• Clinical and Experimental Pediatrics
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• 제65권3호
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• pp.142-149
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• 2022

Background: Individuals with Down syndrome present with several impairments such as hypotonia, ligament laxity, decreased muscle strength, insufficient muscular cocontraction, inadequate postural control, and disturbed proprioception. These factors are responsible for the developmental challenges faced by children with Down syndrome. These individuals also present with balance dysfunctions. Purpose: This systematic review aims to describe the motor dysfunction and balance impairments in children and adolescents with Down syndrome. Methods: We searched the Scopus, ScienceDirect, MEDLINE, Wiley, and EBSCO databases for observational studies evaluating the motor abilities and balance performance in individuals with Down syndrome. The review was registered on PROSPERO. Results: A total of 1,096 articles were retrieved; after careful screening and scrutinizing against the inclusion and exclusion criteria, 10 articles were included in the review. Overall, the children and adolescents with Down syndrome showed delays and dysfunction in performing various activities such as sitting, pulling to stand, standing, and walking. They also presented with compensatory mechanisms to maintain their equilibrium in static and dynamic activities. Conclusion: The motor development of children with Down syndrome is significantly delayed due to structural differences in the brain. These individuals have inefficient compensatory strategies like increasing step width, increasing frequency of mediolateral center of pressure displacement, decreasing anteroposterior displacement, increasing trunk stiffness, and increasing posterior trunk displacement to maintain equilibrium. Down syndrome presents with interindividual variations; therefore, a thorough evaluation is required before a structured intervention is developed to improve motor and balance dysfunction.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.173-182
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• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.

• 대한원격탐사학회지
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• 제39권4호
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Composites Research
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• 제19권3호
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• pp.7-14
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• 2006

본 논문에서는 내압 하중을 받는 복합재 압력 용기의 신뢰도를 구하기 위해 확률적 강도 해석이 수행되었다. 이때 확률적 강도 해석은 점진적 파손 모델과 몬테카를로 시뮬레이션으로 구성된 확률 연속 파손 모델과 상용 유한 요소 해석 코드인 ABAQUS가 연계한 형태로서 복잡한 형상 및 경계 조건을 갖는 복합재 구조물의 확률적 파손 해석을 수행하게 된다. 설계확률 변수로서 복합재 층의 각 방향 별 강도가 고려되었다. 최종적으로, 확률 강도 해석을 통해 복합재 압력 용기의 파열 압력 분산 현상이 설명되었고, 복합재 압력 용기의 각 부위별 신뢰도 값이 제시되었다. 양산 중인 복합재 구조물인 경우, 재료 및 제작 공정의 불확실성이 구조물 성능에 미치는 영향이 더욱 커지게 되어 확률 강도 해석을 이용한 구조 설계가 필수적이다.

• Steel and Composite Structures
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• 제42권5호
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• pp.633-647
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• 2022

This paper presents the results from reliability analyses of the current Eurocode 4 (EN 1994-1-1) and AISC 360-16 design models for predicting the resistance of headed stud shear connectors within profiled steel sheeting, when the ribs are oriented transverse to the supporting beam. For comparison purposes, the performance of the alternative "Luxembourg" and "Stuttgart" model were also considered. From an initial database of 611 push-out tests, 269 cases were included in the study, which ensured that the results were valid over a wide range of geometrical and material properties. It was found that the current EN 1994-1-1 design rules deliver a corrected partial safety factor γ_M^* of around 2.0, which is significantly higher than the target value 1.25. Moreover, 179 tests fell within the domain of the concrete-related failure design equation. Notwithstanding this, the EN 1994-1-1 equations provide satisfactory results for re-entrant profiled sheeting. The AISC 360-16 design equation for steel failure covers 263 of the tests in the database and delivers 𝛾_M^*≈2.0. Conversely, whilst the alternative "Stuttgart" model provides an improvement over the current codes, only a corrected partial safety factor of 𝛾_M^*=1.47 is achieved. Finally, the alternative "Luxembourg" design model was found to deliver the required target value, with a corrected partial safety factor 𝛾_M^* between 1.21 and 1.28. Given the fact that the Luxembourg design model is the only model that achieved the target values required by EN 1990, it is recommended as a potential candidate for inclusion within the second generation of Eurocodes.

• 무역학회지
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• 제41권5호
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• pp.285-304
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• 2016

본 연구는 국제무역에 있어서 전자상거래가 중소기업의 성공에 어떠한 영향을 미치는지를, 중국 전자상거래 기업들의 설문조사를 통하여 실증적으로 분석한다. 또한 구조방정식 모형을 통하여 중국 기업의 전자상거래 핵심 성공요인을 추출, 분석하여 중국 중소기업의 전자상거래 발전 핵심요인으로 최고경영자의 혁신성, 최고경영자의 IT지원 및 기업 전략의 세 가지 요인을 통계적으로 유의미하게 도출하였고, 중국 중소기업 맞춤형 해외진출 플랫폼이 필요하다는 정책적 시사점을 함께 제언한다.

• 산업경영시스템학회지
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• 제46권3호
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• pp.198-208
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• 2023

In order to enhance competitiveness in the industry, financial companies are building a high level of customer satisfaction and repurchase intention by further strengthening not only the technical quality of the platform business but also the customer-oriented service quality. Theoretically, it is time for a theoretical review of whether the expansion of service quality using platform business in the financial industry is directly linked to the performance of financial companies, such as satisfaction and repurchase intention of existing customers. Based on the rapid growth of mobile and the main activities of financial platform companies above, This study attempted to test a significant impact on customer satisfaction and reuse intention on information services and system services, which are service quality of mobile financial platforms. Even if a number of financial companies compete with each other, they could survive by dividing the market, In the digital environment, customers have free access, so the winner can monopolize the market. It is an environment in which customers can move to platform companies that provide better services. The contents presented through the results in this study will be able to be used strategically in terms of the implementation and operation of the financial platform. In addition, it served as an opportunity to find independent variables that affect customer satisfaction and reuse intention, which are financial platform service quality, and suggested the possibility of continuous development of the platform in the future. In summary, the service quality of financial platforms can further expand users by emphasizing user visibility in terms of information services and utilizing user-centered financial platforms that increase customer satisfaction and reliability by strengthening the responsiveness and ease of system services. This study is of important value and is believed to have laid an important foundation for future research.

• 한국재난정보학회 논문집
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• 제19권4호
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• pp.882-896
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• 2023

연구목적: 국가산업단지 공장시설물이 보유한 내진설계의 잠재적 위험과 안전지도 및 점검의 미비점을 효과적으로 향상시킬 수 있는 안전관리 및 내진보강대책 마련을 목적으로 한다. 연구방법: 본 연구에서는 국가산업단지의 지진재난 대비 안전관리 현황 및 관리체계 등 지진재난 안전관리 전반에 관한 조사·분석을 통해 문제점 및 개선방안을 도출하였고, 국가산업단지 내 입주기업체 내진설계 실태조사를 실시하여 시설유형과 구조적 특성에 기반한 개선방안을 제시하였다. 연구결과: 결론적으로 국가산업단지 지진재난 대비 안전관리 및 내진보강에 대한 문제점을 정리하여 4가지 유형별(내진성능 평가 및 관련 제도 보완, 입주기업 및 지자체 권한, 내진보강 및 안전관리 지원대책, 조직의 역량강화)로 개선방안을 도출하였다. 결론: 이를 기반으로 국가산업단지 입주기업이 지진재난을 대비하여 추진해야 하는 내진보강 대책을 마련하였으며, 각 대책별 세부적인 방안을 제시하였다.

• 한국방재안전학회논문집
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• 제17권1호
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• pp.27-35
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• 2024

본 논문은 순환골재 표면에 부착된 시멘트 페이스트 및 모르타르를 제거하는 데 있어 화학적 중화반응 및 초음파 세척의 적용 가능성을 평가하기 위한 실험적 연구 결과를 제시한다. 최적의 초음파 세척 효율 및 화학적 중화반응을 도출하기 위하여 초음파 진동수, 화학용액의 종류 등을 변수로 한 실험이 수행되었다. 그 결과 최적 진동수는 24 kHz로 나타났으며, 염산 15% 용액에 침지시킨 후 30분 가진하는 것이 최적 조건이라는 결론이 도출되었다. 또한, 품질 개선된 순환 굵은 골재의 비중, 흡수율, 마모율은 일반골재와 유사하며 KS F 2527 기준을 모두 만족하는 것으로 나타났다. 따라서, 본 연구를 통해 제안된 방법을 통해 품질 개선된 순환골재는 콘크리트용으로 사용이 가능할 것으로 판단된다.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.