Female Faculty Perspectives On Blended Learning At Universities In Saudi Arabia

Adopting technology is one of the priorities in the Saudi education system. The reform of Saudi education leads to the need to focus on BL as a tool for adopting technology into any college classroom. This study focuses specifically on technology usage for teaching among female faculty. In 2018, women’s education in Saudi Arabia has undergone an incredible social transition, and women’s education is different and more advanced than before. This study looks to learn about female faculty members’ views and experiences as they relate to the adoption of BL in their classrooms. It seeks to provide in-depth knowledge essential to adopt BL according to a Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM) theoretical framework. DOI explains adoption in the social setting with all the possible social culture factors. The TAM explains faculty level acceptance, specifically explaining external factors’ effects on faculty members’ beliefs and influencing them toward the BL. Because this study gathers information on female faculty members’ experiences with BL, a qualitative theme analysis was the appropriate research design to use. In particular, I used a qualitative research method to study female faculty members’ perspectives, collecting data via individual interviews. It is included interviews with female faculty members from four public, 4-year institutions in Saudi Arabia. Faculty members’ flexibility and their relationship to the adoption of BL depended on their benefits and challenges. According to this study’s participants, the challenges of the BL approach in Saudi Arabia necessitate urgent strategic plans at all levels. Faculty members’ knowledge and understanding regarding the definitions of BL showed their acceptance. Faculty support should be a priority for these institutions, which should adopt policies to help achieve Vision 2030—a natural, well-organized way to reform higher education. The gender aspect of the teaching culture considerably impacts the female faculty’s use of BL in Saudi Arabia. This study’s results—that the women faculty believe there is a need for evaluation, and official leadership rules for the adoption of BL. To summarize, the results indicated that women adopted BL when circumstances permitted. The obstacles, in their eyes, were a lack of faculty support, poor strategic evaluation plans, and insufficient empowerment at the institutional level

Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

The Structural and Functional Properties of a Double Mutant of Human Acidic Fibroblast Growth Factor (hFGF-1)

Human acidic Fibroblast Growth Factor 1 (FGF-1), a member of the FGF superfamily, is a potent mitogen and heparin-binding protein involved in a broad spectrum of biological processes, including angiogenesis, cell proliferation, and wound healing. Design of hFGF-1 with an increased thermal stability and an enhanced cell proliferation activity is highly desired for wound healing applications. Herein, we have designed the variant of FGF-1 by substituting two important amino residues in the heparin-binding pocket. The variant was overexpressed in Escherichia coli and was successfully purified to homogeneity using an affinity chromatographic procedure. Far-UV circular dichroism spectroscopic analysis showed that the backbone conformation of the hFGF-1 did not alter due to the introduction of mutations in the heparin-binding pocket. The designed hFGF-1 variant exhibited an increased resistance to limited trypsin digestion. Isothermal titration calorimetry study confirmed that approximately 20-fold decrease in heparin binding affinity (Kd ~90µM) was observed in case of the double mutant compared to that of the wild-type FGF1 (~5µM). Incorporation of positively charged Lys135 adjacent to the negatively charged E136 might have reduced the repulsive effect to heparin. 8-Anilino naphthalene 1-sulfonate (ANS) binding assay revealed that the introduced mutations cause a subtle change in the solvent-accessible non-polar surface of the protein. These results were in concordant with other biophysical data obtained from limited trypsin digestion, 1H 15N HSQC analysis. In addition, no significant change in bioactivity was observed between the mutant and the wild-type FGF1 proteins. This confirms that introduction of positive charge adjacent to E136 nullified the effects of this unique mutation

Probing Pre-Trained Language Models for Disease Knowledge

Pre-trained language models such as ClinicalBERT have achieved impressive results on tasks such as medical Natural Language Inference. At first glance, this may suggest that these models are able to perform medical reasoning tasks, such as mapping symptoms to diseases. However, we find that standard benchmarks such as MedNLI contain relatively few examples that require such forms of reasoning. To better understand the medical reasoning capabilities of existing language models, in this paper we introduce DisKnE, a new benchmark for Disease Knowledge Evaluation. To construct this benchmark, we annotated each positive MedNLI example with the types of medical reasoning that are needed. We then created negative examples by corrupting these positive examples in an adversarial way. Furthermore, we define training-test splits per disease, ensuring that no knowledge about test diseases can be learned from the training data, and we canonicalize the formulation of the hypotheses to avoid the presence of artefacts. This leads to a number of binary classification problems, one for each type of reasoning and each disease. When analysing pre-trained models for the clinical/biomedical domain on the proposed benchmark, we find that their performance drops considerably.Comment: Accepted by ACL 2021 Finding

Existence results for a coupled system of nonlinear fractional functional differential equations with infinite delay and nonlocal integral boundary conditions

This article is devoted to studying a new class of nonlinear coupled systems of fractional differential equations supplemented with nonlocal integro-coupled boundary conditions and affected by infinite delay. We first transform the boundary value problem into a fixed-point problem, and, with the aid of the theory of infinite delay, we assume an appropriate phase space to deal with the obtained problem. Then, the existence result of solutions to the given system is investigated by employing Schaefer's fixed-point theorem, while the uniqueness result is established in view of the Banach contraction mapping principle. The illustrative examples are constructed to ensure the availability of the main results

Effect of light intensity on carbohydrates, lipids contents, and bioethanol production in two algal species of Coelastrella saipanensis and Oscillatoria duplisecta

This study aimed to examine the feasibility of two algal species of Coelastrella saipanensis (Chlorophyceae) and Oscillatoria duplisecta (Cyanophyceae) to produce bioethanol production at different light intensities. In the present study, light-intensity treatments at 27, 36, and 67 µmol m-2 s-1 were used to stimulate bioethanol production from microalga. The effects of these treatments on C. saipanensis and O. duplisecta were investigated on their growth, carbohydrate and lipids contents. The results showed that the stationary phase of C. saipanensis started on the sixth day under light intensities of 27 and 36 µmol m-2 s-1 and on the eighth day under light intensity of 67 µmol m-2 s-1. The stationary stage of blue-green algae O. duplisecta started on day eight, sixth, and seventh under light intensities of 27, 36, and 67 µmol m-2 s-1, respectively. The highest amount of carbohydrate content was 0.182, and 0.310 mg/l for C. saipanensis and O. duplisecta under light intensity of 36 ?mol m-2 s-1. The highest amount of lipid was 0.95 g/l for C. saipanensis under a light intensity of 36 ?mol m-2 s-1, while 0.74 g/L was the highest amount of lipid for O. duplisecta under 67 µmol m-2 s-1 at a light intensity of 36 µmol m-2 s-1. The highest percentage of bioethanol in C. saipanensis and O. duplisecta were 11.35 and 10.23%, respectively. The 18S rRNA and 16S rRNA genes were used for the identification, and the sequences of algae matched those registered in the GenBank (MT375484.1 for C. saipanensis and MW405018.1 for O. duplisecta). The phylogenetic tree of the ITS area was analyzed inside the 18S rRNA and 16S rRNA and the sequences showed a strong resemblance to those species registered in the Genebank

Interpreting patient case descriptions with biomedical language models

The advent of pre-trained language models (LMs) has enabled unprecedented advances in the Natural Language Processing (NLP) field. In this respect, various specialised LMs for the biomedical domain have been introduced, and similar to their general purpose counterparts, these models have achieved state-of-the-art results in many biomedical NLP tasks. Accordingly, it can be assumed that they can perform medical reasoning. However, given the challenging nature of the biomedical domain and the scarcity of labelled data, it is still not fully understood what type of knowledge these models encapsulate and how they can be enhanced further. This research seeks to address these questions, with a focus on the task of interpreting patient case descriptions, which provides the means to investigate the model’s ability to perform medical reasoning. In general, this task is concerned with inferring a diagnosis or recommending a treatment from a text fragment describing a set of symptoms accompanied by other information. Therefore, we started by probing pre-trained language models. For this purpose, we constructed a benchmark that is derived from an existing dataset (MedNLI). Following that, to improve the performance of LMs, we used a distant supervision strategy to identify cases that are similar to a given one. We then showed that using such similar cases can lead to better results than other strategies for augmenting the input to the LM. As a final contribution, we studied the possibility of fine-tuning biomedical LMs on PubMed abstracts that correspond to case reports. In particular, we proposed a self-supervision task which mimics the downstream tasks of inferring diagnoses and recommending treatments. The findings in this thesis indicate that the performance of the considered biomedical LMs can be improved by using methods that go beyond relying on additional manually annotated datasets

Self-assessment in EFL speaking classroom and its effect on achievement, self-regulated learning, and critical thinking: students’ voices from Saudi Arabia

In light of the growing need to enhance the quality of education to overcome social and economic issues, assessment systems and curriculum have undergone significant modifications and reforms in many countries. Saudi Arabia is no exception. The literature suggests that innovative approaches to assessment, such as self-assessment, have the potential to promote lifelong skills, empower learners, and enhance learning. Nonetheless, traditional assessment practices continue to dominate in Saudi Arabia, particularly in higher education English language courses. Review and reframing of assessment approaches are, therefore, necessary in Saudi Arabia to improve the quality of learning and to develop learners’ lifelong skills, including self-regulated learning skills and critical thinking skills. Despite the growing interest in self-assessment as a practical instructional strategy that draws on formative assessment to promote self-regulated learning and critical thinking, relatively few studies have addressed this topic in English language courses in higher education, and none have addressed it in the context of Saudi Arabia. The evidence regarding the impact of self-assessment on the quality of learning and the empowerment of learners may help to guide the Saudi education reform. Nonetheless, traditional assessment practices continue to dominate in Saudi Arabia, particularly in higher education English language courses. Therefore, reviewing and reframing of assessment approaches, specifically to improve the quality of learning, are necessary in Saudi Arabia to develop learners’ lifelong skills, including self-regulated learning skills and critical thinking skills. Recently, self-assessment has emerged as a practical instructional strategy that draws on formative assessment to promote selfregulated learning. However, most research on formative assessment and self-regulated learning has portrayed results related to self-assessment as generalisable, despite the need for research across various educational contexts. The aim of this research is to explore in depth the participants’ perceptions and experience of self-assessment in speaking classrooms and the impact of self-assessment on learners’ self-regulatory skills, critical thinking, and speaking language performance within the EFL context. This study also examines the relationship between learners’ self-regulated learning and their critical thinking skills. Pre- and post-tests were conducted with 27 EFL students before and after a self-assessment intervention. In addition, a self-assessment proforma, audio recording, and semi-structured interviews were collected and conducted with 10 of the 27 students. All these tools played an essential role in investigating the participants’ perceptions and experience of self-assessment and its impact. Overall, the participants in this study displayed favourable attitudes towards self-assessment. The findings indicate that a variety of factors influenced the learners’ perspectives, including learners’ prior experience with traditional speaking assessment, learners’ motivation and willingness to self-assess, learners’ awareness of assessment criteria, and learners’ perceptions and experiences of feedback. The findings also reveal the positive impact of self-assessment on learners’ self-regulated learning skills, critical thinking skills, and achievement, as well as a positive medium-strength relationship between learners’ self-regulated learning skills and critical thinking skills. The study concludes with recommendations for educational policy-makers who are aiming to establish practices that support and empower learners. For example, the study encourages the use and adaptation of the self-assessment proforma in the English language as a reliable scaffolding method of assessment that can foster deep learning and self-regulated learning