• Animal Bioscience
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• v.36 no.4
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• pp.591-600
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• 2023

Objective: We aimed to evaluate the efficacy of a modified Double-Ovsynch protocol vs artificial insemination following estrus detection (AIED) for the enhancement of reproductive performance in Hanwoo cattle. Methods: Four hundred twelve Hanwoo cows were allocated to two treatment groups. The first group of cows were administered gonadotropin releasing hormone (GnRH) on Day 36 (±0.6), prostaglandin F_2α (PGF_2α) on Day 46 (8 to 12 days later), and GnRH on Day 49, which was followed by Ovsynch, consisting of an injection of GnRH on Day 56, PGF_2α on Day 63, and GnRH 56 h and timed AI (TAI) 16 h later (modified Double-Ovsynch group, n = 203). The second group of cows underwent AIED (AIED group, n = 209) and were designated as controls. Results: The pregnancy per AI 60 days after the first AI was higher in the modified Double-Ovsynch (68.5%) than in the AIED (56.5%) group, resulting in a higher probability of pregnancy per AI (odds ratio: 1.68, p<0.05). Moreover, cows in the modified Double-Ovsynch group were more likely (hazard ratio: 1.28, p<0.05) to be pregnant by 150 days after calving than cows in the AIED group, and this difference was associated with a lower mean number of AIs per conception (1.27 vs 1.39, p<0.05) and a shorter median interval between calving and pregnancy (72 vs 78 days, p<0.1). Conclusion: The modified Double-Ovsynch protocol, adjusted according to the herd visit schedule, can be readily used to increase the pregnancy per AI following the first AI and to shorten the interval between calving and pregnancy in beef herds.

• Smart Structures and Systems
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• v.25 no.4
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• pp.423-432
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• 2020

In this paper, a novel but practical approach named series/parallel multi-sensing technique was proposed to evaluate the bolt looseness in a bolt group. The smart washers (SWs), which were fabricated by embedding a Lead Zirconate Titanate (PZT) transducer into two flat metal rings, were installed to the bolts group. By series connection of SWs, the impedance signals of different bolts can be obtained through only one sweep. Therefore, once the loosening occurred, the shift of different peak frequencies can be used to locate which bolt has loosened. The proposed multi input single output (MISO) damage detection scheme is very suitable for the structural health monitoring (SHM) of joint with a large number of bolts connection. Another notable contribution of this paper is the proposal of 3-dB bandwidth root mean square deviation (3 dB-RMSD) which can quantitatively evaluate the severity of bolt looseness. Compared with the traditional naked-eye observation method, the equivalent circuit based 3-dB bandwidth can accurately define the calculation range of RMSD. An experiment with three bolted connection specimens that installed the SWs was carried out to validate our proposed approach. Experimental result shows that the proposed 3 dB-RMSD based multi-sensing technique can not only identify the loosened bolt but also monitor the severity of bolt looseness.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2547-2553
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• 2015

Purpose: The present study concerns the effectiveness of an educational intervention for improving knowledge, attitudes and practices (KAP) of breast cancer early detection among target group women (TGW) in the district of Gampaha, Sri Lanka. Materials and Methods: The study was a community-based intervention. Two medical officer of health areas in Gampaha district were selected using random sampling as intervention (IA) and control (CA). Public health midwives (PHMs) in the IA were exposed to the educational intervention first, conducted the same among the TGW through PHMs. KAP was assessed using an interviewer- administrated questionnaire among 260 TGW from each area selected using cluster sampling before and six months after the intervention. Results: The overall median scores for KAP among TGW in IG increased significantly from pre intervention level of 54% (IQR: 46-59%), 50% (IQR: 41-59%), and 0% (IQR: 0-20%) to post intervention level of 77% (IQR: 72-82%), 68% (IQR: 59- 76 %) and 40% (IQR: 20-60%) respectively. In CG, overall median scores for KAP remained almost the same at pre intervention 54% (IQR:44-59%), 50% (IQR:36-59%) and 0% (IQR: 0-20%) and post intervention 54% (IQR:46-59%), 50% (IQR:36-64%) and 0% (IQR: 0-20%) respectively. Conclusions: The educational intervention was found to be effective.

• Nuclear Engineering and Technology
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• v.33 no.2
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• pp.225-230
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• 2001

The aim of this study is to describe a usefulness of nuclear analytical technique in assessing and comparing the concentration levels through the analysis of uranium using human hair sample in the field of environment. A fission track detection technique was applied to determine the uranium concentration in human hair. Hair samples were collected from two groups of people - a) workers not dealing with uranium directly, and b) workers possibly contaminated with uranium. The concentration of $^{235}$ U for the first group varied from <1 to 39 ng/g and the second group can be estimated up to the level of $\mu$g/g. Radiographs of heavy-duty work samples contained high dense “hot spots” along a single hair. After washing in acetone and distilled water, external contamination was not totally removed. Insoluble uranium compounds were not completely washed out. The (n, f)- radiography technique, having high sensitivity, and capable of getting information on uranium content at each point of a single hair, is an excellent tool for environmental monitoring.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.11
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• pp.705-710
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• 2011

Fault detection has to be proceeded by steady state filtering to get rid of transient effect associated with thermal capacity. Coefficient of variance (COV), ratio of standard deviation devided by moving average, was employed as steady-state filter. Engine speed and refrigerant pressures were selected as parameters representing system dynamics. The filtered values were registered as members of steady-state DB. They were found to show good functional relationship with ambient temperature. The relationship was fitted with a second order polynomial and the distribution bounds of the data around the fitted curve were expressed by visual inspection because of varying average and random data interval. Fault data were compared with the steady-state data obtained during normal operation. The fault data were easily isolated from the fault-free one. To make such isolation reliable, tests to construct good DB should be designed in a systematic way.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2535-2537
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• 2000

In order to detect and monitor the broken cords of steel-cord belt from being damaged by impact of large lump of materials and the corrosion of steel cord, we developed a non-contact magnetic coil detection system. This measures the deterioration of reinforcing cables in steel cord conveyor belt which transport the ores in raw material plant. In this research, magnetic coil sensor of broken-cord detection system has exciting part and sensing part. The broken-cord detection system is operated by supplying a transmitter coil with electric power to generate magnetic field, and then the change of induced voltage is detected in each receiver coils due to resultant magnetic flux effected by the broken steel cords at the inside of the conveyor belt. By the informations such as the position and size of the broken steel cords obtained by SCBMS(Steel Cord Belt Monitoring System), it is expected that not only the span of belt life will be lengthened, but also this system can enable operators to plan scheduled maintenance and prevent the enlargement of damaged parts in steel cord belt at an early stage

• Smart Structures and Systems
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• v.14 no.2
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• pp.159-189
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• 2014

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.225-234
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• 2018

In this paper, we consider a problem of partitioning a search area into smaller rectangular regions, so that multiple platforms can conduct search operations independently without requiring unnecessary coordination among themselves. The search area consists of cells where each cell has some prior information regarding the probability of target existence. The detection probability in particular cell is evaluated by multiplying the observation probability of the platform and the target existence probability in that cell. The total detection probability within the search area is defined as the cumulative detection probability for each cell. However, since this search area partitioning problem is NP-Hard, we decompose the problem into three sequential phases to solve this computationally intractable problem. Additionally, we discuss a special case of this problem, which can provide an optimal analytic solution. We also examine the performance of the proposed approach by comparing our results with the optimal analytic solution.

• Journal of Biomedical Engineering Research
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• v.12 no.2
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• pp.99-106
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• 1991

For the automatic detection of pulmonary blood vessels, a new algorithm is proposed using the fact that human recognizes a pattern orderly according to their size. This method simulates the human recognition process by the pyramid images. For the detection of vessels using multilevel image, large and wtde ones are detected from the most compressed level, followed by the detection of small and narrow ones from the less compressed images with FCM(fuzzy c means) clustering algorithm which classifies similar data into a group. As the proposed algorithm detects blood vessels orderly according to their size, there is no need to consider the variation of parameters and the branch points which should be considered in other detection algirithms. In the detection of patterns whose size changes successively like pulmonary blood vessels, this proposed algorithm can be properly applied

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.