• Journal of People, Plants, and Environment
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• v.21 no.6
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• pp.565-574
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• 2018

Previous studies have shown that forest therapy program can help prevent dementia. However, few studies have focused on low-income elderly people living alone. The current study examined the meanings that the elderly living alone receiving medical care assigned to the urban forest therapy program, as a way to understand the pathways that nature-based intervention affect preventing dementia. Twenty-one participants were recruited and they participated in a five-week urban forest therapy program. Semi-structured interviews were carried out with 21 participants who experienced the urban forest therapy program, and analyzed qualitative data using thematic analysis. Results showed that all themes identified were related to connectedness with oneself, neighbors and nature. Awarenesses of change were consisted of positive and negative themes. The themes of positive awareness were improvements of mental and emotional condition, feelings of isolation and loneliness, and health-related lifestyle. The negative themes were terminations of short-term programs and inconvenient access to the urban forest. Based on these data, we suggest an urban green welfare framework for future research and interventions for preventing dementia of underprivileged elderly group.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1224-1235
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• 2021

An integrated waste management approach for irradiated graphite was developed during the European Commission project 'Treatment and Disposal of Irradiated Graphite and other Carbonaceous Waste'. This included the identification of potential options for the management of irradiated graphite, taking account of storage, retrieval, treatment and disposal methods. This paper describes how these options can be assessed using multi-criteria decision analysis (MCDA) for a case study relating to a generic power reactor. Criteria have been defined to account for safety, environmental, economic and socio-political factors, including radiological impact, resource usage, economic costs and risks. The impact of each option against each criterion has been assessed using data from the project and the wider literature. A linear additive approach has been used to convert the calculated impacts to scores. To account for the relative importance of the criteria, example weightings were allocated. This application has shown that MCDA approaches can be used to support complex decisions regarding irradiated graphite management, accounting for a wide range of criteria. Use of this approach by individual countries or organisations will need to account for the specific options, scores, weightings and constraints that apply, based on their national strategies, regulatory requirements and public acceptability.

• Smart Media Journal
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• v.10 no.2
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• Steel and Composite Structures
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• v.33 no.2
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• pp.261-275
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• 2019

The main objective of this paper is to study the axial buckling and post-buckling of geometrically imperfect single-layer graphene sheets (GSs) under in-plane loading in the theoretical framework of the nonlocal strain gradient theory. To begin with, a graphene sheet is modeled by a two-dimensional plate subjected to simply supported ends, and supposed to have a small initial curvature. Then according to the Hamilton's principle, the nonlinear governing equations are derived with the aid of the classical plate theory and the von-karman nonlinearity theory. Subsequently, for providing a more accurate physical assessment with respect to the influence of respective parameters on the mechanical performances, the approximate analytical solutions are acquired via using a two-step perturbation method. Finally, the authors perform a detailed parametric study based on the solutions, including geometric imperfection, nonlocal parameters, strain gradient parameters and wave mode numbers, and then reaching a significant conclusion that both the size-dependent effect and a geometrical imperfection can't be ignored in analyzing GSs.

• Journal of Korean Academy of Nursing
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• v.50 no.6
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• pp.848-862
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• 2020

Purpose: The purpose of this study was to develop an instrument to evaluate the needs satisfaction of nurses and examine its validity and reliability. Methods: The initial items for the instrument were developed through a literature review and interviews, using the conceptual framework of Maslow's hierarchy of needs theory. The initial items were evaluated for content validity by 14 experts. Four hundred and eighty-six clinical nurses participated in this study through offline and online surveys to test the reliability and validity of the instrument. The first evaluation (n = 256) was used for item analysis and exploratory factor analysis, and the second evaluation (n = 230) was used to conduct a confirmatory factor analysis and to assess the criterion-related validity and internal consistency of the instrument. Test-retest reliability was analyzed using data from 30 nurses. Results: The final instrument consisted of 30 items with two sub-factors for five needs that were identified through the confirmatory factor analysis. The criterion-related validity was established using the five need satisfaction measures (r = .56). Cronbach's α for total items was .90, and test-retest reliability was .89. Conclusion: The findings from this study indicate that this instrument has sufficient validity and reliability. This instrument can be used for the development of nursing interventions to improve the needs satisfaction of clinical nurses.

• Smart Structures and Systems
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• v.26 no.6
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• pp.735-751
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• 2020

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to identify the educational needs of nurses and nursing students for the development of a simulation module of home visiting care for frail, elderly people. Methods: Focus group interviews were conducted with 15 home visiting nurses working in public health centers and 14 nursing students who experienced home visiting from September 10 to October 10, 2018. Results: Bloom's taxonomy of learning objectives, namely, cognitive, affective, and psychomotor domains was used as a framework for data analysis. The defined educational needs for each domain were as follows: "understanding frail, elderly people" for the cognitive domain; "intervention for mental health" and "building a therapeutic relationship" for the affective domain; and "nursing skills", "health education for healthy lifestyles", "referral to the community resource connection", "protection for visiting nurses" for the psychomotor domain. Conclusion: Based on the findings of this study, a simulation module of home visiting care for frail, elderly people can be developed and used for nursing students and nurses to strengthen the capacity for home visiting care.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.61-79
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• 2022

In the rapidly changing era of knowledge revolution, user-centered IT services are emerging as a very important component of modern business. However, in order to lead IT services into success, traditional capabilities and competences are not good enough. Development of IT services involve service planners and designers as well as traditional systems developers. This detailed segmentation of job and corresponding competences among involved in IT service development brings in new type of conflicts and contradictions that may require special attention for IT services to be properly development and implemented. This study aims to explore and define competences and roles of newly emerging job groups in IT services: planners, designers, and developers. In order to identify underlying competences of these emergeing groups, two stage interviews were conducted. At the first stage, general competence framework is developed across these groups with different skills for similar competence catogories. Using the categories developed at the first stage, members of each groups were asked to rate and assess the competences of other groups. Comparisons of these reciprocal assessment revealed the conceptual differences and biases across these groups. Detail differences are discussed and implications are discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.333-339
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• 2022

The purpose of the article is to identify priority trends of technological innovations and strategic opportunities for using the smart potential to the benefit of the Russian industrial production development in the context of digital transformation. The article substantiates the demand for technological process automation at industrial enterprises in Russia and considers the possibilities of using artificial intelligence and the implementation of smart manufacturing in the industry. The article reveals the priorities of the leading Russian industrial companies in the field of digitalization, namely, an expansion of the use of cloud technologies, predictive analysis, IaaS services (virtual data storage and processing centers), supervisory control, and data acquisition (SCADA), etc. The authors give the characteristics of the monitoring of the smart manufacturing systems development indicators in the Russian Federation, conducted by Rosstat since 2020; presents projected data on the assessment of the required resources in relation to the instruments of state support for the development of smart manufacturing technologies for the period until 2024. The article determines targets for the development of smart technologies within the framework of the Federal Project "Digital Technologies".

• Computers and Concrete
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• v.29 no.1
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• pp.1-14
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• 2022

Several aspects influence corrosive processes in reinforced concrete (RC) structures such as environmental conditions, structural geometry and mechanical properties. Since these aspects present large randomnesses, probabilistic models allow a more accurate description of the corrosive phenomena. Besides, the definition of limit states in the reliability assessment requires a proper mechanical model. In this context, this study proposes a straightforward methodology for the mechanical-probabilistic modelling of RC structures subjected to reinforcements' corrosion. An improved damage approach is proposed to define the limit states for the probabilistic modelling, considering three main degradation phenomena: concrete cracking, rebar yielding and rebar corrosion caused either by chloride or carbonation mechanisms. The stochastic analysis is evaluated by the Monte Carlo simulation method due to the computational efficiency of the Lumped Damage Model for Corrosion (LDMC). The proposed mechanical-probabilistic methodology is implemented in a computational framework and applied to the analysis of a simply supported RC beam and a 2D RC frame. Curves illustrate the probability of failure evolution over a service life of 50 years. Moreover, the proposed model allows drawing the probability of failure map and then identifying the critical failure path for progressive collapse analysis. Collapse path changes caused by the corrosion phenomena are observed.