• Journal of Animal Science and Technology
• /
• v.61 no.4
• /
• pp.185-191
• /
• 2019

The aim of this study was to evaluate different herbicides for optimum growth, yield and nutritive value of corn-soybean mixed forage under randomized complete block design. The experimental site was selected and divided equally into 3 blocks. Each block was further divided into 5 plots that each plot had 15 square meter space ($3{\times}5$). Five herbicidal treatments were randomly applied over 5 plots and herbicides were used under 5 herbicidal treatments, viz. 1) No herbicide (control); 2) Pendimethalin; 3) Linuron; 4) S-metolachlor and 5) Ethalfluralin. The collected data were analyzed using ANOVA through SAS 9.1.3 software. The results indicated that growth characteristics were not influenced (p > 0.05) by any herbicide. However, arithmetically corn stalk height was highest in the field of Pendimethalin treatment, whereas highest soybean height was found in the field of S-metolachlor. Arithmetically dry matter (DM) yield was increased with herbicidal treatments as compared to that of control treatment. Relatively highest DM yield (130%) was recorded in the treatment of Ethalfluralin followed by Pendimethalin (126%), S-metolachlor (126%) and Linuron (108%) as compared to that of control treatment. The weed emergence was significantly reduced in all herbicidal treatments as compared to that of control (p > 0.05), but the difference among herbicidal treatments was non-significant. It was concluded that weed emergence can be effectively controlled by use of any tested herbicide. However, optimum DM yield can be achieved through using herbicides; Ethalfluralin, Pendimethalin and S-metolachlor.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.3
• /
• pp.927-930
• /
• 2012

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

• Journal of Periodontal and Implant Science
• /
• v.50 no.2
• /
• pp.83-96
• /
• 2020

Purpose: The present study aimed to evaluate the clinical benefit of additional toothbrushing accompanying non-surgical periodontal treatment on oral and general health in patients with type 2 diabetes mellitus (T2DM). Methods: We conducted a doubled-blind randomized controlled trial in 60 T2DM patients between June 2013 and June 2014. The patients were randomly assigned to the scaling and root planing (SRP) group; the scaling and root planing with additional toothbrushing (SRPAT) group, in which additional toothbrushing was performed by toothpick methods; or the control group. Microbiological and oral examinations were performed for up to 12 weeks following treatment. Non-surgical treatment was conducted in the experimental groups. The SRP group received scaling and root planing and the SRPAT group received additional toothbrushing with the Watanabe method once a week from the first visit through the fifth visit. The primary outcomes were changes in haemoglobin A1c (or glycated haemoglobin; HbA1c) levels, serum endotoxin levels, and interleukin-1 beta levels. Periodontal health status was measured by periodontal pocket depth, the calculus index, and bleeding on probing (BOP). Results: Both the SRP and SRPAT groups showed improvements in periodontal health and HbA1c, but the SRPAT group showed significantly less BOP than the SRP group. Furthermore, only the SRPAT group showed a statistically significant decrease in serum endotoxin levels. Conclusions: Non-surgical periodontal treatment was effective in improving HbA1c and serum endotoxin levels in T2DM patients. Furthermore, non-surgical treatment with additional tooth brushing had a more favourable effect on gingival bleeding management. Trial RegistrationClinical Research Information Service Identifier: KCT000416.

• Journal of Environmental Science International
• /
• v.12 no.3
• /
• pp.307-317
• /
• 2003

In this study, the material cycle model was applied to suggest alternative management of water quality for Jeju Harbor. The distribution of COD, DIN (dissolved inorganic nitrogen) and DIP (dissolved inorganic phosphorus) concentrations was reasonably reproduced by simulations on the model area of the Jeju Harbor using a material cycle model. The simulations of COD, DIN and DIP concentrations were performed under the conditions of 20∼100% pollution loadings reductions from pollution sources. In case of the 100% reduction of the input loads from Sanzi river, concentrations of COD, DM and DIP were reduced to 39%, 78% and 52%, respectively at Jeju harbor. In contrast, in case of the pollutant loadings reductions from sediment, the effect of DIN and DIP reduction relatively seemed to increase around the center of study area. The 95% reduction of the pollutant loadings from river and sediment is required to meet the COD and nutrients concentration of second grade of ocean water quality criteria.

• Asian-Australasian Journal of Animal Sciences
• /
• v.13 no.11
• /
• pp.1598-1603
• /
• 2000

Broiler litter can be used as a feedstuff for ruminants. Fifty seven litter samples collected from 47 farms in Kyungkee Province of Korea were analyzed to assess the effects of type and amount of bedding (rice hulls vs. sawdust), season (winter vs. summer) and drinkers (bell- vs. trough-type) on composition of broiler litter. Rearing conditions of broilers were also surveyed from the farms to estimate annual production rate of litter. Nutrient composition of broiler litter varied widely and moisture and ash concentrations were higher than observed by other researchers. Ash concentration was higher (p<0.05) for samples taken in winter than in summer and higher (p<0.05) in the rice hulls- than in the sawdust-based litter both in winter and summer. Only minor differences in litter composition were noted between drinkers. Ash was negatively correlated with crude protein and neutral detergent fiber (p<0.01), and acid detergent fiber (p<0.05). The estimated litter production rate was 2.7 kg per bird per flock on a wet basis (60% DM) and the annual production rate was 12.7 kg per bird per yr (60% DM). Therefore, the 42 million broilers per month grown in Korea in 1999 produced a total of 533,400 metric tons of litter.

• Asian-Australasian Journal of Animal Sciences
• /
• v.9 no.5
• /
• pp.483-493
• /
• 1996

Advances in silage technology, including precision chop forage harvesters, improved silos, polyethylene sheeting, shear cutting silo unloaders, and the introduction of total mixed rations, have made silage the principal method of forage preservation. A better understanding of the biochemistry and microbiology of the four phases of the ensiling process has also led to the development of numerous silage additives. Although acids and acid salts still are used to ensile low-DM forages in wet climates, bacterial inoculants have become the most widely used silage additives in the past decade. Commercial inoculants can assure a rapid and efficient fermentation phase; however, in the future, these products also must contribute to other areas of silage management, including the inhibition of enterobacteria, clostridia, and yeasts and molds. Nonprotein nitrogen additives have the problems of handling, application, and reduced preservation efficiency, which have limited their wide spread use. Aerobic deterioration in the feedout phase continues to be a serious problem, especially in high-DM silages. The introduction of competitive strains of propionic acid-producing bacteria, which could assure aerobically stable silages, would improve most commercial additives. New technologies are needed that would allow the farmer to assess the chemical and microbial status of the silage crop on a given day and then use the appropriate additive(s).

• Korean journal of food and cookery science
• /
• v.23 no.1 s.97
• /
• pp.99-106
• /
• 2007

This study investigated the effect of UV-B irradiation on the quality of Pleurotus ostreatus. The changes of vitamin $D_2$ contents, color value and flavor pattern in mushrooms were analyzed by high-performance liquid chromatography (HPLC), chromameter and gas chromatography - surface acoustic wave (GC-SAW) electronic nose. By exposure to UV-B irradiation (0 kj/m$^2$, 10 kj/m$^2$, 20 kj/m$^2$), vitamin $D_2$ content increased from 0 (control) to 48.50 g/g (DM: dry matter, 10 kj/m$^2$) and 61.58 g/g (DM, 20 kj/m$^2$). Although there was no significant difference in L, a, b values among the three groups, flavor changes were detected by GC-SAW electronic nose. The number of peaks increased from 10 in the control group (0 kj/m$^2$), to 14 and 15 for the 10 kj/m$^2$ and 20 kj/m$^2$ groups, respectively. Nevertheless, the changes of flavor pattern were not detrimental to the mushroom quality. These results suggested that UV-B irradiation is an effective method to increase the vitamin $D_2$ content without degrading the quality.

• Journal of Advanced Navigation Technology
• /
• v.19 no.6
• /
• pp.595-600
• /
• 2015

The implementation possibility of the flexible optical network configuration using the non-midway optical phase conjugator (OPC) in the dispersion-managed (DM) optical link for wavelength division multiplexed (WDM) transmission is demonstrated in this paper. It is confirmed that the implementation possibility of flexible link configuration is more increased, as number of fiber spans is more bigger and the residual dispersion per span (RDPS) is more large. It is also confirmed that the non-midway OPC link, in which RDPS of the latter half transmission section (after OPC) is decided by the averaged RDPS of the former half transmission section (before OPC), has more advantage for the flexible network configuration.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.99-104
• /
• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Journal of Intelligence and Information Systems
• /
• v.9 no.2
• /
• pp.19-38
• /
• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.