• Educational Technology International
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• v.9 no.2
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• pp.97-121
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• 2008

The purposes of the present study are to describe the design of mobile learning scenarios based on learning sciences theories, and to discuss implications for the future research in this area. To move beyond mere speculations about the abundant possibilities of mobile learning and to make real impact in K-12 school settings, it is critical to conduct school-based research grounded on the learning sciences theories. Towards this end, this paper describes school-based mobile learning projects conducted by a research team at the Learning Sciences Lab in Singapore, and then discusses the possibilities and challenges of mobile learning to further inform future research. Specifically, this paper explores the affordances of mobile technology, such as portability, connectivity and context-sensitivity, to design seamless learning scenarios that bridge formal and informal learning experiences. The authors present a framework for re-conceptualizing different types of learning based on physical settings and intentionality, and then describe two seamless learning scenarios, namely 3Rs and Chinatown Trail, which were implemented in one primary school in Singapore. In conclusion, the authors discuss the affordances of seamless mobile learning for enhancing one's lived experiences to build a living ecological relationship between the person and the environment, and how mobile technology can play a critical role for enabling such lived experiences.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.37-49
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• 2023

Rainfall data is one of the most important data in hydrologic modeling. In this study, the impacts of spatial resolution of precipitation data on hydrological responses were assessed using SWAT in the Santa Fe River Basin, Florida. High correlations were found between the FAWN and NLDAS rainfall data, which are observed weather data and simulated weather data based on observed data, respectively. FAWN-based scenarios had higher maximum rainfall and more rainfall days and events compared to NLDAS-based scenarios. Downstream areas showed lower correlations between rainfall and peak discharge than upstream areas due to the characteristics of study site. All scenarios did not show significant differences in base flow, and showed less than 5% of differences in high flows among NLDAS-based scenarios. The impact of resolution will appear differently depending on the characteristics of the watershed and topography and the applied model, and thus, is a process that must be considered in advance in runoff simulation research. The study suggests that applying the research method to watersheds in Korea may yield more pronounced results, and highlights the importance of considering data resolution in hydrologic modeling.

• The Journal of Information Systems
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• v.32 no.4
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• pp.149-162
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• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Journal of Korean Biological Nursing Science
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• v.26 no.2
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• pp.71-82
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• 2024

Purpose: This study systematically analyzed the impact of aromatherapy on pain in individuals with diabetes. Methods: A search was performed in seven electronic databases based on the PICO-SD (Population, Intervention, Comparison, Outcome, Study Design) framework. The population (P) of interest was individuals with diabetes, and the intervention (I) included aromatherapy targeting pain reduction. The comparison (C) consisted of control groups that received no intervention, another intervention, or usual care. The outcome (O) measured was pain. The quality of the selected literature was assessed using the Joanna Briggs Institute checklist. In MIX 2.0 Pro, the pooled overall effect of pain was calculated using Hedge's g and a random-effects model, and heterogeneity was calculated using the Q statistic and Higgin's I2 values. Meta-regression and exclusion sensitivity analyses were performed. Results: Five articles and seven studies were included, showing a significant pooled overall effect of aromatherapy on diabetes-related pain (Hedge's g = -1.83, 95% CI: -2.76 to -0.91). Meta-regression demonstrated that effectiveness in reducing pain was associated with studies conducted in West Asia, those with IRB approval, and those receiving funding. Additionally, interventions involving subjects under 60, lavender oil (vs. turpentine oil or blended oils), massage therapy (vs. topical application), fewer hours per session, and more repeated measurements (vs. pre/post measurements) were associated with pain reduction. Conclusion: Aromatherapy, especially with lavender oil, effectively manages diabetes-related pain. Short-duration massage application is also effective. A personalized selection of oil type and application method could optimize therapeutic outcomes for individuals with diabetes.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.197-211
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• 2024

This research studies the effect of geotechnical factors on EPB-TBM performance parameters. The modeling was performed using simple and multivariate linear regression methods, artificial neural networks (ANNs), and Sugeno fuzzy logic (SFL) algorithm. In ANN, 80% of the data were randomly allocated to training and 20% to network testing. Meanwhile, in the SFL algorithm, 75% of the data were used for training and 25% for testing. The coefficient of determination (R²) obtained between the observed and estimated values in this model for the thrust force and cutterhead torque was 0.19 and 0.52, respectively. The results showed that the SFL outperformed the other models in predicting the target parameters. In this method, the R² obtained between observed and predicted values for thrust force and cutterhead torque is 0.73 and 0.63, respectively. The sensitivity analysis results show that the internal friction angle (φ) and standard penetration number (SPT) have the greatest impact on thrust force. Also, earth pressure and overburden thickness have the highest effect on cutterhead torque.

• Steel and Composite Structures
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• v.50 no.2
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• pp.183-199
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• 2024

In this paper, a closed-form rigorous solution for interfacial stress in continuous steel beam with variable section strengthened with bonded prestressed FRP plates and subjected to a uniformly distributed load is developed using linear elastic theory and including the variation of fiber volume fractions with a longitudinal orientation of the fibers of the FRP plates. The results show that there exists a high concentration of both shear and normal stress at the ends of the laminate, which might result in premature failure of the strengthening scheme at these locations. The theoretical predictions are compared with other existing solutions. Overall, the predictions of the different solutions agree closely with each other. A parametric study has been conducted to investigate the sensitivity of interface behavior to parameters such as laminate and adhesive stiffness, the thickness of the laminate and the fiber volume fractions where all were found to have a marked effect on the magnitude of maximum shear and normal stress in the composite member. This research gives a numerical precision in relating to the others studies which neglect the effect of prestressed plate and the shear lag impact. The physical and geometric properties of materials are taken into account, and that may play an important role in reducing the interfacial stresses magnitude.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.419-429
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• 2024

The primary goal was to assess how the addition of cement dust, a byproduct known to be harmful, could be used to stabilize clay. Various percentages of cement dust were added to soil samples, which were then subjected to triaxial testing at different rates of strain using an unconsolidated undrained triaxial machine. Six different rates of strain were applied to analyze the response of the clay under different conditions, resulting in 216 triaxial sample tests. As the percentage of cement dust in the clay samples increased, there was a noticeable increase in the strength properties of the clay, indicating a positive effect of cement dust on the clay's strength characteristics. Higher rates of strain during testing led to increased strength properties of the clay. Varying cement dust content influenced the impact of increasing the rate of strain on the clay's strength properties. Higher cement dust content reduced the sensitivity of the clay to changes in strain rate, indicating that the clay became less responsive to changes in strain rate as cement dust content increased. Potential for Clay Stabilization Cement dust proved the potential to enhance the strength properties of clay, indicating its potential utility in clay stabilization applications. Both higher percentages of cement dust and higher rates of strain were found to increase the clay's strength. It's essential to consider both the percentage of cement dust and the rate of strain when assessing the strength properties of clay in practical applications.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.163-170
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• 2023

Since spent nuclear fuel assemblies (SFA) are transported to interim storage or final disposal facility after cooling the decay heat, finite element analysis (FEA) with simplification is widely used to show their integrity against cladding failure to cause dispersal of radioactive material. However, there is a lack of research addressing the comprehensive impact of shape and element simplification on analysis results. In this study, for the optimization of a typical pressurized water reactor SFA, different types of finite element models were generated by changing number of fuel rods, fuel rod element type and assembly length. A series of FEA in use of these different models were conducted under a shock load data obtained from surrogate fuel assembly transportation test. Effects of number of fuel rods, element type and length of assembly were also analyzed, which shows that the element type of fuel rod mainly affected on cladding strain. Finally, an optimal finite element model was determined for other practical application in the future.

• Transactions of Materials Processing
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• v.33 no.2
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• pp.112-117
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• 2024

In this study, the friction and wear characteristics of Inconel 600 in the superplastic forming process of Ti6Al4V were evaluated through pin-on-disc tests. To achieve an efficient and systematic experimental design, the Taguchi method was employed. The wear track of the Inconel 600 pin showed scratches in the sliding contact direction, confirming that the wear mechanism is abrasive wear. Through sensitivity analysis such as ANOVA and Main effects, it was confirmed that both normal force and sliding distance have a significant impact on the wear. Changes in sliding velocity and distance did not affect the friction coefficient, which remained relatively constant at approximately 0.380. The wear prediction model for Inconel 600 in the superplastic forming of Ti6Al4V was constructed, which can be utilized as a guideline for the prediction and management of tool wear.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4307-4326
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• 2024

This study investigates Equivalent Spherical Diameter (ESD) estimation at high inlet velocity pool scrubbing conditions using the Interfacial Area Transport Equation (IATE) diameter model including bubble-induced turbulence and interphase modeling. The compatibility of area-averaged Sauter Mean Diameter (SMD), areaaveraged Local Equivalent Diameter (LED) and void-weighted area-averaged LED approaches to estimate the ESD are explored and the proposed model is validated against available experimental data. The study reveals that the prevalent constant ESD assumption in pool scrubbing codes is not universal by showcasing a decreasing trend along the column due to intensive bubble breakup. The area-averaged LED approach fails to capture this trend, while the area-averaged SMD and void-weighted area-averaged LED approaches provide accurate estimations aligned with experimental data. Turbulence parameters, interfacial forces, and diameter modeling are identified as crucial for accurate predictions of flow and geometrical variables by setting up the OpenFOAM framework. A sensitivity analysis indicates that the inlet velocity has an acceptable effect on the ESD along the column. The ESD increases near the exit and decreases in the swarm region by increasing the inlet velocities. Turbulent intensity reduces ESD across all column sections while changes in aspect ratio minimally impact ESD. The study shows promise in developing correlations that take into account the spatial variation of ESD in pool scrubbing conditions.