The major cause of human mortality worldwide is cardiovascular disease, and lowering the mortality rate depends on early detection and intervention. By examining huge datasets of patient data to find similarities, mac...
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The integration of Application-Driven Data Analytics into Engineering and Engineering Technology courses has become increasingly important due to the growing significance of data in the modern industry. This research ...
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ISBN:
(纸本)9780791887325
The integration of Application-Driven Data Analytics into Engineering and Engineering Technology courses has become increasingly important due to the growing significance of data in the modern industry. This research aims to explore the potential of introducing new application-driven data analytics modules to advance the teaching effectiveness of the existing fundamental Engineering and Engineering Technology courses in undergraduate education. This pilot project involves one study module called "The Value of Data Analytics in Engineering" in a Machine Design course at Miami University Engineering Technology Department and a Data Analytics programming course at Oklahoma State University Industrial Engineering & Management School. The lecture in this module includes topics of data collection, manipulation, analysis, and visualization. A lab session is also conducted to analyze the collected tensile test data using python programming, where various analyses are performed to understand the material properties of the test samples. These analyses include stress-strain curves learning and visualization, material properties calculation, and results comparison to reference data. To gather feedback on the effectiveness of this new study module, students are given surveys after the lectures and lab sessions. The surveys focus on the outcomes of this approach in helping students understand the course material, the ease of tools usage, and the student's interests in learning further of Data Analytics topics in their future courses. In total 39 students are surveyed for feedback on the lecture and lab session. Overall, the feedback gathered from the students indicates that the approach has been effective. The survey results show that 72% of the students have gained better understanding of data analytics, and 79% of the students are interested in integrating more of the topics into existing curriculum. The use of python in Google Colab has also been well received by the students,
The rapid rise of digital learning platforms has ushered in an era of educational transformation. While these platforms offer the advantage of scalability, they often fall short in facilitating meaningful interaction,...
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ISBN:
(纸本)9786269689026
The rapid rise of digital learning platforms has ushered in an era of educational transformation. While these platforms offer the advantage of scalability, they often fall short in facilitating meaningful interaction, which is pivotal for effective learning. Addressing this concern, our study introduces PyGuru 2.0, an innovative online learning environment for python programming that aligns with the ICAP framework with an advanced conversational agent. We further investigate the interactions between students and a chatbot, employing a qualitative approach to comprehensively explore the diverse ways in which students interact with the chatbot. The interaction categories encompass a wide spectrum, including code assistance, error resolution, and conceptual explanation. In future, we plan to further elaborate on this coding scheme and see its impact on students' learning outcomes.
Personalized medicine is a tailored method in healthcare that uses specific genetic, environmental, and lifestyle information to develop individualized treatment plans. Its aim is to improve treatment outcomes by stay...
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Groundwater potentiality assessment is an effective tool for groundwater management. Data mining modeling techniques have been efficacious in this regard. This study explored the potential of a GIS-based PROMETHEE met...
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Groundwater potentiality assessment is an effective tool for groundwater management. Data mining modeling techniques have been efficacious in this regard. This study explored the potential of a GIS-based PROMETHEE method in the field of groundwater hydrology. The approach was applied to model aquifer potential conditioning factors derived from interpreted geoelectrical parameters. 68 depth sounding (VES) data locations were identified in Ipinsa, a typical multifaceted geologic hardrock terrain. The acquired VES data were quantitatively interpreted to determine the subsurface lithologic parameters in the form of resistivity and thickness. The interpreted results were used to derive the groundwater potential conditioning factors (GPCFs), namely: longitudinal conductance (Lc), transverse resistance (TR), transmissivity (T), reflection coefficient (Rc) and recharge rate (Re). Using the derived geoelectrical-based GPCFs values, the GPCFs themes were produced in the GIS platform. The produced GPCFs themes were multi-critically analyzed using the mechanism of python programming-based Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II) data mining model algorithm to produce groundwater potentiality indexing (GPI) map. Furthermore, to compare the performance of the (PROMETHEE-II) data mining model result, multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model was applied. The efficiency of the PROMETHEE-II and MCDA-AHP-based GPI maps were evaluated using well data records. The results of the well data correlation with the predictive model maps show regression coefficients of 78% and 75% for PROMETHEE-II and MCDA-AHP data mining models, respectively. These results show that both models have good performance in prediction of groundwater potential zones, with the PROMETHEE-II as a better alternative. These maps and models could be used as future planning tool and part of decision support for decision making for locating appropr
In this study, we proposed novel metrics for evaluating volleyball technical performance in relation to the action context. To assess each player's relative participation, we also introduced relative contextual co...
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In this study, we proposed novel metrics for evaluating volleyball technical performance in relation to the action context. To assess each player's relative participation, we also introduced relative contextual coefficients. We analyzed 20 games played by the world's top eight teams during the 2019 FIVB Women's Club World Championship, using Data Volley software and python programming. We evaluated inter- and intra-observer reliability and used binomial logistic regression models to estimate each variable's probability of having contributed to the team's set win. We calculated estimated confidence intervals, standard errors, and Wald values;and we employed Akaike's and Bayesian criteria to evaluate the model's goodness of fit. We identified optimal cut-off points using receiver operating characteristic curves, and we found that the proposed contextual evaluation coefficients prevented overestimation of a player's technical performance in uneven situations. We addressed the issue in which the winning team may be the one that scored the fewest points, and we were able to predict a team's victory with confidence. The proposed coefficients made multiple technical and contextual aspects of the game easily accessible and comprehensible, offering significant beneficial implications for coaches and players.
Margaret Atwood is a Canadian author of more than thirty-five books and the winner of prestigious literary prizes,such as the Booker Prize,the Giller Prize,and the Governor General's *** influence on Canadian lite...
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Margaret Atwood is a Canadian author of more than thirty-five books and the winner of prestigious literary prizes,such as the Booker Prize,the Giller Prize,and the Governor General's *** influence on Canadian literature and contemporary literature as a whole is ***,little is known with respect to how Atwood represents animals covering the full range of her *** paper reports on the analysis of animal representations in Atwood's seventeen novels through python programming and close reading under the framework of a new semiotic research finding,a pan-indexicality model within the context of literature and the *** study investigates the frequencies of animal vocabulary in the seventeen novels,the changes of animal representations in her novels before 1990s and after 1990s,and the implication of the ever-changing animal representations during the fty *** paper concludes that nonhuman animal descriptions in Atwood's novels of 1970s and 1980s run at a high level and decrease in her novels of 1990s,while scientific animal descriptions increase in her novels of 2000s and *** animals in her novels of 1970s and 1980s are instrumentalized as a vehicle for indigenization and national individuation from the United States,and scientific animals in her novels of 2000s and 2010s are instrumentalized in the service of environmental *** study suggests that the pan-indexicality model can be employed to understand the meaning of signs in literature and the environment from the perspective of authorial intention,with reference to authors'encyclopedic knowledge,personal experience,social,and cultural background information.
When students in CS1 (Introductory programming) write erroneous code, course staff can use automated tools to provide various types of helpful feedback. In this paper, we focus on syntactically correct student code co...
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ISBN:
(纸本)9798400708404
When students in CS1 (Introductory programming) write erroneous code, course staff can use automated tools to provide various types of helpful feedback. In this paper, we focus on syntactically correct student code containing logical errors. Tools that explain logical errors typically require course staff to invest greater effort than tools that detect such errors. To reduce this effort, prior work has investigated the use of Large Language Models (LLMs) such as GPT3 to generate explanations. Unfortunately, these explanations can be incomplete or incorrect, and therefore unhelpful if presented to students directly. Nevertheless, LLM-generated explanations may be of adequate quality for Teaching Assistants (TAs) to efficiently craft helpful explanations on their basis. We evaluate the quality of explanations generated by an LLM (GPT-3.5-turbo) in two ways, for 30 buggy student solutions across 6 code-writing problems. First, in a study with 5 undergraduate TAs, we compare TA perception of LLM-generated and peer-generated explanation quality. TAs were unaware which explanations were LLM-generated, but they found them to be comparable in quality to peer-generated explanations. Second, we performed a detailed manual analysis of LLM-generated explanations for all 30 buggy solutions. We found at least one incorrect statement in 15/30 explanations (50%). However, in 28/30 cases (93%), the LLM-generated explanation correctly identified at least one logical error. Our results suggest that for large CS1 courses, TAs with adequate training to detect erroneous statements may be able to extract value from such explanations.
It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent ...
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It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly applicable statistical computing methods. This article describes efforts to teach Monte Carlo simulation using python. A series of simulation assignments are completed first in Google Sheets, as described in a previous article. Then, the same simulation assignments are completed in python, as detailed in this article. This pedagogical strategy appears to support student learning for those who are unfamiliar with statistical computing but familiar with the use of spreadsheets. for this article are available online.
Citrus fruits are among the most important food components. Orange is one of the most important citrus fruits because of its vitamin C content and delicious taste, and it is planted and harvested in most countries. Ap...
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ISBN:
(纸本)9798350393897
Citrus fruits are among the most important food components. Orange is one of the most important citrus fruits because of its vitamin C content and delicious taste, and it is planted and harvested in most countries. Aphids, pests, and fungi cause the destruction of orange fruits. Therefore, it is very important to quickly identify diseases. Several factors are important to improve the ripening of the orange fruit, so that it does not suffer from viruses or fungi. First, several important viruses and fungi and their causes in oranges should be investigated, and ways to improve orange ripening should be made easy;here, we used an image processing system and Red, Green, Blue (RGB) to Hue, Saturation, Lightness (HSL) conversion to identify several important viruses and fungi from the color of orange peel. In addition, the proposed algorithm detects both healthy and diseased oranges. The accuracy of the present study was 93.6%. In this study, an algorithm for diagnosing orange-fruit diseases was proposed. This algorithm is performed using python programming and executed by the Raspberry Pi. Therefore, oranges with Melanosis, Canker, and Black Spot disease were detected. The defective part of the orange fruit is shown in the form of mask and HSL images as well as the integration of the mask and HSL images in the output. After running the program, the severity of the disease is shown through the number of pixels of the defective part of the orange, and guidance for the prevention and treatment of the disease is described.
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