• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.221-241
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• 2005

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.182-192
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• 2022

Objectives: Understanding the factors influencing mothers' decision to breastfeed their infants is essential to formulate effective breastfeeding interventions. This study explored the determinants of optimal breastfeeding indicators in Indonesia. Methods: We used the 2017 Indonesia Demographic and Health Survey to analyze factors associated with early initiation of breastfeeding, exclusive breastfeeding (EBF), and continued breastfeeding at 1 year (CBF-1) and 2 years (CBF-2). Multivariate logistic regression models were used to examine bio-demographic, socio-cultural, and behavioral characteristics associated with breastfeeding after considering the survey design effect. Results: The risk of delayed breastfeeding initiation was higher among infants who were born smaller, first-born children, were delivered via cesarean delivery, and did not have immediate skin-to-skin contact (p<0.01). Infant's age, birth pattern, household wealth index, and the mother's occupation and smoking status were predictors of EBF (p<0.05). CBF-1 was less common among first-time mothers and those working in the non-agricultural sector, mothers from wealthier families, and mothers who had cesarean deliveries (p<0.01). Infant's age was negatively associated with CBF-2 (adjusted odds ratio [aOR], 0.85; 95% confidence interval [CI], 0.74 to 0.99). Mothers attending college were less likely to practice CBF-2 than those with no education or primary education (aOR, 0.45; 95% CI, 0.26 to 0.77). The absence of postnatal visits was a risk factor for CBF-1 and CBF-2 (p<0.05). Conclusions: Breastfeeding interventions in Indonesia should pay particular attention to at-risk groups such as women from wealthier families, working outside the agricultural sector, and with a higher education level. Nutrition-sensitive programs (e.g., postnatal care and smoking cessation) should also be encouraged.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.2
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• pp.159-167
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• 2022

Backgrounds/Aims: The optimal timing of percutaneous transhepatic gallbladder drainage (PTGBD) and subsequent laparoscopic cholecystectomy (LC) according to the severity of acute cholecystitis (AC) has not been established yet. Methods: This single-center, retrospective study included 695 patients with grade I or II AC without common bile duct stones who underwent PTGBD and subsequent LC between January 2010 and December 2019. Difficult surgery (DS) (open conversion, subtotal cholecystectomy, adjacent organ injury, transfusion, operation time ≥ 90 minutes, or estimated blood loss ≥ 100 mL) and poor postoperative outcome (PPO) (postoperative hospital stay ≥ 7 days or Clavien-Dindo grade ≥ II postoperative complication) were defined to comprehensively evaluate intraoperative and postoperative outcomes, respectively. Results: Of 695 patients, 403 had grade I AC and 292 had grade II AC. According to the receiver operating characteristic curve and multivariate logistic regression analyses, an interval from symptom onset to PTGBD of > 3.5 days and an interval from PTGBD to LC of > 7.5 days were significant predictors of DS and PPO, respectively, in grade I AC. In grade II AC, the timing of PTGBD and subsequent LC were not statistically related to DS or PPO. Conclusions: In grade I AC, performing PTGBD within 3.5 days after symptom onset can reduce surgical difficulties and subsequently performing LC within 7.5 days after PTGBD can improve postoperative outcomes. In grade II AC, early PTGBD cannot improve the surgical difficulty. In addition, the timing of subsequent LC is not correlated with surgical difficulties or postoperative outcomes.

• Clinical and Experimental Pediatrics
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• v.56 no.3
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• pp.101-106
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• 2013

Despite developments in surgical techniques and other interventions, right ventricular (RV) failure remains an important clinical problem in several congenital heart diseases (CHD). RV function is one of the most important predictors of mortality and morbidity in patients with CHD. RV failure is a progressive disorder that begins with myocardial injury or stress, neurohormonal activation, cytokine activation, altered gene expression, and ventricular remodeling. Pressure-overload RV failure caused by RV outflow tract obstruction after total correction of tetralogy of Fallot, pulmonary stenosis, atrial switch operation for transposition of the great arteries, congenitally corrected transposition of the great arteries, and systemic RV failure after the Fontan operation. Volume-overload RV failure may be caused by atrial septal defect, pulmonary regurgitation, or tricuspid regurgitation. Although the measurement of RV function is difficult because of many reasons, the right ventricle can be evaluated using both imaging and functional modalities. In clinical practice, echocardiography is the primary mode for the evaluation of RV structure and function. Cardiac magnetic resonance imaging is increasingly used for evaluating RV structure and function. A comprehensive evaluation of RV function may lead to early and optimal management of RV failure in patients with CHD.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• Women's Health Nursing
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• v.21 no.4
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• pp.308-320
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• 2015

Purpose: The aim of this study was to determine the predictive factors for maternal role development for mothers of premature infants. Methods: A descriptive correlational study was conducted. A total sample of 121 mothers of premature infants following discharge from the neonatal intensive care unit were recruited using two strategies; an internet-based survey and an in-person data collection in a tertiary university hospital in Korea. A self-report questionnaire was used to collect data regarding personal, birth variables, marital intimacy, maternal attachment, maternal identity and maternal role development. Results: A hierarchical multiple regression analysis indicated that parity, maternal attachment, marital intimacy and maternal identity were predictors for maternal role development for mothers of premature infants, accounting for 70% of the variance. Among these variables, maternal attachment is the most powerful predictor for maternal role development. Conclusion: Nursing interventions during hospitalization to post-discharge education that includes parents of premature babies with positive interaction between couples strengthening marital intimacy and promotes maternal attachment that leads to integrate maternal identity should be considered by priority. Community-based family services such as home visits should be focused on maximizing the predictive factors for maternal role development in transition to motherhood that can contribute to maternal health as well as optimal growth and development of premature infants.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2211-2215
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• 2014

Background: To examine the incidence of positive vaginal surgical margins and determine the predicting factors following radical hysterectomy for stage IB1 carcinoma of the cervix. Materials and Methods: The clinical and histological data of 656 FIGO stage IB1 cervical cancer patients who had radical hysterectomy with bilateral pelvic lymphadenectomy (RHPL) from January 2003 to December 2012 were retrospectively reviewed and were analyzed for their association with a positive vaginal surgical margin. A p-value of < 0.05 was considered significant. Results: Thirty-five patients (5.3%) had positive vaginal surgical margins following RHPL; 24 (3.7%) for intraepithelial lesions and 11 (1.7%) for carcinoma. On multivariate analysis, microscopic vaginal involvement by high-grade squamous intraepithelial lesion and/or carcinoma (adjusted odd ratio (OR) 186.8; 95% confidence interval (CI) 48.5-718.5) and squamous histology (OR 8.7; 95% CI 1.7-44.0), were significantly associated with positive vaginal surgical margin. Conclusions: Microscopic vaginal involvement by HSIL and/or carcinoma are strong predictors for positive vaginal surgical margins for stage IB1 cervical cancer patients undergoing radical hysterectomy. Preoperative 'mapping' colposcopy or other strategies should be considered to ensure optimal vaginal resection.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.