• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.31.3-32
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• 2020

The surface brightness fluctuation (SBF) is one of the most crucial distance indicators for unresolved stellar systems at large distances. Here, we present an evolutionary population synthesis model of the surface brightness fluctuation (SBF) for normal and He-enriched simple stellar populations (SSPs). Our SBF model for the normal-He population agrees well with other existing models, but the He-rich populations bring about a substantial change in the SBF of SSPs. Our normal-He SBF model well reproduces the observed SBFs of the Milky Way globular clusters, but the SBFs of early-type galaxies in the Virgo Cluster are placed between the normal-He and He-rich SBF models. We show that the SBF-based distance estimation would be affected by up to a 10-20% level in I- and near-IR bands at given colors. Finally, we propose that when combined with independent metallicity and age indicators such as Mg2 and H��, the UV and optical SBFs can readily detect underlying He-rich populations in unresolved stellar systems. Given the degree of the SBF variation resulting from the population difference, we suggest that the distance measurement before the proper in-depth analysis of stellar populations should be done with great caution.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.32.2-32.2
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• 2020

Massive Compact Elliptical Galaxies (MCEGs) found in the local universe are as massive as normal galaxies but extremely compact (M∗ > 1011 Msun, Reff < 1.5 kpc). They are considered to be the relics of red nugget galaxies found at high redshift. They are not likely to have undergone many mergers, keeping their original mass and size. Moreover, it is expected that they host a dominant population of red (metal-rich) globular clusters rather than blue (metal-poor) ones. Indeed, Beasley et al. (2018) found that the color distribution of the cluster system of NGC 1277 is unimodal, showing only a red population. However, NGC 1277 is the only case whose cluster system was studied among MCEGs. In this study, we investigate globular cluster systems of 14 nearby MCEGs with a homogeneous data set of HST/WFC3 F814W/F160W archive images. We detect tens to hundreds of globular clusters in each galaxy and examine their color distributions. Surprisingly, the fractions of red globular clusters are similar to those of normal galaxies, and are much lower than that of NGC 1277. We additionally obtain Gemini/GMOS-N g'r'i' images of PGC 70520, one of the 14 nearby MCEGs, to detect more globular clusters from deeper and wider images. We will discuss the results from the Gemini data combined with the results from the HST data in relation with the formation of MCEGs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1916-1934
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• 2023

The phishing attack is a malicious emerging threat on the internet where the hackers try to access the user credentials such as login information or Internet banking details through pirated websites. Using that information, they get into the original website and try to modify or steal the information. The problem with traditional defense systems like firewalls is that they can only stop certain types of attacks because they rely on a fixed set of principles to do so. As a result, the model needs a client-side defense mechanism that can learn potential attack vectors to detect and prevent not only the known but also unknown types of assault. Feature selection plays a key role in machine learning by selecting only the required features by eliminating the irrelevant ones from the real-time dataset. The proposed model uses Hyperparameter Optimized Artificial Neural Networks (H-ANN) combined with a Hybrid Firefly and Grey Wolf Optimization algorithm (H-FFGWO) to detect and block phishing websites in Internet of Things(IoT) Applications. In this paper, the H-FFGWO is used for the feature selection from phishing datasets ISCX-URL, Open Phish, UCI machine-learning repository, Mendeley website dataset and Phish tank. The results showed that the proposed model had an accuracy of 98.07%, a recall of 98.04%, a precision of 98.43%, and an F1-Score of 98.24%.

• Advances in nano research
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• v.15 no.3
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• pp.225-237
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• 2023

Combination of novel technologies with traditional physiotherapy in rehabilitation in injured athletes have shown to provide improved time of recovery. In specific, nanodrugs delivery systems are widely utilized as a counterpart to the physiotherapy in injuries in sports. In the present study, we focus on the common injuries in dance-sports, their recovery and the effect combination of nano-particle drug delivery with the physiotherapy practices. In this regard, a comprehensive review on the common injuries in dance sport is provided. Moreover, the researches on the effectiveness of the nano-particle drug delivery in therapy of such injuries and in similar cases are provided. The possibility of using combination of nano-particle drug delivery and physiotherapy is discussed in detail. Finally, using artificial intelligence methods, predictions on the recovery time and after-treatment side-effects is investigated. Artificial Neural Network (ANN) predictions suggested that using nano-particle drug delivery systems along with physiotherapy practices could provide shortened treatment time to recovery in comparison to conventional drugs. Moreover, the post-recover effects are less than the conventional methods.

• Smart Structures and Systems
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• v.32 no.6
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• pp.383-391
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• 2023

Energy harvesting in trams may become a prevalent source of passive energy generation due to the high density of vibrational energy, and this may help power structural health monitoring systems for the trams. This paper presents a broadband vibrational energy harvesting device design that utilizes a varied frequency from a tram vehicle using a 2 DOF vibrational system combined with electromagnetic energy conversion. This paper will demonstrate stepwise optimization processes to determine mechanical parameters for frequency tuning to adjust to the trams' operational conditions, and electromagnetic parameters for the whole system design to maximize power output. The initial optimization will determine 5 important design parameters in a 2 DOF vibrational system, namely the masses (m₁, m₂ (and spring constants (k₁, k₂, k₃). The second step will use these parameters as initial guesses for the second optimization which will maintain the ratios of these parameters and present electrical parameters to maximize the power output from this system. The obtained values indicated a successful demonstration of design optimization as the average power generated increased from 1.475 mW to 17.44 mW (around 12 times).

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.49-90
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• 2024

Intradisciplinary research refers to research that integrates ideas often associated with different research domains in a discipline. Such cross-fertilization leverages abundant diversity present in the IS discipline to tackle increasingly complex IS problems and grand challenges. Despite its importance and recent attention, a concerted, sustained effort toward intradisciplinary research is lagging. A fundamental issue we see is a lack of an elaborate IS research map that effectively shows similarities and differences among research domains and demonstrates types of ideas that may travel and integrate into different domains. We thus aim to propose an elaborate IS research map and compile research elements that can be tried and combined across research domains. To do so, we utilize two IS classics (i.e., IT views and IS phenomena), identify their complementarity, and interweave the two disparate ways of portraying the IS research field. The resultant view-phenomenon (VP) matrix specifies research domains based on two consistent, comprehensive criteria and helps researchers discern similarities and differences among research domains more effectively. The VP matrix also sheds light on a variety of research elements that can flow across research domains. The VP matrix along with the research elements together facilitate intradisciplinary efforts and, more broadly, help the IS discipline to prosper. The VP matrix is particularly helpful for doctoral students and young scholars.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.92-124
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• 2022

Providing services using kiosks is actively carried out between suppliers and consumers. These service processes have recently begun to play a dominant role in transactions. However, previous self-service technology (SST) studies or kiosks have not fully reflected the changing environment surrounding these different technologies. To cover the updated business environments, we combined qualitative and quantitative research methods. Through qualitative research and a review of previous studies, the variables emphasized as motivations and constraints for kiosks use and those that can be newly illuminated were selected for this study. We then applied the variables to the research model to assess their influence. In terms of the motivations for using kiosks, the results suggest that perceived usefulness and compatibility as service quality, forced use, and perceived service providers' efficiency as provider polices, absorptive capacity, and habit as an individual characteristic and social influence as a subjective norm have a significant effect on the attitude toward kiosks. In terms of constraints, difficult to use and need for interaction predicts the attitude toward kiosks. Attitude toward kiosks, perceived behavioral control, and social influence are directly related to the intention to use kiosks. Lastly, intention to use kiosks plays a significant role as an antecedent of revisit intention. Using these empirical results, we propose both academic and practical implications for future kiosks use.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.32-50
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• 2022

Technology acceptance is one of the most popular research areas. Rapid developments in technology are making human life more comfortable. However, still most of the rural area has been deprived of benefits of technological advancement. Seventy percent population of India resides in rural area. Leveraging the improved penetration of the internet; mobile friendly population in rural India has been increasingly shopping online in the last few years. e-Governance is one of the important vehicles to provide efficient services to the citizens by Governments. One major obstacle is acceptance of e-Governance platforms by the citizens. Considering the increasing trend of using e-Commerce in rural area, this paper attempts to investigate moderating effect of online shopping experience on intention to use e-Governance portals. We surveyed 365 villagers across Maharashtra: one of the leading states in India. The result confirmed online shopping experience moderates the relationship between: 'perceived security & privacy' and 'attitude'; 'perceived security & privacy' and 'intention to use'; 'Perceived usefulness' and 'attitude'; and, 'attitude' and 'intention to use'. In this study definition of moderating variable 'experience' is unique and different than most of the popular studies. We defined experience as: 'prior use of any application of technology similar to the target application of technology'. Whereas prior studies considered experience as prior experience with target application of the technology.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.275-306
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• 2022

Crowdfunding has emerged as an important financing source for diverse cultural projects and commercial ventures in the early stages. Unlike traditional investment evaluation, where structured financial data is critical, such information is typically unavailable for crowdfunding campaigns. Instead, campaign creators prepare pitches containing essential information about themselves and the campaigns, which are crucial in attracting and persuading contributors. Prior literature has examined the effects of different aspects in campaign pitches, but a comprehensive understanding of the theme is lacking. This study aims to fill this gap by identifying the lexicon of frequently used vocabulary in campaign pitches and examining how they are associated with crowdfunding success. Moreover, we examine how the association differs between culture and commercial crowdfunding campaigns. We randomly collected 50,000 campaigns from the cultural and commercial categories on Kickstarter and extracted the 100 most used verbs in the campaign pitches. Based on a machine learning approach combined with principal component analysis, we constructed sets of verbal factors statistically significant in predicting crowdfunding success. The findings also show that cultural and commercial campaigns consist of different verbal components with different effects on crowdfunding success.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.