http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
개별검색 DB통합검색이 안되는 DB는 DB아이콘을 클릭하여 이용하실 수 있습니다.
통계정보 및 조사
예술 / 패션
<해외전자자료 이용권한 안내>
- 이용 대상 : RISS의 모든 해외전자자료는 교수, 강사, 대학(원)생, 연구원, 대학직원에 한하여(로그인 필수) 이용 가능
- 구독대학 소속 이용자: RISS 해외전자자료 통합검색 및 등록된 대학IP 대역 내에서 24시간 무료 이용
- 미구독대학 소속 이용자: RISS 해외전자자료 통합검색을 통한 오후 4시~익일 오전 9시 무료 이용
※ 단, EBSCO ASC/BSC(오후 5시~익일 오전 9시 무료 이용)
Development of closed-form solutions and algorithms for constructing sub-surface swept proﬁles (SWP) of toroidal and conical bodies is presented in this paper. While the problem of identifying the entire SWP of such surfaces has been extensively investigated in extant studies, construction of subsurface SWPs has rarely been addressed despite the subject being of great signiﬁcance to machining process employing nonstandard-shaped NC tools. Torus shapes considered in extant literature are restricted to the fourth quadrant of a tube cross section. In industrial applications, however, proﬁle cutters contain different regions of a toroidal surface. To identify SWP elements in the proposed study, a single analytical expres-sion in one variable has been deduced using two moving frames. The basic idea behind such a formula-tion is to employ the one-to-many strategy, which greatly reduces the computational cost and effort. Algorithms to identify feasible domains of SWP parameters at each level cut, where toroidal and conical surfaces meet, have also been proposed in this study. This is important, since cutting a tool surfaces along the rotation axis divides SWP-parameter domains into non overlapping sets of intervals that must be addressed for each tool posture. In addition, this study demonstrates that for certain tool postures, while C1 continuity between sub-surfaces is satisﬁed, the SWP connectivity is lost at some points. To locate these so called singular-characteristic points, some precomputation steps have been performed. Lastly, several factors affecting the smoothness of SWPs have been identiﬁed and discussed.
In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of R2, RMS and MAPE. For the testing of dataset, the R2, RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the R2, RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.
In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.
The objective of this study was to investigate the effect of superplasticizer and mineral admixture contents on the properties of Self-Consolidating Concrete (SCC). Silica fume was used as a mineral admixture and polycarboxylate based third generation superplasticizer was used as a chemical admixture. In order to determine the optimum admixture dosages; trial mixes were prepared with varying admixture dosages. Nine concrete mixtures with different admixture dosages were prepared from trial mixes. Hardened concrete properties and self-compactability criteria of these series were determined and test results were compared between these SCC mixtures. It was observed that 10S1.3A (10% Silica Fume, 1.3% Superplasticizer) and 10S1.5A (10% Silica Fume, 1.5% Superplasticizer) mixtures show the best performance with regard to fresh and hardened concrete properties.
'스콜라' 이용 시 소속기관이 구독 중이 아닌 경우, 오후 4시부터 익일 오전 7시까지 원문보기가 가능합니다.
Purpose: To evaluate the effectiveness of intravitreal injection of ranibizumab (IVR) in treating diabetic macular edema (DME) with serous retinal detachment (SRD) based on spectral domain optical coherence tomography (SD-OCT) patterns. Methods: One hundred thirty-four eyes of 134 patients with DME who underwent SD-OCT evaluation were included in this study. We retrospectively analyzed the medical records of patients who received IVR for the treatment of DME. Their eyes were classified into three groups according to the following SD-OCT features: SRD, diffuse retinal thickness and cystoid macular edema. The three groups were compared regarding changes in best-corrected visual acuity and central foveal thickness (CFT) after IVR. Results: The mean age was 61.4 ± 9.2 years (range, 44 to 81 years). The average length of the follow-up period was 9.4 ± 3.4 months (range, 6 to 24 months). The mean CFT value was significantly reduced in all groups (p < 0.001) after treatment. Increases in best-corrected visual acuity were statistically significant for the diffuse retinal thickness and cystoid macular edema groups (p < 0.001 and p < 0.001, respectively). However, there was no significant improvement after IVR injection in the SRD group (p = 0.252). In the SRD group, patients with ellipsoid zone disruption and external limiting membrane disruption demonstrated poorer visual gains at the last follow-up visit (p < 0.005 and p = 0.002, respectively). Conclusions: A significant reduction in CFT with required IVR injections in DME with SRD was achieved but was accompanied by a worse functional outcome in the SRD group. The presence of subretinal fluid on SD-OCT in study eyes may be a poor prognostic factor for visual acuity.
The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.
In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days\' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.
Ozbek,,Emin,Otunctemur,,Alper,Sahin,,Suleyman,Dursun,,Murat,Besiroglu,,Huseyin,Koklu,,Ismail,Polat,,Emre,Can,Erkoc,,Mustafa,Danis,,Eyyup,Bozkurt,,Muammer Asian Pacific Organization for Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.12
Background: Metabolic syndrome (MetS) is a multifactorial disease characterized by impaired glucose tolerance/diabetes, obesity, high triglyceride levels, low HDL levels, and hypertension. In this study we evaluate the relationship between tumor size and grade, and presence of the metabolic syndrome in patients with renal cell carcinoma. Materials and Methods: Between 2007-2013, radical nephrectomy was performed for 310 patients with renal tumors in our clinic and those with pathology reported renal cell carcinoma were enrolled and divided into two groups, with and without metabolic syndrome diagnosed on the basis of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria. The relationship between tumor size and grade of the two groups (Fuhrman nuclear degree) was evaluated statistically. Results: The metabolic syndrome was found in 70 patients, with a mean age of 65.5 (40-87), as compared to 58.8 (31-84) years in the non-metabolic syndrome group. Tumor size over 7 cm was found in 54% and 33%, respectively, and tumor grade over Fuhrman 3 in 56% and 32% of patients. Patients with metabolic syndrome had significantly higher tumor size and grade (p<0.05). In the presence of hypertension, diabetes and high triglyceride levels, significant assocations were again observed (p<0.05). Tumor size and degree also increased with increasing body mass index but this was not statistically significant (p>0.05). Conclusions: Renal cancer is more aggressive in patients with metabolic syndrome. Lifestyle and risk factors were revealed to be significant influences in renal cancer patients.
Otunctemur,,Alper,Ozbek,,Emin,Sahin,,Suleyman,Dursun,,Murat,Besiroglu,,Huseyin,Koklu,,Ismail,Erkoc,,Mustafa,Danis,,Eyyup,Bozkurt,,Muammer,Gurbuz,,Ahmet Asian Pacific Organization for Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.9
Background: Diabetes is a chronic disease characterized by impaired fasting blood glucose that leads to disturbances in various organs. In this study, we evaluated relationships between tumor size and grade in a population of diabetic and non-diabetic patients with renal cell carcinoma. Materials and Methods: Between 2007-2013, in our clinic radical nephrectomy performed to 310 patients for renal tumors and pathology reported renal cell carcinoma cases were enrolled in the study. Patients with and without a history of diabetes regarding fasting glucose and HgA1c levels were evaluated during surgery for tumor size and Fuhrman grade. Results: Diabetes was found in 95 patients. The mean age of the patients with and without diabetes mellitus was 64.3 (40-79) and 58.4 (31-87) years, respectively. In the diabetes group 51% of patients had a tumor size over 7 cm and 54% a tumor grade over Fuhrman 3. The respective figures in the non-diabetes group were 35% and 30% (p<0.05 in both cases). Conclusions: Renal cancer appears more aggressive in patients with diabetes. In this study lifestyle and risk factors with diabetes regulation were observed to be important for renal cancer patients. Multicenter studies are needed in larger series for more accurate results.
Ozbek,,Emin,Otunctemur,,Alper,Dursun,,Murat,Koklu,,Ismail,Sahin,,Suleyman,Besiroglu,,Huseyin,Erkoc,,Mustafa,Danis,,Eyyup,Bozkurt,,Muammer Asian Pacific Organization for Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.3
Purpose: To compare histopathologic findings of patients who underwent transurethral resection of a bladder tumor (TUR-B) between groups with and without the metabolic syndrome. Materials and Methods: We retrospectively analyzed data of 535 patients who underwent TUR-B in our department between October 2005 and March 2011. All patients had primary urethelial cell carcinoma (UCB). Histologic stage, grade, the presence of hypertension, diabetes mellitus, body mass index (BMI), waist circumference, HDL and trigliseride levels were evaluated. The TNM classification was used, with Ta tumor accepted as lower stage and T1 and T2 tumors as higher stage bladder cancers. Also, the pathological grading adopted by the 2004 World Health Organization grading system were applied. Non-invasive papillary urothelial neoplasms of low malignant potential were regarded as low grade. Results: Among the total of 509 patients analyzed in our study, there were 439 males (86.2%) and 70 females (13.8%). Metabolic syndrome was significantly associated with high histologic grade, and high pathologic stage (p<0.001). Conclusions: The patients with metabolic syndrome were found to have statistically significant higher T stage and grade of bladder cancer. Further studies with more patients are needed to confirm our study.