Search results for: innovative learning

Authors: Tahira Zaman

Abstract:

The paper intends to discover the utility of hybrid teaching methods to aid undergraduate students to improve their English academic writing skills. A total of 45 undergraduate students were selected randomly from three classes from varying language abilities, with the research design of monitoring and rubrics evaluation as a means of measure. Language skills of the students were upgraded with the help of experiential learning methods using reflective writing technique, guided method in which students were merely directed to correct form of writing techniques along with self-guided method for the students to produce a library research-based article measured through a standardized rubrics provided. The progress of the students was monitored and checked through rubrics and self-evaluation and concluded that a change was observed in the students’ writing abilities.

Keywords: self evaluation, hybrid, self evaluation, reflective writing

Procedia PDF Downloads 155

Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

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Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

Procedia PDF Downloads 168

Authors: Rachel Koshy, Nazrila Hairizan Bt. Nasir, Samsiah Bt. Awang, Kamaliah Bt. Mohamad Noh

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Malaysia collaborated as a ‘trailblazer’ country with PHCPI (Primary Health Care Performance Initiative) to populate the Primary Health Care (PHC) Vital Signs Profile (VSP) for the country. The PHC VSP provides an innovative snapshot of the primary health care system's performance. Four domains were assessed: system financing, system capacity, system performance, and system equity, and completed in 2019. There were two phases using a mixed method study design. The first phase involved a quantitative study, utilising existing secondary data from national and international sources. In the case of unavailability of data for any indicators, comparable alternative indicators were used. The second phase was a mixed quantitative-qualitative approach to measure the functional capacity based on governance and leadership, population health needs, inputs, population health management, and facility organisation and management. PHC spending constituted 35% of overall health spending in Malaysia, with a per capita PHC spending of $152. The capacity domain was strong in the three subdomains of governance and leadership, information system, and funds management. The two subdomains of drugs & supplies and facility organisation & management had low scores, but the lowest score was in empanelment of the population under the population health management. The PHC system performed with an access index of 98%, quality index of 84%, and service coverage of 62%. In the equity domain, there was little fluctuation in the coverage of reproductive, maternal, newborn, and child health services by mother’s level of education and under-five child mortality between urban and rural areas. The public sector was stronger in the capacity domain as compared to the private sector. This is due to the different financing, organisational structures, and service delivery mechanism. The VSP has identified areas for improvement in the effort to provide high-quality PHC for the population. The gaps in PHC can be addressed through the system approach and the positioning of public and private primary health care delivery systems.

Keywords: primary health care, health system, system domains, vital signs profile

Procedia PDF Downloads 120

Authors: Mohammad Reza Ostad Ali Naghi Kashani, Esfanji Elia

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Nowadays, the amounts of companies which tend to have an Enterprise Resource Planning (ERP) application are increasing particularly in developing countries like Iran. ERP projects are expensive, time consuming, and complex, in addition the failure rate is high among these projects. It is important to know whether these projects could meet their goals or not. Furthermore, the area which should be improved should be identified. In this paper we made a framework to evaluate ERP projects success implementation. First, based on literature review we made a framework based on BSC model, financial, customer, processes, learning and knowledge, because of the importance of change management it was added to model. Then an organization was divided in three layers. We choose corporate, managerial, and operational levels. Then to find criteria to assess each aspect, we use Delphi method in two rounds. And for the second round we made a questionnaire and did some statistical tasks on them. Based on the statistical results some of them are accepted and others are rejected.

Keywords: ERP, BSC, ERP project evaluation, IT projects

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Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: D. Hassell, D. De Focatiis

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This paper presents work on the application of wiki based coursework for a fourth-year engineering module delivered as part of both a MEng and MSc programme in Chemical Engineering. The module was taught with an equivalent structure simultaneously on two separate campuses, one in the United Kingdom (UK) and one in Malaysia, and the subsequent results were compared. Student feedback was sought via questionnaires, with 45 respondents from the UK and 49 from Malaysia. Results include discussion on; perceived difficulty; student enjoyment and experiences; differences between MEng and MSc students; differences between cohorts on different campuses. The response of students to the use of wiki-based coursework was found to vary based on their experiences and background, with UK students being generally more positive on its application than those in Malaysia.

Keywords: engineering education, student differences, student learning, web based coursework

Procedia PDF Downloads 288

Authors: Zheng Baichuan, Li Shenghui, Li Bingqian, Zhang Ning, Chen Kai

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Entity alignment (EA) tasks in knowledge graphs often play a pivotal role in the integration of knowledge graphs, where structural differences often exist between the source and target graphs, such as the presence or absence of attribute information and the types of attribute information (text, timestamps, images, etc.). However, most current research efforts are focused on improving alignment accuracy, often along with an increased reliance on specific structures -a dependency that inevitably diminishes their practical value and causes difficulties when facing knowledge graph alignment tasks with varying structures. Therefore, we propose a universal knowledge graph alignment approach that only utilizes the common basic structures shared by knowledge graphs. We have demonstrated through experiments that our method achieves state-of-the-art performance in fair comparisons.

Keywords: knowledge graph, entity alignment, transformer, deep learning

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 110

Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

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Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

Procedia PDF Downloads 219

Authors: Stephen Kirkup

Abstract:

This paper discusses the implementation of the boundary element method (BEM) on an Excel spreadsheet and how it can be used in teaching vector calculus and simulation. There are two separate spreadheets, within which Laplace equation is solved by the BEM in two dimensions (LIBEM2) and axisymmetric three dimensions (LBEMA). The main algorithms are implemented in the associated programming language within Excel, Visual Basic for Applications (VBA). The BEM only requires a boundary mesh and hence it is a relatively accessible method. The BEM in the open spreadsheet environment is demonstrated as being useful as an aid to teaching and learning. The application of the BEM implemented on a spreadsheet for educational purposes in introductory vector calculus and simulation is explored. The development of assignment work is discussed, and sample results from student work are given. The spreadsheets were found to be useful tools in developing the students’ understanding of vector calculus and in simulating heat conduction.

Keywords: boundary element method, Laplace’s equation, vector calculus, simulation, education

Procedia PDF Downloads 153

Authors: Derradji A., Ben El Mamoun M., Zakaria E., Charadi I. Anrur

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The issue of dilapidated buildings represents a significant opportunity for constructive and beneficial interventions in Morocco. Faced with challenges associated with aging constructions and rapid urbanization, the country is committed to developing innovative strategies aimed at revitalizing urban areas and enhancing the sustainability of infrastructure, thereby ensuring citizens' safety. Through targeted investments in the renovation and modernization of existing buildings, Morocco aims to stimulate job creation, boost the local economy, and improve the quality of life for residents. Additionally, the integration of sustainable construction standards and the strengthening of regulations will promote resilient and environmentally friendly urban development. In this proactive perspective, LABOTEST has been commissioned by the National Agency for Urban Renewal (ANRUR) to conduct an in-depth study. This study focuses on the technical expertise of 1800 buildings identified as dilapidated in the prefectures of Rabat and Skhirat-Témara following an initial clearance operation. The primary objective of this initiative is to conduct a comprehensive diagnosis of these buildings and define the necessary interventions to eliminate potential risks while ensuring appropriate treatment. The article presents the adopted intervention methodology, taking into account the social dimensions involved, as well as the results of the technical expertise. These results include the classification of buildings according to their degree of urgency and recommendations for appropriate conservatory measures. Additionally, different pathologies are identified and accompanied by specific treatment proposals for each type of building. Since this study, the adopted approach has been generalized to the entire territory of Morocco. LABOTEST has been solicited by other cities such as Casablanca, Chefchaouen, Ouazzane, Azilal, Bejaad, and Demnate. This extension of the initiative demonstrates Morocco's commitment to addressing urban challenges in a proactive and inclusive manner. These efforts also illustrate the endeavors undertaken to transform urban challenges into opportunities for sustainable development and socio-economic progress for the entire population.

Keywords: building, dilapidated, rehabilitation, Morocco

Procedia PDF Downloads 54

Authors: Carol C. Opara, Olukemi E. Adetuyi-Olu-Francis

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The teaching of French language, like every other language, has its numerous challenges. A multilingual community, however, is a linguistic environment housing diverse languages, each with its peculiarity, both pros, and cones. A foreign language will have to strive hard for survival in an environment where various indigenous languages, as well as an established official language, exist. This study examined the challenges and prospects of the teaching of French as a foreign language in a multilingual community. A 22-item questionnaire was used to elicit information from 40 Nigerian Secondary school teachers of French. One of the findings of this study showed that the teachers of the French language are not motivated. Also, the linguistic environment is not favourable for the teaching and learning of French language in Nigeria. One of the recommendations was that training and re-training of teachers of French should be of utmost importance to the Nigerian Federal Ministry of Education.

Keywords: challenges, french as foreign language, multilingual community, teaching

Procedia PDF Downloads 197

Authors: Diane Ghanem, Oscar Covarrubias, Michael Raad, Dawn LaPorte, Babar Shafiq

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Introduction: Standardized exams have long been considered a cornerstone in measuring cognitive competency and academic achievement. Their fixed nature and predetermined scoring methods offer a consistent yardstick for gauging intellectual acumen across diverse demographics. Consequently, the performance of artificial intelligence (AI) in this context presents a rich, yet unexplored terrain for quantifying AI's understanding of complex cognitive tasks and simulating human-like problem-solving skills. Publicly available AI language models such as ChatGPT have demonstrated utility in text generation and even problem-solving when provided with clear instructions. Amidst this transformative shift, the aim of this study is to assess ChatGPT’s performance on the orthopaedic surgery in-training examination (OITE). Methods: All 213 OITE 2021 web-based questions were retrieved from the AAOS-ResStudy website. Two independent reviewers copied and pasted the questions and response options into ChatGPT Plus (version 4.0) and recorded the generated answers. All media-containing questions were flagged and carefully examined. Twelve OITE media-containing questions that relied purely on images (clinical pictures, radiographs, MRIs, CT scans) and could not be rationalized from the clinical presentation were excluded. Cohen’s Kappa coefficient was used to examine the agreement of ChatGPT-generated responses between reviewers. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT Plus. The 2021 norm table was used to compare ChatGPT Plus’ performance on the OITE to national orthopaedic surgery residents in that same year. Results: A total of 201 were evaluated by ChatGPT Plus. Excellent agreement was observed between raters for the 201 ChatGPT-generated responses, with a Cohen’s Kappa coefficient of 0.947. 45.8% (92/201) were media-containing questions. ChatGPT had an average overall score of 61.2% (123/201). Its score was 64.2% (70/109) on non-media questions. When compared to the performance of all national orthopaedic surgery residents in 2021, ChatGPT Plus performed at the level of an average PGY3. Discussion: ChatGPT Plus is able to pass the OITE with a satisfactory overall score of 61.2%, ranking at the level of third-year orthopaedic surgery residents. More importantly, it provided logical reasoning and justifications that may help residents grasp evidence-based information and improve their understanding of OITE cases and general orthopaedic principles. With further improvements, AI language models, such as ChatGPT, may become valuable interactive learning tools in resident education, although further studies are still needed to examine their efficacy and impact on long-term learning and OITE/ABOS performance.

Keywords: artificial intelligence, ChatGPT, orthopaedic in-training examination, OITE, orthopedic surgery, standardized testing

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Authors: O. Fiquet, P. Lemarignier

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Additive manufacturing may find numerous applications in the nuclear industry, for the same reason as for other industries, to enlarge design possibilities and performances and develop fabrication methods as a flexible route for future innovation. Additive Manufacturing applications in the design of structural metallic components for reactors are already developed at a high Technology Readiness Level (TRL). In the case of a Pressured Water Reactor using uranium oxide fuel pellets, which are ceramics, the transposition of already optimized Additive Manufacturing (AM) processes to UO₂ remains a challenge, and the progress remains slow because, to our best knowledge, only a few laboratories have the capability of developing processes applicable to UO₂. After the Fukushima accident, numerous research fields emerged with the study of ATF (Accident tolerant Fuel) fuel concepts, which aimed to improve fuel behaviour. One item concerns the increase of the pellet thermal performance by, for example, the addition of high thermal conductivity material into fissile UO₂. This additive phase may be metallic, and the end product will constitute a CERMET composite. Innovative designs of an internal metallic framework are proposed based on predictive calculations. However, because the well-known reference pellet manufacturing methods impose many limitations, manufacturing such a composite remains an arduous task. Therefore, the AM process appears as a means of broadening the design possibilities of CERMET manufacturing. If the external form remains a standard cylindrical fuel pellet, the internal metallic design remains to be optimized based on process capabilities. This project also considers the limitation to a maximum of 10% volume of metal, which is a constraint neutron physics considerations impose. The AM technique chosen for this development is robocasting because of its simplicity and low-cost equipment. It remains, however, a challenge to adapt a ceramic 3D printing process for the fabrication of UO₂ fuel. The investigation starts with surrogate material, and the optimization of slurry feedstock is based on alumina. The paper will present the first printing of Al2O3-Mo CERMET and the expected transition from ceramic-based alumina to UO₂ CERMET.

Keywords: nuclear, fuel, CERMET, robocasting

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Authors: Inna V. Krotova

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The study pays special attention to the definition of the psychological potential of a specialist-defectologist, which determines his desire to increase the level of his or her professional competence. The group included participants of the educational environment – an additional professional program 'Technologies of psychological and pedagogical assistance for children with complex developmental disabilities' implemented by the department of defectology and clinical psychology of the KFU jointly with the Support Fund for the Deafblind people 'Co-Unity'. The purpose of our study was to identify the psychological aspects of the readiness of the specialist-defectologist to his or her professional development. The study assessed the indicators of psychological preparedness, and its four components were taken into account: motivational, cognitive, emotional and volitional. We used valid and standardized tests during the study. As a result of the factor analysis of data received (from Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization, Rotation converged in 12 iterations), there were identified three factors with maximum factor load from 24 indices, and their correlation coefficients with other indicators were taken into account at the level of reliability p ≤ 0.001 and p ≤ 0.01. Thus the system making factor was determined – it’s a 'motivation to achieve success'; it formed a correlation galaxy with two other factors: 'general internality' and 'internality in the field of achievements', as well as with such psychological indicators as 'internality in the field of family relations', 'internality in the field of interpersonal relations 'and 'low self-control-high self-control' (the names of the scales used is the same as names in the analysis methods. In conclusion of the article, we present some proposals to take into account the psychological model of readiness of specialists-defectologists for their professional development, to stimulate the growth of their professional competence. The study has practical value for all providers of special education and organizations that have their own specialists-defectologists, teachers-defectologists, teachers for correctional and ergotherapeutic activities, specialists working in the field of correctional-pedagogical activity (speech therapists) to people with special needs who need true professional support.

Keywords: psychological readiness, defectologist, professional development, psychological factors, special education, professional competence, innovative educational environment

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Authors: Opeyemi Awau Adepoju

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The task of nation-building for Nigeria, like other developing nations, has continued to be riddled with audacious challenges that kept threatening to consume the state itself. Among the destabilizing factors that are sometimes mutually reinforcing are poverty and insecurity. Nigeria has been bedeviled with poverty since the onset of the 1980s when the country metamorphosed from an agricultural to an oil-based economy coupled with unbridled political corruption and wasteful management of resources by successive governments. The crippling poverty started manifesting in the scourge of criminalities and a general state of insecurity. Poverty gradually becomes the breeder of insecurity and threats to human life in Nigeria. Interestingly, successive governments tended to recognize the destructive tendencies of poverty and took several interventionist initiatives towards abating or slowing down the spate of poverty so as to reverse the trend of insecurity, but none of those initiatives can be adjudged good or enduring legacies. The emergence of the Buhari administration in 2015 provided a new opportunity to tackle poverty and, in turn, insecurity that had permeated every aspect of national life before that year’s presidential elections. Expectedly, the government took ambitious steps through its innovative ideas of intervention through its National Social Intervention Programmes (NSIP). Therefore, this paper is an assessment of the Buhari administration’s initiatives in poverty eradication in Nigeria as one of its strategies to fight insecurity, and the paper adopted a qualitative approach. The theoretical arguments put up by this paper are with respect to the connection between poverty and insecurity sourced from the theory of Relative Deprivation. The paper found that the Buhari administration has done better than any government since 1999 in inventing a social intervention program and that the poverty of the people has been addressed to a notable extent. However, the problem of politicization of intervention programs has continued to be the practice under the administration, and if this is not abated, the post-Buhari era may as well be like the eras before it. The paper recommends legislation that can make poverty ameliorating programs permanent, at least for some years to come, so as to avoid the usual policy summersault at every instance of political transition, which has limited the sustainability of public policies and indeed hindered nation-building efforts in Nigeria.

Keywords: insecurity, poverty alleviation, public policies, social intervention

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Authors: Achim Kampker, Heiner H. Heimes, Mathias Ordung, Christoph Lienemann, Ansgar Hollah, Nemanja Sarovic

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Electric cars with their fast innovation cycles and their disruptive character offer a high degree of freedom regarding innovative design for remanufacturing. Remanufacturing increases not only the resource but also the economic efficiency by a prolonged product life time. The reduced power train wear of electric cars combined with high manufacturing costs for batteries allow new business models and even second life applications. Modular and intermountable designed battery packs enable the replacement of defective or outdated battery cells, allow additional cost savings and a prolongation of life time. This paper discusses opportunities for future remanufacturing value chains of electric cars and their battery components and how to address their potentials with elaborate designs. Based on a brief overview of implemented remanufacturing structures in different industries, opportunities of transferability are evaluated. In addition to an analysis of current and upcoming challenges, promising perspectives for a sustainable electric car circular economy enabled by design for remanufacturing are deduced. Two mathematical models describe the feasibility of pursuing a circular economy of lithium ion batteries and evaluate remanufacturing in terms of sustainability and economic efficiency. Taking into consideration not only labor and material cost but also capital costs for equipment and factory facilities to support the remanufacturing process, cost benefit analysis prognosticate that a remanufacturing battery can be produced more cost-efficiently. The ecological benefits were calculated on a broad database from different research projects which focus on the recycling, the second use and the assembly of lithium ion batteries. The results of this calculations show a significant improvement by remanufacturing in all relevant factors especially in the consumption of resources and greenhouse warming potential. Exemplarily suitable design guidelines for future remanufacturing lithium ion batteries, which consider modularity, interfaces and disassembly, are used to illustrate the findings. For one guideline, potential cost improvements were calculated and upcoming challenges are pointed out.

Keywords: circular economy, electric mobility, lithium ion batteries, remanufacturing

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Authors: Matthew J. Zagumny

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The current study reports an examination of the QUEST-25 (Q-Assessment of Undergraduate Epistemology and Scientific Thinking) online version for assessing the dispositional attitudes toward scientific thinking and intellectual curiosity among undergraduate students. The QUEST-25 consists of scientific thinking (SIQ-25) and intellectual curiosity (ICIQ-25), which were correlated in hypothesized directions with the Religious Commitment Inventory, Curiosity and Exploration Inventory, Belief in Science scale, and measures of academic self-efficacy. Additionally, concurrent validity was established by the resulting significant differences between those identifying the centrality of religious belief in their lives and those who do not self-identify as being guided daily by religious beliefs. This study demonstrates the utility of the QUEST-25 for research, evaluation, and theory development.

Keywords: guided-inquiry learning, intellectual curiosity, psychometric assessment, scientific thinking

Procedia PDF Downloads 254

Authors: Jonas Gradauskas, Oleksandr Masalskyi, Ihor Zharchenko

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The advancement in improving the efficiency of conventional solar cells toward the Shockley-Queisser limit seems to be slowing down or reaching a point of saturation. The challenges hindering the reduction of this efficiency gap can be categorized into extrinsic and intrinsic losses, with the former being theoretically avoidable. Among the five intrinsic losses, two — the below-Eg loss (resulting from non-absorption of photons with energy below the semiconductor bandgap) and thermalization loss —contribute to approximately 55% of the overall lost fraction of solar radiation at energy bandgap values corresponding to silicon and gallium arsenide. Efforts to minimize the disparity between theoretically predicted and experimentally achieved efficiencies in solar cells necessitate the integration of innovative physical concepts. Hot carriers (HC) present a contemporary approach to addressing this challenge. The significance of hot carriers in photovoltaics is not fully understood. Although their excessive energy is thought to indirectly impact a cell's performance through thermalization loss — where the excess energy heats the lattice, leading to efficiency loss — evidence suggests the presence of hot carriers in solar cells. Despite their exceptionally brief lifespan, tangible benefits arise from their existence. The study highlights direct experimental evidence of hot carrier effect induced by both below- and above-bandgap radiation in a singlejunction solar cell. Photocurrent flowing across silicon and GaAs p-n junctions is analyzed. The photoresponse consists, on the whole, of three components caused by electron-hole pair generation, hot carriers, and lattice heating. The last two components counteract the conventional electron-hole generation-caused current required for successful solar cell operation. Also, a model of the temperature coefficient of the voltage change of the current–voltage characteristic is used to obtain the hot carrier temperature. The distribution of cold and hot carriers is analyzed with regard to the potential barrier height of the p-n junction. These discoveries contribute to a better understanding of hot carrier phenomena in photovoltaic devices and are likely to prompt a reevaluation of intrinsic losses in solar cells.

Keywords: solar cell, hot carriers, intrinsic losses, efficiency, photocurrent

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Authors: Maryam Kalkatechi

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Architecture students spent most of their qualitative time in studios during their years of study. The studio table’s importance as furniture in the studio is that it elevates the quality of the projects and positively influences the student’s productivity. This paper first describes the aspects considered in designing comprehensive studio table and later details on each aspect. Comprehensive studio tables are meant to transform the studio space to an efficient yet immense place of learning, collaboration, and participation. One aspect of these tables is that the surface transforms to a place of accommodation for design conversations, the other aspect of these tables is the efficient interactive platform of the tools. The discussion factors of the comprehensive studio include; the comprehensive studio setting of workspaces, the arrangement of the comprehensive studio tables, the collaboration aspects in the studio, the studio display and lightings shaped by the tables and lighting of the studio.

Keywords: studio tables, student performance, productivity, hologram, 3D printer

Procedia PDF Downloads 182

Authors: Jabulani Sibanda, Clemence Chikiwa

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This paper traces diverse language practices representative of equally diverse conceptions of language. To be dynamic with languaging practices, one needs to appreciate nuanced languaging practices, their challenges, prospects, and opportunities. The paper presents what we envision as three major conceptions of language that give impetus to diverse language practices. It examines theoretical models of the bilingual mental lexicon and how they inform language practices. The paper explores classroom languaging practices that have been promulgated and experimented with. The paper advocates the deployment of multisensory semiotic systems to complement linguistic classroom communication and the acknowledgement of learners’ linguistic and semiotic resources as valid in the learning enterprise. It recommends the enactment of specific clauses on language in education policies and curriculum documents that empower classroom interactants to exercise discretion in languaging practices.

Keywords: languaging, monolingual, multilingual, semiotic and linguistic repertoire

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Authors: Cristina Munoz-Shuguli, Francisco Rodriguez, Julio Bruna, M. Jose Galotto, Abel Guarda

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The microbial contamination in fruits due to the presence of fungal is the most important cause of their deterioration and loss. The development of active food packaging materials with antifungal properties has been proposed as an innovative strategy in order to prevent this problem. In this way, natural compounds as the essential oils or their derivatives, also called volatile compounds (VC), can be incorporated in the food packaging materials to control the fungal growth during fruit packaging. However, if the VC is incorporated directly in the packaging material, it is released very fast due to VC high volatility. For this reason, the formation of inclusion complexes through the encapsulation of VC into beta-cyclodextrin (β-CD) and their incorporation in package materials is an alternative to maintain an antifungal atmosphere around the packaged fruits for longer times. In this context, the aim of this work was to develop inclusion complexes based in β-CD and VC (β-CD:VC) for further application in the antifungal food packaging materials development. β-CD:VC inclusion complexes were obtained with two different molar ratios 2:1 and 1:1, through co-precipitation method. The entrapment efficiency of β-CD:VC as well the release of antifungal compound from inclusion complexes exposed to different relative humidity (25, 50, and 97 %) to headspace were determined by gaseous chromatography (GC). Also, thermal and antimicrobial properties of β-CD:VC were determined through thermogravimetric analysis (TGA) and antifungal assays against Botrytis cinerea, respectively. GC results showed that β-CD:VC 2:1 had a higher entrapment efficiency than β-CD:VC 1:1, with values of 75.5 ± 3.71 % and 59.6 ± 1.51 %, respectively. It was probably because during the synthesis of β-CD:VC 1:1, there was less molecular space to the movement of VC molecules. Furthermore, the release of VC from β-CD:VC was directly related with the relative humidity. High amount of VC was released when the inclusion complexes were exposed to high humidity, possibly due to the interactions between the water molecules and the β-CD hydrophilic wall. On the other hand, a better thermal stability of VC in inclusion complexes allowed to verify its effective encapsulation into β-CD. Finally, antimicrobial assays showed that the inclusion complexes had a high antifungal activity at very low concentrations. Therefore, the results obtained in this work allow suggesting the β-CD:VC inclusion complexes as potential candidates to the development of fruit antifungal packaging materials, which activity is relative humidity dependent.

Keywords: Botrytis cinerea, fruit packaging, headspace release, volatile compounds

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Authors: Chinaza Uleanya, Martin Duma, Bongani Gamede

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The research investigated pedagogic malpractices by lecturers in sub-Sahara African universities. The population of the study consisted of undergraduates and lecturers in selected universities in Nigeria and South Africa. Mixed method approach was adopted for data collection. The sample population of the study was 480 undergraduate students and 16 lecturers. Questionnaires with 4 point Likert-scale were administered to 480 respondents while interviews were conducted with 6 lecturers. In addition, the teaching strategies of 10 lecturers were observed. Data analyses indicated that poor work environment demotivates lecturers and makes them involved in pedagogic malpractice which is one of the causes of learning challenges faced by undergraduates. The finding of the study also shows that pedagogic malpractice contributes to the high rate of dropout in sub-Sahara African universities. Based on the results, it was recommended that qualified lecturers be employed and given conducive environments to work.

Keywords: malpractice, pedagogy, pedagogic malpractice, correlates

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Authors: Érick Aragão Ribeiro, George André Pereira Thé, José Marques Soares

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Graphical Human Interface, also known as supervision software, are increasingly present in industrial processes supported by Supervisory Control and Data Acquisition (SCADA) systems and so it is evident the need for qualified developers. In order to make engineering students able to produce high quality supervision software, method for the development must be created. In this paper we propose model, based on the international standards ISO/IEC 25010 and ISO/IEC 25040, for the development of graphical human interface. When compared with to other methods through experiments, the model here presented leads to improved quality indexes, therefore help guiding the decisions of programmers. Results show the efficiency of the models and the contribution to student learning. Students assessed the training they have received and considered it satisfactory.

Keywords: software development models, software quality, supervision software, fuzzy logic

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Authors: Ana Hol

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Digital systems are in the cognitive wave of the eTransformations and are now extensively aimed at meeting the individuals’ demands, both those of customers requiring services and those of service providers. It is also apparent that successful future systems will not just simply open doors to the traditional owners/users to offer and receive services such as Uber for example does today, but will in the future require more customized and cognitively enabled infrastructures that will be responsive to the system user’s needs. To be able to identify what is required for such systems, this research reviews the historical and the current effects of the eTransformation process by studying: 1. eTransitions of company websites and mobile applications, 2. Emergence of new sheared economy business models as Uber and, 3. New requirements for demand driven, cognitive systems capable of learning and just in time decision making. Based on the analysis, this study proposes a Cognitive eTransformation Framework capable of guiding implementations of new responsive and user aware systems.

Keywords: system implementations, AI supported systems, cognitive systems, eTransformation

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Authors: Collins Molua

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This paper investigated the effect of Teaching Strategies on Academic Achievement in Secondary Physics Education as a quality assurance process for the teaching and learning of the subject. Teaching strategies investigated were the interactive, independent and dependent strategies. Three null hypotheses were tested at p< 0.05 using one instrument, physics achievement test(PAT).The data were analyzed using analysis of covariance (ANCOVA).Results showed that teaching strategies have significant effect on students achievement; the joint effect of the teaching strategies was also significant on students achievement in Physics. The interactive teaching strategies was recommended for teaching the subject and the students should be exposed to practical, computer literacy to stimulate interest and curiosity to enhance quality.

Keywords: quality, assurance, secondary education, strategies, physics

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Authors: Ivana Prochazkova

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Cognitive linguistics offers biblical scholarship a specific methodological tool for analysis and interpretation of metaphorical expressions. Its methodology makes it possible to study processes involved in constructing the meaning of individual metaphorical expressions and whole conceptual metaphors; to analyze their function in the text; to follow the semantic development of concepts and conceptual domains, and to trace semantic changes and their motivation. The legal language in the Hebrew canon is extremely specific and formalized. Especially in the preambles to the collections of laws in the Pentateuch, more general considerations of the motif of keeping and breaking the law are encountered. This is also true in the psalms and wisdom literature. Legal theory and the philosophy of law deal with these motifs today. Metaphors play an important role in texts that reflect on more general issues. The purpose of this conference contribution is to write all over the central metaphorical concept, conceptual metaphor ךרד תורה (TORAH/LAW IS A JOURNEY), its function in the Torah and principal trends of the further development in the Prophets and the Writings. The conceptual metaphor תורה ךרד (TORAH/LAW IS A JOURNEY) constitutes a coherent system in conjunction with other metaphors that include e.g., conceptual metaphors נחה תורה (TORAH/LAW LEADS); its variant רעה תורה (TORAH IS A SHEPHERD/GUIDE); מקור תורה (TORAH/LAW IS A FOUNTAIN/A SOURCE OF LIFE). Some conceptual metaphors are well known, and their using are conventional (עשׁר תורה TORAH/LAW IS RICHES, שׂשׂון תורה TORAH/LAW IS DELIGHT, דבשׁ תורה TORAH/LAW IS HONEY, שׁמשׁ תורה TORAH/LAW IS SUN ). But some conceptual metaphors are by its occurrence innovative and unique (e.g., שׁריון תורה TORAH /LAW IS BODY ARMOR, כובע תורה TORAH /LAW IS A HELMET, בגד תורה TORAH/LAW IS A GARMENT, etc.). There will be given examples. Conceptual metaphors will be described by means of some 'metaphorical vehicles,' which are Hebrew expressions in the source domain that are repeatedly used in metaphorical conceptualizations of the target domain(s). Conceptual metaphors will be further described by means of 'generic narrative structures,' which are the particular aspects of a conceptual metaphor that emerge during the metaphorical structuring of concepts. They are the units of the metaphorical vehicles – the Hebrew expressions in the source domain – that structure concepts in much the same way that the conceptual metaphor in the target domain does. And finally, they will be described by means of the network of correspondences that exist between metaphorical vehicles – or generic metaphorical structures – and the Hebrew expressions in the target domain.

Keywords: cognitive theology, conceptual metaphor in the Old Testament, conceptual metaphors of the Torah, conceptual domain of law, righteousness, and justice

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

Procedia PDF Downloads 137