Search results for: learning evaluation

Authors: Nebojsa Stefanovic, Sasa Milijic, Natasa Danilovic Hristic

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Spatial plan of the special purpose area constitutes a basic tool in the planning of infrastructure corridor of a highway. The aim of the plan is to define the planning basis and provision of spatial conditions for the construction and operation of the highway, as well as for developing other infrastructure systems in the corridor. This paper presents a methodology and approach to the preparation of the Spatial Plan for the special purpose area for the infrastructure corridor of the highway E-80, Section Niš-Merdare in Serbia. The applied methodological approach is based on the combined application of the integrative and participatory method in the decision-making process on the sustainable development of the highway corridor. It was found that, for the planning and management of the infrastructure corridor, a key problem is coordination of spatial and urban planning, strategic environmental assessment and sectoral traffic planning and designing. Through the development of the plan, special attention is focused on increasing the accessibility of the local and regional surrounding, reducing the adverse impacts on the development of settlements and the economy, protection of natural resources, natural and cultural heritage, and the development of other infrastructure systems in the corridor of the highway. As a result of the applied methodology, this paper analyzes the basic features such as coverage, the concept, protected zones, service facilities and objects, the rules of development and construction, etc. Special emphasis is placed to methodology and results of the Strategic Environmental Assessment of the Spatial Plan, and to the importance of protection measures, with the special significance of air and noise protection measures. For evaluation in the Strategic Environmental Assessment, a multicriteria expert evaluation (semi-quantitative method) of planned solutions was used in relation to the set of goals and relevant indicators, based on the basic set of indicators of sustainable development. Evaluation of planned solutions encompassed the significance and size, spatial conditions and probability of the impact of planned solutions on the environment, and the defined goals of strategic assessment. The framework of the implementation of the Spatial Plan is presented, which is determined for the simultaneous elaboration of planning solutions at two levels: the strategic level of the spatial plan and detailed urban plan level. It is also analyzed the relationship of the Spatial Plan to other applicable planning documents for the planning area. The effects of this methodological approach relate to enabling integrated planning of the sustainable development of the infrastructure corridor of the highway and its surrounding area, through coordination of spatial, urban and sectoral traffic planning and design, as well as the participation of all key actors in the adoption and implementation of planned decisions. By the conclusions of the paper, it is pointed to the direction for further research, particularly in terms of harmonizing methodology of planning documentation and preparation of technical-design documentation.

Keywords: corridor, environment, highway, impact, methodology, spatial plan, urban

Procedia PDF Downloads 206

Authors: Bongs Lainjo

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Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focuses on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through the education process. Such mentality is coupled with a tough curriculum that does not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study, the focus is on developing a model that can systematically be implemented by school administrations to prevent possible dropout scenarios. At the elementary level, especially the lower grades, a child's perception of education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models are installed in every educational system with a view to helping prevent an imminent school dropout just before it happens. In a competency-based curriculum that most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduce the rate of dropout.

Keywords: elementary school, predictive models, machine learning, risk factors, data mining, classifiers, dropout rates, education system, competency-based curriculum

Procedia PDF Downloads 171

Authors: Kanika Gupta, Ashok Kumar

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Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

Procedia PDF Downloads 287

Authors: Emiel Caron

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Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

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Authors: Kaoutar Jraida

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Growing evidence demonstrates that thermal conditions in office buildings broadly influence productivity of workers. The purpose of this study is to evaluate and analyze the indoor thermal comfort in an office space based on the calculation of predicted mean vote and predicted percentage of dissatisfied (PMV-PPD) model and field survey.

Keywords: Office, Predicted Mean Vote (PMV), Percentage People Dissatisfied (PPD), Thermal comfort

Procedia PDF Downloads 193

Authors: Sedat Simsek, Tugay Arat

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Today's communication concept, which has a great revolution with the printing press which has been found by Gutenberg, has no boundary thanks to advanced communication devices and the internet. It is possible to take advantage in many areas, such as from medicine to social sciences or from mathematics to education, from the computers that was first produced for the purpose of military services. The use of these developing technologies in the field of education has created a great vision changes in both training and having education. Materials, which can be considered as basic communication resources and used in traditional education has begun to lose its significance, and some technologies have begun to replace them such as internet, computers, smart boards, projection devices and mobile phone. On the other hand, the programs and applications used in these technologies have also been developed. University students use virtual books instead of the traditional printed book, use cell phones instead of note books, use the internet and virtual databases instead of the library to research. They even submit their homework with interactive methods rather than printed materials. The traditional education system, these technologies, which increase productivity, have brought a new dimension to education. The aim of this study is to determine the influence of technologies in the learning process of students and to find whether is there any similarities and differences that arise from the their faculty that they have been educated and and their learning process. In addition to this, it is aimed to determine the level of ICT usage of students studying at the university level. In this context, the advantages and conveniences of the technology used by students are also scrutinized. In this study, we used surveys to collect data. The data were analyzed by using SPSS 16 statistical program with the appropriate testing.

Keywords: education, communication technologies, role of technology, teaching

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Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

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Authors: Hasti Ebrahimi

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This study sheds light on the challenges and difficulties caused by the Iranian clerical system’s intervention in the country’s school education during the COVID-19 pandemic, when schools remained closed for almost two years. The pandemic brought Iranian elementary school education to a standstill for almost 6 months before the country developed a nationwide learning platform – a customized television network. While the initiative seemed to have been welcomed by the majority of Iranian parents, it resented some of the more traditional strata of the society, including the influential Friday Prayer Leaders who found the televised version of the elementary education ‘less spiritual’ and ‘more ‘material’ or science-based. That prompted the Iranian Channel of Education, the specialized television network that had been chosen to serve as a nationally televised school during the pandemic, to try to redefine much of its online elementary school educational content within the religious ideology of the Islamic Republic of Iran. As a result, young clergies appeared on the television screen as preachers of Islamic morality, religious themes and even sociology, history, and arts. The present research delves into the consequences of such an intervention, how it might have impacted the infrastructure of Iranian elementary education and whether or not the new ideology-infused curricula would withstand the opposition of students and mainstream teachers. The main methodology used in this study is Critical Discourse Analysis with a cognitive approach. It systematically finds and analyzes the alternative ideological structures of discourse in the Iranian Channel of Education from September 2021 to July 2022, when the clergy ‘teachers’ replaced ‘regular’ history and arts teachers on the television screen for the first time. It has aimed to assess how the various uses of the alternative ideological discourse in elementary school content have influenced the processes of learning: the acquisition of knowledge, beliefs, opinions, attitudes, abilities, and other cognitive and emotional changes, which are the goals of institutional education. This study has been an effort aimed at understanding and perhaps clarifying the relationships between the traditional textual structures and processing on the one hand and socio-cultural contexts created by the clergy teachers on the other. This analysis shows how the clerical portion of elementary education on the Channel of Education that seemed to have dominated the entire televised teaching and learning process faded away as the pandemic was contained and mainstream classes were restored. It nevertheless reflects the deep ideological rifts between the clerical approach to school education and the mainstream teaching process in Iranian schools. The semantic macrostructures of social content in the current Iranian elementary school education, this study suggests, have remained intact despite the temporary ideological intervention of the ruling clerical elite in their formulation and presentation. Finally, using thematic and schematic frameworks, the essay suggests that the ‘clerical’ social content taught on the Channel of Education during the pandemic cannot have been accepted cognitively by the channel’s target audience, including students and mainstream teachers.

Keywords: televised elementary school learning, Covid 19, critical discourse analysis, Iranian clerical ideology

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Authors: Martin Lauzier, Benjamin Lafreniere-Carrier, Nathalie Delobbe

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Supervisor support is one of the main levers to foster transfer of training. Although past and current studies voice its effects, few have sought to identify the factors that may explain why supervisors offer support to their subordinates when they return from training. Based on Goal Orientation Theory and following the principles of supportive supervision, this study aims to improve our understanding of the factors that influence supervisors’ involvement in the transfer process. More specifically, this research seeks to verify the influence of supervisors’ goal orientation on the adoption of post-training support behaviors. This study also assesses the mediating role of the supervisors’ interest in subordinates’ development on this first relationship. Conducted in two organizations (Canadian: N₁ = 292; Belgian: N₂ = 80), the results of this study revealed three main findings. First, supervisors’ who adopt learning mastery goal orientation also tend to adopt more post-training supportive behaviors. Secondly, regression analyses (using the bootstrap method) show that supervisors' interest in developing their subordinates’ skills mediate the relationship between supervisors’ goal orientation and post-training supportive behaviors. Thirdly, the observed mediation effects are consistent in both samples, regardless of supervisors’ gender or age. Overall, this research is part of the limited number of studies that have focused on the determining factors supervisors’ involvement in the learning transfer process.

Keywords: supervisor support, transfer of training, goal orientation, interest in the development of subordinates’ skills

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Authors: David Ofosu-Hamilton, John K. E. Edumadze

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Universities have come to recognize the role Information and Communication Technology (ICT) skills plays in the daily activities of tertiary students. The ability to use ICT – essentially, computers and their diverse applications – are important resources that influence an individual’s economic and social participation and human capital development. Our society now increasingly relies on the Internet, and the Cloud as a means to communicate and disseminate information. The educated individual should, therefore, be able to use ICT to create and share knowledge that will improve society. It is, therefore, important that universities require incoming students to demonstrate a level of computer proficiency or trained to do so at a minimal cost by deploying advanced educational technologies. The training and standardized assessment of all in-coming first-year students of the University of Cape Coast in Information Technology Skills (ITS) have become a necessity as students’ most often than not highly overestimate their digital skill and digital ignorance is costly to any economy. The one-semester course is targeted at fresh students and aimed at enhancing the productivity and software skills of students. In this respect, emphasis is placed on skills that will enable students to be proficient in using Microsoft Office and Google Apps for Education for their academic work and future professional work whiles using emerging digital multimedia technologies in a safe, ethical, responsible, and legal manner. The course is delivered in blended mode - online and self-paced (student centered) using Alison’s free cloud-based tutorial (Moodle) of Microsoft Office videos. Online support is provided via discussion forums on the University’s Moodle platform and tutor-directed and assisted at the ICT Centre and Google E-learning laboratory. All students are required to register for the ITS course during either the first or second semester of the first year and must participate and complete it within a semester. Assessment focuses on Alison online assessment on Microsoft Office, Alison online assessment on ALISON ABC IT, Peer assessment on e-portfolio created using Google Apps/Office 365 and an End of Semester’s online assessment at the ICT Centre whenever the student was ready in the cause of the semester. This paper, therefore, focuses on the digital culture approach of hybrid teaching, learning and paperless examinations and the possible adoption by other courses or programs at the University of Cape Coast.

Keywords: assessment, blended, cloud, paperless

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Authors: Gaurav Kumar Sinha

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The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements.

Keywords: energy efficiency, sustainability analytics, carbon emissions, oil refineries, data analytics, machine learning, predictive modeling, process optimization, greenhouse gas reduction, environmental performance

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Sarah Kalaouze, Maxine Iannucelli, Kristen Dunfield

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Children’s implicit beliefs regarding intelligence (i.e., intelligence mindsets) influence their motivation, perseverance, and success. Previous research suggests that the way parents perceive failure influences the development of their child’s intelligence mindsets. We invited 151 children-parent dyads (Age= 5–6 years) to complete a series of difficult puzzles over zoom. We assessed parents’ intelligence and failure mindsets using questionnaires and recorded parents’ person/performance-oriented (e.g., “you are smart” or "you were almost able to complete that one) and process-oriented (e.g., “you are trying really hard” or "maybe if you place the bigger pieces first") failure feedback. We were interested in observing the relation between parental mindsets and the type of feedback provided. We found that parents’ intelligence mindsets were not predictive of the feedback they provided children. Failure mindsets, on the other hand, were predictive of failure feedback. Parents who view failure-as-debilitating provided more person-oriented feedback, focusing on performance and personal ability. Whereas parents who view failure-as-enhancing provided process-oriented feedback, focusing on effort and strategies. Taken all together, our results allow us to determine that although parents might already have a growth intelligence mindset, they don’t necessarily have a failure-as-enhancing mindset. Parents adopting a failure-as-enhancing mindset would influence their children to view failure as a learning opportunity, further promoting practice, effort, and perseverance during challenging tasks. The focus placed on a child’s learning, rather than their performance, encourages them to perceive intelligence as malleable (growth mindset) rather than fix (fixed mindset). This implies that parents should not only hold a growth mindset but thoroughly understand their role in the transmission of intelligence beliefs.

Keywords: mindset(s), failure, intelligence, parental feedback, parents

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Chuan Yang, Jing Bie, Yueh-Lung Lin, Zhong Wang

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Transportation planning is an academic integration discipline combining research and practice with the aim of mobility and accessibility improvements at both strategic-level policy-making and operational dimensions of practical planning. Transportation planning could build the linkage between traffic and social development goals, for instance, economic benefits and environmental sustainability. The transportation planning analysis and evaluation tend to apply empirical quantitative approaches with the guidance of the fundamental principles, such as efficiency, equity, safety, and sustainability. Space syntax theory has been applied in the spatial distribution of pedestrian movement or vehicle flow analysis, however rare has been written about its application in transportation planning. The correlated relationship between the variables of space syntax analysis and authentic observations have declared that the urban configurations have a significant effect on urban dynamics, for instance, land value, building density, traffic, crime. This research aims to explore the potentials of applying Space Syntax methodology to evaluate urban transportation planning through studying the effects of urban configuration on cities transportation performance. By literature review, this paper aims to discuss the effects that urban configuration with different degrees of integration and accessibility have on three elementary components of transportation planning - transportation efficiency, transportation safety, and economic agglomeration development - via intensifying and stabilising the nature movements generated by the street network. And then the potential and limits of Space Syntax theory to study the performance of urban transportation and transportation planning would be discussed in the paper. In practical terms, this research will help future research explore the effects of urban design on transportation performance, and identify which patterns of urban street networks would allow for most efficient and safe transportation performance with higher economic benefits.

Keywords: transportation planning, space syntax, economic agglomeration, transportation efficiency, transportation safety

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Horng-Ji Lai

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The intuitive design features and friendly interface of smartphone attract older adults. In Taiwan, many senior education institutes offer smartphone training courses for older adult learners who are interested in learning this innovative technology. It is expected that the training courses can help them to enjoy the benefits of using smartphone and increase their life satisfaction. Therefore, it is important to investigate the factors that influence older adults’ behavior of using smartphone. The purpose of the research was to develop and test a research model that investigates the factors (self-efficacy, social connection, the need to seek health information, and the need to seek financial information) affecting older adult learners’ Internet behaviour and their life satisfaction with use of smartphone. Also, this research sought to identify the relationship between the proposed variables. Survey method was used to collect research data. A Structural Equation Modeling was performed using Partial Least Squares (PLS) regression for data exploration and model estimation. The participants were 394 older adult learners from smartphone training courses in active aging learning centers located in central Taiwan. The research results revealed that self-efficacy significantly affected older adult learner’ social connection, the need to seek health information, and the need to seek financial information. The construct of social connection yielded a positive influence in respondents’ life satisfaction. The implications of these results for practice and future research are also discussed.

Keywords: older adults, smartphone, internet behaviour, life satisfaction

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Authors: Khawaja A. Wahaj Wani, Aykut Erkal

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Pakistan's construction industry, a key player in economic development, has experienced remarkable growth. However, the surge in activities has been accompanied by dangerous working conditions, attributed to legislative gaps and flaws. Unhealthy construction practices, uncertain site conditions, and hazardous environments contribute to a concerning rate of injuries and fatalities. The principal aim of this research study is to undertake a thorough evaluation based on the assessment of the current situation of Health & Safety policies and the surveys performed by stakeholders of Pakistan with the aim of providing solution-centric methodologies for the enforcement of health and safety regulations within construction companies operating on project sites. Recognizing the pivotal role that the construction industry plays in bolstering a nation's economy, it is imperative to address the pressing need for heightened awareness among site engineers and laborers. The study adopts a robust approach, utilizing questionnaire surveys and interviews. As an exclusive investigative study, it encompasses all stakeholders: clients, consultants, contractors, and subcontractors. Targeting PEC-registered companies. Safety performance was assessed through the examination of sixty safety procedures using SPSS-18. A high Cronbach's alpha value of 0.958 ensures data reliability, and non-parametric tests were employed due to the non-normal distribution of data. The safety performance evaluation revealed significant insights. "Using Hoists and Cranes" and "Precautionary Measures (Shoring and Excavation)" exhibited commendable safety levels. Conversely, "Trainings on Safety" displayed a lower safety performance, alongside areas such as "Safety in Contract Documentation," "Meetings for Safety," and "Worker Participation," indicating room for improvement. These findings provide stakeholders with a detailed understanding of current safety measures within Pakistan's construction industry.

Keywords: construction industry, health and safety regulations, Pakistan, risk management

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Authors: Ida Fatimawati Adi Badiozaman, Augustus Raymond Segar

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The Covid-19 pandemic transformed teaching contexts at an unprecedented level. Although studies have focused mainly on its impact on students, little is known about how emergency online teaching affects faculty members in higher education. Given that the pandemic has robbed teachers of opportunities for adequate preparation, it is vital to understand how teaching competencies were perceived in the crisis-response transition to online teaching and learning (OTL). Therefore, the study explores how academics perceive their readiness for OTL and what competencies were perceived to be central. Therefore, through a mixed-methods design, the study first explores through a survey how academics perceive their readiness for OTL and what competencies were perceived to be central. Emerging trends from the quantitative data of 330 academics (three public and three private Higher learning institutions) led to the formulation of interview guides for the subsequent qualitative phase. The authors use critical sensemaking (CSM) to analyse interviews with twenty-two teachers (n = 22) (three public; three private HEs) toward understanding the interconnected layers of influences they draw from as they make sense of their teaching competence. The sensemaking process reframed competence and readiness in that agentic competency emerged as crucial in shaping resilience and adaptability during the transition to OTL. The findings also highlight professional learningcriticalto teacher competence: course design, communication, time management, technological competence, and identity (re)construction. The findings highlight opportunities for strategic orientation to change during crisis. Implications for pedagogy and policy are discussed.

Keywords: online teaching, pedagogical competence, agentic competence, agency, technological competence

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Authors: Jaheon Gu, Wooyong Chung, Mijung Koo, Seonbok Lee, Gyoutae Park, Sangguk Ahn, Hiesik Kim, Jungil Park

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In this paper, we present a portable ammonia gas detector for performing the gas safety management efficiently. The display of the detector is separated from its body. The display module is received the data measured from the detector using ZigBee. The detector has a rechargeable li-ion battery which can be use for 11~12 hours, and a Bluetooth module for sending the data to the PC or the smart devices. The data are sent to the server and can access using the web browser or mobile application. The range of the detection concentration is 0~100ppm.

Keywords: ammonia, detector, gas, portable

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Authors: Kateriina Rannula, Elle Sõrmus, Siret Piirsalu

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Mixed generation classroom is a phenomenon that current higher education establishments are faced with daily trying to meet the needs of modern labor market with its emphasis on lifelong learning and retraining. Representatives of mainly X and Y generations in one classroom acquiring higher education is a challenge to lecturers considering all the characteristics that differ one generation from another. The importance of outlining different strategies and considering the needs of the students lies in the necessity for everyone to acquire the maximum of the provided knowledge as well as to understand each other to study together in one classroom and successfully cooperate in future workplaces. In addition to different generations, there are also learners with different native languages which have an impact on reading and understanding texts in third languages, including possible translation. Current research aims to investigate, describe and compare reading strategies among the representatives of generation X and Y. Hypotheses were formulated - representatives of generation X and Y use different reading strategies which is also different among first and third year students of the before mentioned generations. Current study is an empirical, qualitative study. To achieve the aim of the research, relevant literature was analyzed and a semi-structured questionnaire conducted among the first and third year students of Tallinn Health Care College. Questionnaire consisted of 25 statements on the text reading strategies, 3 multiple choice questions on preferences considering the design and medium of the text, and three open questions on the translation process when working with a text in student’s third language. The results of the questionnaire were categorized, analyzed and compared. Both, generation X and Y described their reading strategies to be 'scanning' and 'surfing'. Compared to generation X, first year generation Y learners valued interactivity and nonlinear texts. Students frequently used strategies of skimming, scanning, translating and highlighting together with relevant-thinking and assistance-seeking. Meanwhile, the third-year generation Y students no longer frequently used translating, resourcing and highlighting while Generation X learners still incorporated these strategies. Knowing about different needs of the generations currently inside the classrooms and on the labor market enables us with tools to provide sustainable education and grants the society a work force that is more flexible and able to move between professions. Future research should be conducted in order to investigate the amount of learning and strategy- adoption between generations. As for reading, main suggestions arising from the research are as follows: make a variety of materials available to students; allow them to select what they want to read and try to make those materials visually attractive, relevant, and appropriately challenging for learners considering the differences of generations.

Keywords: generation X, generation Y, learning strategies, reading strategies

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Authors: Cristina-Corina Bențea, Teodora-Mihaela Iconomescu, Laurențiu-Gabriel Talaghir, Claudiu Mereuță, Anamaria Berdilă

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The study aims to examine the relationships between motor abilities, self-evaluation of athletic competence, and demographic characteristics in a sample of late-childhood participants. Defined as physical elements that enable the movements, motor skills are classified according to movement precision as gross and fine motor skills. Across their development, children enhance their ability to coordinate the limbs to produce different actions. In educational settings, they perform various instructional activities that involve the improvement of their athletic prowess and are taught how to strengthen their gross and fine motor abilities. Also, in relation to their activities, children tend to evaluate themselves differently across the various domains of their life. Starting from childhood, athletic competence is one of the area-specific evaluations of competence that refers to one’s ability to do well at sports, including outdoor games. Method: The sample consisted of fifty-eight primary school children, thirty girls, and twenty-eight boys, with ages between 8-10 years. The Bruininks-Oseretsky test of motor proficiency was used to assess both gross and fine motor skills in eight specific areas (fine motor precision, fine motor integration, manual dexterity, bilateral coordination, balance, running speed and agility, upper-limb coordination, strength). Athletic competence self-perceived was assessed with one of the six subscales of the Self-Perception Profile for Children. Results: Were examined both the relationships between each motor skills scale and subscales and between motor skills and general self-perceived athletic competence. Results indicated correlations between the athletic competence and four motor skills subscales depending on the gender and age of the children. The findings of the study were discussed related to the possibility to improve children's physical proficiency in educational settings according to the level of self-perceived athletic competence.

Keywords: gross motor skills, fine motor skills, athletic competence, self-evaluation, children, education

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Authors: Abdool Qaiyum Mohabuth

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Transferrable skills are learnt abilities which are mainly acquired when experiencing work. University students have the opportunities to develop the knowledge and aptitude at work when they undertake WBL placement during their studies. There is a range of transferrable skills which students may acquire at their placement settings. Several studies have tried to identify a core set of transferrable skills which students can acquire at their placement settings. However, the different lists proposed have often been criticised for being exhaustive and duplicative. In addition, assessing the achievement of students on practice learning based on the transferrable skills is regarded as being complex and tedious due to the variability of placement settings. No attempt has been made in investigating whether these skills are assessable at practice settings. This study seeks to define a set of generic transferrable skills that can be assessed during WBL practice. Quantitative technique was used involving the design of two questionnaires. One was administered to University of Mauritius students who have undertaken WBL practice and the other was slightly modified, destined to mentors who have supervised and assessed students at placement settings. To obtain a good representation of the student’s population, the sample considered was stratified over four Faculties. As for the mentors, probability sampling was considered. Findings revealed that transferrable skills may be subject to formal assessment at practice settings. Hypothesis tested indicate that there was no significant difference between students and mentors as regards to the application of transferrable skills for formal assessment. A list of core transferrable skills that are assessable at any practice settings has been defined after taking into account their degree of being generic, extent of acquisition at work settings and their consideration for formal assessment. Both students and mentors assert that these transferrable skills are accessible at work settings and require commitment and energy to be acquired successfully.

Keywords: knowledge, skills, assessment, placement, mentors

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Bouami Hind, Millot Patrick

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The implementation of automated devices in life-critical systems such as hospitals can bring a new set of challenges related to automation malfunctions. While automation has been identified as great leverage for the medication dispensing system’s security and efficiency, it also increases the complexity of the organization. In particular, the installation and operation stage of automated devices can be complex when malfunctions related to automated systems occur. This paper aims to document operators’ situation awareness about the malfunctions of automated drug delivery systems (ADCs) during their implementation through Saint Brieuc hospital’s feedback. Our evaluation approach has been deployed in Saint Brieuc hospital center’s pharmacy, which has been equipped with automated nominative drug dispensing systems since January of 2021. The analysis of Saint Brieuc hospital center pharmacy’s automation revealed numerous malfunctions related to the implementation of Automated Delivery Cabinets. It appears that the targeted performance is not reached in the first year of implementation in this case study. Also, errors have been collected in patients' automated treatments’ production such as lack of drugs in pill boxes or nominative carnets, excess of drugs, wrong location of the drug, drug blister damaged, non-compliant sachet, or ticket errors. Saint Brieuc hospital center’s pharmacy is doing a tremendous job of setting up and monitoring performance indicators from the beginning of automation and throughout ADC’s operation to control ADC’s malfunctions and meet the performance targeted by the hospital. Health professionals, including pharmacists, biomedical engineers and directors of work, technical services and safety, are heavily involved in an automation project. This study highlights the importance of the evaluation of ADCs’ performance throughout the implementation process and the hospital’s team involvement in automation supervision and management.

Keywords: life-critical systems, situation awareness, automated delivery cabinets, implementation, risks and malfunctions

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Authors: Tatiana Ponomarenko, Oksana Marinina, Marina Nevskaya

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Modern corporate social responsibility (CSR) is a sphere of multilevel responsibility of a company toward society represented by various stakeholders. The relevance of CSR management grows due to the active development of socially responsible investing (principles for responsible investment) taking into account factors of environmental, social and corporate governance (ESG), growing attention of the investment community in general to the long-term stability of companies and the quality of control of nonfinancial risks. The modern approach to CSR strategic management is aimed at the creation of trustful relationships with stakeholders, on the basis of which a contribution to the sustainable development of companies, regions, and national economics is insured. However, the practical concepts of social responsibility in mining companies are different, which leads to various degrees of application of CSR. A number of companies implement CSR using a traditional (limited) understanding of responsibility toward employees and counteragents, the others understand CSR much wider and try to use leverages of efficient cooperation. As in large mining companies the scope of CSR measures is diverse and characterized by different indices, the study was aimed at evaluating CSR efficiency on the basis of a proprietary methodology and determining the level of development of CSR management in terms of anti-crisis, reactive and proactive development. The methodology of the research includes analysis of integrated global reporting initiative (GRI) reports of large mining companies; choice of most representative sectoral agents by a criterion of the regularity of issuance and publication of reports; calculation of indices of evaluation of CSR level of the selected companies in dynamics. The methodology of evaluation of CSR level is based on a rating score of changes in standard indices of GRI reports by economic, environmental, and social directions. Result. By the results of the analysis, companies of fuel and energy and metallurgic complexes, in overwhelming majority, reflecting three indices out of a wide range of possible indicators of SDGs (Sustainable Development Goals), were selected for the study. The evaluation of the scopes of CSR of the companies Gazprom, LUKOIL, Metalloinvest, Nornikel, Rosneft, Severstal, SIBUR, SUEK corresponds to the reactive type of development according to a scale of CSR strategic management, which is the average value out of the possible values. The chief drawback is that companies, in the process of analyzing global goals, often choose the goals which relate to their own activities, paying insufficient attention to the interests of the stakeholders inside the country. This fact evidences the necessity of searching for more effective mechanisms of CSR control. Acknowledgment: This article is prepared within grant support of the RFBR, project 19-510-44013 'Development of the concept of mineral resources value formation in the context of sustainable development in resource-oriented economies'.

Keywords: sustainable development, corporate social responsibility, development strategies, efficiency assessment

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Authors: M. Assaad, J. Mäkiö, T. Mäkelä, M. Kankaanranta, N. Fachantidis, V. Dagdilelis, A. Reid, C. R. del Rio, E. V. Pavlysh, S. V. Piashkun

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To bring science and society together in Europe, thus increasing the continent’s international competitiveness, STEM (science, technology, engineering and mathematics) education must be more relatable to European youths in their everyday life. STIMEY (Science, Technology, Innovation, Mathematics, Engineering for the Young) project researches and develops a hybrid educational environment with multi-level components that is being designed and developed based on a well-researched pedagogical framework, aiming to make STEM education more attractive to young people aged 10 to 18 years in this digital era. This environment combines social media components, robotic artefacts, and radio to educate, engage and increase students’ interest in STEM education and careers from a young age. Additionally, it offers educators the necessary modern tools to deliver STEM education in an attractive and engaging manner in or out of class. Moreover, it enables parents to keep track of their children’s education, and collaborate with their teachers on their development. Finally, the open platform allows businesses to invest in the growth of the youths’ talents and skills in line with the economic and labour market needs through entrepreneurial tools. Thus, universities, schools, teachers, students, parents, and businesses come together to complete a circle in which STEM becomes part of the daily life of youths through a hybrid educational environment that also prepares them for future careers.

Keywords: e-learning, entrepreneurship, pedagogy, robotics, serious gaming, social media, STEM education

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Authors: Ivan Ashif Ardhana, Intan Mahanani

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Chemistry education tends to change from triplet representation among macroscopic, microscopic, and symbolic to tetrahedron shape. This change set the aspect of human element on the top of learning. Meaning that students are expected to solve the problems involving the ethic, morality, and humanity through the class. Ability to solve the problems connecting either theories or applications is called scientific literacy which have been implemented in curriculum 2013 implicitly. Scientific literacy has an aspect of nature science and critical thinking. Both can be integrated to learning using scientific approach and scientific inquiry. Unfortunately, students’ ability of scientific literacy in Indonesia is far from expectation. A survey from PISA had proven it. Scientific literacy of Indonesian students is always at bottom five position from 2002 till 2012. Improving a scientific literacy needs many efforts against them. Developing an instructional material based on scientific approach is one kind of that efforts. Instructional material contains both aspect of nature of science and critical thinking which is instructed explicitly to improve the students’ understanding about science. Developing goal is to produce a prototype and an instructional material using scientific approach whose chapter is chemical bonding and intermolecular forces for high school students grade ten. As usual, the material is subjected to get either quantitative mark or suggestion through validation process using validation sheet instrument. Development model is adapted from 4D model containing four steps. They are define, design, develop, and disseminate. Nevertheless, development of instructional material had only done until third step. The final step wasn’t done because of time, cost, and energy limitations. Developed instructional material had been validated by four validators. They are coming from chemistry lecture and high school’s teacher which two at each. The result of this development research shown the average of quantitative mark of students’ book is 92.75% with very proper in criteria. Given at same validation process, teacher’s guiding book got the average mark by 96.98%, similar criteria with students’ book. Qualitative mark including both comments and suggestions resulted from validation process were used as consideration for the revision. The result concluded us how the instructional materials using scientific approach to explicit nature of science and critical thinking on the topic of chemical bonding and intermolecular forces are very proper if they are used at learning activity.

Keywords: critical thinking, instructional material, nature of science, scientific literacy

Procedia PDF Downloads 262