• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3567-3579
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• 2022

This paper provides an assessment of fluid transport and mixing processes inside the primary circuit of the test facility ROCOM through the numerical simulation of Test 2.1 with the system code ATHLET. The experiment represents an asymmetric injection of cold and non-borated water into the reactor coolant system (RCS) of a pressurized water reactor (PWR) to restore core cooling, an emergency procedure which may subsequently trigger a core re-criticality. The injection takes place at low velocity under single-phase subcooled conditions and presents a major challenge for the simulation in lumped parameter codes, due to multidimensional effects in horizontal piping and vessel arising from density gradients and gravity forces. Aiming at further validating ATHLET 3-D capabilities against horizontal geometries, the experiment conditions are applied to a ROCOM model, which includes a newly developed horizontal pipe object to enhance code prediction inside coolant loops. The obtained results show code strong simulation capabilities to represent multidimensional flows. Enhanced prediction is observed at the vessel inlet compared to traditional 1-D approach, whereas mixing overprediction from the descending denser plume is observed at the upper-half downcomer region, which leads to eventual deviations at the core inlet.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.221-239
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• 2021

This paper presents a multiscale modeling method for sheet molding compound (SMC) composites through a novel bundle packing reconstruction algorithm based on a micro-CT (Computed Tomography) image processing. Due to the complex flow pattern during the compression molding process, the SMC composites show a spatially varying orientation and overlapping of fiber bundles. Therefore, significant inhomogeneity and anisotropy are commonly observed and pose a tremendous challenge to predicting SMC composites' properties. For high-fidelity modeling of the SMC composites, the statistical distributions for the fiber orientation and local volume fraction are characterized from micro-CT images of real SMC composites. After that, a novel bundle packing reconstruction algorithm for a high-fidelity SMC model is proposed by considering the statistical distributions. A method for evaluating specimen level's strength and stiffness is also proposed from a set of high-fidelity SMC models. Finally, the proposed multiscale modeling methodology is experimentally validated through a tensile test.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.281-300
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• 2023

Recently, the call for better accountability and social responsibility from corporations has been regularly voiced, both in the academic literature and in public discussions. This poses a challenge to the existing literature in understanding consumption behaviors to direct them toward sustainable development. This study investigates the purchase intention of Gen Z consumers in Vietnam with green packaging products. Data were collected from 914 respondents by online questionnaire and then analyzed using OLS. The results suggest the significant influence of customers' income and packaging in driving customers' intention to use environmentally-friendly products. Specifically, consumers in a higher income class participate more actively in green purchases. However, problems associated with inadequate packaging are also illustrated, resulting in the poor perception of green messages and poor practice of ecological actions. Besides, subjective norms and green trust are found to be adversely related to green consumer intention. In addition, gender disparity in green behavior is reported, where female consumers show a higher tendency to ecological consumption than their male counterparts. Other demographic factors are also included in the model as control variables, which are age, education, price, environmental literacy, environmental concern, and psychological awareness, but they do not have a significant impact on green purchase intention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.

• Transactions of Materials Processing
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• v.32 no.3
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• pp.122-128
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• 2023

As electric vehicles (EV) have increasingly replaced the conventional vehicles with internal combustion engines (ICE), most of automotive makers are actively devoting to the technology development of EV parts. Accordingly, the manufacturing process for power source has been also shifting from engine/transmission to EV motor/reducer system. However, lack of experience in developing the EV motor still remains as a technical challenge. In this paper, we employed the forming simulation based on finite element modeling to solve this problem. In particular, in order to increase the accuracy of the forming simulation, we introduced the elastic-plastic constitutive model parameters for polymer-copper hybrid wire by investigating the individual strain-stress curves, and elastic modulus of polymer and copper. Then, the reliability of modeling procedure was confirmed by comparing the simulated results with experiments. Finally, the identified mechanical properties and finite element modeling were applied to a hairpin forming process, which involves multiple deformation paths such as bending, pressing, widening, and twisting. The proposed numerical approach can replace common experience or experiment based trials by reducing production time and cost in the future.

• Journal of Distribution Science
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• v.21 no.5
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• pp.53-61
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• 2023

Purpose: The purpose of this study is to consolidate research trends about the distribution of 'other customer perspective' on 'brand love' and 'brand anthropomorphism', as well as to identify prospective research topics and provide managers with suggestions. Design, data, and technique of research: The purpose of this article is to examine the distribution relationship between brand love and brand anthropomorphism using a systematic review and bibliographic mapping analysis (VOS viewer) using 23 documents from 2014 to 2023. Results: This will be a step in the correct path if brand managers can have a great interaction with their clients by using common anthropomorphism. Yet, a second challenge will be how to anthropomorphize the brand. Moreover, there is nothing simpler than discovering oneself in a brand when there are several pictures, life ethics, sentiments, and experiences that coincide. From a different perspective, the brand sometimes looks to be the ideal model for consumers to identify with, and even fall in love with since it makes them feel close to their significant other. Conclusion: The findings may help companies create a long-term brand strategy and anticipate additional consumer rewards and value. They may also enhance brand-customer theory.

• Educational Technology International
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• v.18 no.1
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• pp.1-25
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• 2017

Educational applications of big data analysis have been of interest in order to improve learning effectiveness and efficiency. As a basic challenge for educational applications, the purpose of this study is to develop a comprehensive data set scheme for learning analytics in the context of digital textbook usage within the K-12 school environments of Korea. On the basis of the literature review, the Start-up Mega Planning model of needs assessment methodology was used as this study sought to come up with negotiated solutions for different stakeholders for a national level of learning metrics framework. The Ministry of Education (MOE), Seoul Metropolitan Office of Education (SMOE), and Korean Education and Research Information Service (KERIS) were involved in the discussion of the learning metrics framework scope. Finally, we suggest a proposal for the national learning metrics framework to reflect such considerations as dynamic education context and feasibility of the metrics into the K-12 Korean schools. The possibilities and limitations of the suggested framework for learning metrics are discussed and future areas of study are suggested.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Journal of Distribution Science
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• v.20 no.6
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• pp.99-108
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• 2022

Purpose: This study aims to provide a further research challenge in digital marketing distribution and co-creation from relevant literature. The concept of digital marketing and co-creation has been known to impact the business sector positively, but it has not been utilized much in the government sector. Therefore, further research is needed to identify the role of digital marketing distribution and co-creation in increasing non-tax state revenue services of government institutions in Indonesia. Research design, data, and methodology: This study is based on a systematic literature review. The stages are (1) research scope review, (2) article extraction from journals, (3) article quality assessment, (4) article analysis, and (5) comprehensive report. Fifty articles published from 2011 to 2021 were collected from the Google Scholar website. Result: This study provides a proposed model that depicts all of the potential connections between digital marketing, co-creation, and non-tax state revenue. In addition, we also identify that the customer experience influences non-tax state revenue. Conclusions: This study attributes the use of the digital marketing distribution and co-creation concept in the government sector and its benefits for state organizations, which have not been investigated in previous studies.