• 유통과학연구
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• 제22권2호
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• pp.41-50
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• 2024

Purpose: This study aims to identify the distribution of skill evolution for accounting personnel during the health crisis and investigate the impact of accounting skills in developing resilience among accounting personnel. Research design, data, and methodology: A total of 131 respondents of accounting personnel participated in a self-administered survey questionnaire. This data is analysed using the partial least square structural equation modeling. Results: The results show that accounting skills, digital skills, and writing skills have a significant impact on developing accounting personnel's motivation, subsequently leading to resilience. Conclusions: This study adds to the literature on the new requirements and future profiles of Malaysian organisation and the accounting profession. This will be a good reference for the practitioners to identify the relevant skills required for accountants after the pandemic. Furthermore, this study includes encouraging motivation and skills to improve resilience in the Malaysian context further to understand the push factors on skills evolution among the accountants. Higher education institutions with accounting courses would consider the potential future skills of accountants to meet market demands on time when updating the institutions' curricula program. Hence, the relevant skills required can be developed and practiced at the education level, especially secondary and tertiary levels.

• ALGAE
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• 제38권4호
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• pp.295-306
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• 2023

Human coronavirus diseases, particularly severe acute respiratory syndrome coronavirus 2, still remain a persistent public health issue, and many recent studies are focusing on the quest for new leads against coronaviruses. To contribute to this growing pool of knowledge and explore the available marine natural products against coronaviruses, this study investigated the antiviral effects of fucoxanthin isolated from Sargassum siliquastrum-a brown alga found on Jeju Island, South Korea. The antiviral effects of fucoxanthin were confirmed in severe acute respiratory syndrome coronavirus 2-infected Vero cells, and its structural characteristics were verified in silico using molecular docking and molecular dynamic simulations and in vitro colorimetric method. Fucoxanthin inhibited the infection in a concentration-dependent manner, without showing cytotoxicity. Molecular docking simulations revealed that fucoxanthin binds to the angiotensinconverting enzyme 2-spike protein (binding energy -318.306 kcal mol^-1) and main protease (binding energy -205.118 kcal mol^-1). Moreover, molecular dynamic simulations showed that fucoxanthin remains docked to angiotensin-converting enzyme 2-spike protein for 20 ns, whereas it breaks away from main protease after 3 ns. Also, the in silico prediction of the fucoxanthin was verified through the in vitro colorimetric method by inhibiting the binding between angiotensinconverting enzyme 2 and spike protein in a concentration-dependent manner. These results indicate that fucoxanthin exhibits antiviral effects against severe acute respiratory syndrome coronavirus 2 by blocking the entry of the virus. Therefore, fucoxanthin from S. siliquastrum can be a potential candidate for treating coronavirus infection.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권12호
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• pp.357-362
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• 2023

교량은 중요한 교통 인프라지만 다양한 환경적 요인과 지속적인 교통 부하로 손상 및 균열을 겪게 되며, 이러한 요인들은 교량의 노후화를 가속화시킨다. 현재 건설한 지 오래된 교량이 많아지면서 안전성을 보장하고 노후화를 진단하기 위한 시스템의 필요성이 대두되고 있다. 이미 교량에서는 실시간 또는 주기적으로 교량의 상태를 모니터링하기 위해 구조물 건전도 모니터링(SHM) 기술이 활용되고 있다. 이 기술과 함께 인공지능과 사물인터넷 기술을 활용한 지능형 교량 모니터링 기술 개발이 진행 중이다. 본 논문에서는 노후화된 교량의 유지관리를 위해 고속 푸리에 변환과 차원 축소 알고리즘을 활용한 교량 안전성을 예측 엣지 시스템 기법을 연구한다. 특히, 기존 연구와는 다르게 실제 교량에서 수집된 센서 데이터를 이용하여 데이터셋을 형성하고 교량의 안전성을 확인할 수 있는지 알아본다.

• 한국방사선학회논문지
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• 제12권1호
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• pp.93-100
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• 2018

흡연 유무의 남성을 대상으로 뇌 백질의 손상 유무를 파악 할 수 있는 확산텐서영상을 검사하여 영상을 획득 한 후 Tract-Based Spatial Statics(TBSS)방법으로 뇌 백질 부위의 신경섬유로의 비등방도 FA(fractional anisotropy)값을 측정 분석한 결과 모든 영역에서 흡연자가 비흡연자보다 비등방성 측정값이 낮게 나타났지만 FA값은 통계적으로 유의하지 않았으며 오른쪽 맥락총 부위에 대한 FA값에서만 통계적으로 유의하였다. 본 연구의 결과 값으로 추측하자면 즉 흡연이 뇌 백질의 미세구조성 변화에 크게 영향을 미치지는 않지만 맥락총 부위에는 영향을 미친다고 할 수 있다.

• Smart Structures and Systems
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• 제33권2호
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Steel and Composite Structures
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• 제51권2호
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• pp.185-202
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• 2024

In this research paper, and for the first time, wave propagations in sigmoidal imperfect functionally graded material plates are investigated using a simplified quasi-three-dimensionally higher shear deformation theory (Quasi-3D HSDTs). By employing an indeterminate integral for the transverse displacement in the shear components, the number of unknowns and governing equations in the current theory is reduced, thereby simplifying its application. Consequently, the present theories exhibit five fewer unknown variables compared to other Quasi-3D theories documented in the literature, eliminating the need for any correction coefficients as seen in the first shear deformation theory. The material properties of the functionally graded plates smoothly vary across the cross-section according to a sigmoid power law. The plates are considered imperfect, indicating a pore distribution throughout their thickness. The distribution of porosities is categorized into two types: even or uneven, with linear (L)-Type, exponential (E)-Type, logarithmic (Log)-Type, and Sinus (S)-Type distributions. The current quasi-3D shear deformation theories are applied to formulate governing equations for determining wave frequencies, and phase velocities are derived using Hamilton's principle. Dispersion relations are assumed as an analytical solution, and they are applied to obtain wave frequencies and phase velocities. A comprehensive parametric study is conducted to elucidate the influences of wavenumber, volume fraction, thickness ratio, and types of porosity distributions on wave propagation and phase velocities of the S-FGM plate. The findings of this investigation hold potential utility for studying and designing techniques for ultrasonic inspection and structural health monitoring.

• 통합자연과학논문집
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• 제16권4호
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• pp.123-131
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• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.

• Smart Structures and Systems
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• 제34권2호
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• pp.129-143
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• 2024

In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects.

• East Asian Economic Review
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• 제28권3호
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• pp.359-388
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• 2024

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study's findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.

• Parasites, Hosts and Diseases
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• 제62권2호
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• pp.205-216
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• 2024

Sigma-class glutathione transferase (GST) proteins with dual GST and prostaglandin synthase (PGS) activities play a crucial role in the establishment of Clonorchis sinensis infection. Herein, we analyzed the structural and enzymatic properties of sigma-class GST (CsGST-σ) proteins to obtain insight into their antioxidant and immunomodulatory functions in comparison with mu-class GST (CsGST-µ) proteins. CsGST-σ proteins conserved characteristic structures, which had been described in mammalian hematopoietic prostaglandin D₂ synthases. Recombinant forms of these CsGST-σ and CsGST-µ proteins expressed in Escherichia coli exhibited considerable degrees of GST and PGS activities with substantially different specific activities. All recombinant proteins displayed higher affinities toward prostaglandin H₂ (PGS substrate; average K_m of 30.7 and 3.0 ㎛ for prostaglandin D₂ [PGDS] and E₂ synthase [PGES], respectively) than those toward CDNB (GST substrate; average K_m of 1,205.1 ㎛). Furthermore, the catalytic efficiency (K_cat/K_m) of the PGDS/PGES activity was higher than that of GST activity (average K_cat/K_m of 3.1, 0.7, and 7.0×10^-3 s^-1㎛^-1 for PGDS, PGES, and GST, respectively). Our data strongly suggest that the C. sinensis sigma- and mu-class GST proteins are deeply involved in regulating host immune responses by generating PGD₂ and PGE₂ in addition to their roles in general detoxification.