The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.
Journal of the Korean Society of Fisheries and Ocean Technology
/
v.60
no.1
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pp.87-99
/
2024
This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.
Background: Airway obstruction and the extent of emphysema are reported to be responsible for reduced bone mineral density (BMD). Corresponding to different phenotypes of a pulmonary disease, different severity in extra pulmonary features may exist. We compared BMDs of subjects with or without airway obstruction and/or emphysema and investigated the relationships among BMD, the severity of airway obstruction, and the extent of emphysema. Methods: Using a university hospital database, we reviewed patients over 40 years old who performed spirometry, computed tomography of chest, and measurement of BMD of the lumbar (L) spine. According to the presence or absence of airway obstruction and/or emphysema, four groups were classified. Results: Among a total of 59 subjects, 33 (56%) had osteoporosis. The prevalence of osteoporosis in subjects with no airway obstruction and no emphysema, those with only emphysema, those with only airway obstruction, and those with both airway obstruction and emphysema were 42%, 57%, 64%, and 73%, respectively (p=0.047 by linear-by-linear association). The mean T-scores of BMD of L1 (p=0.032) and L1-4 spines were different among the four groups (p=0.034). Although the T-score of L1 BMD negatively correlated with the extent of emphysema (r=-0.275, p=0.035) and positively with each of body mass index (BMI) (r=0.520, p<0.001), forced expiratory volume in one second ($FEV_1$) (r=0.330, p=0.011), $FEV_1$/forced vital capacity (r=0.409, p=0.001), and forced expiratory flow at 25~75% of FVC ($FEF_{25-75%}$) (r=0.438, p=0.0001), respectively, multiple linear regression analysis indicated that BMI (p<0.001) and $FEF_{25-75%}$ were predictive of BMD (p=0.012). Conclusion: Low BMI and airway obstruction were strongly associated with reduced bone density rather than the extent of emphysema.
Journal of the Korea Academia-Industrial cooperation Society
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v.18
no.6
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pp.302-311
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2017
This is predictive correlational study to identify the influence of emotional labor and resourcefulness on turnover intention among clinical nurses. The participants were 138 clinical nurses and the data were collected by an online survey using a self-administered questionnaire from 10th to 17th April, 2016.The collected data were analyzed using SPSS program through t-test, One-way ANOVA, Scheffe's test and multiple linear regression. In the results, there were differences in emotional labor by age, marital status, job position, clinical career, shift work, in resourcefulness by gender, clinical career, and in turnover intention by age. As a result of multiple linear regression, emotional labor and resourcefulness were selected as significant related variables affecting nurse's turnover intentions. These factors accounted for 3.7% of turnover intention, which necessitates the consideration of a specific plan to reduce emotional labor and increase resourcefulness for decreasing clinical nurse's turnover intention.
Journal of the Institute of Electronics Engineers of Korea SP
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v.42
no.2
s.302
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pp.131-142
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2005
The low rate speech coders under 4 kbit/s are based on sinusoidal transform coding (STC) or multiband excitation (MBE). Since the harmonic coders are not efficient to reconstruct the transient segments of speech signals such as onsets, offsets, non-periodic signals, etc, the coders do not provide a natural speech quality. This paper proposes method of a efficient transient model :d a multi-mode low rate coder at 2.4 kbit/s that uses harmonic model for the voiced speech, stochastic model for the unvoiced speech and a model using aperiodic pulse location tracking (APPT) for the transient segments, respectively. The APPT utilizes the harmonic model. The proposed method uses different models depending on the characteristics of LPC residual signals. In addition, it can combine synthesized excitation in CELP coding at time domain with that in harmonic coding at frequency domain efficiently. The proposed coder shows a better speech quality than 2.4 kbit/s version of the mixed excitation linear prediction (MELP) coder that is a U.S. Federal Standard for speech coder.
A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.
Lee, Sung Sil;Kim, Dong Un;Park, Deuk Hyun;Cho, Hyun Young;Ahn, Seung Jun;Kho, Chan Young;Shin, Tae Yong;Kim, Young Sik;Ha, Young Rock
Journal of Trauma and Injury
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v.20
no.2
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pp.130-137
/
2007
Purpose: Ultrasound is of proven accuracy in abdominal and thoracic trauma and may be useful for diagnosing extremity injury in situations where radiography is not available, such as disasters and military and space applications. However, the diagnosis of fractures is suggested by history and physical examination and is typically confirmed with radiography. As a alternative to radiography, we prospectively evaluated the utility of extremity ultrasound performed by trained residents of emergency medicine (EM) one patient with wrist and ankle extremity injuries. Methods: Initially, residents of EM performed physical examinations for fractures. The emergency ultrasound (EM US) was performed by trained residents, who used a portable ultrasound device with a 10- to 5-MHz linear transducer, on suspected patients before radiography examination. The results of emergency ultrasound and radiography and the final diagnosis were recorded, and correlation among them were determined by using Kappa s test Results: Thirty-nine patients were enrolled in our study. The average age was $36.6\;{\pm}\;19.3$ years. There were radius Fx. (n=21), radius-ulna Fx. (n=1), ulna Fx. (n=1), and contusion (n=2) injuries among the wrist injury and lat.-med. malleolar Fx. (n=13), lat. malleolar Fx. (n=6), and med. malleolar Fx. (n=3) injuries among the ankle injury. Comparing EM US with radiography, we found the sensitivity, specificity, positive predictive value, and negative predictive value of EM US for Fx. diagnosis to be 100%, 66.7%, 97.3%, 100% and those of radiography to be 97.2%, 100%, 100%, and 75%, respectively. Kappa s test for a correlation between the Fx. diagnosis of EM US and the final diagnosis of Fx was performed, and Kappa's value was 0.787 (P = 0.004).Conclusion: EM US for Fx. can be performed quickly and accurately by EM residents with excellent accuracy in remote locations such as disaster areas and in military and aerospace applications. EM US was as useful as radiography in our study and had a high correlation to the final diagnosis of Fx. Therefore, ultrasound should performed on patients with extremity injury to determine whether extremity evaluation should be added to the FAST (focused abdominal sonography trauma) examination.
Shahraki, Hadi Raeisi;Pourahmad, Saeedeh;Paydar, Shahram;Azad, Mohsen
Asian Pacific Journal of Cancer Prevention
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v.17
no.4
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pp.1861-1864
/
2016
Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was $40.9{\pm}13.4years$ (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.
Purpose: This study aims to derive a predictive empirical equation for PGV prediction from P-wave using earthquake records in Korea and to verify the reliability of Onsite EEW. Method: The noise of P wave is removed from the observations of 627 seismic events in Korea to derive an empirical equation with PGV on the base rock, and reliability of Onsite alarms is verified from comparing PGV's predictions and observations through simulation using the empirical equation. Result: P-waves were extracted using the Filter Picker from earthquake observation records that eliminated noises, a linear regression with PGV was used to derive a predictive empirical equation for Onsite EEW. Through the on-site warning simulation we could get a success rate of 80% within the MMI±1 error range above MMI IV or higher. Conclusion: Through this study, the design feasibility and performance of Onsite EEWS using domestic earthquake records were verified. In order to increase validity, additional medium-sized seismic observations from abroad are required, the mis-detection of P waves is controlled, and the effect of seismic amplification on the surface is required.
Objectives : The objective of this study is to determine the present state of patients with breast cancer use of Korean medicine(KM) and predictive factors for the use. Through this, the present study is intended to present reasonable treatment approaches for patients with breast cancer as well as communicating correct information on KM to healthcare providers and presenting objective alternatives for patients with breast cancer management based on the subjects' experience in health benefits obtained from their use of KM. Methods : To collect data for the present study, questionnaire surveys were conducted on outpatients who visited four hospitals located in Seoul, Korea during around three weeks from May 31, 2012. Although the total number of the questionnaire sheet distributed in the form of directly asking questionnaire questions was 300, 12 incomplete questionnaire sheets were excluded. Therefore, the number of questionnaire sheets actually used in analyses was 288 and thus the collect rate was 96%. Results : Major results of this study are as follows. First, the number of subjects who responded to the questionnaire was 288 in total. Forty-six percent of the patients reported KM usage and the most commonly used ginseng and qigong/exercise. KM use was found to be associated with age, experiencing side effects of cancer treamnent. Factors that affect the use of KM were analyzed by Linear Logistic Regression and the results showed that age, experiencing side effects of cancer treatment, effectiveness of cancer treatment, and satisfaction of the treatment were factors that were related with relatively more frequent use of KM. Conclusions : Comparing the previous studies, it could be seen that patients with breast cancer were highly interested in and used KM in which conventional medicine and KM are used simultaneously. Knowledge on the integrative use of KM and conventional therapies is necessary for cancer physicians and traditional Korean medical doctors to help patients make informed choices. KM use may play a role in the positive benefits associated with process of breast center treatment. Healthcare providers should communicate correct information on the KM use that has been scientifically verified and talk with each other openly. The fact that the significant correlation between predictive factors for the use of KM was identified trough the present study is quite meaningful.
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