• 대한영상의학회지
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• 제84권1호
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• pp.75-91
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• 2023

전립선영상 판독과 자료체계 버전 2.1에서는 다중 매개 자기공명영상(multiparametric MRI; 이하 mpMRI)을 사용하는 버전 2의 기술적인 변수와 영상 판독 기준이 개정되었다. 이러한 변화를 통해 전립선암 평가의 발전이 예상지만, 어떤 사항들은 아직까지 해결되지 않았고 새로운 문제점들이 부각되고 있다. 본 종설에서는 전립선영상 판독과 자료체계 2.1 버전의 간단한 개요와 새롭게 부상하는 다음과 같은 문제들에 대해 비판적인 관점에서 논의하고자 한다: mpMRI의 보다 자세한 프로토콜에 대한 필요, 개정된 이행부 판독기준에 대한 검증 부족, 개정된 확산강조영상 및 조영 증강 영상 판독기준, anterior fibromuscular stroma, 중심부 평가, 주변부 신호 및 종양 공격성, 구조화된 판독문 변화에 대한 명료화의 필요, 영상 품질과 수행능력 제어에 대한 필요 및 기타 적응증을 포함하도록 시스템 확장을 위한 적응증.

• 한국지리정보학회지
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• 제20권4호
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• pp.55-64
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• 2017

본 연구에서는 전국 단위의 방치되어 있는 대나무 임분을 대상으로 GIS를 이용하여 분포 유형구분 및 확산 특성을 평가하였다. 대나무 분포 유형은 확산형, 관리형, 혼합형, 쇠퇴형, 분리형 순으로 분류되었다. 이 중 대표적인 확산 특성을 보이고 있는 대나무 임분을 대상으로 1980년~2010년 30년간 공간 시뮬레이션 한 결과, 사천지역의 죽종혼효 임분은 확산면적 2.5ha, 확산속도 0.08ha/yr, 확산거리 1.1m/yr로 나타났다. 거제지역의 죽순대 임분은 확산면적 1.9ha, 확산속도 0.06ha/yr, 확산거리 0.9m/yr로 나타났으며, 계곡, 능선을 따라 확산 이동하는 특성을 보이는 것으로 나타났다. 향후 본 결과는 대나무 확산 방지 기술 개발에 필요한 기초적인 자료로 활용될 수 있으며, 대나무 자원의 관리 기반을 구축하는데 기여할 것으로 판단된다.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.287-295
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• 2019

The study aims to build a model for evaluating the effectiveness of activities and the effectiveness of financial investments in high-tech industries in Kazakhstan. The development of high-tech industries plays an important role in the economic growth of a country. In this regard, it is relevant to study the effectiveness of financing the most important industry in Kazakhstan. The development of the high-tech sector ensures the efficient functioning of the national innovation system. High-tech enterprises are one of the competitive sectors that allow us to develop and implement leading-edge innovations with the goal of their subsequent commercialization domestically and abroad. The author defines the multicriteria of efficiency in a knowledge-based economy associated with achieving an economic effect with multivariate correlation of results with costs. A multivariate dynamic model, an integral indicator of performance, an integral indicator of cost-effectiveness is proposed. The assessment of the effectiveness of financial costs and performance indicators in all regions of Kazakhstan have the positive dynamics of indicators, as well as a high economic effect. The results of the study can be applied in regional management to adequately assess the effectiveness of high-tech organizations and the effectiveness of financial investments, contribution to ensuring the economic security of the region.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.719-726
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• 2020

Education policy is a dynamic process featuring social development trends. The world countries have focused their education program on empowering the learners for future life and work. This paper aims to assess the higher education curriculum based on a survey of 280 students, employers, alumni, and lecturers in both social sciences and natural sciences in Ho Chi Minh City, Vietnam. The fuzzy decision-making method, namely the Fuzzy Extent Analysis Method (F-EAM), was applied to measure the relative weight of each parameter. Seven factors under the curriculum development have been put in the ranking. Input with emphasis on foreign language was the highest priority in curriculum development, given the expected demand of the labor market. Objective and learning outcome and teaching activities ranked second and third, respectively. The traditional triangle of teaching content, methodology, and evaluation and assessment are still proven their roles, but certain modifications have been defined in the advanced curriculum. Teaching facilities had the least weight among the seven dimensions of curriculum development. The findings are helpful for education managers to efficiently allocate scarce resources to reform the curriculum to bridge the undergraduate quality gap between labor supply and demand, meeting the dynamic trends of social development.

• Journal of Periodontal and Implant Science
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• 제52권1호
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• pp.65-76
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• 2022

Purpose: The present study measured changes in arteriolar and venular capillary flow and structure in the gingival tissues during the development of plaque-induced gingival inflammation by combining dynamic optical coherence tomography (OCT), laser perfusion, and capillaroscopic video imaging. Methods: Gingival inflammation was induced in 21 healthy volunteers over a 3-week period. Gingival blood flow and capillary morphology were measured by dynamic OCT, laser perfusion imaging, and capillaroscopy, including a baseline assessment of capillary glycocalyx thickness. Venular capillary flow was estimated by analysis of the perfusion images and mean blood velocity/acceleration in the capillaroscopic images. Readings were recorded at baseline and weekly over the 3 weeks of plaque accumulation and 2 weeks after brushing was resumed. Results: Perfusion imaging demonstrated a significant reduction of gingival blood flow after 1 and 2 weeks of plaque accumulation (P<0.05), but by 3 weeks of plaque accumulation there was a more mixed picture, with reduced flow in some participants and increased flow in others. Participants with reduced flux at 3 weeks also demonstrated venular-type flow as determined by perfusion images and evidence of the development of venular capillaries as assessed by the velocity/acceleration ratio in capillaroscopic images. After brushing resumed, these venular capillaries were broken down and replaced by arteriolar capillaries. Conclusions: After 3 weeks of plaque accumulation, there was wide variation in microvascular reactions between the participants. Reduced capillary flow was associated with the development of venular capillaries in some individuals. This is noteworthy, as an early increase in venous capillaries is a key vascular feature of cardiovascular disease, psoriasis, Sjögren syndrome, and rheumatoid arthritis-diseases with a significant association with the development of severe gingival inflammation, which leads to periodontitis. Future investigations of microvascular changes in gingival inflammation might benefit from accurate capillary flow velocity measurements to assess the development of venular capillaries.

• Journal of Periodontal and Implant Science
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• 제54권3호
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• pp.189-204
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• 2024

Purpose: This pilot study was conducted to develop a novel test setup for the in vitro assessment of the primary stability of dental implants. This was achieved by characterising their long-term behaviour based on the continuous recording of micromotions resulting from dynamic and cyclic loading. Methods: Twenty screw implants, each 11 mm in length and either 3.8 mm (for premolars) or 4.3 mm (for molars) in diameter, were inserted into the posterior region of 5 synthetic mandibular models. Physiological masticatory loads were simulated by superimposing cyclic buccal-lingual movement of the mandible with a vertically applied masticatory force. Using an optical 3-dimensional (3D) measuring system, the micromotions of the dental crowns relative to the alveolar bone resulting from alternating off-centre loads were concurrently determined over 10,000 test cycles. Results: The buccal-lingual deflections of the dental crowns significantly increased from cycle 10 to cycle 10,000 (P<0.05). The deflections increased sharply during the first 500 cycles before approaching a plateau. Premolars exhibited greater maximum deflections than molars. The bone regions located mesially and distally adjacent to the loaded implants demonstrated deflections that occurred synchronously and in the same direction as the applied loads. The overall spatial movement of the implants over time followed an hourglass-shaped loosening pattern with a characteristic pivot point 5.5±1.1 mm from the apical end. Conclusions: In synthetic mandibular models, the cyclic reciprocal loading of dental implants with an average masticatory force produces significant loosening. The evasive movements observed in the alveolar bone suggest that its anatomy and yielding could significantly influence the force distribution and, consequently, the mechanical behaviour of dental implants. The 3D visualisation of the overall implant movement under functional cyclic loading complements known methods and can contribute to the development of implant designs and surgical techniques by providing a more profound understanding of dynamic bone-implant interactions.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• 한국농공학회논문집
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• 제62권5호
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• pp.105-119
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• 2020

Although there have been a lot of efforts to improve water quality in the estuarine reservoir, overall the water quality problems of the estuarine reservoirs remain. So, it is essential to establish water quality management plans under a comprehensive understanding of the environmental characteristics of the estuarine reservoir. Therefore, in this study, a resilience analysis framework for evaluating the estuarine reservoir's water quality was suggested for improving existing assessment method for water quality management plan. First, as a result of analyzing the static resilience to each scenario, it was found that from the S3 scenario in which dredging was conducted considerably, the resilience of about 30% more than the current estuarine reservoir system was restored. Second, as a result of analyzing the dynamic resilience, if cost and time are considered, there is no significant difference in robustness and resourcefulness, so it can be seen that the resilience of the estuarine reservoir can be efficiently improved by simply performing dredging up to the level of Scenario 3. Finally, as a result of comparing static and dynamic resilience, since static resilience is only presented as a single value, the differences and characteristics of the resilience capacity of the estuarine reservoir might be overlooked only by the static resilience analysis. However, in the aspect that it is possible to interpret the internal recovery capacity of the estuarine reservoir in multiple ways with various indicators (robustness, redundancy, resourcefulness, rapidity), evaluating water quality based on dynamic resilience analysis is useful.

• 한국지리정보학회지
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• 제13권2호
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• pp.41-53
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• 2010

도로상에서 발생하는 교통사고의 원인을 정확하게 파악하기 위해서는 사고지점의 도로 기하구조, 교통시설물, 교통운영방식, 날씨 등 주변적 요인과 사고특성과의 상관관계가 시공간적으로 분석되어야 한다. 본 연구에서는 도로 폐쇄 전후 교통사고의 특성을 비교하기 위하여, 미국 미주리주 교통부가 개발, 활용중인 통합교통관리시스템의 교통사고 자료 및 GIS기법을 이용하여 세인트루이스의 주요도로인 I-64 도로를 대상으로 분석을 하였다. 이러한 시공간적 분석은 일반적 통계분석으로 획득하기 어려운 공간적 특성을 용이하게 파악할 수 있는 장점이 있으며, 분석을 통해 얻어진 결과는 추후 교통사고 감소를 위한 개선책 마련에 근거자료로 활용될 수 있을 것으로 기대된다. 또한 본 연구에서는 교통자료의 통합관리를 위해 개발된 통합교통관리시스템의 구성요소 및 자료의 특성을 살펴봄으로써, 향후 우리나라의 실정에 맞는 통합 교통 데이터 시스템 구축의 고려사항으로 삼고자 한다.