• Wind and Structures
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• v.14 no.6
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• pp.517-536
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• 2011

The assessment of wind-induced motion plays an important role in the development and design of the majority of today's structures that push the limits of engineering knowledge. A vital part of the design is the prediction of wind-induced tall building motion and the assessment of its effects on occupant comfort. Little of the research that has led to the development of the various international standards for occupant comfort criteria have considered the effects of the low-frequency motion on task performance and interference with building occupants' daily activities. It has only recently become more widely recognized that it is no longer reasonable to assume that the level of motion that a tall building undergoes in a windstorm will fall below an occupants' level of perception and little is known about how this motion perception could also impact on task performance. Experimental research was conducted to evaluate the performance of individuals engaged in a manual tracking task while subjected to low level vibration in the frequency range of 0.125 Hz-0.50 Hz. The investigations were carried out under narrow-band random vibration with accelerations ranging from 2 milli-g to 30 milli-g (where 1 milli-g = 0.0098 $m/s^2$) and included a control condition. The frequencies and accelerations simulated are representative of the level of motion expected to occur in a tall building (heights in the range of 100 m -350 m) once every few months to once every few years. Performance of the test subjects with and without vibration was determined for 15 separate test conditions and evaluated in terms of time taken to complete a task and accuracy per trial. Overall, the performance under the vibration conditions did not vary significantly from that of the control condition, nor was there a statistically significant degradation or improvement trend in performance ability as a function of increasing frequency or acceleration.

• Korean Medical Education Review
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• v.23 no.2
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• pp.80-89
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• 2021

Professional identity formation (PIF) is an essential concept in professional education. Many scholars have explored conceptual frameworks of PIF and conducted empirical studies to advance an understanding of the construct in medical education. Despite its importance, it is unclear what educational approaches and assessment practices are actually implemented in medical education settings. Therefore, we conducted a literature review of empirical studies reporting educational practices for medical learners' PIF. We searched the Web of Science database using keywords and chose 37 papers for analysis based on inclusion and exclusion criteria. Thematic analysis was conducted. Most empirical papers (92%) were from North America and Western Europe and used qualitative research methods, including mixed methods (99%). The papers reported the use of reflection activities and elective courses for specific purposes, such as art as an educational activity. Patient and healthcare experiences were also found to be a central theme in medical learners' PIF. Through an iterative analysis of the key themes that emerged from the PIF studies, we derived the following key concepts and implications: (1) the importance of creating informal and incidental learning environments, (2) ordinary yet authentic patient experiences, (3) a climate of psychosocial safety in a learning environment embracing individual learners' background and emotional development, and (4) the reconceptualization of PIF education and assessment. In conclusion, research on PIF should be diversified to include various cultural and social contexts. Theoretical frameworks should also be diversified and developed beyond Kegan's developmental framework to accommodate the nonlinear and dynamic nature of PIF.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.75-91
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• 2023

The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.55-64
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• 2017

In order to assess the spatial and dynamic changes in bamboo forests, this study used the national-level spatial data between 1980 and 2010 to extract spatial information of bamboo forests through GIS technology. The results showed that the distribution types were mainly expansion, normal, mixed, damage, and separation. In case of mixed bamboo forest in the Sacheon region, the expansion characteristics were: area 2.5 ha, velocity 0.08 ha/yr, and distance 1.1 m/yr. The Phyllostachys pubescens forest in the Geojae region showed the following characteristics: area 1.9 ha, velocity 0.06 ha/yr, and distance 0.9 m/yr with where along from valley to ridge. This approach could provide a valuable tool for decision-making and implementations such as the bamboo forest management plan, environmental impact assessment for a preventing the bamboo expansion, and sustainable managing the bamboo resources.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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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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• v.7 no.9
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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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• v.52 no.1
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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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• v.54 no.3
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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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• v.23 no.9
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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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• v.23 no.8
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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.