• Animal Bioscience
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• v.36 no.1
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• pp.53-62
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• 2023

Objective: In this study, metabolites that changed in the rumen fluid, urine and feces of dairy cows fed different feed ratios were investigated. Methods: Eight Holstein cows were used in this study. Rumen fluid, urine, and feces were collected from the normal concentrate diet (NCD) (Italian ryegrass 80%: concentrate 20% in the total feed) and high concentrate diet (HCD) groups (20%: 80%) of dairy cows. Metabolite analysis was performed using proton nuclear magnetic resonance (NMR) identification, and statistical analysis was performed using Chenomx NMR software 8.4 and Metaboanalyst 4.0. Results: The two groups of rumen fluid and urine samples were separated, and samples from the same group were aggregated together. On the other hand, the feces samples were not separated and showed similar tendencies between the two groups. In total, 160, 177, and 188 metabolites were identified in the rumen fluid, urine, and feces, respectively. The differential metabolites with low and high concentrations were 15 and 49, 14 and 16, and 2 and 2 in the rumen fluid, urine, and feces samples, in the NCD group. Conclusion: As HCD is related to rumen microbial changes, research on different metabolites such as glucuronate, acetylsalicylate, histidine, and O-Acetylcarnitine, which are related to bacterial degradation and metabolism, will need to be carried out in future studies along with microbial analysis. In urine, the identified metabolites, such as gallate, syringate, and vanillate can provide insight into microbial, metabolic, and feed parameters that cause changes depending on the feed rate. Additionally, it is thought that they can be used as potential biomarkers for further research on subacute ruminal acidosis.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Journal of Soil and Groundwater Environment
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• v.27 no.6
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• pp.47-57
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• 2022

A number of different methods have been used for modeling the sorption of volatile organic chlorinated compounds such as tetrachloroethylene/perchloroethylene (PCE). In this study, PCE was adsorbed in several natural sorbents, i.e., Pahokee peat, vermicompost, BionSoil^®, and natural soil, in the batch experiments. Several sorption models such as linear, Freundlich, solubility-normalized Freundlich model, and Polanyi-Manes model (PMM) were used to analyze sorption isotherms. The relationship between sorption model parameters, organic carbon content (f_oc), and elemental C/N ratio was studied. The organic carbon normalized partition coefficient values (log K_oc = 1.50-3.13) in four different sorbents were less than the logarithm of the octanol-water partition coefficient (log K_ow = 3.40) of PCE due to high organic carbon contents. The log K_oc decreased linearly with log f_oc and log C/N ratio, but increased linearly with log O/C, log H/C, and log (N+O)/C ratio. Both log K_F,oc or log K_F,oc decreased linearly with log f_oc (R² = 0.88-0.92) and log C/N ratio (R² = 0.57-0.76), but increased linearly with log (N+O)/C (R² = 0.93-0.95). The log q_max,oc decreased linearly as log f_oc and log C/N increased, whereas it increased with log O/C, log H/C and log (N+O)/C ratios. The log q_max,oc increased linearly with (N+O)/C indicating a strong dependence of q_max,oc on the polarity index. The results showed that PCE sorption behaviors were strongly correlated with the physicochemical properties of soil organic matter (SOM).

• Journal of dental hygiene science
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• v.22 no.4
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• pp.249-255
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• 2022

Background: Sexual dimorphism is important for sex determination in the field of forensics. However, sexual dimorphism is commonly assessed using cone beam computed tomography (CBCT) rather than three-dimensional (3D) modeling software; therefore, studies using a more accurate measurement approach are necessary. This study assessed the sexual dimorphism of the MS using a 3D modeling program to obtain information that could contribute to the fields of surgery and forensics. Methods: The CBCT data of 60 patients (age, 20~29 y; 30 males and 30 females) admitted to the Department of Orthodontics at the Dankook University School of Dentistry were provided in Digital Imaging and Communications in Medicine (DICOM) format. The left MS and right MS were modeled based on the DICOM files using the Mimics (version 22; Materialise, Leuven, Belgium) 3D program and converted to stereolithography (STL) files used to measure the width, length, and height of the MS, infraorbital foramen (IOF), right MS, and left MS. The average of three repeated measurements was calculated, and a reliability test was performed to ensure data reliability (Cronbach's α=0.618). A canonical discriminant analysis was performed using a standard approach (left: Box's M=0.096; right: Box's M=0.115). Results: Males had greater values for all parameters (MS width, MS length, MS height, IOF, right MS, left MS) than females. The discriminant analysis identified six independent variables (MS width, MS height, MS length, IOF, right MS, left MS) that could identify sex. The left MS and right MS correctly identified the sex of 81.7% and 71.7% of the patients, respectively, with the left MS having higher accuracy. Conclusion: This study confirmed that, for Korean individuals, the left MS has a better ability to identify sex than the right MS. These results may contribute to sex identification in the fields of surgery and forensics.

• Wind and Structures
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• v.36 no.4
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• pp.221-236
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• 2023

The lateral component of turbulence and the vortices shed in the wake of a structure result in introducing dynamic wind load in the acrosswind direction and the resulting level of motion is typically larger than the corresponding alongwind motion for a dynamically sensitive structure. The underlying source mechanisms of the acrosswind load may be classified into motion-induced, buffeting, and Strouhal components. This study proposes a frequency domain framework to decompose the overall load into these components based on output-only measurements from wind tunnel experiments or full-scale measurements. First, the total acrosswind load is identified based on measured acceleration response by solving the inverse problem using the Kalman filter technique. The decomposition of the combined load is then performed by modeling each load component in terms of a Bayesian filtering scheme. More specifically, the decomposition and the estimation of the model parameters are accomplished using the unscented Kalman filter in the frequency domain. An aeroelastic wind tunnel experiment involving a tall circular cylinder was carried out for the validation of the proposed framework. The contribution of each load component to the acrosswind response is assessed by re-analyzing the system with the decomposed components. Through comparison of the measured and the re-analyzed response, it is demonstrated that the proposed framework effectively decomposes the total acrosswind load into components and sheds light on the overall underlying mechanism of the acrosswind load and attendant structural response. The delineation of these load components and their subsequent modeling and control may become increasingly important as tall slender buildings of the prismatic cross-section that are highly sensitive to the acrosswind load effects are increasingly being built in major metropolises.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.154-160
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• 1996

The control of diamond turning is usually achieved through a laser-interferometer feedback of slide position. The limitation of this control scheme is that the feedback signal does not account for additional dynamics of the tool post and the material removal process. If the tool post is rigid and the material removal process is relatively static, then such a non-collocated position feedback control scheme may surfice. However, as the accuracy requirement gets tighter and desired surface cnotours become more complex, the need for a direct tool-tip sensing becomes inevitable. The physical constraints of the machining process prohibit any reasonable implementation of a tool-tip motion measurement. It is proposed that the measured force normal to the face of the workpiece can be filtered through an appropriate admittance transfer function to result in the estimated dapth of cut. This can be compared to the desired depth of cut to generate the adjustment control action in additn to position feedback control. In this work, the design methodology on the admittance model-based control with a conventional controller is presented. The recursive least-squares algorithm with forgetting factor is proposed to identify the parameters and update the cutting process in real time. The normal cutting forces are measured to identify the cutting dynamics in the real diamond turning process using the precision dynamoneter. Based on the parameter estimation of cutting dynamics and the admitance model-based nanodynamic control scheme, simulation results are shown.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.1
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• pp.85-95
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• 1998

The broadband ultrasonic transducers have been designed to use in obtaining the broadband echo signals from fish schools in relation to the identification of fish species. The broadening of bandwidth was achieved by attaching double acoustic matching layers on the front face of a Tonpilz transducer consisted of an aluminum head, a piezoelectric ring, a brass tail and to evaluate the performance characteristics, such as the transmitting voltage response(TVR) of transducers. The constructed transducers were tested experimentally and numerically by changing the parameters such as impedances and thicknesses of the head, tail and matching layers, in the water tank. Also, the developed transducer was excited by a chirp signal and the received chirp waveforms were analyzed. According to the measured TVR results, the available 3 dB bandwidth of the transducer with double matching layers of an $Al_O_3/epoxy$ composite of 7 mm thick and a polyurethane window of 18 mm thick was 7.3 kHz with a center frequency of 38.8 kHz, and the maximum and the minimum values of the TVR in this frequency region were 135.7 dB and 132.7 dB re $1\;{\mu}Pa/V$ at 1 m, respectively. Also, the available 3 dB bandwidth of the transducer with double matching layers of an $Al_O_3/epoxy$ composite of 11 mm thick and a polyurethane window of 15 mm thick was 6.2 kHz with a center frequency of 38.6 kHz, and the maximum TVR value in the frequency region was 136.3 dB re $1\;{\mu}Pa/V$ at 1 m. Reasonable agreement between the experimental results and the numerical results for the TVR of the developed transducers was achieved. The frequency dependant characteristics of experimentally observed chirp signals closely matched to the measured TVR results. These results suggest that there is potential for increasing the bandwidth by varying other parameters in the transducer design and the material of the acoustic matching layers.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Parasites, Hosts and Diseases
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• v.55 no.4
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• pp.367-374
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• 2017

Despite the broad distribution of leishmaniasis among Iranians and animals across the country, little is known about the genetic characteristics of the causative agents. Applying both HSP70 PCR-RFLP and sequence analyses, this study aimed to evaluate the genetic diversity and phylogenetic relationships among Leishmania spp. isolated from Iranian endemic foci and available reference strains. A total of 36 Leishmania isolates from almost all districts across the country were genetically analyzed for the HSP70 gene using both PCR-RFLP and sequence analysis. The original HSP70 gene sequences were aligned along with homologous Leishmania sequences retrieved from NCBI, and subjected to the phylogenetic analysis. Basic parameters of genetic diversity were also estimated. The HSP70 PCR-RFLP presented 3 different electrophoretic patterns, with no further intraspecific variation, corresponding to 3 Leishmania species available in the country, L. tropica, L. major, and L. infantum. Phylogenetic analyses presented 5 major clades, corresponding to 5 species complexes. Iranian lineages, including L. major, L. tropica, and L. infantum, were distributed among 3 complexes L. major, L. tropica, and L. donovani. However, within the L. major and L. donovani species complexes, the HSP70 phylogeny was not able to distinguish clearly between the L. major and L. turanica isolates, and between the L. infantum, L. donovani, and L. chagasi isolates, respectively. Our results indicated that both HSP70 PCR-RFLP and sequence analyses are medically applicable tools for identification of Leishmania species in Iranian patients. However, the reduced genetic diversity of the target gene makes it inevitable that its phylogeny only resolves the major groups, namely, the species complexes.