• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3438-3457
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• 2022

As location-based services (LBS) are widely used in vehicular ad-hoc networks (VANETs), location privacy has become an utmost concern. Spatial cloaking is a popular location privacy protection approach, which uses a cloaking area containing k-1 collaborative vehicles (CVs) to replace the real location of the requested vehicle (RV). However, all CVs are assumed as honest in k-anonymity, and thus giving opportunities for dishonest CVs to submit false location information during the cloaking area construction. Attackers could exploit dishonest CVs' false location information to speculate the real location of RV. To suppress this threat, an edge-assisted Trusted Collaborative Anonymity construction scheme called TCA is proposed with trust mechanism. From the design idea of trusted observations within variable radius r, the trust value is not only utilized to select honest CVs to construct a cloaking area by restricting r's search range but also used to verify false location information from dishonest CVs. In order to obtain the variable radius r of searching CVs, a multiple linear regression model is established based on the privacy level and service quality of RV. By using the above approaches, the trust relationship among vehicles can be predicted, and the most suitable CVs can be selected according to RV's preference, so as to construct the trusted cloaking area. Moreover, to deal with the massive trust value calculation brought by large quantities of LBS requests, edge computing is employed during the trust evaluation. The performance analysis indicates that the malicious response of TCA is only 22% of the collaborative anonymity construction scheme without trust mechanism, and the location privacy leakage is about 32% of the traditional Enhanced Location Privacy Preserving (ELPP) scheme.

• Asian Journal for Public Opinion Research
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• v.9 no.2
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• pp.188-211
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• 2021

Decreasing response rates to traditional survey methods, like face-to-face and telephone interviews, have led survey practitioners around the world to seek new ways of conducting surveys in recent years." The COVID-19 pandemic exacerbated this problem because it made conducting face-to-face interviews even more difficult than before. For example, it made conducting face-to-face surveys infeasible in 2020 in South Korea, and so the Korean Academic Multimode Open Survey (KAMOS) was unable to conduct a planned face-to-face survey to recruit new panel members. The entire 8,514-member panel, established via two-stage probability-based sampling from 2016 to 2019, was invited to take three online/telephone surveys in 2020. Of these panel members, 1,352 responded to at least one survey in 2020. To test to what extent the panel remained representative of the adult South Korean population, we compared the two groups of panel members: those who responded to at least one survey in 2020 and those who did not. After weighting both groups on the basis of age, sex, and geographical area, we analyzed their responses to some of the questions that were asked during multiple rounds of the face-to-face panel-recruiting interviews. Using Cohen's d for survey items that could be analyzed numerically and Cramér's V for categorical items, we were able to conclude that the respondents to the 2020 surveys were equivalent to the non-respondents in terms of both demographics and in the answers they originally gave to substantive questions on a variety of topics related to social science or public opinion research, including questions about quality of life, societal issue, and politics (Cohen's d items <0.2, 95% CI; Cramér's V items <0.1, 95% CI). This analysis may provide a model for others who wish to test the continued representativeness of their panel or who would like to use a different survey mode or change some other aspect of their methodology and test whether it is equivalent to their former methodology. Our success in building a panel that retained its representativeness may be useful to those in other countries where face-to-face surveys had previously been the norm but are becoming increasingly difficult to conduct.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.127-133
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• 2021

This study studies the effect of social welfare service quality on facility reuse for users of social welfare facilities. The purpose of this study is to study the effect of the parallel triple mediation effect of facility image user loyalty and facility satisfaction. To this end 219 users of social welfare facilities were analyzed using SPSS 23.0 for Windows and Process macro. As a result, first the variables of service quality facility image user loyalty facility satisfaction and facility reuse all formed significant positive relationships. Second facility image user loyalty and facility satisfaction had multiple mediating effects. This study presented a new model using multivariate in response to social welfare facilities in pandemic situations.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Safety and Health at Work
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• v.13 no.4
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• pp.394-400
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• 2022

Background: Impacts of exposure are generally monitored and recorded after injuries or illness occur. Yet, absence of conventional after-the-effect impacts (i.e., lagging indicators), tend to focus on physical health and injuries, and fail to inform if workers are not exposed to safety and health hazards. In contrast to lagging indicators, leading indicators are proactive, preventive, and predictive indexes that offer insights how effective safety and health. The present study is to validate an extended Voluntary Protection Programs (VPP) that consists of six leading indicators. Methods: Questionnaires were distributed to 13 organizations (response rate = 93.1%, 1,439 responses) in Taiwan. Cronbach α, multiple linear regression and canonical correlation were used to test the reliability of the extended Voluntary Protection Programs (VPP) which consists of six leading indicators (safe climate, transformational leadership, organizational justice, organizational support, hazard prevention and control, and training). Criteria-related validation strategy was applied to examine relationships of six leading indicators with six criteria (perceived health, burnout, depression, job satisfaction, job performance, and life satisfaction). Results: The results showed that the Cronbach's α of six leading indicators ranged from 0.87 to 0.92. The canonical correlation analysis indicated a positive correlation between the six leading indicators and criteria (1st canonical function: correlation = 0.647, square correlation = 0.419, p < 0.001). Conclusions: The present study validates the extended VPP framework that focuses on promoting safety and physical and mental health. Results further provides applications of the extended VPP framework to promote workers' safety and health.

• Analyses & Alternatives
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• v.3 no.1
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• pp.29-61
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• 2019

Within the discipline of International Relations (IR), the literatures on global governance (GG) and great power management (GPM) at best ignore each other, and at worst treat the other as a rival or enemy. On the one hand, the GPM literature, like both realism in all its forms, and neoliberalism, takes for granted the ongoing, disproportionate influence of the great powers in the management of the international system/society, and does not look much beyond that. On the other hand, the GG literature emphasizes the roles of smaller states, non-state actors and intergovernmental organizations (IGOs), and tends to see great powers more as part of the problem than as part of the solution. This paper argues that the rise to prominence of a non-traditional security agenda, and particularly of human security, has triggered a de facto merger of GPM and GG that the IR literature usually treated as separate and often opposed theories. We use the Ebola crisis of 2014-15 to show how an issue framed as human security brought about a multi-actor response that combined the key elements of GPM and GG. The security framing overrode many of the usual inhibitions between great powers and non-state actors in humanitarian crises, including even the involvement of great power military forces. Through examining broadly the way in which the Ebola crisis is tackled, we argue that in an age of growing human security challenges, GPM and GG are necessarily and fruitfully merging. The role of great powers in this new human security environment is moving away from the simple means and ends of traditional GPM. Now, great powers require the ability to cooperate and coordinate with multiple-level actors to make the GG/GPM nexus more effective and sustainable. In doing so they can both provide crucial resources quickly, and earn respect and status as responsible great powers. IGOs provide legitimation and coordination to the GPM/GG package, and non-state actors (NSAs) provide information, specialist knowledge and personnel, and links into public engagement. In this way, the unique features of the Ebola crisis provide a model for how the merger of GPM and GG might be taken forward on other shared-fate threats facing global international society.

• Nutrition Research and Practice
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• v.17 no.5
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• pp.1019-1027
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• 2023

BACKGROUND/OBJECTIVES: Sustainability has become one of the top priorities in the foodservice industry. With an increase in consumer interest in sustainability and educational opportunities in higher education, it is important to know what sustainable practices are implemented in campus dining and how sustainable practices affect consumers' responses. This study aims to identify the key sustainable practices in the campus dining context, and investigate the relationship by applying the stimulus-organism-response framework to determine whether the key sustainable practices influence consumers' perception and behavioral intentions. SUBJECTS/METHODS: The self-administered online survey was distributed to college students in 8 dining halls at a large southeastern university in the United States from September 20-October 10, 2019. A total of 382 valid questionnaires were collected, and factor analysis and multiple regressions were utilized to test the research model. RESULTS: This study identified 4 dimensions of campus sustainability with a total of sustainable practices: sustainable food, waste management, energy/water conservation, and recycling/reuse. Three dimensions of sustainable campus practices (i.e., sustainable food, waste management, recycling/reuse) played a significant role in consumers forming a perceived value while energy/water conservation did not significantly influence the consumers' perceived value toward the campus dining. Waste management was identified as the most important practice to enhance consumers' perceived value (β = 0.330). Using sustainable food and recycling/reuse were ranked second and third, respectively (β = 0.262, β = 0.154). The findings confirmed the significant positive relationship between perceived value and revisit intentions. CONCLUSIONS: The findings support the inclusion of dining sustainability as a critical component in explaining college students' perceived value and revisit intention toward campus dining. Furthermore, this study provides practical implications for university administrators and foodservice operators to consider the key sustainable practices to meet the consumers' value and revisit intentions.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Journal of Veterinary Science
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• v.25 no.3
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• pp.35.1-35.13
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• 2024

Importance: Experimental autoimmune encephalomyelitis (EAE) is an animal model of multiple sclerosis characterized by inflammation within the central nervous system. However, inflammation in non-neuronal tissues, including the lungs, has not been fully evaluated. Objective: This study evaluated the inflammatory response in lungs of EAE mice by immunohistochemistry and histochemistry. Methods: Eight adult C57BL/6 mice were injected with myelin oligodendrocyte glycoprotein_35-55 to induce the EAE. Lungs and spinal cords were sampled from the experimental mice at the time of sacrifice and used for the western blotting, histochemistry, and immunohistochemistry. Results: Histopathological examination revealed inflammatory lesions in the lungs of EAE mice, characterized by infiltration of myeloperoxidase (MPO)- and galectin-3-positive cells, as determined by immunohistochemistry. Increased numbers of collagen fibers in the lungs of EAE mice were confirmed by histopathological analysis. Western blotting revealed significantly elevated level of osteopontin (OPN), cluster of differentiation 44 (CD44), MPO and galectin-3 in the lungs of EAE mice compared with normal controls (p < 0.05). Immunohistochemical analysis revealed both OPN and CD44 in ionized calcium-binding adapter molecule 1-positive macrophages within the lungs of EAE mice. Conclusions and Relevance: Taken together, these findings suggest that the increased OPN level in lungs of EAE mice led to inflammation; concurrent increases in proinflammatory factors (OPN and galectin-3) caused pulmonary impairment.

• IMMUNE NETWORK
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• v.20 no.4
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• pp.33.1-33.19
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• 2020

We tested how adjuvants effect in a cancer vaccine model using an epitope derived from an autoactivation loop of membrane-type protease serine protease 14 (PRSS14; loop metavaccine) in mouse mammary tumor virus (MMTV)-polyoma middle tumor-antigen (PyMT) system and in 2 other orthotopic mouse systems. Earlier, we reported that loop metavaccine effectively prevented progression and metastasis regardless of adjuvant types and TH types of hosts in tail-vein injection systems. However, the loop metavaccine with Freund's complete adjuvant (CFA) reduced cancer progression and metastasis while that with alum, to our surprise, were adversely affected in 3 tumor bearing mouse models. The amounts of loop peptide specific antibodies inversely correlated with tumor burden and metastasis, meanwhile both T_H1 and T_H2 isotypes were present regardless of host type and adjuvant. Tumor infiltrating myeloid cells such as eosinophil, monocyte, and neutrophil were asymmetrically distributed among 2 adjuvant groups with loop metavaccine. Systemic expression profiling using the lymph nodes of the differentially immunized MMTV-PyMT mouse revealed that adjuvant types, as well as loop metavaccine can change the immune signatures. Specifically, loop metavaccine itself induces T_H2 and T_H17 responses but reduces T_H1 and T_reg responses regardless of adjuvant type, whereas CFA but not alum increased follicular T_H response. Among the myeloid signatures, eosinophil was most distinct between CFA and alum. Survival analysis of breast cancer patients showed that eosinophil chemokines can be useful prognostic factors in PRSS14 positive patients. Based on these observations, we concluded that multiple immune parameters are to be considered when applying a vaccine strategy to cancer patients.