4101 research outputs found
Sort by
Mastering teaching: thriving as an early career teacher
No full texthis book builds on the experiences of school leaders, early career teachers and their mentors and responds to the challenges that new teachers face as they move beyond initial teacher training. Practiced educators provide research-informed guidance in each chapter to scaffold new teachers' workplace learning when the learning curve is steepest. Support for new teachers is vitally important in enhancing teaching quality, promoting teacher wellbeing, and reducing staff burnout rates.Each chapter, co-authored by school-based and university-based teacher educators, contains rich illustrative examples and vignettes from lead practitioners in UK primary and secondary schools. The book is relevant across curriculum areas and phases of education so that all new teachers can ease their transition into teaching, build their confidence and lay foundations for their career-long professional growth
Predicting and curing depression using long short term memory and global vector
No full textIn today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms which can share data with a variety of IoT devices and smart systems to better understand and analyze the user’s condition. We compared the accuracy and loss of the training dataset and the validation dataset of the two models namely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer had an accuracy of 83%, while Bi-LSTM with a global vector layer had an accuracy of 86% with a precision of 86.4%, and an F1 score of 0.861. In addition to providing basic therapies for the treatment of identified cases, our model also helps prevent the deterioration of associated conditions, making our method a real-world solution
Application of Benefit Incidence Analysis (BIA) as a Tool to Evaluate Climate Action Spending on Climate Smart Agriculture Initiatives: An Experimental Study of the Usage of BIA on Agriculture-Related Spending in Zambia
No full textUsing survey data obtained through semi-structured questionnaires which were administered using a multi-stage random sampling process, this study sought to undertake an experimental application of the Benefit Incidence Analysis (BIA) socio-economic evaluation tool on 117 rural farming households in Chongwe District of Zambia. The sampled households were receiving agricultural support through the Government-financed Farmer Input Support Programme (FISP). Specifically, this experimental study of the usage of BIA on agriculture-related spending in Zambia was aimed at proving possible replication of the usage of BIA for evaluating socio-economic and distributional impacts of financing for Climate Smart Agricultural (CSA) practices. Results prove that BIA assessment variables such as income/expenditure quintiles, education status, gender and age are also applicable to and essential in evaluating CSA initiatives. Despite this study proving applicability to CSA assessments, undertaking a BIA is highly technical and data intensive. Such an undertaking would heavily rely on the timely availability of complementary economic and financial data and an intermediate to advanced level of technical capacity in order to administer the analysis
Learner interrupted: understanding the stories behind the codes – a qualitative analysis of HE distance-learner withdrawals
No full textSuccessful retention of students through understanding their motivations and behaviours is a challenge to universities worldwide. Whilst the impact of withdrawals is an issue for all institutions, attrition for distance-learning providers is particularly problematic owing to higher non-completion rates, less physical visibility, and because distance-learners tend to have more complex lives. This paper examines students’ personal stories explaining their decisions to withdraw from university study. It considers 641 written discourses initiated by students as part of their requests to withdraw, covering the challenges they face, and the complex combinations of factors that contribute to their decisions to give up. This qualitative approach was adopted as a necessary complement to the quantitative rush of metrics information that universities now provide on withdrawal figures. Three themes selected are: deferral/withdrawal, time available, and preparedness for study. The paper concludes that complementary qualitative insights both add clarity and detail to institutional understanding and reduces oversimplification of complex decision-making from unidimensional quantitative approaches
Human emotions recognition, analysis and transformation by bioenergy field in smart grid using image processing
No full textThe passage of electric signals throughout the human body produces an electromagnetic field, known as the human-biofield, carries information about a person's psychological health. The human biofield can be rehabilitated by using healing techniques like sound therapy, and many others in smart grid. However, psychiatrists, and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results as a novel. The present research is based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization-interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on prediction and overall satisfaction. The participants were strongly satisfied with the prediction level (~81%) and future recommendation level (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In future, experimentation using different therapies, and integrating more advanced techniques are anticipated to open a new gateways in healthcare
Exploring service quality and its impact on customer satisfaction and customer loyalty within Islamic banks in Palestine - from customers perspective
No full textIslamic banks in Palestine are operating in a very competitive environment, considering the existence of other banks that are offering similar products there. Noticeably, the intense competition in the banking industry has resulted from the customers’ expectations of receiving highest services and products. However, there is a lack of evidence on whether compliance with Islamic principles influences the Customer’s satisfaction and loyalty.This study aimed at testing the relationship between CARTER model dimensions, customer satisfaction, and customer loyalty in Palestine’s Islamic banks. CARTER defined as an assessment of quality of service at the Islamic banking business was developed by Othman and Owen (2001) based on the dimensions CARTER, namely: Compliance, Assurance, Reliability, Tangible, Empathy, and Responsiveness.Another significant aim was to analyse the moderating influence of competitive advantage and Islamic compliance. Objectives included examining whether there was a significant correlation between CARTER model dimensions, competitive advantage, customer satisfaction, and customer loyalty among Islamic Banks’ customers in Palestine.Moreover, there was an objective of determining whether competitive advantage and Islamic compliance significantly could moderate the relationship between customer satisfaction and customer loyalty among Islamic Banks’ customers in Palestine.In order to achieve the aims and objectives of the study, SERVQUAL model from Parasuraman et al. (1985) and CARTER from Othman and Owen (2001b) have been adapted.Given that, the research started with an exhaustive review of related literature, while defining the found gap. Finding gaps in literature investigated led to develop the hypotheses of the study. Conducting the methodological study to answer the research questions, meet the aims and objectives of the research and testing the hypotheses was the followed step.The empirical study adopted the quantitative approach, whereas 93 participants have provided with questionnaires to conduct the aimed survey. The questionnaire adopted a 33-items questionnaire from Othman and Owen (2001b). In the process of analysing the answers of the participants, the researcher used SmartPLS (v3.3.3). Accordingly, the findings revealed that only Islamic Compliance and Assurance from the CARTER model and Competitive Advantage were significant. The study concluded that Competitive Advantage moderated the relationship between Customer Satisfaction and Customer Loyalty but not Islamic Compliance. This study has provided a new perspective on customer satisfaction for Islamic banks in Palestine. The study contributed to literature, in particular the researches on Islamic banks, while recommended the enhancement of customer satisfaction understanding. Though there were some limitations in the study, such as small sample size collected of 93 participants and the targeted sample to be 100 participants. One can admit the small sample because of the limits of this study to be in Palestine. However, the study actually, provided a better knowledge to the academicians and the literature of banking concerning customer satisfaction
Capability and robustness of novel hybridized artificial intelligence technique for sediment yield modeling in Godavari River, India
No full textSuspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications
On the utility of thermogravimetric analysis for exploring the kinetics of thermal degradation of lignins
No full textThe kinetics of pyrolysis of organosolv (TcA) and hydroxypropyl-modified (TcC) lignins have been investigated using thermogravimetric analysis (TGA). Three isothermal models (single first order, Guggenheim and Avrami-Erofeev) and one non-isothermal model (Kissinger) were used to analyse the mass-loss data. Sensible derived kinetic parameters, i.e., activation energy and pre-exponential factor, were obtained only for the initial stages of pyrolysis where the kinetics were approximately first order. Models that analysed TGA data beyond the initial stage gave inconsistent results, indicating the complexity of subsequent decomposition steps occurring at higher temperatures and/or longer times. The kinetics of the initial stage are important for designing routes to lignin's valorisation into useful products, such as carbon fibres, activated carbons, polymer additives, etc. TcC had a higher activation energy (41.5 kJ/mol) for initial decomposition than TcA (39 kJ/mol), consistent with its greater thermal stability observed previously during conversion of lignin-based fibres into carbon fibres
Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals
No full textThe proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies
Are changes in vital signs, mobility, and mental status while in hospital measures of the quality of care?
No full textIntroduction Little is known of the changes in patients' health condition while in hospital in low-resource settings. The aim of this exploratory study is to examine dependency of patients on hospital admission and discharge in a low-resource sub-Saharan hospital.Methods We carried out a retrospective observational study of changes in the health condition, as reflected by their mental status, mobility and vital signs, of 5,888 consecutive patients between hospital admission and discharge.Results Mental status, mobility and vital signs were normal in 25% of patients on hospital admission and 30% of patients at discharge. Although very few patients with normal mental status, mobility and vital signs on admission died in hospital, the condition of 40% of them deteriorated.Conclusion No comparative data on changes in health condition between hospital admission and discharge have been published. Our proposed health condition categories identify changes that may matter most to patients and should be considered as a standard metric of hospital care