Search results for: collaborative learning approach

Authors: Bayan Bevrani, Robert L. Burdett, Prasad K. D. V. Yarlagadda

Abstract:

This article focuses upon multi-modal transportation systems (MMTS) and the issues surrounding the determination of system capacity. For that purpose a multi-objective framework is advocated that integrates all the different modes and many different competing capacity objectives. This framework is analytical in nature and facilitates a variety of capacity querying and capacity expansion planning.

Keywords: analytical model, capacity analysis, capacity query, multi-modal transportation system (MMTS)

Procedia PDF Downloads 342

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 106

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 211

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 147

Authors: Mahmoud Y. M. Ahmed, Ahmed Konsowa, Mostafa Sami, Ayman Mosallam

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Fukushima nuclear accident (Japan 2011) has drawn attention to the issue of gas leakage from hazardous facilities through building boundaries. The rapidly increasing investments in nuclear stations have made the ability to predict, and prevent, gas leakage a rather crucial issue both environmentally and economically. Leakage monitoring for underground facilities is rather complicated due to the combination of Reinforced Concrete Shear Wall (RCSW) and soil. In the framework of a recent research conducted by the authors, the gas insulation capabilities of RCSW-soil combination have been investigated via a lab-scale experimental work. Despite their accuracy, experimental investigations are expensive, time-consuming, hazardous, and lack for flexibility. Numerically simulating the gas leakage as a fluid flow problem based on Computational Fluid Dynamics (CFD) modeling approach can provide a potential alternative. This novel implementation of CFD approach is the topic of the present paper. The paper discusses the aspects of modeling the gas flow through porous media that resemble the RCSW both isolated and combined with the normal soil. A commercial CFD package is utilized in simulating this fluid flow problem. A fixed RCSW layer thickness is proposed, air is taken as the leaking gas, whereas the soil layer is represented as clean sand with variable properties. The variable sand properties include sand layer thickness, fine fraction ratio, and moisture content. The CFD simulation results almost demonstrate what has been found experimentally. A soil layer attached next to a cracked reinforced concrete section plays a significant role in reducing the gas leakage from that cracked section. This role is found to be strongly dependent on the soil specifications.

Keywords: RCSW, gas leakage, Pressure Decay Method, hazardous underground facilities, CFD

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Authors: Madhav Khadilkar

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Construction of a large standby reservoir was required to provide secure water supply. The new reservoir was required to be constructed at the same location of an abandoned old open pond due to space constraints. Some investigations were carried out earlier to improvise and re-commission the existing pond. But due to a lack of quantified risk of settlement from voids in the underlying limestone, the shallow foundations were not found feasible. Since the reservoir was resting on hard strata for about three-quarter of plan area and one quarter was resting on soil underlying with limestone and considerably low subgrade modulus. Further investigations were carried out to ascertain the locations and extent of voids within the limestone. It was concluded that the risk due to lime dissolution was acceptably low, and the site was found geotechnically feasible. The hazard posed by limestone dissolution was addressed through the integrated structural and geotechnical analysis and design approach. Finite Element Analysis was carried out to quantify the stresses and differential settlement due to various probable loads and soil-structure interaction. Walls behaving as cantilever under operational loads were found undergoing in-plane bending and tensile forces due to soil-structure interaction. Sensitivity analysis for varying soil subgrade modulus was carried out to check the variation in the response of the structure and magnitude of stresses developed. The base slab was additionally checked for the loss of soil contact due to lime pocket formations at random locations. The expansion and contraction joints were planned to receive minimal additional forces due to differential settlement. The reservoir was designed to sustain the actions corresponding to allowable deformation limits per code, and geotechnical measures were proposed to achieve the soil parameters set in structural analysis.

Keywords: differential settlement, limestone dissolution, reservoir, soil structure interaction

Procedia PDF Downloads 140

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 192

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: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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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 247

Authors: Khodr Fakih

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The Ombudsman is a procedural mechanism that provides a different approach of dispute resolution. The ombudsman primarily deals with specific grievances from the public against governmental injustice and misconduct. The ombudsman theory is considered an important instrument to any democratic government. This is true since it improves the transparency of the governmental activities in a world in which executive power are rising. Many countries have adopted the concept of Ombudsman but under different terminologies. This paper will provide the different types of Ombudsman and the common activities/processes of fulfilling their mandates.

Keywords: administration, citizens, government, mediator, ombudsman, presidential mediator

Procedia PDF Downloads 318

Authors: M. Louise Humbert, Karen E. Chad, Natalie E. Houser, Marta E. Erlandson

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Physical literacy is the motivation, confidence, physical competence, knowledge, and understanding to value and takes responsibility for engagement in physical activities for life. In recent years, physical literacy has emerged as a determinant of health, promoting a positive lifelong physical activity trajectory. Physical literacy’s holistic approach and emphasis on the intrinsic valuation of movement provide an encouraging avenue for intervention among children to develop competent and confident movers. Although there is research on physical literacy interventions, no evidence exists on the outcomes of multi-sectoral interventions involving a combination of home, school, and community contexts. Since children interact with and in a wide range of contexts (home, school, community) daily, interventions designed to address a combination of these contexts are critical to the development of physical literacy. Working with school administrators and teachers, sports and recreation leaders, and community members, our team of university and community researchers conducted and evaluated one of the first multi-contextual and multi-sectoral physical literacy interventions in Canada. Schools played a critical role in this multi-sector intervention, and in this project, teachers and administrators focused their actions on developing physical literacy in students 10 to 14 years of age through the instruction of physical literacy-focused physical education lessons. Little is known about the experiences of educators when they work alongside an array of community representatives to develop physical literacy in school-aged children. Given the uniqueness of this intervention, we sought to answer the question, ‘What were the experiences of school-based educators involved in a multi-sectoral partnership focused on creating a physical literacy enriched community intervention?’ A thematic analysis approach was used to analyze data collected from interviews with educators and administrators, informal conversations, documents, and observations at workshops and meetings. Results indicated that schools and educators played the largest role in this multi-sector intervention. Educators initially reported a limited understanding of physical literacy and expressed a need for resources linked to the physical education curriculum. Some anxiety was expressed by the teachers as their students were measured, and educators noted they wanted to increase their understanding and become more involved in the assessment of physical literacy. Teachers reported that the intervention’s focus on physical literacy positively impacted the scheduling and their instruction of physical education. Administrators shared their desire for school and division-level actions targeting physical literacy development like the current focus on numeracy and literacy, treaty education, and safe schools. As this was one of the first multi-contextual and multi-sectoral physical literacy interventions, it was important to document creation and delivery experiences to encourage future growth in the area and develop suggested best practices.

Keywords: physical literacy, multi sector intervention, physical education, teachers

Procedia PDF Downloads 90

Authors: Maryam Kalkatechi

Abstract:

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 175

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

Procedia PDF Downloads 54

Authors: F. S. Al-Anzi, M. Sarfraz, A. Elmi, A. R. Khan

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Researchers are responding to the environmental challenges of Kuwait in localized, innovative, effective and economic ways. One of the vital and significant examples of the natural challenges is lack or water and desertification. In this research, the project team focuses on redesigning a prototype, using Value Engineering Methodology, which would provide similar functionalities to the well-known technology of Waterboxx kits while reducing the capital and operational costs and simplifying the process of manufacturing and usability by regular farmers. The design employs used tires and recycled plastic sheets as raw materials. Hence, this approach is going to help not just fighting desertification but also helping in getting rid of ever growing huge tire dumpsters in Kuwait, as well as helping in avoiding hazards of tire fires yielding in a safer and friendlier environment. Several alternatives for implementing the prototype have been considered. The best alternative in terms of value has been selected after thorough Function Analysis System Technique (FAST) exercise has been developed. A prototype has been fabricated and tested in a controlled simulated lab environment that is being followed by real environment field testing. Water and soil analysis conducted on the site of the experiment to cross compare between the composition of the soil before and after the experiment to insure that the prototype being tested is actually going to be environment safe. Experimentation shows that the design was equally as effective as, and may exceed, the original design with significant savings in cost. An estimated total cost reduction using the VE approach of 43.84% over the original design. This cost reduction does not consider the intangible costs of environmental issue of waste recycling which many further intensify the total savings of using the alternative VE design. This case study shows that Value Engineering Methodology can be an important tool in innovating new designs for reducing costs.

Keywords: desertification, functional analysis, scrap tires, value engineering, waste recycling, water irrigation rationing

Procedia PDF Downloads 191

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

Procedia PDF Downloads 361

Authors: C. Bolakis, I. S. Karanasiou, D. Grbovic, G. Karunasiri, N. Uzunoglu

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A new approach for efficient detection of THz radiation in biomedical imaging applications is proposed. A double-layered absorber consisting of a 32 nm thick aluminum (Al) metallic layer, located on a glass medium (SiO2) of 1 mm thickness, was fabricated and used to design a fine-tuned absorber through a theoretical and finite element modeling process. The results indicate that the proposed low-cost, double-layered absorber can be tuned based on the metal layer sheet resistance and the thickness of various glass media taking advantage of the diversity of the absorption of the metal films in the desired THz domain (6 to 10 THz). It was found that the composite absorber could absorb up to 86% (a percentage exceeding the 50%, previously shown to be the highest achievable when using single thin metal layer) and reflect less than 1% of the incident THz power. This approach will enable monitoring of the transmission coefficient (THz transmission ‘’fingerprint’’) of the biosample with high accuracy, while also making the proposed double-layered absorber a good candidate for a microbolometer pixel’s active element. Based on the aforementioned promising results, a more sophisticated and effective double-layered absorber is under development. The glass medium has been substituted by diluted poly-si and the results were twofold: An absorption factor of 96% was reached and high TCR properties acquired. In addition, a generalization of these results and properties over the active frequency spectrum was achieved. Specifically, through the development of a theoretical equation having as input any arbitrary frequency in the IR spectrum (0.3 to 405.4 THz) and as output the appropriate thickness of the poly-si medium, the double-layered absorber retains the ability to absorb the 96% and reflects less than 1% of the incident power. As a result, through that post-optimization process and the spread spectrum frequency adjustment, the microbolometer detector efficiency could be further improved.

Keywords: bio-imaging, fine-tuned absorber, fingerprint, microbolometer

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Authors: Giles Balbinotti, Lucas Balbinotti, Paula Hembecker

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The management of the productive process project activities, for the conception of future work and for the financial health of the companies, is an important condition in an organizational model that corroborates the management of the human aspects and their variabilities existing in the work. It is important to seek, at all levels of the organization, understanding and consequent cultural change, and so that factors associated with human aspects are considered and prioritized in the projects. In this scenario, the central question of research for this study is placed from the context of the work, in which the managers and project coordinators are inserted, as follows: How is the top management convinced, in the design stages, to take The ‘Ergonomics’ as strategy for the performance and sustainability of the business? In this perspective, this research has as general objective to analyze how the application of the management of the human aspects in a real project of productive process in the automotive industry, including the activity of the manager and coordinator of the project beyond the strategies of convincing to act in the ergonomics of design. For this, the socio-technical and ergonomic approach is adopted, given its anthropocentric premise in the sense of acting on the social system simultaneously to the technical system, besides the support of the Modapts system that measures the non-value-added times and the correlation with the Critical positions. The methodological approach adopted in this study is based on a review of the literature and the analysis of the activity of the project coordinators of an industry, including the management of human aspects in the context of work variability and the strategies applied in project activities. It was observed in the study that the loss of performance of the serial production lines reaches the important number of the order of 30%, which can make the operation with not value-added, and this loss has as one of the causes, the ergonomic problems present in the professional activity.

Keywords: human aspects in production process project, ergonomics in design, sociotechnical project management, sociotechnical, ergonomic principles, sustainability

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Authors: O. R. Razanakoto, L. Temple

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Living in a least developed country, the Malagasy society has a weak capacity to internalize progress, including health concerns. Since the arrival in the fifteenth century of Arabic script, called Sorabe, that was mainly dedicated to the aristocracy, until the colonial era beginning at the end of the nineteenth century and that has popularized the current usual script of the occidental civilization, the upcoming manuscripts that deal with apparent scientific or at least academic issue have been slowly established. So that, the Malagasy communities’ way of life is not well documented yet to allow a precise understanding of the major concerns, reason, and purpose of the existence of the farmers that compose them. A question arises, according to literature, how does Malagasy community that is dominated by agrarian society conceive the conservation of its wellbeing? This study aims to emphasize the scope and the limits of the « One Health » concept or of the Health Integrated Approach (HIA) that evolves at global scale, with regard to the specific context of local Malagasy smallholder farms. It is expected to identify how this society represents linked risks and the mechanisms between human health, animal health, plant health, and ecosystem health within the last 100 years. To do so, the framework to conduct systematic review for agricultural research has been deployed to access available literature. This task has been coupled with the reading of articles that are not indexed by online scientific search engine but that mention part of a history of agriculture and of farmers in Madagascar. This literature review has informed the interactions between human illnesses and those affecting animals and plants (breeded or wild) with any unexpected event (ecological or economic) that has modified the equilibrium of the ecosystem, or that has disturbed the livelihoods of agrarian communities. Besides, drivers that may either accentuate or attenuate the devasting effects of these illnesses and changes were revealed. The study has established that the reasons of human worries are not only physiological. Among the factors that regulate global health, food system and contemporary medicine have helped to the improvement of life expectancy from 55 to 63 years in Madagascar during the last 50 years. However, threats to global health are still occurring. New human or animal illnesses and livestock / plant pathology or enemies may also appear, whereas ancient illnesses that are supposed to have disappeared may be back. This study has highlighted how much important are the risks associated to the impact of unmanaged externalities that weaken community’s life. Many risks, and also solutions, come from abroad and have long term effects even though those happen as punctual event. Thus, a constructivist strategy is suggested to the « One Health » global concept throughout the record of local facts. This approach should facilitate the exploration of methodological pathways and the identification of relevant indicators for research related to HIA.

Keywords: agrarian system, health integrated approach, history, madagascar, resilience, risk

Procedia PDF Downloads 98

Authors: Ana Hol

Abstract:

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

Procedia PDF Downloads 223

Authors: Andrey V. Timofeev

Abstract:

This paper presents development results of the method of seismoacoustic activity monitoring based on usage vibrosensitive properties of optical fibers. Analysis of Rayleigh backscattering radiation parameters changes, which take place due to microscopic seismoacoustic impacts on the optical fiber, allows to determine seismoacoustic emission sources positions and to identify their types. Results of using this approach are successful for complex monitoring of railways.

Keywords: C-OTDR systems, monitoring of railways, Rayleigh backscattering, eismoacoustic activity

Procedia PDF Downloads 376

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: Sylvester S. Horvey, Jones Mensah

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The growing complexities and dynamics in the business environment have led to a new approach to risk management, known as enterprise risk management (ERM). ERM is a system and an approach to managing the risks of an organization in an integrated manner to achieve the corporate goals and strategic objectives. Regardless of the diversities in the business environment, ERM has become an essential factor in managing individual and business risks because ERM is believed to enhance shareholder value and firm growth. Despite the growing number of literature on ERM, the question about what factors drives ERM remains limited. This study provides a comprehensive literature review of the main factors that contribute to ERM implementation. Google Scholar was the leading search engine used to identify empirical literature, and the review spanned between 2000 and 2020. Articles published in Scimago journal ranking and Scopus were examined. Thirteen firm characteristics and sixteen articles were considered for the empirical review. Most empirical studies agreed that firm size, institutional ownership, industry type, auditor type, industrial diversification, earnings volatility, stock price volatility, and internal auditor had a positive relationship with ERM adoption, whereas firm size, institutional ownership, auditor type, and type of industry were mostly seen be statistically significant. Other factors such as financial leverage, profitability, asset opacity, international diversification, and firm complexity revealed an inconclusive result. The growing literature on ERM is not without limitations; hence, this study suggests that further research should examine ERM determinants within a new geographical context while considering a new and robust way of measuring ERM rather than relying on a simple proxy (dummy) for ERM measurement. Other firm characteristics such as organizational culture and context, corporate scandals and losses, and governance could be considered determinants of ERM adoption.

Keywords: enterprise risk management, determinants, ERM adoption, literature review

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Authors: Iuliana-Erika Köpeczi

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‘Language is no longer regarded as peripheral to our grasp of the world we live in, but as central to it. Words are not mere vocal labels or communicational adjuncts superimposed upon an already given order of things. They are collective products of social interaction, essential instruments through which human beings constitute and articulate their world’, said Roy Harris. If we were to accept the above-mentioned premise, then we surely must accept that discourse, generally, - and political discourse, specifically -, bears a crucial importance to one’s perception of reality. The way in which political rhetoric constructs reality changes the relationship between the voter and his/her view of the world, which, in turn, influences greatly the future trends of political participation. In this context, our inquiry focuses on the role of populist discourses in the post 9/11 political rhetoric, and how this led to the formation, construction and reconstruction of identity within the ‘us’ vs. ‘them’ dichotomy. It is our hypothesis that anti-intellectualistic elements played a significant role in the manner in which identity construction had been carried out on a discursive level. By adopting a comparative approach, we intend to identify the similarities and differences between the use of such anti-intellectualist elements in the United States of America on one hand – within the discourse of Rick Santorum, – and France on the other – with Marine le Pen’s discourse. Our methodological approach uses close textual analysis of primary source material (discourse analysis); historical contextualization of both primary documents and broader socio-political and cultural framework through archival research and secondary sources; as well as interpretation of primary texts through theoretical frameworks (qualitative research). We hope that the output of our endeavor will be useful in better understanding the different correlations that exist between anti-intellectualism and populism and how the interactions between these two elements aids in political identity construction through discourse.

Keywords: anti-intellectualism, discourse theory, France, identity construction, populism, United States of America

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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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: Luigi Pari, Alessandro Suardi, Enrico Santangelo

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In the last decade Jatropha curcas L. (an oleaginous crop native to Central America and part of South America) has raised particular interest owing to of its properties and uses. Its capsules may contain up to 40% in oil and can be used as feedstock for biodiesel production. The harvesting phase is made difficult by the physiological traits of the specie, because fruits are in bunches and do not ripen simultaneously. Three harvesting methodologies are currently diffused and differ for the level of mechanization applied: manual picking, semi-mechanical harvesting, and mechanical harvesting. The manual picking is the most common in the developing countries but it is also the most time consuming and inefficient. Mechanical harvesting carried out with modified grape harvesters has the higher productivity, but it is very costly as initial investment and requires appropriate schemes of cultivation. The semi-mechanical harvesting method is achieved with shaker tools employed to facilitate the fruit detachment. This system resulted much cheaper than the fully mechanized one and quite flexible for small and medium scale applications, but it still requires adjustments for improving the productive performance. CRA-ING, within the European project Jatromed (http://www.jatromed.aua.gr) has carried out preliminary studies on the applicability of such approach, adapting an olive shaker to harvest Jatropha fruits. The work is a survey of the harvesting methods currently available for Jatropha, show the pros and cons of each system, and highlighting the criteria to be considered for choosing one respect another. The harvesting of Jatropha curcas L. remains a big constrains for the spread of the species as energy crop. The approach pursued by CRA-ING can be considered a good compromise between the fully mechanized harvesters and the exclusive manual intervention. It is an attempt to promote a sustainable mechanization suited to the social context of developing countries by encouraging the concrete involvement of local populations.

Keywords: jatropha curcas, energy crop, harvesting, central america, south america

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Authors: Diksha Sapkota, Kathleen Baird, Amornrat Saito, Debra Anderson

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Background: Violence during pregnancy is global public health problem incurring huge amount of social, economic and human costs. It is of particular concern as it affects health of mother, neonates and also disrupt family functioning. Mental illness is one of its commonest consequences affecting both mother and baby and likely to be chronic if left unattended. Past decade has seen advances in knowledge about different forms of violence, its health impacts and intervention/s helping to confront the violence. However, limited range and lack of consistency in measurable outcomes undermine overall effect of interventions, and available evidence are largely slanted towards high-income countries. Despite recognition of integrating screening and counselling for abused pregnant women in health settings, there is a dearth of evidence on its effectiveness from developing countries limiting its applicability and feasibility. This study intends to summarise the high-quality evidence on intimate partner violence interventions in reducing violence and improving mental health and implement the promising intervention in our context. Methods: Quantitative systematic review will be done using PRISMA statement and based on its finding; randomised controlled intervention will be carried out. The study will be conducted among women attending ANC clinic of Dhulikhel Hospital, Nepal. Being the pilot study, samples just adequate to draw the inferences i.e. not less than 30 in each arm will be taken. Phenomological approach will be used to explore the strengths and weaknesses of tested intervention and recommendations for better planning in future. Conclusion: This study intends to provide concrete evidence on what works best in our context and will assist policymakers, programme planners, donors in informed decision making.

Keywords: intimate partner violence/prevention and control, mental health, Nepal, pregnant

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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 134