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The use of storytelling as a pedagogic tool in the English language classroom
No full textThis chapter explores the practice of storytelling as a pedagogic tool in English language teaching and learning, offering an overview of its different uses. It defines the use of storytelling in English language teaching with English for Speakers of Other languages (ESOL) learners. ESOL learners are often young adult or adult asylum seekers or refugees, who bring stories to the classroom in the form of life experiences from their own cultures, based on their beliefs, customs and language identity. ESOL learners are often young adult or adult asylum seekers or refugees, who bring stories to the classroom in the form of life experiences from their own cultures, based on their beliefs, customs and language identity. The National Storytelling Network defines storytelling as an ancient art form and a valuable form of human expression, describing it as the interactive art of using words and actions to reveal the elements and images of a story while encouraging the listener’s imagination
Evolution and evaluation: sarcasm analysis for Twitter data using sentiment analysis
No full textThis paper addresses the evolution and evaluation of sarcasm in textual form. The growing popularity of social networking sites is well known, and every individual generates a whole new set of opinions in form of blogs, micro-posts, etc. Sentiment analysis is one of the fastest evolving aspects of artificial intelligence categorizing opinions under positive, negative, or neutral sentiments. One such part of sentiment analysis is sarcasm. Sarcasm is becoming a common phenomenon in networking sites where expressing murky feelings wrapped by positive words for conveying contempt is highly used, making it difficult to understand the actual meaning of a statement. When reading customer reviews or complaints, it might be helpful to understand the consumers' genuine intentions in order to enhance the efficiency of customer support or after-sales services. In this paper, different classifiers- Decision Tree, Naïve Bayes, K-Nearest, and Support Vector machine are used to predict a statement under the category sarcastic or non-sarcastic using tweeter data, the following proposed methodology is used for the experimental evaluation concluding that the given classifiers SVM gains the highest accuracy of 93%, whereas Naïve Bayes and Decision Tree are performing well with an accuracy of 83% and 86% respectively along with the lowest of 51% attained by KNN
Thermoplastic composites: modelling melting, decomposition and combustion of matrix polymers
No full textIn thermoplastic composites, the polymeric matrix upon exposure to heat may melt, decompose and deform prior to burning, as opposed to the char-forming matrices of thermoset composites, which retain their shape until reaching a temperature at which decomposition and ignition occur.In this work, a theoretical and numerical heat transfer model to simulate temperature variations during the melting, decomposition and early stages of burning of commonly used thermoplastic matrices is proposed. The scenario includes exposing polymeric slabs to one-sided radiant heat in a cone calorimeter with heat fluxes ranging from 15 to 35 kW/m2. A one-dimensional finite difference method based on the Stefan approach involving phase-changing and moving boundary conditions was developed by considering convective and radiative heat transfer at the exposed side of the polymer samples. The polymers chosen to experimentally validate the simulated results included polypropylene (PP), polyester (PET), and polyamide 6 (PA6). The predicted results match well with the experimental result
Time-series data modelling using advanced machine learning and AutoML – experimental work
No full textA prominent area of data analytics is "time-series modeling" where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods.The datasets this empirical research utilized were secondary quantitative of the real prices of the currently most used cryptocurrencies. We found that AutoML for time-series is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting time-series data
Experiential Learning in Entrepreneurship Education for Sustainable Agricultural Development: A Bibliometric Analysis
No full textEntrepreneurs are viewed as agents of change. Educating university students in all fields to become entrepreneurs must be a priority for Higher Education Institutions. This study aims to review literature on experiential learning in entrepreneurship education as an agent of change and analyse the conceptual and social structure of research carried out between 1991 and 2020. A review of existing literature combined with a bibliometric analysis of entrepreneurship education were utilised. The findings reveal that entrepreneurship in the agricultural sector is still an emerging phenomenon under consolidation especially in comparison with other disciplines such as management and commerce. In general, while entrepreneurship is taught as a discipline there is no clear application of theory to practice. Findings show that research gained momentum from 2003 and it has been on the rise. Most of the research has been conducted in the field of Education and Training with USA leading in country productions. A collaboration index of 2.5 was attained and the United Kingdom has the highest cross-national collaborations. African countries need to holistically embrace entrepreneurship education in their curricula as this is paramount to a sustainable green industrial development. Collaborations with the USA, UK and other countries like Malaysia would be beneficial in the transfer of knowledge and skills. This would enhance technological diffusion and consequently increase productivity within the agricultural sector in Africa
Virtual Reality-Based Interventions for Treating Depression in the Context of COVID-19 Pandemic: Inducing the Proficit in Positive Emotions as a Key Concept of Recovery and a Path Back to Normality
No full textBACKGROUND: During the COVID-19 pandemic as much as 40% of the global population reported deterioration in depressive mood, whereas 26% experienced increased need for emotional support. At the same time, the availability of on-site psychiatric care declined drastically because of the COVID-19 preventive social restriction measures. To address this shortfall, telepsychiatry assumes a greater role in mental health care services. Among various on-line treatment modalities, immersive virtual reality (VR) environments provide an important resource for adjusting the emotional state in people living with depression. Therefore, we reviewed the literature on VR-based interventions for depression treatment during the COVID-19 pandemic. SUBJECTS AND METHODSWe searched the PubMed and Scopus databases, as well as the Internet, for full-length articles published during the period of 2020-2022 citing a set of following key words: "virtual reality", "depression", "COVID-19", as well as their terminological synonyms and word combinations. The inclusion criteria were: 1) the primary or secondary study objectives included the treatment of depressive states or symptoms; 2) the immersive VR intervention used a head-mounted display (HMD); 3) the article presented clinical study results and/or case reports 4) the study was urged by or took place during the COVID-19-associated lockdown period. RESULTSOverall, 904 records were retrieved using the search strategy. Remarkably, only three studies and one case report satisfied all the inclusion criteria elaborated for the review. These studies included 155 participants: representatives of healthy population (n=40), a case report of a patient with major depressive disorder (n=1), patients with cognitive impairments (n=25), and COVID-19 patients who had survived from ICU treatment (n=89). The described interventions used immersive VR scenarios, in combination with other treatment techniques, and targeted depression. The most robust effect, which the VR-based approach had demonstrated, was an immediate post-intervention improvement in mood and the reduction of depressive symptoms in healthy population. However, studies showed no significant findings in relation to both short-term effectiveness in treatment of depression and primary prevention of depressive symptoms. Also, safety issues were identified, such as: three participants developed mild adverse events (e.g., headache, "giddiness", and VR misuse behavior), and three cases of discomfort related to wearing a VR device were registered. CONCLUSIONSThere has been a lack of appropriately designed clinical trials of the VR-based interventions for depression since the onset of the COVID-19 pandemic. Moreover, all these studies had substantial limitations due to the imprecise study design, small sample size, and minor safety issues, that did not allow us making meaningful judgments and conclude regarding the efficacy of VR in the treatment of depression, taking into account those investigations we have retrieved upon the inclusion criteria of our particularistic review design. This may call for randomized, prospective studies of the short-term and long-lasting effect of VR modalities in managing negative affectivity (sadness, anxiety, anhedonia, self-guilt, ignorance) and inducing positive affectivity (feeling of happiness, joy, motivation, self-confidence, viability) in patients suffering from clinical depression
Predicting daily PM2.5 using Classic Azure ML Studio
No full textWith the development of various industries, air pollution is also increasing day by day, and hence the air is getting harmful for living beings whether it be humans or animals. Some of the major factors in determining air pollution are Aerosol Optical Thickness/Depth (AOT/AOD), Ml, and M2. This paper proposes determining the most important factors in determining the PM2.5 level using feature selection methods and developing a model to predict the PM2.5 level using neural network regression on the Azure ML studio platform
How Pooh Sticks ... and Comes Unstuck Derrida in the Hundred Acre Wood Edward Bear after 100 Years
No full textIn this chapter, David Rudd examines Milne’s use of language in the Winnie-the-Pooh stories. It applies Derrida’s concept of “différance,” in particular, to explore the language slippages that characterize the verbal play in the stories. The chapter places particular emphasis on the difficulty of naming in the stories
Characterization of aerosolized particles in effluents from carbon fibre composites incorporating nanomaterials during simultaneous fire and impact
No full textThis work investigates the aerosols emitted from carbon fibre-reinforced epoxy composites (CFC) incorporating nanomaterials (nanoclays and nanotubes), subjected to simultaneous fire and impact, representing an aeroplane or automotive crash. Simultaneous fire and impact tests were performed using a previously described bespoke testing methodology with the capability to collect particles released from the front/back faces of the impacted composites plus the effluents. In this work the methodology has been further developed by connecting the Dekati Low Pressure Impactor (DLPI) and Mini Particle Sampler (MPS) sampling system in the extraction chimney. The aerosols emitted have been characterized using various devices devoted to the analysis of aerosols. The influence of the nanoadditives in the matrix on the number concentration and the size distribution of airborne particles produced, was studied with a cascade impactor in the 5 nm–10 µm range. The morphology of the separated soot fractions was examined by SEM. The measurement of aerodynamic size of particles that can deposit in human respiratory tract indicate that 75% of the soot and particles released from CFC could deposit in the lungs reaching the bronchi region at a minimum. There was however, a minimal difference between the number particle concentrations or particle-size mass distribution of particles from CFC and CFC containing nanoadditives. Moreover, no fibres were found in the effluents