Search results for: machine learning approach for neurological disorder assessment

Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Renata L. C. Leão, João E. Nascimento, Natalia C. E. S. Nascimento, Elaine S. Vasconcelos, Mércia L. Oliveira

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The quality control procedures of a radiopharmaceutical include the assessment of its chemical purity. The method suggested by international pharmacopeias consists of a thin layer chromatographic run. In this paper, the method proposed by the United States Pharmacopeia (USP) is compared to a direct method to determine the final concentration of aminopolyether in Fludeoxyglucose (¹⁸F-FDG) preparations. The approach (no chromatographic run) was achieved by placing the thin-layer chromatography (TLC) plate directly on an iodine vapor chamber. Both methods were validated and they showed adequate results to determine the concentration of aminopolyether in ¹⁸F-FDG preparations. However, the direct method is more sensitive, faster and simpler when compared to the reference method (with chromatographic run), and it may be chosen for use in routine quality control of ¹⁸F-FDG.

Keywords: chemical purity, Kryptofix 222, thin layer chromatography, validation

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Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

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Authors: Jiang Niu, Yue Jiang

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With the ultimate goal to automatically generate translated texts with high quality, machine translation has made tremendous improvements. However, its translations of literary works are still plagued with problems in coherence, esp. the translation between distant language pairs. One of the causes of the problems is probably the lack of linguistic knowledge to be incorporated into the training of machine translation systems. In order to enable readers to better understand the problems of machine translation in coherence, to seek out the potential knowledge to be incorporated, and thus to improve the quality of machine translation products, this study applies Theme-Rheme structure to examine how a machine-translated literary text is organized and developed in terms of coherence. Theme-Rheme structure in Systemic Functional Linguistics is a useful tool for analysis of textual coherence. Theme is the departure point of a clause and Rheme is the rest of the clause. In a text, as Themes and Rhemes may be connected with each other in meaning, they form thematic and rhematic progressions throughout the text. Based on this structure, we can look into how a text is organized and developed in terms of coherence. Methodologically, we chose Chinese and English as the language pair to be studied. Specifically, we built a comparable corpus with two modes of English translations, viz. machine translation (MT) and human translation (HT) of one Chinese literary source text. The translated texts were annotated with Themes, Rhemes and their progressions throughout the texts. The annotated texts were analyzed from two respects, the different types of Themes functioning differently in achieving coherence, and the different types of thematic and rhematic progressions functioning differently in constructing texts. By analyzing and contrasting the two modes of translations, it is found that compared with the HT, 1) the MT features “pseudo-coherence”, with lots of ill-connected fragments of information using “and”; 2) the MT system produces a static and less interconnected text that reads like a list; these two points, in turn, lead to the less coherent organization and development of the MT than that of the HT; 3) novel to traditional and previous studies, Rhemes do contribute to textual connection and coherence though less than Themes do and thus are worthy of notice in further studies. Hence, the findings suggest that Theme-Rheme structure be applied to measuring and assessing the coherence of machine translation, to being incorporated into the training of the machine translation system, and Rheme be taken into account when studying the textual coherence of both MT and HT.

Keywords: coherence, corpus-based, literary translation, machine translation, Theme-Rheme structure

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Authors: Rob Leslie, Taher Karimian

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The blockage of cross drainage structures is a risk that needs to be understood and managed or lessened through the design. A blockage is a random event, influenced by site-specific factors, which needs to be quantified for design. Under and overestimation of blockage can have major impacts on flood risk and cost associated with drainage structures. The importance of this matter is heightened for those projects located within sensitive lands. It is a particularly complex problem for large linear infrastructure projects (e.g., rail corridors) located within floodplains where blockage factors can influence flooding upstream and downstream of the infrastructure. The selection of the appropriate blockage factors for hydraulic modeling has been subject to extensive research by hydraulic engineers. This paper has been prepared to review the current Australian Rainfall and Runoff 2019 (ARR 2019) methodology for blockage assessment by applying this method to a transport corridor brownfield upgrade case study in New South Wales. The results of applying the method are also validated against asset data and maintenance records. ARR 2019 – Book 6, Chapter 6 includes advice and an approach for estimating the blockage of bridges and culverts. This paper concentrates specifically on the blockage of cross drainage structures. The method has been developed to estimate the blockage level for culverts affected by sediment or debris due to flooding. The objective of the approach is to evaluate a numerical blockage factor that can be utilized in a hydraulic assessment of cross drainage structures. The project included an assessment of over 200 cross drainage structures. In order to estimate a blockage factor for use in the hydraulic model, a process has been advanced that considers the qualitative factors (e.g., Debris type, debris availability) and site-specific hydraulic factors that influence blockage. A site rating associated with the debris potential (i.e., availability, transportability, mobility) at each crossing was completed using the method outlined in ARR 2019 guidelines. The hydraulic results inputs (i.e., flow velocity, flow depth) and qualitative factors at each crossing were developed into an advanced spreadsheet where the design blockage level for cross drainage structures were determined based on the condition relating Inlet Clear Width and L10 (average length of the longest 10% of the debris reaching the site) and the Adjusted Debris Potential. Asset data, including site photos and maintenance records, were then reviewed and compared with the blockage assessment to check the validity of the results. The results of this assessment demonstrate that the estimated blockage factors at each crossing location using ARR 2019 guidelines are well-validated with the asset data. The primary finding of the study is that the ARR 2019 methodology is a suitable approach for culvert blockage assessment that has been validated against a case study spanning a large geographical area and multiple sub-catchments. The study also found that the methodology can be effectively coded within a spreadsheet or similar analytical tool to automate its application.

Keywords: ARR 2019, blockage, culverts, methodology

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Authors: Till Gramberg

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In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

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Authors: Mahdi Farajzadeh Ajirlou

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The detailed serious adverse effects raised the stresses around the safety of individuals who have gotten COVID-19 vaccines. Numerous verification referrers that disease with COV-19 causes neurological dysfunction in a significant proportion of influenced patients, where these side effects show up seriously amid the disease, and still less is known approximately the potential long-term results for the brain, where the loss of olfaction could be a neurological sign and simple indications of COVID-19. Since publishing effective clinical trial results of mRNA coronavirus disease 2019 (COVID-19) and injecting it to the volunteers in 2020, numerous reports have emerged approximately about cardiovascular complications followed by the mRNA vaccination. Vaccination-associated adenopathy could be a constant imaging finding after the organization of COVID-19 antibodies that will lead to a symptomatic problem in patients with shown or suspected cancer, in whom it may be vague from dangerous nodal inclusion. In spite of all the benefits and viability of the coronavirus infection 2019 (COVID-19) antibodies specified in later clinical trials, a few other post-vaccination side impacts, such as lymphadenopathy (LAP), were observed. Also, numerous variables, including financial conditions, have played a critical part in expanding the number of people with COVID-19 infection and also much more side effects in that country. Amid the Coronavirus widespread, Iran has been experiencing extreme sanctions, which has faced this nation with an extreme financial crisis. Additionally, with COVID-19 widespread, there was a developing concern around the abuse of imaging exams extraordinarily within the pediatric populace, which highlights the issues pointed out by this review.

Keywords: radiology, vaccines, COVID-19, side effect

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Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

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Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

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Authors: Petros Roussos

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The present study focused on students' statistical reasoning related to Null Hypothesis Statistical Testing and p-values. Its objective was to test the hypothesis that neither the place (classroom, at a distance, online) nor the medium that actually supports the learning (ICT, internet, books) has an effect on understanding of statistical concepts. In addition, it was expected that students' attitudes towards statistics would not predict understanding of statistical concepts. The sample consisted of 385 undergraduate and postgraduate students from six state and private universities (five in Greece and one in Cyprus). Students were administered two questionnaires: a) the Greek version of the Survey of Attitudes Toward Statistics, and b) a short instrument which measures students' understanding of statistical significance and p-values. Results suggest that attitudes towards statistics do not predict students' understanding of statistical concepts, whereas the medium did not have an effect.

Keywords: attitudes towards statistics, blended learning, e-learning, statistical reasoning

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Authors: Ayesha M. Facey

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The evaluation sought to determine the factors associated with the high failure rate among students taking a first-year introductory statistics course. By utilizing Tyler’s Objective Based Model, the main objectives were: to assess the effectiveness of the lecturer’s teaching strategies; to determine the proportion of students who attends lectures and tutorials frequently and to determine the impact of infrequent attendance on performance; to determine how the assigned activities assisted in students understanding of the course content; to ascertain the possible issues being faced by students in understanding the course material and obtain possible solutions to the challenges and to determine whether the learning outcomes have been achieved based on an assessment of the second in-course examination. A quantitative survey research strategy was employed and the study population was students enrolled in semester one of the academic year 2015/2016. A convenience sampling approach was employed resulting in a sample of 98 students. Primary data was collected using self-administered questionnaires over a one-week period. Secondary data was obtained from the results of the second in-course examination. Data were entered and analyzed in SPSS version 22 and both univariate and bivariate analyses were conducted on the information obtained from the questionnaires. Univariate analyses provided description of the sample through means, standard deviations and percentages while bivariate analyses were done using Spearman’s Rho correlation coefficient and Chi-square analyses. For secondary data, an item analysis was performed to obtain the reliability of the examination questions, difficulty index and discriminant index. The examination results also provided information on the weak areas of the students and highlighted the learning outcomes that were not achieved. Findings revealed that students were more likely to participate in lectures than tutorials and that attendance was high for both lectures and tutorials. There was a significant relationship between participation in lectures and performance on examination. However, a high proportion of students has been absent from three or more tutorials as well as lectures. A higher proportion of students indicated that they completed the assignments obtained from the lectures sometimes while they rarely completed tutorial worksheets. Students who were more likely to complete their assignments were significantly more likely to perform well on their examination. Additionally, students faced a number of challenges in understanding the course content and the topics of probability, binomial distribution and normal distribution were the most challenging. The item analysis also highlighted these topics as problem areas. Problems doing mathematics and application and analyses were their major challenges faced by students and most students indicated that some of the challenges could be alleviated if additional examples were worked in lectures and they were given more time to solve questions. Analysis of the examination results showed that a number of learning outcomes were not achieved for a number of topics. Based on the findings recommendations were made that suggested adjustments to grade allocations, delivery of lectures and methods of assessment.

Keywords: evaluation, item analysis, Tyler’s objective based model, university statistics

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Authors: Hiromi Kawasaki, Satoko Yamasaki, Mika Iwasa, Tomoko Iki, Akiko Takaki

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In basic nursing, the conditions necessary for maintaining human health -temperature, humidity, illumination, distance from others, noise, moisture, meals, and excretion- were explained. Nursing students often think of these conditions in the context of a hospital room. In order to make students think of these conditions in terms of an environment necessary for maintaining health and preventing illness for residents, in the third year of community health nursing, students learned how to assess and improve the environment -particularly via the case of shelters in the event of a disaster. The importance of environmental management has increased in 2020 as a preventive measure against COVID-19 infection. We verified the effect of the lessons, which was decided to be conducted through distance learning. Sixty third-year nursing college students consented to participate in this study. Environmental standard knowledge for conducting environmental assessment was examined before and after class, and the percentage of correct answers was compared. The χ² test was used for the test, with a 5% significance level employed. Measures were evaluated via a report submitted by the students after class. Student descriptions were analyzed both qualitatively and descriptively with respect to expected health problems and suggestions for improvement. Students have already learned about the environment in terms of basic nursing in their second year. The correct answers for external environmental values concerning interpersonal distance, illumination, noise, and room temperature (p < 0.001) increased significantly after taking the class. Humidity was registered 83.3% before class and 93.3% after class (p = 0.077). Regarding the body, the percentage of students who answered correctly was 70% or more, both before and after the class. The students’ reports included overcrowding, high humidity/high temperature, and the number of toilets as health hazards. Health disorders to be prevented were heat stroke, infectious diseases, and economy class syndrome; improvement methods were recommended for hyperventilation, stretching, hydration, and waiting at home. After the public health nursing class, the students were able to not only propose environmental management of a hospital room but also had an understanding of the environment in terms of the lives of individuals, environmental assessment, and solutions to health problems. The response rate for basic items learned in the second year was already high before and after class, and interpersonal distance and ventilation were described by students. Students were able to use what they learned in basic nursing about the standards of the human mind and body. In the external environment, the memory of specific numerical values was ambiguous. The environment of the hospital room is controlled, and interest in numerical values may decrease. Nursing staff needs to maintain and improve human health as well as hospital rooms. With COVID-19, it was thought that students would continue to not only consider this point in reference to hospital rooms but also in regard to places where people gather. Even in distance learning, students were able to learn the important issues and lessons.

Keywords: environmental assessment, evacuation center, nursing education, nursing students

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Coarse-grained soils are known and commonly used in a wide range of geotechnical projects, including high earth dams or embankments for their high shear strength. The most important engineering property of these soils is friction angle which represents the interlocking between soil particles and can be applied widely in designing and constructing these earth structures. Friction angle and dilative behavior of coarse-grained soils can be estimated from empirical correlations with in-situ testing and physical properties of the soil or measured directly in the laboratory performing direct shear or triaxial tests. Unfortunately, large-scale testing is difficult, challenging, and expensive and is not possible in most soil mechanic laboratories. So, it is common to remove the large particles and do the tests, which cannot be counted as an exact estimation of the parameters and behavior of the original soil. This paper describes a new methodology to simulate particles grading distribution of a well-graded gravel sample to a smaller scale sample as it can be tested in an ordinary direct shear apparatus to estimate the stress-strain behavior, friction angle, and dilative behavior of the original coarse-grained soil considering its confining pressure, and relative density using a machine learning method. A total number of 72 direct shear tests are performed in 6 different sizes, 3 different confining pressures, and 4 different relative densities. Multivariate Adaptive Regression Spline (MARS) technique was used to develop an equation in order to predict shear strength and dilative behavior based on the size distribution of coarse-grained soil particles. Also, an uncertainty analysis was performed in order to examine the reliability of the proposed equation.

Keywords: MARS, coarse-grained soil, shear strength, uncertainty analysis

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Authors: Efleda Preclaro Tolentino

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Research indicates that an educator’s belief system is reflected in the way they structure the learning environment. Their values and belief system have the potential to positively impact school readiness through an understanding of children’s development and the creation of a stable, motivating environment. Based on the premise that the social environment influences the development of social skills, knowledge construct, and shared values of young children, this study examined verbal and nonverbal exchanges between early childhood teachers and their preschool students within the context of remote learning. Using the qualitative method of data collection, the study determined the nature of interactions between preschoolers and their teachers within a remote learning environment at a preschool in Southeast Asia that utilized the Mother Tongue-based Multilingual Education (MTBMLE) Approach. From the lens of sociocultural theory, the study investigated preschoolers’ use of literacies to convey meaning and to interact within a remote learning environment. Using a Strengths Perspective, the study revealed the creativity and resourcefulness of preschoolers in expressing themselves through trans-literacies that were made possible by the use of online mode of learning within cultural and subcultural norms. The study likewise examined how social skills acquired by young children were transmitted (verbally or nonverbally) in their interactions with peers during Zoom meetings. By examining the dynamics of social exchanges between teachers and children, the findings of the study underscore the importance of providing support for preschool students as they apply acquired values and shared practices within a remote learning environment. The potential of distance learning in the early years will be explored, specifically in supporting young children’s language and literacy development. At the same time, the study examines the role of teachers as cultivators of trans-literacies. The teachers’ skillful use of technology in facilitating young children’s learning, as well as in supporting interactions with families, will be examined. The findings of this study will explore the potential of distance learning in early childhood education to establish continuity in learning, supporting young children’s social and emotional transitions, and nurturing trans-literacies that transcend prevailing definitions of learning contexts. The implications of teachers and parents working collaboratively to support student learning will be examined. The importance of preparing teachers to be resourceful, adaptable, and innovative to ensure that learning takes place across a variety of modes and settings will be discussed.

Keywords: transliteracy, preschoolers, remote learning, strengths perspective

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Hatim Laalej, Jon Stammers

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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.

Keywords: machining, manufacturing, tool wear, signal processing

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Authors: Calvin Chiu, Samar Saade Needham

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The factors associated with the development of major depressive disorder (MDD) have been an ongoing area of concern within the field of psychopathology. Among parents, the rise in stress factors for individuals receiving less social support contributes to an increase in MDD cases. Understanding the causal aspects of MDD through the interworking of stress development within social support disparities provides critical insights into preventive measures for depressive symptoms. The present study seeks to assess the impact of social support on stress formation within MDD. Such that single parents lacking social support prompt an increase in stress formation, which proliferates the progression of MDD. Participants in this study were 450 ethnic minority mothers and fathers experiencing health inequities during pregnancy and early childhood. Perceived stress, social support, and depression are assessed by multi-item questionnaires that produce score ranges for general findings. Results indicated that lower social support scores resulted in higher depression scores, and higher perceived stress scores produced higher depression scores. Furthermore, single parents reported higher depression scores. These findings overlap with studies on paternal depression and suggest that MDD is a product of stress accumulation due to declining social support systems. Future studies may specify effective social support systems for decreasing stress accumulation in MDD formation in preventive strategies.

Keywords: major depressive disorder, stress formation, cognitive-behavioral outcomes, deficit-based behaviors

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Authors: Hector Barrios-Piña, Georgia García-Arellano, Salvador García-Rodríguez, Gerardo Bocanegra-García, Shashi Kant

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This work shows the methodology of application and the effectiveness of the Flipped Learning technique for Civil Engineering laboratory classes. It was experimented by some of the professors of the Department of Civil Engineering at Tecnológico de Monterrey while teaching their laboratory classes. A total of 28 videos were created. The videos primarily demonstrate instructions of the experimental practices other than the usage of tools and materials. The technique allowed the students to prepare for their classes in advance. A survey was conducted on the participating professors and students (semester of August-December 2019) to quantify the effectiveness of the Flipped Learning technique. The students reported it as an excellent way of improving their learning aptitude, including self-learning whereas, the professors felt it as an efficient technique for optimizing their class session, which also provided an extra slot for class-interaction. A comparison of grades was analyzed between the students of the traditional classes and with Flipped Learning. It did not distinguish the benefits of Flipped Learning. However, the positive responses from the students and the professors provide an impetus for continuing and promoting the Flipped Learning technique in future classes.

Keywords: flipped learning, laboratory classes, civil engineering, competences development

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Shishir Lamichhane, Saurav Dulal, Bibek Gautam, Madan Thapa Magar, Indraman Tamrakar

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Renewable energies such as wind turbines and solar photovoltaic have gained significance as a result of global environmental pollution and energy crises. These sources of energy are converted into electrical energy and delivered to end-users through the utility system. As a result of the widespread use of power electronics-based grid-interfacing technologies to accommodate renewable sources of energy, the prevalence of converters has expanded as well. As a result, the power system's rotating inertia is decreasing, endangering the utility grid's stability. The use of Virtual Synchronous Machine (VSM) technology has been proposed to overcome the grid stability problem due to low rotating inertia. The grid-connected inverter used in VSM can be controlled to emulate inertia, which replicates the external features of a synchronous generator. As a result, the rotating inertia is increased to support the power system's stability. A power angle control strategy is proposed in this paper and its model is simulated in MATLAB/Simulink to study the effects of parameter disturbances on the active power and frequency for a VSM. The system consists of a synchronous generator, which is modeled in such a way that the frequency drops to an unacceptable region during transient conditions due to a lack of inertia when VSM is not used. Then, the suggested model incorporating VSM emulates rotating inertia, injecting a controllable amount of energy into the grid during frequency transients to enhance transient stability.

Keywords: damping constant, inertia–constant, ROCOF, transient stability, distributed sources

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Authors: Petcharat Lovichakorntikul, John Walsh

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Meditation is in the process of becoming a globalized practice and its benefits have been widely acknowledged. The first wave of internationalized meditation techniques and practices was represented by Chan and Zen Buddhism and a new wave of practice has arisen in Thailand as part of the Phra Dhammakaya temple movement. This form of meditation is intended to be simple and straightforward so that it can easily be taught to people unfamiliar with the basic procedures and philosophy. This has made Phra Dhammakaya an important means of outreach to the international community. One notable aspect is to encourage adults to become like children to perform it – that is, to return to a naïve state prior to the adoption of ideology as a means of understanding the world. It is said that the Lord Buddha achieved the point of awakening at the age of seven and Phra Dhammakaya has a program to teach meditation to both children and adults. This brings about the research question of how practitioners respond to the practice of meditation and how should they be taught? If a careful understanding of how children behave can be achieved, then it will help in teaching adults how to become like children (albeit idealized children) in their approach to meditation. This paper reports on action research in this regard. Personal interviews and focus groups are held with a view to understanding self-learning methods with respect to Buddhist meditation and understanding and appreciation of the practices involved. The findings are considered in the context of existing knowledge about different learning techniques among people of different ages. The implications for pedagogical practice are discussed and learning methods are outlined.

Keywords: Buddhist meditation, Dhammakaya, meditation technique, pedagogy, self-learning

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: green home, resident aware, resident profile, activity learning, machine learning

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Authors: Ahmed Shuhaiber

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The explosion of the World Wide Web and the electronic trend of university teaching have transformed the learning style to become more learner-centred, Which has popularized the digital delivery of mediated lectures as an alternative or an adjunct to traditional lectures. Despite its potential and popularity, virtual lectures have not been adopted yet in Jordanian universities. This research aimed to fill this gap by studying the factors that influence student’s willingness to accept virtual lectures in one Jordanian University. A quantitative approach was followed by obtaining 216 survey responses and statistically applying the UTAUT model with some modifications. Results revealed that performance expectancy, effort expectancy, social influences and self-efficacy could significantly influence student’s attitudes towards virtual lectures. Additionally, facilitating conditions and attitudes towards virtual lectures were found with significant influence on student’s intention to take virtual lectures. Research implications and future work were specified afterwards.

Keywords: E-learning, student willingness, UTAUT, virtual Lectures, web-based learning systems

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Authors: Nouf Nasser, Nada Alotaibi, Jazzal Kandiel

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This research study is about the use of artificial intelligence in PhotoRoom. When an individual selects a photo, PhotoRoom automagically removes or separates the background from other parts of the photo through the use of artificial intelligence. This will allow an individual to select their desired background and edit it as they wish. The methodology used was an observation, where various reviews and parts of the app were observed. The review section's findings showed that many people actually like the app, and some even rated it five stars. The conclusion was that PhotoRoom is one of the best photo editing apps due to its speed and accuracy in removing backgrounds.

Keywords: removing background, app, artificial intelligence, machine learning

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Authors: Schröde C., Rodriguez D., Sánchez A., Abdul Malak, Churchill J., Boksmati T., Alharbi, Alsulmi H., Maghrabi S., Mowalad, Mutwalli R., Abualnaja Y.

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Formulating a strategy for sustainable development of the Kingdom of Saudi Arabia’s coastal and marine environment is at the core of the “Marine and Coastal Protection Assessment Study for the Kingdom of Saudi Arabia Coastline (MCEP)”; that was set up in the context of the Vision 2030 by the Saudi Arabian government and aimed at providing a first comprehensive ‘Status Quo Assessment’ of the Kingdom’s marine environment to inform a sustainable development strategy and serve as a baseline assessment for future monitoring activities. This baseline assessment relied on scientific evidence of the drivers, pressures and their impact on the environments of the Red Sea and Arabian Gulf. A key element of the assessment was the cumulative pressure hotspot analysis developed for both national waters of the Kingdom following the principles of the Driver-Pressure-State-Impact-Response (DPSIR) framework and using the cumulative pressure and impact assessment methodology. The ultimate goals of the analysis were to map and assess the main hotspots of environmental pressures, and identify priority areas for further field surveillance and for urgent management actions. The study identified maritime transport, fisheries, aquaculture, oil, gas, energy, coastal industry, coastal and maritime tourism, and urban development as the main drivers of pollution in the Saudi Arabian marine waters. For each of these drivers, pressure indicators were defined to spatially assess the potential influence of the drivers on the coastal and marine environment. A list of hotspots of 90 locations could be identified based on the assessment. Spatially grouped the list could be reduced to come up with of 10 hotspot areas, two in the Arabian Gulf, 8 in the Red Sea. The hotspot mapping revealed clear spatial patterns of drivers, pressures and hotspots within the marine environment of waters under KSA’s maritime jurisdiction in the Red Sea and Arabian Gulf. The cascading assessment approach based on the DPSIR framework ensured that the root causes of the hotspot patterns, i.e. the human activities and other drivers, can be identified. The adapted CPIA methodology allowed for the combination of the available data to spatially assess the cumulative pressure in a consistent manner, and to identify the most critical hotspots by determining the overlap of cumulative pressure with areas of sensitive biodiversity. Further improvements are expected by enhancing the data sources of drivers and pressure indicators, fine-tuning the decay factors and distances of the pressure indicators, as well as including trans-boundary pressures across the regional seas.

Keywords: Arabian Gulf, DPSIR, hotspot, red sea

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Authors: M. H. Yee, J. Md Yunos, W. Othman, R. Hassan, T. K. Tee, M. M. Mohamad

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The generation of an idea that goes through several phases is affected by individual factors, interests, preferences and motivation. The purpose of this research was to analyze the difference in difficulties of generating ideas according to individual learning styles. A total of 375 technical students from four technical universities in Malaysia were randomly selected as samples. The Kolb Learning Styles Inventory and a set of developed questionnaires were used in this research. The results showed that the most dominant learning style is among technical students is Doer. A total of 319 (85.1%) technical students faced difficulties in solving individual assignments. Most of the problem faced by technical students is the difficulty of generating ideas for solving individual assignments. There was no significant difference in difficulties of generating ideas according to students’ learning styles. Therefore, students need to learn higher order thinking skills enabling students to generate ideas and consequently complete assignments.

Keywords: difference, difficulties, generating idea, learning styles, Kolb Learning Styles Inventory

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Authors: Isabel Medina, Mario Duran

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Although Problem-based learning provides students with multiple opportunities for rigorous instructional experiences in which students are challenged to address problems in the community; there are still gaps in connecting community leaders to the PBL process. At a south Texas high school, community participation serves as an integral component of the PBL process. Problem-based learning (PBL) has recently gained momentum due to the increase in global communities that value collaboration and critical thinking. As an instructional approach, PBL engages high school students in meaningful learning experiences. Furthermore, PBL focuses on providing students with a connection to real-world situations that require effective peer collaboration. For PBL leaders, providing students with a meaningful process is as important as the final PBL outcome. To achieve this goal, STEM high school strategically created a space for community involvement to be woven within the PBL fabric. This study examines the impact community members had on PBL students attending a STEM high school in South Texas. At STEM High School, community members represent a support system that works through the PBL process to ensure students receive real-life mentoring from business and industry leaders situated in the community. A phenomenological study using a semi-structured approach was used to collect data about students’ perception of community involvement within the PBL process for one South Texas high school. In our proposed presentation, we will discuss how community involvement in the PBL process academically impacted the educational experience of high school students at STEM high school. We address the instructional concerns PBL critics have with the lack of direct instruction, by providing a representation of how STEM high school utilizes community members to assist in impacting the academic experience of students.

Keywords: phenomenological, STEM education, student engagement, community involvement

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Authors: Du T. Tran, Quyen T. L. Hoang

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The role of language learning strategies in second language acquisition has received increased attention across several disciplines in recent years. Language learning strategies have been shown to occur in many studies over the passing years with the aim of improving the efficiency of language learning. Following previous studies, this study endeavors to scrutinize language learning strategies employed by the students at Vo Minh Duc high school and the effect of motivation on students’ learning strategy choices. The responses are examined quantitatively and qualitatively to enhance their validity and reliability. Data are collected from 342 students’ responses to the questionnaire, interviews with ten teachers and fifteen students, and classroom observations. The findings reveal that students’ motivation has an enormous impact on the choice of language learning strategies. The results simultaneously show that students use many language learning strategies to enhance their communicative competence, but the most frequently used ones are cognitive and affective ones. Significant correlations among types of learning strategies and the influence of motivation on the choices of language learning strategies were consistent with previous studies. The study’s results are expected to be beneficial to teachers of English and students in terms of narrowing the gap between the students' language learning strategies and their teaching methodologies preferences and sketching out the best strategies to enhance students’ speaking skills. The implications of these findings and the importance of viewing learners holistically are discussed, and recommendations are made for ongoing research.

Keywords: learning strategies, speaking skills, memorization strategies, cognitive strategies, affective strategies

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, contrastive learning, unsupervised training

Procedia PDF Downloads 80