The Role of Morphological Awareness in Vocabulary Acquisition in English of Saudi EFL Learners

This study aimed to assess the impact of explicit morphology instruction on Saudi EFL students’ morphological awareness and vocabulary knowledge. The experimental group received for six weeks in their English class morphological lessons that focused on the basic analytical and synthetic word formation rules to increase students’ morphological awareness and help in improving their vocabulary knowledge. On the other hand, the control group received the regular instruction in their English class. At the end of the instruction period, the pre- and post-test scores of the experimental group were compared. Then, the experimental group scores were compared to the control group. The results showed that explicit morphology instruction improved students’ morphological awareness and vocabulary knowledge. There was a statistically significant difference between the experimental group scores in the pre- and post-tests, and a statistically significant difference exists between the experimental group scores and the scores of the control group on the New Vocabulary level test and the Morphological Awareness test. As the results indicated that the intervention (morphology instruction) had a significant impact on the participants performance, this study suggests that teaching morphology plays a significant role in increasing students’ morphological awareness and vocabulary knowledge

Perceptions of developers in green home project in Kuala Lumpur

Green home is rapidly becoming a strong momentum in the construction industry after recognizing many negative environmental issues & problems and potential social and economical benefits around the world. However, developers still using conventional way to construct the housing. This gives huge impact to the environment and also human health. Meanwhile, there are actually some barriers hinder developers to adopt this in their projects. This study examines the perceptions of the developers in Kuala Lumpur on the future of the green housing sector for the next 20 years, their commitment about green housing and propose solution for improvement green home project in Kuala Lumpur. The methodology used in this study is questionnaire and it is targeted 200 developer’s firms in Kuala Lumpur.The introduction of Green home rating system, improvement of awareness and knowledge among the stakeholders, support from the government and local industry and the effect of competitive advantage would support brighter future. Meanwhile, the status quo in rules and regulation, lack of public interest and demand, organization disinterest, local authority enforcement and project cost escalation would hinder a faster progress. Finally, this study could really help the awareness on environment and in improve the green home project among developers

Improving Software Model Inference by Combining State Merging and Markov Models

Labelled-transition systems (LTS) are widely used by developers and testers to model software systems in terms of their sequential behaviour. They provide an overview of the behaviour of the system and their reaction to different inputs. LTS models are the foundation for various automated verification techniques such as model-checking and model-based testing. These techniques require up-to-date models to be meaningful. Unfortunately, software models are rare in practice. Due to the effort and time required to build these models manually, a software engineer would want to infer them automatically from traces (sequences of events or function calls). Many techniques have focused on inferring LTS models from given traces of system execution, where these traces are produced by running a system on a series of tests. State-merging is the foundation of some of the most successful LTS inference techniques to construct LTS models. Passive inference approaches such as k-tail and Evidence-Driven State Merging (EDSM) can infer LTS models from these traces. Moreover, the best-performing methods of inferring LTS models rely on the availability of negatives, i.e. traces that are not permitted from specific states and such information is not usually available. The long-standing challenge for such inference approaches is constructing models well from very few traces and without negatives. Active inference techniques such as Query-driven State Merging (QSM) can learn LTSs from traces by asking queries as tests to a system being learnt. It may lead to infer inaccurate LTSs since the performance of QSM relies on the availability of traces. The challenge for such inference approaches is inferring LTSs well from very few traces and with fewer queries asked. In this thesis, investigations of the existing techniques are presented to the challenge of inferring LTS models from few positive traces. These techniques fail to find correct LTS models in cases of insufficient training data. This thesis focuses on finding better solutions to this problem by using evidence obtained from the Markov models to bias the EDSM learner towards merging states that are more likely to correspond to the same state in a model. Markov models are used to capture the dependencies between event sequences in the collected traces. Those dependencies rely on whether elements of event permitted or prohibited to follow short sequences appear in the traces. This thesis proposed EDSM-Markov a passive inference technique that aimed to improve the existing ones in the absence of negative traces and to prevent the over-generalization problem. In this thesis, improvements obtained by the proposed learners are demonstrated by a series of experiments using randomly-generated labelled-transition systems and case studies. The results obtained from the conducted experiments showed that EDSM-Markov can infer better LTSs compared to other techniques. This thesis also proposes modifications to the QSM learner to improve the accuracy of the inferred LTSs. This results in a new learner, which is named ModifiedQSM. This includes considering more tests to the system being inferred in order to avoid the over-generalization problem. It includes investigations of using Markov models to reduce the number of queries consumed by the ModifiedQSM learner. Hence, this thesis introduces a new LTS inference technique, which is called MarkovQSM. Moreover, enhancements of LTSs inferred by ModifiedQSM and MarkovQSM learners are demonstrated by a series of experiments. The results from the experiments demonstrate that ModifiedQSM can infer better LTSs compared to other techniques. Moreover, MarkovQSM has proven to significantly reduce the number of membership queries consumed compared to ModifiedQSM with a very small loss of accuracy

The Level of Awareness of the Coronavirus Pandemic and Its Prevention for Students With Learning Difficulties in the State of Kuwait

This study aimed to identify the level of awareness of the emerging coronavirus pandemic and prevent infection among (18) students with learning difficulties in the State of Kuwait. The study applied a scale prepared by researchers in the period of 12th till the 17th of May 2020. The alpha was between (0.74- 0.76), indicating that the scale has a high degree of validity and reliability. The results showed high levels of awareness and prevention among the study sample, as their awareness rate reached 83%, while the rate of infection prevention reached 88%, while as a whole, the model showed the rate of 87% on the scale

Distributed reflection denial of service attack: A critical review

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks

Exact Solutions for Nonlinear Partial Differential Equations: A Fusion of Classical Methods and Innovative Approaches

This article demonstrates how variation of parameters can be successfully implemented in combination with other classical techniques, such as the method of characteristics, to derive novel classes of solutions to nonlinear partial differential equations (NLPDES) by considering specific initial conditions. This innovative approach offers the advantage of generating exact solutions. The results underscore this method's potential to address additional NLPDE classes.Comment: 15 pages, 24 figure

Potentiating stem cell-derived hepatocyte function

Ph. D. ThesisThe rat pancreatic AR42J-B13 (B-13) cell line differentiates into non-replicative hepatocyte-like (B-13/H) cells in response to glucocorticoid. As this response is dependent on the induction of serine/threonine protein kinase 1 (SGK1), this suggests a general essential role for SGK1 in hepatocyte maturation. To test this hypothesis, B-13 cells infected with AdV-SGK1F in the absence of glucocorticoid resulted in the expression of Flag-tagged SGK1F protein; increases in β-catenin phosphorylation; decreases in Tcf/Lef transcriptional activity; expression of hepatocyte marker genes and the conversion of B-13 cells to a cell phenotype near-similar to B-13/H cells. Given this demonstration of functionality, the effects of expressing adenoviral-encoded flag-tagged human SGK1F (AdV-SGK1F) in induced pluripotent human stem cells (iPSCs) was investigated. iPSCs directed to differentiate to hepatocyte-like cells using the standard protocol for chemical inhibitors and mixtures of growth factors, were infected with AdV-SGK1F at different stages of their differentiation to hepatocytes, either at an early point during differentiation to endoderm; during endoderm differentiation to anterior definitive endoderm and hepatoblasts and once converted to hepatocyte-like cells. SGK1F expression did not affect differentiation to endoderm, most possibly due to low levels of expression. However, expression of SGK1F in both iPSCs-derived endoderm and hepatocyte-like cells both resulted in the promotion of cells to an hepatoblast phenotype. These data demonstrate that the effect of expressing SGK1F in human iPSC-derived cells contrasts with its effects when expressed in B-13 cells. Given that SGK1 expression promotes an hepatoblast phenotype rather than maturation of human iPSC towards a mature hepatocyte phenotype, these data suggest a temporary role for Sgk1 in promoting a hepatoblast state in B-13 trans-differentiation to B-13/H cells.Taif University and the Ministry of Education of the Kingdom of Saudi Arabia

The integration of user-generated content and networked journalism within professional journalistic frameworks at the Arabic news channels

This research examines the integration of networked journalism and user-generated content into Arab broadcasting news. It aims to investigate how traditional media organisations collaborate and interact with their audiences. The investigation takes the form of a case-study with two Pan-Arab news channels, Al-Arabiya and Al Hadath. It studies journalists' perceptions and attitudes toward user-generated content and collaboration with the audience to form networked journalism practices. The research uses a methodological triangulation comprising in-depth interviews, observation and content analysis of two selected programmes that embrace audience collaboration: the I see and Your interaction TV programmes. Furthermore, the research explores the impact of the adoption of user generated content on gatekeeping and decision making in broadcasting newsrooms. It also highlights the emergence of social media units in newsrooms and the use of editorial analytics metrics to track audience interactions and to monitor trends and UGC relevant to journalists and news programmes. The results of this study identified five factors related to the adoption of UGC and networked journalism: personal perceptions, editorial direction, crisis reporting, the pressure of competition and the impact of deadlines and workload. The research findings indicate that user-generated content and the gathering of content from social media have become integrated into newsroom daily routines. They identify the methods used to verify and integrate such content into news bulletins and programmes, as well as to collaborate with activists and citizen journalists on the ground. Moreover, this study offers an insight into the growing challenges of verification and fact checking in the digital age. The research results emphasise the importance of clear practical guidelines and codes of conduct for journalists and newsrooms in the digital age. The findings also highlight potential risks that could be overlooked by news organisations, such as the impact of user generated content on the mental health of journalists, and the safety of citizen collaborators, especially those operating in conflict zones

Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and new membership function to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a new membership function to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images