189 research outputs found

    Exploring Ways of Vocabulary Instructional Practices in ESL Classrooms in Pakistan

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    Word Power offers a key role when it comes to effective communication and comprehensive reading. Language teaching-learning is a complex phenomenon especially in learning L2 vocabulary. A learner is provided with a complete package of textual and non-textual language-based content to develop all four skills of L2 of a learner. A language curriculum is based on text aided by co-curricular activities like role plays, presentations, speeches/debates or declamation, essay writing competitions, etc. to improve the language proficiency of a learner. In such activities, vocabulary has a secondary role to play, whereas, it is hard to find such activities where the prime focus is to be given on the development of language learner’s vocabulary size. Language learners view vocabulary enhancement as one of the most challenging domains while learning a language Çelik & Toptaş, (2010). The present study reflects a brief overview of the most frequently practiced pedagogical practices of vocabulary items. This study is qualitative, where secondary level school learners are the target population. The researcher has chosen semi-structured interviews with students (n=20). The study finds that the vocabulary instructional process in Pakistani ESL learners lacks the proper induction of vocabulary teaching strategies. It doesn’t target the utilization of vocabulary items in a meaningful context. In addition, vocabulary items are heavily taught explicitly and implicit instructional methods are not taken much into consideration. Thus, the researcher recommends the integration of best teaching practices into the traditional language pedagogical process. It may involve CALL methods and other incidental and intentional approaches that can enable learners to enhance vocabulary size, retain, and use vocabulary items in a real-life context. Keywords: Vocabulary teaching/learning, ESL learners, CALL, traditional practices, pedagogical practices, vocabulary development, language learners DOI: 10.7176/RHSS/10-24-09 Publication date: December 31st 2020

    Pregnancy outcomes of patients with ultrasound-indicated and history-indicated mcdonald cervical cerclage

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    Objective: To assess pregnancy outcomes of patients with ultrasound-indicated and history-indicated McDonald cervical cerclage. Materials and methods: This was a cross-sectional study of pregnant women who had cervical cerclage performed at Aga Khan University Hospital, Karimabad campus. We obtained Institutional Review Board approval and reviewed the medical records of patients. A purposive continuous sampling technique was used. A total of 88 patients were included. There were no exclusion criteria. Outcome data were collected from the medical record of patients from January 2010 to December 2016. Results: Analysis reported a statistically significant lower gravidity and parity in the scan-indicated group as compared with other groups (p = 0.000 and p = 0.001, respectively). Previous history of cervical cerclage, history of mid-trimester miscarriage, and preterm labor were significantly associated with indication for cerclage (p = 0.001, 0.046, and 0.001, respectively). Cervical length was also significantly associated with the indication for cerclage (p p = 0.003 and p = 0.04 and 0.004, respectively). Conclusion: Our study showed that patients with a history suggestive of cervical incompetence or short cervix on ultrasound should be offered cerclage to prevent preterm birth and to improve neonatal outcomes. Ultrasound-indicated cerclage after the first trimester indicates that universal cervical-length screening by transvaginal ultrasound in the mid-trimester can pick silent cases, and inserting cerclage can prolong pregnancy to term. Clinical significance: Screening cervical length will help clinicians to reduce preterm birth rate, especially in resource-limited underdeveloped countries

    A systematic review of economic evaluations of local authority commissioned preventative public health interventions in overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation in the United Kingdom

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    Background Since 2013, local authorities in England have been responsible for commissioning preventative public health interventions. The aim of this systematic review was to support commissioning by collating published data on economic evaluations and modelling of local authority commissioned public health preventative interventions in the UK. Methods Following the PRISMA protocol, we searched for economic evaluations of preventative intervention studies in four different areas: overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation. The systematic review identified studies between January 1994 and February 2015, using five databases. We synthesized the studies to identify the key methods and examined results of the economic evaluations. Results The majority of the evaluations related to cost-effectiveness, rather than cost-benefit analyses or cost-utility analyses. These analyses found preventative interventions to be cost effective, though the context of the interventions differed between the studies. Conclusions Preventative public health interventions in general are cost-effective. There is a need for further studies to support justification of continued and/or increased funding for public health interventions. There is much variation between the types of economically evaluated preventative interventions in our review. Broader studies incorporating different contexts may help support funding for local authority-sponsored public health initiatives

    Developing a quality-of-life measure for autistic children and young people in schools

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    Aim: To develop a quality-of-life measure to be used by both school staff and autistic children and young people (CYP) in schools, in which public and community involvement (PCIE) is at the heart of the research.  Method: A prevalence questionnaire survey will be conducted with 20 primary and secondary schools across Kent, Surrey, and Sussex (KSS). 10 semi-structured interviews will be conducted with autistic CYP. This will inform development of a quality-of-life measure for autistic CYP. Results: Work so far has included PCIE in the early stages of the project, in which young people, school staff and collaborators have contributed to the study set up and designing study materials. Conclusion: With PCIE included in the planning of the project, the research team is confident that this will be useful further along in the project with dissemination and validating and implementing a quality-of-life measure for autistic CYP in schools

    A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection

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    The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.publishedVersio
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