ketika dilakukan pengujian keseluruhan didapat hasil akurasi sebesar 80%. ======================================================================================================== PT.X experienced problems related to the failure of the sorting process on the robotic palletizer system, before the sorting process there was a check on the quality of the carton product box by humans. The defective condition of the weight of the box or the contents of the product in the carton box does not match it properly and the defect in the physical condition of the box can cause the sorting process on the robotic palletizer system to be disrupted and maintenance occurs on the system which can cause the company to suffer losses. Therefore, a tool is designed that can detect box quality using image processing with the Gray Level Co-occurrence Matrix (GLCM) method as a texture detector from objects by issuing energy, correlation, homogeneity, dissimilarity, and contrast values. Then the results of GLCM readings will be classified using SVM (Support Vector Machine) to sort out the conditions of goodbox and badbox. Then the weight of the box will be weighed using a loadcell. The condition of the quality of the box in terms of physical and weight both must be in good condition or must be fulfilled (using AND logic), if one of them does not meet then a reject process is carried out. The results of this box quality detection tool, when detecting the physical quality of the box in an open space that is tested during the day, produces an accuracy value of 85%, When testing at night produces an accuracy of 80%, When testing in a closed room produces an accuracy of 90% and when the overall test was carried out, the results of accuracy were 80%