• Journal of Distribution Science
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• v.21 no.1
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• pp.53-63
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• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.114-114
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• 2020

Water contamination in a water distribution network (WDN) is harmful since it directly induces the consumer's health problem and suspends water service in a wide area. Actions need to be taken rapidly to countermeasure a contamination event. A contaminant source ident ification (CSI) is an important initial step to mitigate the harmful event. Here, a CSI approach focused on determining the contaminant intrusion possible location and time (PLoT) is introduced. One of the methods to discover the PLoT is an inverse calculation to connect all the paths leading to the report specification of a sensor. A filtering procedure is then applied to narrow down the PLoT using the results from individual sensors. First, we spatially reduce the suspect intrusion points by locating the highly suspicious nodes that have similar intrusion time. Then, we narrow the possible intrusion time by matching the suspicious intrusion time to the reported information. Finally, a likelihood-score is estimated for each suspect. Another important aspect that needs to be considered in CSI is that there are inherent uncertainties, such as the variations in user demand and inaccuracy of sensor data. The uncertainties can lead to overlooking the real intrusion point and time. To reflect the uncertainties in the CSI process, the Monte-Carlo Simulation (MCS) is conducted to explore the ranges of PLoT. By analyzing all the accumulated scores through the random sets, a spread of contaminant intrusion PLoT can then be identified in the network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.94-96
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• 2021

In this paper, we develop a new navigation algorithm for industrial mobile robots to arrive at the destination in unknown environment. To achieve this, we suggest a navigation algorithm that combines Dynamic Window Approach (DWA) and Dijkstra path planning algorithm. We compare Local Dynamic Window Approach (LDWA), Global Dynamic Window Approach(GDWA), Rapidly-exploring Random Tree (RRT) Algorithm. The navigation algorithm using Dijkstra algorithm combined with LDWA and GDWA makes mobile robots to reach the destination. and obstacles faced during the path planning process of LDWA and GDWA. Then, we compare on time taken to arrive at the destination, obstacle avoidance and computation complexity of each algorithm. To overcome the limitation, we seek ways to use the optimized navigation algorithm for industrial use.

• Physical Therapy Rehabilitation Science
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• v.12 no.2
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• pp.177-184
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• 2023

Objective: Manual therapy is a commonly utilized approach in managing chronic pain, but its specific impact on pain catastrophizing remains uncertain. The objective of this systematic review and meta-analysis was to examine the effects of manual therapy on pain catastrophizing in individuals with chronic pain. Design: A systematic review and meta-analysis Methods: A comprehensive search was conducted in electronic databases to identify relevant studies published from 2014 onwards. Studies that evaluated the impact of manual therapy on pain catastrophizing in individuals with chronic pain were incorporated. The risk of bias in the selected studies was evaluated using the Cochrane tool for risk of bias in qualitative analysis. For the quantitative analysis, RevMan 5.4 software was utilized, employing a random-effects model as the analysis model. The effect measure used in the analysis was the standardized mean difference (SMD). Results: In total, 26 studies were collected, and following the screening process, three of them were incorporated into the final analysis. The included studies involved a total of 153 patients with chronic pain. The interventions comprised various manual therapy techniques targeting different areas of the body. Pain catastrophizing and pain intensity were the primary outcomes of interest. The meta-analysis revealed a significant reduction in pain catastrophizing scores following manual therapy intervention compared to control conditions (SMD = -0.91, 95% CI: -1.25 to -0.58). However, heterogeneity between the studies was observed. Conclusions: Despite the limited quantity and heterogeneity of studies, it has been demonstrated that manual therapy intervention is effective in reducing pain catastrophizing in individuals with chronic pain.

• Computers and Concrete
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• v.32 no.4
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.4
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• pp.63-68
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• 2023

Purpose: This study aims to investigate the national obligation of public health support for residents in medically vulnerable areas in Korea, and to propose a suitable model for public health institutions in this region. Research design, data, and methodology: A survey targeting residents was conducted from August 10 to August 17, 2021, with a sample size of 177 general citizens. The survey utilized a structured questionnaire administered online through Google, employing convenience random sampling. After an editing process to ensure data accuracy, the final dataset of 174 valid samples underwent encoding, coding, and cleaning using the IBM SPSS Statistics 22.0 program for analysis. Results: Health status revealed a moderate level, and 63.8% reported having chronic diseases, particularly prevalent among the elderly. External healthcare institutions were commonly utilized, with proximity and competence of doctors being primary reasons. Respondents expressed a need for improving the public health and medical system, emphasizing the establishment of a County Health Centre and expanding medical departments. Conclusions: In this region, the region's unique challenges, including education, employment, population decline, aging, and transportation, require multidimensional efforts and urgent intervention by public entities. Long-term strategies involve considering the establishment of a health and medical institute, adjusting health centre resources to local realities, and fostering a cooperative system for collaboration among residents and institutions.

• Journal of Ecology and Environment
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• v.47 no.3
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• pp.75-84
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• 2023

Background: Knowledge of the spatial trends of plant invasions in different habitats is essential for a better understanding of the process of these invasions. We examined the variation in invasive alien plant species (IAS) richness and composition at two spatial scales defined by elevation and habitat types (roadside, forest, and cultivated lands) in the Makawanpur district of Nepal. Following an elevation gradient ranging from 500 to 2,400 m asl along a mountain road, plant species cover was recorded within sample plots of size 10 m × 5 m. Systematic random sampling was adopted in every 100 m elevation intervals on three habitat types. Results: Altogether 18 invasive alien plants belonging to eight families were recorded within 60 plots, of which 14 species (representing 80%) were from tropical North and South America. The most common plants by their frequency were Ageratina adenophora, Chromolaena odorata, Bidens pilosa, Lantana camara, and Parthenium hysterophorus. We found a significant relationship between species composition and elevation in the study area. Low-elevation regions had a higher number of alien species as compared to high-elevation regions within different habitat types. Conclusions: The species richness and density of IAS were higher in the road site followed by the cultivated land and forest sites. This pattern occurred throughout the elevation range and habitats. IAS were found mostly in the open land with high sunlight availability. Information from such scientific assessment of invasive alien plants will assist in developing appropriate management plans in the Makawanpur district.

• ETRI Journal
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• v.45 no.5
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• pp.822-835
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• 2023

Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.