Search results for: English learning strategies

Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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Authors: Fazl E. Ahad, Shi Dongyan, Anees Ur Rehman

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Plates are one of the most important structural components used in many industries like aerospace, marine and various other engineering fields and thus motivate designers and engineers to study the vibrational characteristics of these structures. This paper is a review of existing literature on vibration analysis of plates. Focus has been kept on prominent studies related to isotropic plates based on Mindlin plate theory; however few citations on orthotropic plates and higher order shear deformation theories have also been included. All citations are in English language. This review is aimed to provide contemporarily relevant survey of papers on vibrational characteristics of thick plates and will be useful for scientists, designers and researchers to locate important and relevant literature/research quickly.

Keywords: mindlin plates, vibrations, arbitrary boundary conditions, mode shapes, natural frequency

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Authors: Neha Singh, Ananya Arora

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Facial expression measurement plays a crucial role in the identification of emotion. Facial expression plays a key role in psychophysiology, neural bases, and emotional disorder, to name a few. The Facial Action Coding System (FACS) has proven to be the most efficient and widely used of the various systems used to describe facial expressions. Coders can manually code facial expressions with FACS and, by viewing video-recorded facial behaviour at a specified frame rate and slow motion, can decompose into action units (AUs). Action units are the most minor visually discriminable facial movements. FACS explicitly differentiates between facial actions and inferences about what the actions mean. Action units are the fundamental unit of FACS methodology. It is regarded as the standard measure for facial behaviour and finds its application in various fields of study beyond emotion science. These include facial neuromuscular disorders, neuroscience, computer vision, computer graphics and animation, and face encoding for digital processing. This paper discusses the conceptual basis for FACS, a numerical listing of discrete facial movements identified by the system, the system's psychometric evaluation, and the software's recommended training requirements.

Keywords: facial action, action units, coding, machine learning

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Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

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Authors: Fatih Frank Alparslan

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The aviation industry is at a crucial turning point due to modern technologies, environmental concerns, and changing ways of transporting people and goods globally. The paper examines these challenges and opportunities comprehensively. It emphasizes the role of innovative management and advanced technology in shaping the future of air travel. This study begins with an overview of the current state of the aviation industry, identifying key areas where innovation and technology could be highly beneficial. It explores the latest advancements in airplane design, propulsion, and materials. These technological advancements are shown to enhance aircraft performance and environmental sustainability. The paper also discusses the use of artificial intelligence and machine learning in improving air traffic control, enhancing safety, and making flight operations more efficient. The management of these technologies is critically important. Therefore, the research delves into necessary changes in organization, culture, and operations to support innovation. It proposes a management approach that aligns with these modern technologies, underlining the importance of forward-thinking leaders who collaborate across disciplines and embrace innovative ideas. The paper addresses challenges in adopting these innovations, such as regulatory barriers, the need for industry-wide standards, and the impact of technological changes on jobs and society. It recommends that governments, aviation businesses, and educational institutions collaborate to address these challenges effectively, paving the way for a more innovative and eco-friendly aviation industry. In conclusion, the paper argues that the future of aviation relies on integrating new management practices with innovative technologies. It urges a collective effort to push beyond current capabilities, envisioning an aviation industry that is safer, more efficient, and environmentally responsible. By adopting a broad approach, this research contributes to the ongoing discussion about resolving the complex issues facing today's aviation sector, offering insights and guidance to prepare for future advancements.

Keywords: aviation innovation, technology integration, environmental sustainability, management strategies, multidisciplinary approach

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Sally Abu Sabaa, Lindsey Gutt

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In an attempt to use education as a way of raising a socially responsible and engaged global citizen, the YU-Bridge program, the largest and fastest pathway program of its kind in North America, has embarked on the journey of integrating general themes from the UN’s sustainable developmental goals (SDGs) in its English for Academic Purposes (EAP) curriculum. The purpose of this initiative was to redefine the general philosophy of education in the middle of a pandemic and align with York University’s University Academic Plan that was released in summer 2020 framed around the SDGs. The YUB program attracts international students from all over the world but mainly from China, and its goal is to enable students to achieve the minimum language requirement to join their undergraduate courses at York University. However, along with measuring outcomes, objectives, and the students’ GPA, instructors and academics are always seeking innovation of the YUB curriculum to adapt to the ever growing challenges of academics in the university context, in order to focus more on subject matter that students will be exposed to in their undergraduate studies. However, with the sudden change that has happened globally with the advance of the COVID-19 pandemic, and other natural disasters like the increase in forest fires and floods, rethinking the philosophy and goal of education was a must. Accordingly, the SDGs became the solid pillars upon which we, academics and administrators of the program, could build a new curriculum and shift our perspective from simply ESL education to education with moral and ethical goals. The preliminary implementation of this initiative was supported by an institutional-wide consultation with EAP instructors who have diverse experiences, disciplines, and interests. Along with brainstorming sessions and mini-pilot projects preceding the integration of the SDGs in the YUB-EAP curriculum, those meetings led to creating a general outline of a curriculum and an assessment framework that has the SDGs at its core with the medium of ESL used for language instruction. Accordingly, a community of knowledge exchange was spontaneously created and facilitated by instructors. This has led to knowledge, resources, and teaching pedagogies being shared and examined further. In addition, experiences and reactions of students are being shared, leading to constructive discussions about opportunities and challenges with the integration of the SDGs. The discussions have branched out to discussions about cultural and political barriers along with a thirst for knowledge and engagement, which has resulted in increased engagement not only on the part of the students but the instructors as well. Later in the program, two surveys will be conducted: one for the students and one for the instructors to measure the level of engagement of each in this initiative as well as to elicit suggestions for further development. This paper will describe this fundamental step into using ESL methodology as a mode of disseminating essential ethical and socially correct knowledge for all learners in the 21st Century, the students’ reactions, and the teachers’ involvement and reflections.

Keywords: EAP, curriculum, education, global citizen

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Authors: Sankaran Potti Narasimhan

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Costal Karnataka is sandwiched between Arabian Sea and the biodiversity hotspot of Western Ghats. This has provided a rich vegetation, canopy and humidity for the sustainable growth and evolution of many orchid populations. Similar to many other biodiversity hostpot regions of India and the world, this region also faces threat from anthropogenic activities and climate change. Hence, there is a need to study the current orchid diversity and trends in population as well as an effective conservation strategy. Costal belt of Karnataka state of India extends over 325 kilometers and an area of 18,000 km2. The region encompasses two national parks such as the Anshi National Park and the Kudremukh National Park. The study regions also include two Wild Life Sanctuaries such as the Someshwara Wildlife Sanctuary and Mookambika Wildlife Sanctuary. The estimated number of orchids in the region includes 30 genera and 45 species. Both terrestrial and epiphytic orchids are found in this region. The region contains many red listed orchids such as Trias stocksii (Critically endangered), Eriad alzellii (Lower risk vulnerable) and Dendrobnium ovatum (Vulnerable). The important terrestrial orchids of the region are Geodorum, Habenaria, Lipparis, Malaxis, Nervilia, Pachystoma, Pectelis, Peristylus, Tropidia and Zeuxine. The epiphytic forms includes Acampe, Aerides, Bulbophyllum, Cleisostoma, Conchidum, Cottonia, Cymbidium, Dendronium, Eria, Flickingeria, Gastrochilus, Kingidium, Luisia, Oberonia, Phalaenopsis, Pholidota, Porpax, Rhynchostylis, Sirhookera and Trias. The current paper discusses the population strength and changes in the population structure of these orchids along with proposed conservation strategies.

Keywords: orchid diversity, bulbophyllum, dendrobium, orchid conservation

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Authors: Rajender Dahiya

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Investigation outcomes of major process incidents have been consistent for decades and validate that the causes and consequences are often identical. The debate remains as we continue to experience similar process incidents even with enormous development of new tools, technologies, industry standards, codes, regulations, and learning processes? The objective of this paper is to investigate the most common causes of major industrial incidents and reveal industry challenges and best practices to prevent such incidents. The author, in his current role, performs audits and inspections of a variety of high-hazard industries in North America, including petroleum refineries, chemicals, petrochemicals, manufacturing, etc. In this paper, he shares real life scenarios, examples, and case studies from high hazards operating facilities including key challenges and best practices. This case study will provide a clear understanding of the importance of near miss incident investigation. The incident was a Safe operating limit excursion. The case describes the deficiencies in management programs, the competency of employees, and the culture of the corporation that includes hazard identification and risk assessment, maintaining the integrity of safety-critical equipment, operating discipline, learning from process safety near misses, process safety competency, process safety culture, audits, and performance measurement. Failure to identify the hazards and manage the risks of highly hazardous materials and processes is one of the primary root-causes of an incident, and failure to learn from past incidents is the leading cause of the recurrence of incidents. Several investigations of major incidents discovered that each showed several warning signs before occurring, and most importantly, all were preventable. The author will discuss why preventable incidents were not prevented and review the mutual causes of learning failures from past major incidents. The leading causes of past incidents are summarized below. Management failure to identify the hazard and/or mitigate the risk of hazardous processes or materials. This process starts early in the project stage and continues throughout the life cycle of the facility. For example, a poorly done hazard study such as HAZID, PHA, or LOPA is one of the leading causes of the failure. If this step is performed correctly, then the next potential cause is. Management failure to maintain the integrity of safety critical systems and equipment. In most of the incidents, mechanical integrity of the critical equipment was not maintained, safety barriers were either bypassed, disabled, or not maintained. The third major cause is Management failure to learn and/or apply learning from the past incidents. There were several precursors before those incidents. These precursors were either ignored altogether or not taken seriously. This paper will conclude by sharing how a well-implemented operating management system, good process safety culture, and competent leaders and staff contributed to managing the risks to prevent major incidents.

Keywords: incident investigation, risk management, loss prevention, process safety, accident prevention

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Authors: Saule Shazhanbayeva, Denise van der Merwe

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Study hypothesis: Nazarbayev Intellectual School of Kyzylorda’s Biology teachers can use STEM-integrated learning to improve students' problem-solving ability and responsibility as global citizens. The significance of this study is to indicate how the use of STEM integrative learning during Biology lessons could contribute to forming globally-minded students who are responsible community members. For the purposes of this study, worldview is defined as a view that is broader than the country of Kazakhstan, allowing students to see the significance of their scientific contributions to the world as global citizens. The context of worldview specifically indicates that most students have never traveled outside of their city or region within Kazakhstan. In order to broaden student understanding, it is imperative that students are exposed to different world views and contrasting ideas within the educational setting of Biology as the science being used for the research. This exposure promulgates students understanding of the significance they have as global citizens alongside the obligations which would rest on them as scientifically minded global citizens. Integrative learning should be Biological Science - with Technology and engineering in the form of problem-solving, and Mathematics to allow improved problem-solving skills to develop within the students of Nazarbayev Intellectual School (NIS) of Kyzylorda. The school's vision is to allow students to realise their role as global citizens and become responsible community members. STEM allows integrations by combining four subject skills to solve topical problems designed by educators. The methods used are based on qualitative analysis: for students’ performance during a problem-solution scenario; and Biology teacher interviews to ascertain their understanding of STEM implementation and willingness to integrate it into current lessons. The research indicated that NIS is ready for a shift into STEM lessons to promote globally responsible students. The only additional need is for proper STEM integrative lesson method training for teachers.

Keywords: global citizen, STEM, Biology, high-school

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Authors: Dan Xie

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The use of discourse markers (DMs) can play a crucial role in representing discourse interaction and pragmatic competence. Learners’ use of DMs and differences between native speakers (NSs) and non-native speakers (NNSs) in the use of various DMs have been the focus of considerable research attention. However, some commonly used DMs, such as you know, have not received as much attention in comparative studies, especially in the Chinese context. This study analyses data in two corpora (COLSEC and Spoken BNC 2014 (14-25)) to investigate how Chinese learners differ from NNSs in their use of the DM you know and its functions in speech. The results show that there is a significant difference between the two corpora in terms of the frequency of use of you know. In terms of the functions of you know, the study shows that six functions can all be present in both corpora, although there are significant differences between the five functional dimensions, especially in introducing a claim linked to the prior discourse and highlighting particular points in the discourse. It is hoped to show empirically how Chinese learners and NSs use DMs differently.

Keywords: you know, discourse marker, native speaker, Chinese learner

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Authors: Salwa Enneifer

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The research is intended to investigate Tunisian students’ own perception of the difficulties they encounter in the writing task. To achieve this objective, a questionnaire was administered to students enrolled in the ‘Faculty of Letters Arts and Humanities’ in Kairouan, in Tunisia. Students were classified into three groups: first-, second-, and third-year students. The researcher used 120 questionnaires filled in by the students as data for this study; moreover, 30 students participated in a semi-structured interview to complete the data. The questionnaire results revealed that Tunisian EFL students faced spelling and grammar difficulties. ANOVA also revealed that the first-year students did not recognise that Arabic and English greatly differ in their respective punctuation systems. The second-year class, however, was fully aware of this difference. Additionally, the interview shed light on other aspects or different difficulties experienced by students in writing: a cruel ‘lack of vocabulary’, Arabic language interference, the organisation of the essay and especially the academic essay, and difficulty with writing an argumentative essay.

Keywords: difficulties, writing, Tunisian, EFL students

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Authors: N. M. Zukani, E. O. Adu

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Homeschooling has been a primary method for parents to educate their children. It has become a growing educational phenomenon across the globe. However, homeschooling is, therefore, an alternative form of education in which children are instructed at home rather than in mainstream schools. This study evaluated the effectiveness of homeschooling in East London Education District, looking at the stakeholder’s perceptions, reviewing issues that impact on this as reflected in literature. This is a qualitative study done in selected homeschools. Semi structured interviews were used as a form of collecting data. Data was scrutinized and grouped into themes. The study revealed the importance of differentiation of instruction, and the need for flexibility in the process of homeschooling for children who faced difficulties, special needs in learning in mainstream schooling. It is therefore concluded that the participants in the study clearly showed that homeschooling is an educational choice for parents who have concerns about the quality of education of their children. Furthermore, homeschooling has the potential to be the most learner centered, nurturing educational approach. It was recommended that an effective homeschooling practice mainly, the practice should consider attention to children-parent’s goals and learning structure. Although homeschooling looks at how to overcome the drawbacks of mainstream schooling, there are also cases that reflected, the incompetency of parents or tutors conducting the homeschooling and also a need for the support material and other educational supports from the government.

Keywords: homeschooling, effectiveness, stakeholders, parents, perception

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Athena Johnson

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Special education is an important field but often under-researched, particularly for the cause of learning deficiencies. Often times special education looks at the symptoms rather than the cause, and this can lead to many misdiagnoses. Student trauma, as measured by the Adverse Childhood Experiences (ACE) test, is extremely common, often resulting in Post Traumatic Stress Disorder (PTSD). PTSD affects the brain's ability to learn properly, making students have a much more difficult time with auditory learning and memory due to always being in flight or fight mode, and due to this, students with PTSD are often misdiagnosed with Attention Deficit and Hyperactivity Disorder (ADHD). This can lead to them getting the wrong support, with PTSD students needing more counseling than anything else. Through these research papers' methodologies, a literature review on article research from the perspectives of students who were misdiagnosed, and imperial research, the major findings of this study were the importance of trauma-informed care in schools. Trauma-informed care in the school system is crucial for helping the many students who experience traumatic life events and struggle in school due to it. It is important to support students with PTSD so that they are able to integrate and learn better in society and school with trauma-informed school care.

Keywords: ACE test, ADHD, misdiagnoses, special education, trauma, trauma-informed care, PTSD

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Authors: Oloruntegbe Kunle Oke, Alake Ese Monica, Odutuyi Olubu Musili

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This research explores the differing effectiveness of cascade and cluster models in professional development programs for educators in Ondo State, Nigeria. The cascade model emphasizes a top-down approach, where training is cascaded from expert trainers to lower levels of teachers. In contrast, the cluster model, a bottom-up approach, fosters collaborative learning among teachers within specific clusters. Through a review of the literature and empirical studies of the implementations of the former in two academic sessions followed by the cluster model in another two, the study examined their effectiveness on teacher development, productivity and students’ achievements. The study also drew a comparative analysis of the strengths and weaknesses associated with each model, considering factors such as scalability, cost-effectiveness, adaptability in various contexts, and sustainability. 2500 teachers from Ondo State Primary Schools participated in the cascade with intensive training in five zones for a week each in two academic sessions. On the other hand, 1,980 and 1,663 teachers in 52 and 34 clusters, respectively, were in the first and the following session. The programs were designed for one week of rigorous training of teachers by facilitators in the former while the latter was made up of four components: sit-in-observation, need-based assessment workshop, pre-cluster and the actual cluster meetings in addition to sensitization, and took place one day a week for ten weeks. Validated Cluster Impact Survey Instruments, CISI and Teacher’s Assessment Questionnaire (TAQ) were administered to ascertain the effectiveness of the models during and after implementation. The findings from the literature detailed specific effectiveness, strengths and limitations of each approach, especially the potential for inconsistencies and resistance to change. Findings from the data collected revealed the superiority of the cluster model. Response to TAQ equally showed content knowledge and skill update in both but were more sustained in the cluster model. Overall, the study contributes to the ongoing discourse on effective strategies for improving teacher training and enhancing student outcomes, offering practical recommendations for the development and implementation of future professional development projects.

Keywords: cascade model, cluster model, teachers’ development, productivity, students’ achievement

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Authors: Fundime Miri, Emanuela Sulaj

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Benthic ecosystems constitute important ecological habitats, providing fundamental services for spawning, foraging, and sheltering aquatic organisms. Benthic faunal communities are characterized by a large biological diversity, supported by a great physical variety of benthic habitats. Until late, the study of meiobenthic communities in Albania has been neglectedthus excluding an important component of benthos. The present study aims to bring characteristics of distribution pattern of meiofaunal communities with further focus on nematode genus-based diversity from different intertidal habitats along Albanian Adriatic Sea Coast. The investigation area is extended from Shkodra to Vlora District, including six sandy sampling sites in beaches and areas near river estuaries. Sediment samples were collected manually in low intertidal zone by using a cylindrical corer, with an internal diameter of 5 cm. The richness onmeiofaunalmajor taxon level did not show any significant change between different sampling sites compare to significant changes in nematode diversity at genus level, with distinct nematode assemblages per sampling sites and presence of exclusive genera. All meiofaunal communities under study were dominated by nematodes. Further assessment of functional diversity on nematode assemblages exhibited changes as well on trophic groups and life strategies due to diverse feeding behaviors and c-p values of nematode genera. This study emphasize the need for lower level taxonomic identification of meiofaunal organisms and extending of ecological assessments on trophic diversity and life strategies to understanding functional consequences.

Keywords: benthos, meiofauna, nematode genus-based diversity, functional diversity, intertidal, albanian adriatic coast

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Ruwandika Perera

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Even though the Sri Lankan government provides a reasonable level of support for students at all levels of the school system, for example, free education, textbooks, school uniforms, subsidized public transportation, and school meals, low participation in learning among secondary students is an issue warranting investigation, particularly in low socio-economic districts. This study attempted to determine the levels of motivation and engagement amongst students in a number of schools in two low socio-economic districts of Sri Lanka. This study employed quantitative research design in an attempt to determine levels of motivation and engagement amongst Sri Lankan secondary school students. Motivation and Engagement Scale-Junior School (MES-JS) was administered among 100 Sinhala-medium and 100 Tamil-medium eighth-grade students (50 students from each gender). The mean age of the students was 12.8 years. Schools were represented by type 2 government schools located in Monaragala and Nuwara Eliya districts in Sri Lanka. Confirmatory factor analysis (CFA) was conducted to measure the construct validity of the scale. Since this did not provide a robust solution, exploratory factor analysis (EFA) was conducted. Four factors were identified; Failure Avoidance and Anxiety (FAA), Positive Motivation (PM), Uncertain Control (UC), and Positive Engagement (PE). An independent-samples t-test was conducted to compare PM, PE, FAA, and UC in gender and ethnic groups. There was no significant difference identified for PE, FAA, and UC scales based upon gender. These results indicate that for the participants in this study, there were no significant differences based on gender in the levels of failure avoidance and anxiety, uncertain control, and positive engagement in the school experience. But, the result for the PM scale was close to significant, indicating there may be differences based on gender for positive motivation. A significant difference exists for all scales based on ethnicity, with the mean result for the Tamil students being significantly higher than that for the Sinhala students. These results indicate those Sinhala-medium students’ levels of positive motivation and positive engagement in learning was lower than Tamil-medium students. Also, these results indicate those Tamil-medium students’ levels of failure avoidance, anxiety, and uncertain control was higher than Sinhala-medium students. It could be concluded that male students levels of PM were significantly lower than female students. Also, Sinhala-medium students’ levels of PM and PE was lower than Tamil-medium students, and Tamil-medium students levels of FAA and UC was significantly higher than Sinhala-medium students. Thus, there might be particular school-related conditions affecting this situation, which are related to early adolescents’ motivation and engagement in learning.

Keywords: early adolescents, engagement, low socio-economic districts, motivation

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Authors: Emmanuel Olugbenga Fakoya

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The study examined the perceived impact of climate change on the livelihood of arable crop farmers in Ipokia Local Government Area of Ogun State, Nigeria. Multistage sampling technique was used to select 80 arable crop farmers in the study area. Data collected were analyzed using percentages, frequencies and Chi square analysis. The result showed that 63.8 percent of the respondents were male while 55.0 percent were married. Less than half (30.0 percent) of the respondents were between the age bracket of 41-50 years and 50.0 percent had 6-10 household size. Furthermore, majority (40.0 percent) of the arable crop farmers farmed on an inherited land and 51.3 percent had 2-3 hectares of land. Majority (38.8 percent) of the farmers intercrop maize with cassava and maize with yam. Various strategies adapted to reduce the effect of climate change on their crop and livelihood include: crop rotation (53.8 percent), planting of leguminous crop (35.0 percent), application of organic fertilizers (45.0 percent), mulching (56.3 percent) and by planting drought resistance crops (46.5 percent). Reported among the effects of climate change on crop and farmers’ livelihood were: discoloration of crop leave (63.8 percent), increase infestation of pests and diseases (58.8 percent) and reduction of crop yield (60.0 percent). Chi- square analysis showed significant relationship between impact of climate change on arable crop production and thus famers’ livelihood. It was concluded from the study that climate change is an impinging factor that seriously affect arable crop production and hence farmers’ livelihood despite coping strategies to minimize its effect. It was however recommended that Agricultural policies and practices that could minimize or eliminate its effect should be seriously enacted to boost production and increase farmers’ livelihood.

Keywords: agricultural extension, extension agent, private sector, perception

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Wonder Ngezimana, Benjamin Alex Madzivire

Abstract:

As new public universities are being applauded for being promulgated in Zimbabwe, there is need for comprehensive plan for ensuring sustainable competitive advantages in their niche mandated areas. Unhealthy competition between university faculties for enrolment hinders growth of the newly established universities faculties, especially in the agricultural sciences related disciplines. Blue ocean metaphor is based on creation of competitor-free market unlike 'red oceans', which are well explored and crowded with competitors. This study seeks to explore the potential contribution of blue oceans strategy (BOS) for growth of universities with bias towards faculties of agriculture in public universities in Zimbabwe. Case studies with agricultural sciences related disciplines were selected across three universities for interviewing. Data was collected through 10 open ended questions on academics in different management positions within university faculties of agriculture. Summative analysis was thereafter used during coding and interpretation of the data. Study findings show that there are several important elements for making offerings more comprehendible towards fostering faculty growth and performance with bias towards student enrolment. The results points towards BOS form of value innovations with various elements to consider in faculty offerings. To create valued innovation beyond the red oceans, the cases in this study have to be modelled to foster changes in enrolment, modes of delivery, certification, being research oriented with excellence in teaching, ethics, service to the community and entrepreneurship. There is, therefore, need to rethink strategy towards reshaping inclusive enrolment, industry relevance, affiliations, lifelong learning, sustainable student welfare, ubuntu, exchange programmes, research excellence, alumni support and entrepreneurship. Innovative strategic collaborations and partnerships, anchored on technology boost the strategic offerings henceforth leveraging on various offerings in this study. Areas of further study include the amplitude of blue oceans shown in the university faculty offerings and implementation strategies of BOS.

Keywords: blue oceans strategy, collaborations, faculty offerings, value innovations

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Authors: Ivy Chumo, Sheillah Simiyu, Hellen Gitau, Isaac Kisiangani, Caroline Kabaria Kanyiva Muindi, Blessing Mberu

Abstract:

The global sanitation workforce bridges the gap between sanitation infrastructure and the provision of sanitation services through essential public service work. Manual pit emptiers often perform the work at the cost of their dignity, safety, and health as their work requires repeated heavy physical activities such as lifting, carrying, pulling, and pushing. This exposes them to occupational and environmental health hazards and risking illness, injury, and death. The study will extend the studies by presenting occupational health risks and suggestions for improvement in informal settlements of Nairobi, Kenya. This is a qualitative study conducted among sanitation stakeholders in Korogocho, Mukuru and Kibera informal settlements in Nairobi. Data were captured using digital voice recorders, transcribed and thematically analysed. The discussion notes were further supported by observational notes made during the interviews. These formed the basis for a robust picture of occupational health of manual pit emptiers; a lack or inappropriate use of protective clothing, and prolonged duration of working hours were described to contribute to the occupational health hazard. To continue working, manual pit emptiers had devised coping strategies which include working in groups, improvised protective clothing, sharing the available protective clothing, working at night and consuming alcohol drinks while at work. Many of these strategies are detrimental to their health. Occupational health hazards among pit emptiers are key for effective working and is as a result of a lack of collaboration amongst stakeholders linked to health, safety and lack of PPE of pit emptiers. Collaborations amongst sanitation stakeholders is paramount for health, safety, and in ensuring the provision and use of personal protective devices.

Keywords: sanitation, occupational health, manual emptiers, informal settlements

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Authors: Aysen Handan Girginer

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This study analyzes the possible causes of miscommunication between pilots and air traffic controllers by looking into a number of variables such as pronunciation, L1 interference, use of non-standard vocabulary. The purpose of this study is to enhance the knowledge of the aviation LSP instructors and to apply this knowledge to the design of new curriculum. A 16-item questionnaire was administered to 60 Turkish pilots who work for commercial airlines in Turkey. The questionnaire consists of 7 open-ended and 9 Likert-scale type questions. The analysis of data shows that there are certain pit holes that may cause communication problems for pilots that can be avoided through proper English language training. The findings of this study are expected to contribute to the development of new materials and to develop a language training model that is tailored to the needs of students of flight training department at the Faculty of Aeronautics and Astronautics. The results are beneficial not only to the instructors but also to the new pilots in training. Specific suggestions for aviation students’ training will be made during the presentation.

Keywords: curriculum design, materials development, LSP, pilot training

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Authors: Xiguan Jiangfan Yang

Abstract:

This study investigates the tea consumption based on the Double-Hurdle model. The results of a CHNS survey of 12,745 samples in China offer two preliminary insights: First, we can’t apply the conclusions we get by using all samples to the men or women subgroups. Second, men and women are impacted by different demographic not only on the intention to drink tea, but also on the quantities of tea consumed. These two findings suggest that appropriate and corresponding marketing strategies should be developed to targeting on the different groups of tea consumers.

Keywords: Chinese, CHNS, Double-Hurdle model, tea consumption

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Authors: Dangut Maren David, Skaf Zakwan

Abstract:

Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Cesar Vazquez-Gonzalez, Sophie Avila-Foucat, Leonardo Ortiz-Lozano, Patricia Moreno-Casasola, Alejandro Granados-Barba

Abstract:

In the last twenty years, academic and scientific literature has been focused on understanding the processes and factors of coastal social-ecological systems vulnerability and resilience. Some scholars argue that resilience and vulnerability are isolated concepts due to their epistemological origin, while others note the existence of a strong resilience-vulnerability relationship. Here we present an ordinal logistic regression model based on the analytical framework about dynamic resilience-vulnerability interaction along adaptive cycle of complex systems and disasters process phases (during, recovery and learning). In this way, we demonstrate that 1) during the disturbance, absorptive capacity (resilience as a core of attributes) and external response capacity explain the probability of households capitals to diminish the damage, and exposure sets the thresholds about the amount of disturbance that households can absorb, 2) at recovery, absorptive capacity and external response capacity explain the probability of households capitals to recovery faster (resilience as an outcome) from damage, and 3) at learning, adaptive capacity (resilience as a core of attributes) explains the probability of households adaptation measures based on the enhancement of physical capital. As a result, during the disturbance phase, exposure has the greatest weight in the probability of capital’s damage, and households with absorptive and external response capacity elements absorbed the impact of floods in comparison with households without these elements. At the recovery phase, households with absorptive and external response capacity showed a faster recovery on their capital; however, the damage sets the thresholds of recovery time. More importantly, diversity in financial capital increases the probability of recovering other capital, but it becomes a liability so that the probability of recovering the household finances in a longer time increases. At learning-reorganizing phase, adaptation (modifications to the house) increases the probability of having less damage on physical capital; however, it is not very relevant. As conclusion, resilience is an outcome but also core of attributes that interacts with vulnerability along the adaptive cycle and disaster process phases. Absorptive capacity can diminish the damage experienced by floods; however, when exposure overcomes thresholds, both absorptive and external response capacity are not enough. In the same way, absorptive and external response capacity diminish the recovery time of capital, but the damage sets the thresholds in where households are not capable of recovering their capital.

Keywords: absorptive capacity, adaptive capacity, capital, floods, recovery-learning, social-ecological systems

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Authors: Dina C. Castro, Rossana Boyd, Eugenia Papadaki

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Immigration within and across continents is currently a global reality. The number of people leaving their communities in search for a better life for them and their families has increased dramatically during the last twenty years. Therefore, young children of the 21st century around the World are growing up in diverse communities, exposed to many languages and cultures. One consequence of these migration movements is the increased linguistic diversity in school settings. Depending on the linguistic history and the status of languages in the communities (i.e., minority-majority; majority-majority) the instructional approaches will differ. This session will discuss how bilingualism is addressed in early education programs in both minority-majority and majority-majority language communities, analyzing experiences in three countries with very distinct societal and demographic characteristics: Peru (South America), the United States (North America), and Italy (European Union). The ultimate goal is to identify commonalities and differences across the three experiences that could lead to a discussion of bilingualism in early education from a global perspective. From Peru, we will discuss current national language and educational policies that have lead to the design and implementation of bilingual and intercultural education for children in indigenous communities. We will also discuss how those practices are being implemented in preschool programs, the progress made and challenges encountered. From the United States, we will discuss the early education of Spanish-English bilingual preschoolers, including the national policy environment, as well as variations in language of instruction approaches currently being used with these children. From Italy, we will describe early education practices in the Bilingual School of Monza, in northern Italy, a school that has 20 years promoting bilingualism and multilingualism in education. While the presentations from Peru and the United States will discuss bilingualism in a majority-minority language environment, this presentation will lead to a discussion on the opportunities and challenges of promoting bilingualism in a majority-majority language environment. It is evident that innovative models and policies are necessary to prevent inequality of opportunities for bilingual children beginning in their earliest years. The cross-cultural examination of bilingual education experiences for young children in three part of the World will allow us to learn from our success and challenges. The session will end with a discussion of the following question: To what extent are early care and education programs being effective in promoting positive development and learning among all children, including those from diverse language, ethnic and cultural backgrounds? We expect to identify, with participants to our session, a set of recommendations for policy and program development that could ensure access to high quality early education for all bilingual children.

Keywords: early education for bilingual children, global perspectives in early education, cross-cultural, language policies

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

Abstract:

This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

Procedia PDF Downloads 429