• Current Optics and Photonics
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• v.8 no.2
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• pp.138-150
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• 2024

This study is essential for advancing our knowledge about the interaction between long-range high-power lasers and energetic materials, with a particular emphasis on understanding the response of a 155-mm shell under various surface irradiations, taking into account external factors such as atmospheric disturbances. The analysis addresses known limitations in understanding the use of non-realistic targets and the negligence of ambient conditions. The model employs the three-dimensional level-set method, computer-aided design (CAD)-based target design, and a message-passing interface (MPI) parallelization scheme that enables rapid calculations of the complex chemical reactions of the irradiated high explosives. Important outcomes from interaction modeling include the accurate prediction of the initiation time of ignition, transient pressure, and temperature responses with the location of the initial hot spot within the shell, and the relative magnitude of noise with and without the presence of physical ambient disturbances. The initiation time of combustion was increased by approximately a factor of two with atmospheric disturbance considered, while slower heating of the target resulted in an average temperature rise of approximately 650 K and average pressure increase of approximately 1 GPa compared to the no ambient disturbance condition. The results provide an understanding of the interaction between the high-power laser and energetic target at a long distance in an atmospheric condition.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Steel and Composite Structures
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• v.32 no.1
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• pp.141-156
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• 2019

This paper presents the flexural behaviour of stainless steel beam-to-tubular column joints with extended endplates subjected to static loading. Moment-rotation relationships were investigated numerically by using Abaqus software with geometric and material nonlinearity considered. The prediction of damages among components was achieved through ductile damage models, and the influence of initial geometric imperfections and residual stresses was evaluated in large fabricated stainless steel joints involving hollow columns and concrete-filled columns. Parametric analysis was subsequently conducted to assess critical factors that could affect the flexural performance significantly in terms of the initial stiffness and moment resistance. A comparison between codes of practice and numerical results was thereafter made, and design recommendations were proposed for further applications. Results suggest that the finite element model can predict the structural behaviour reasonably well with the component damage consistent with test outcomes. Initial geometric imperfections and residual stresses are shown to have little effect on the moment-rotation responses. A series of parameters that can influence the joint behaviour remarkably include the strain-hardening exponents, stainless steel strength, diameter of bolts, thickness of endplates, position of bolts, section of beams and columns. AS/NZS 2327 is more reliable to predict the joint performance regarding the initial stiffness and moment capacity compared to EN 1993-1-8.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.38-38
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• 2023

Pleikrong reservoir with a concrete gravity dam that impound more than 1 billion cubic meter storage volume is one of the largest reservoir in Central Highland of Vietnam. Sedimentation is a major problem in this area and it becomes more severe due to the effect of climate change. Over time, it gradually reduces the reservoir storage capacity affecting to the reliability of water and power supply. This study aims to integrate the soil and water assessment tool (SWAT) model with 14 bias-corrected GCM/RCM models under two emissions scenarios, representative concentration pathway (RCP) 4.5 and 8.5 to estimate sediment inflow to Pleikrong reservoir in the long term period. The result indicated that the simulated total amount of sediment deposited in the reservoir from 2010 to 2018 was approximately 39 mil m³ which is a 17% underestimate compared with the observed value of 47 mil m³. The results also show the reduction in reservoir storage capacity due to sedimentation ranges from 25% to 62% by 2050, depending on the different climate change models. The reservoir reduced storage volume's rate in considering the impact of climate change is much faster than in the case of no climate change. The outcomes of this study will be helpful for a sustainable and climate-resilient plan of sediment management for the Pleikrongreservoir.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• Journal of Preventive Medicine and Public Health
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• v.45 no.4
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• pp.259-266
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• 2012

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

• Journal of Environmental Impact Assessment
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• v.1 no.1
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• pp.41-50
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• 1992

To prevent unwanted visual impacts of proposed projects before they are actually built, Visual Impact Assessment(VIA) is conducted in current landscape planning and management process. The application of VIA to actual projects raises some important questions: "What views will the project affect?" "What tools and techniques are effective for predicting and portraying future landscape conditions?" "Who should determine the value of the impacts?" and "How can the impacts be measured?" Types and levels of visual impacts should be decided through analyzing both the existing landscape and the proposed project. Computer-based visual simulations will play a pivotal role as effective prediction and communication tools. With professionals' assistance, the public participation in the VIA process will produce meaningful solutions for planning and managing the future landscape. Also, the use of a proper response format and sensitive assessment criteria in measuring the public's opinion will enrich outcomes of the assessment. Based on the methodological framework, the case study briefly explains an application of VIA to an actual project.

• Journal of Korean Academy of Nursing
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• v.50 no.1
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• pp.1-13
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• 2020

Purpose: The purpose of this study was to construct and test a structural equation model on nursing work outcomes based on Youssef and Luthans' positive psychological capital and integrated conceptual framework of work performance. Methods: This study used a structured questionnaire administered to 340 nurses. Data were analyzed using structural equation modeling. Results: Positive psychological capital showed indirect and direct effects on job satisfaction, retention intention, organizational citizenship behavior, and nursing performance. While, the nursing work environment had direct and indirect effects on job satisfaction and nursing performance, it only had indirect effects on intention to work and organizational citizenship behavior. Additionally, a mediating effect on retention intention and organizational citizenship behavior was found between job satisfaction and nursing performance variables. Conclusion: The nursing organization needs to build a supportive work environment and reinforce positive psychological capital to improve nursing performance. Additionally, it needs to actively manage the necessary parameters involved in the stages of job satisfaction, retention intention, nursing performance, and organizational citizenship behavior of nurses. The findings propose the continuous management of nursing personnel based on nurses' attitude outcome, behavioral intention, behavioral outcome, and stage of role performance.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.237-258
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• 2022

This paper studies detecting and locating loose bolts using nonlinear guided waves. The 3D Finite Element (FE) simulation is used for the prediction of guided waves' interactions with loose bolted joints. The numerical results are verified by experimentally obtained data. The study considers bolted joints consisting of two bolts. It is shown that the guided waves' interaction with surfaces of a loose bolted joint generates Contact Acoustic Nonlinearity (CAN). The study uses CAN for detecting and locating loose bolts. The processed experimentally obtained data show that the CAN is able to successfully detect and locate loose bolted joints. A 3D FE simulation scheme is developed and validated by experimentally obtained data. It is shown that FE can predict the propagation of guided waves in loose bolts and is also able to detect and locate them. Several numerical case studies with various bolt sizes are created and studied using the validated 3D FE simulation approach. It is shown that the FE simulation modeling approach and the signal processing scheme used in the current study are able to detect and locate the loose bolts in imperfect bolted joints. The outcomes of this research can provide better insights into understanding the interaction of guided waves with loose bolts. The results can also enhance the maintenance and repair of imperfect joints using the nonlinear guided waves technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.