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In July 2023, cases of highly pathogenic avian influenza (HPAI) were reported at 2 shelters for stray cats in Seoul, South Korea. The cause of infection was suspected to be improperly sterilized raw food made from domestic duck meat, which was manufactured in South Korea. All viruses isolated from cats at the shelters and from the raw food belonged to HPAI A(H5N1) clade 2.3.4.4b. The gene constellation of all viruses was most similar to that of viruses isolated in Korea in November 2022. Of note, the viruses isolated from infected cats harbored mutations E627K or D701N in polymerase basic 2, which are indicative of adaptation to mammals. Postmortem examination revealed systemic pathologic lesions and the presence of widespread virus in different tissues. Thus, consumption of raw duck meat contaminated with HPAI virus likely caused systemic symptoms and death in cats, indicating the introduction of mammal-adapted mutations of the virus.

Objective To systematically translate the Duchenne muscular dystrophy Functional Ability Self-Assessment Tool (DMDSAT) into Korean and verify the reliability and validity of the Korean version (K-DMDSAT). Methods The original DMDSAT was translated into Korean by two translators and two pediatric physiatrists. A total of 88 patients with genetically confirmed Duchenne muscular dystrophy (DMD) participated in the study. They were evaluated using the K-DMDSAT once as a self-assessment and once by an interviewer. The interviewer evaluated the K-DMDSAT again 1 week later using a test-retest approach. The intraclass correlation coefficient (ICC) was used to verify the interrater and test-retest reliabilities. Pearson correlation analysis between the K-DMDSAT and the Brooke or Vignos scales were used to assess validity. Results The total score and all domains of the K-DMDSAT showed excellent interrater and test-retest reliability, with an ICC for total scores of 0.985 and 0.987, respectively. All domains had an ICC >0.90. From the Pearson correlation analysis, the total K-DMDSAT score was significantly correlated with the Vignos and Brooke scales (r=0.918 and 0.825, respectively; p

Schistosomus reflexus (SR) is one of the most common congenital anomalies found in cases of cattle dystocia; this disorder occurs mostly in cattle. Congenital anomalies such as SR are caused by various genetic and environmental factors, but no specific cause has been elucidated for SR. This study reports a case of SR in a Holstein dairy cattle fetus with congenital anomalies in Korea. Grossly, a distinct spine curvature was observed between the thoracic and lumbar vertebrae, accompanied by a consequential malformation from the sacrum to the occipital bone. Furthermore, the thoracic and abdominal organs were exposed. In computed tomography (CT) images, mild and severe kyphoscoliosis was observed in T1~11 and L1~6, respectively. Additionally, vertebral dysplasia was observed in S1~5 and Cd 1~5. To pinpoint the causal genes and mutations, we leveraged a custom 50K Hanwoo SNP-Chip and the Online Mendelian Inheritance in Animals (OMIA) database. As a result, we identified a nonsense mutation in apoptotic protease activating factor 1 (APAF1) within HH1 that was associated with a decrease in conception rate and an increase in abortion in Holstein dairy cattle. The genotype of the SR case was A/A, and most of the 1,142 normal Holstein dairy cattle tested as a control group had the genotype G/G. In addition, the A/A genotype did not exist in the control group. Based on the pathological, genetic, and radiological findings, the congenital abnormalities observed were diagnosed as SR.

Abstract In this paper, a local magnitude estimation model using ridge regression is proposed for the accurate determination of the local magnitude of earthquakes. To increase its accuracy, an estimation model was derived from the hypocentral distance correction term and peak amplitude using ridge regression. The improved performance of the proposed estimation model was evaluated via the mean squared error. It was compared with the least squares model to confirm its superiority in predicting the real magnitude.

Mucormycosis is a life-threatening fungal disease caused by opportunistic pathogens present in the environment. This report presents a case of a Lichtheimia ramosa infection that manifested as characteristic lesions in various visceral organs of a pregnant cow in Korea. The post-mortem examination revealed the cause of death to be mucormycosis. The fungi isolated from the liver were found to be Lichtheimia spp. Analysis of the fungi using Internal Transcribed Spacer gene sequencing facilitated the identification of Lichtheimia ramosa. To the best of our knowledge, this is the first clinical case of angioinvasive mucormycosis in cattle.

Downer cow can be caused by muscular paralysis, neurological damage, metabolic disorder, and/or the complication of microbial infection. However, downer cow with unknown etiology is issued because of the non-detection of its bacterial etiological agent. In this study, differences in the bacterial community in brain tissues between downer cattle with unknown etiology and healthy slaughtered cattle are investigated. Bacterial diversity and representative genera between downer and normal cattle were significantly different (p < 0.05). There are significant differences in representative genera of downer and normal cattle, especially the significance, fold change, and area under the receiver operating characteristic curve score (p < 0.05). Furthermore, the prediction of functional genes in brain microbiota between the downer and normal cattle revealed differences in the cluster of orthologous gene categories, such as lipid transport and metabolism, secondary metabolite biosynthesis, and signal transduction (p < 0.05). This study revealed a significant difference in microbiota between the downer and normal cattle. Thus, we demonstrate that representative genera from downer cattle through 16S rRNA gene amplicon sequencing and microbiota analysis have the potential as candidates for bacterial etiological agents for downer cow.

A modified sigmoid sliding mode control (MS-SMC) approach is proposed for stabilizing and tracking a quadrotor system with a nonlinear sliding surface, where the dynamics model is underactuated, highly coupled, and nonlinear. The constructed nonlinear sliding surface is based on the traditional sliding mode surface with a modified sigmoid function, allowing the initial value to quickly reach equilibrium. A new type of nonlinear SMC is applied for performance improvement of the quadrotor using the proposed modified sigmoid sliding surface. To control the quadrotor effectively, a double-loop control method is used to design the control rate, in which the position subsystem is the outer loop, and the attitude subsystem is the inner loop.With the Lyapunov function, the stability of the overall closed-loop system is ensured by stabilizing each subsystem step by step. Moreover, from a practical point of view, the system performance under the model uncertainties and external disturbances are also considered. The simulation results show that the proposed MS-SMC performs better than the conventional sliding mode control (CSMC) and the back-stepping sliding mode control (BS-SMC) in terms of stabilization and tracking against external disturbances.

The discrimination between earthquakes and artificial explosions is a significant issue in seismic analysis to efficiently prevent and respond to seismic events. However, the discrimination of seismic events is challenging due to the low incidence rate. Moreover, the similarity between earthquakes and artificial explosions with a local magnitude derives a nonlinear data distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods—support vector machine, naive Bayes, and logistic regression. Furthermore, to overcome the nonlinear separation problem, the kernel functions and regularized logistic regression are applied to design seismic classifiers. To efficiently design the classifier, P- and S-wave amplitude ratios on the time domain and spectral ratios on the frequency domain, which is converted by fast Fourier transform and short-time Fourier transform are selected as feature vectors. Furthermore, an adaptive synthetic sampling algorithm is adopted to enhance the classifier performance against the seismic data imbalance issue caused by the non-equivalent number of occurrences. The comparisons among classifiers are evaluated by the binary classification performance analysis methods.

In December 2016, highly pathogenic avian influenza (HPAI) infection with systemic pathologic lesions was found in cats in South Korea. Genetic analyses indicated that the feline isolates were similar to HPAI H5N6 viruses isolated in chicken farms nearby. This finding highlights the need for monitoring of domestic mammals during HPAI outbreaks.

With the growing complexity of software-based industrial control applications, the demand for reliable software in the industrial control domain has also been increasing. Although the robustness of complex industrial control applications can benefit from formal modeling and verification, there remains a significant gap between formally verified models and real implementation. A model-driven development approach can be a solution to this problem. This paper aims to introduce a full-fledged model-driven integrated development environment, called UMIICA, for developing reliable industrial control applications. For this goal, we first determined the requirements imposed by the model-driven development process of networked industrial control applications. Based on these requirements, we analyzed the problems to be solved and designed the UMIICA tool according to the problem statements. Our proposed UMIICA tool is a full-fledged development environment for better productivity and software quality in our target application domain in that it incorporates whole model-driven development phases with cross-platform support and preserves the verified model behavior on the final implementation. As a case study, we constructed a three-axis cartesian robot testbed and developed an application for the testbed via the UMIICA tool. The evaluation results on our testbed also proved the effectiveness of UMIICA regarding the functional correctness and real-time performance guarantee of the resulting target application.

Equine herpesvirus-1 (EHV-1) is an important pathogen in horses. It affects horses worldwide and causes substantial economic losses. In this study, for the first time, we characterized EHV-1 isolates from South Korea at the molecular level. We then aimed to determine the genetic divergences of these isolates by comparing them to sequences in databases. In total, 338 horse samples were collected, and 12 EHV-1 were isolated. We performed ORF30, ORF33, ORF68, and ORF34 genetic analysis and carried out multi-locus sequence typing (MLST) of 12 isolated EHV-1. All isolated viruses were confirmed as non-neuropathogenic type, showing N752 of ORF30 and highly conserved ORF33 (99.7–100%). Isolates were unclassified using ORF68 analysis because of a 118 bp deletion in nucleotide sequence 701–818. Seven EHV-1 isolates (16Q4, 19R166-1, 19R166-6, 19/10/15-2, 19/10/15-4, 19/10/18-2, 19/10/22-1) belonged to group 1, clade 10, based on ORF34 and MLST analysis. The remaining 5 EHV-1 isolates (15Q25-1, 15D59, 16Q5, 16Q40, 18D99) belonged to group 7, clade 6, based on ORF34 and MLST analysis.

We propose a control method wherein the estimated angles converge to the desired value for quadrotor attitude stabilization and position tracking. To improve the performance of a quadrotor system, the unmeasured states of the quadrotor are estimated using a sliding mode observer (SMO). We set up a quadrotor dynamic model and augment the quadrotor dynamics by an SMO. We also derive the control inputs by sliding mode control (SMC) and calculate the desired angle of the quadrotor to reach the target position with the control inputs. For fast convergence speed and increased robustness of tracking performance, a nonlinear sliding surface is applied to SMC. The angle of the quadrotor converges to the desired value through the operation of SMC with a nonlinear sliding surface. The target tracking performance is improved by adaptively switching the deceleration curve of the sliding mode surface with a nonlinear curve. Using a tracking system based on a nonlinear surface sliding mode control (NSMC) and SMO, the quadrotor reaches the target position with a decreased settling time. The performance and effectiveness of the proposed system are proved through simulation results.

Among various localization methods, a localization method that uses a radio frequency signal-based wireless sensor network has been widely applied due to its robustness against noise factors and few limits on installation location. In this paper, we focus on an iterative localization scheme for a mobile with a limited number of time difference of arrival (TDOA) and angle of arrival (AOA) data measured from base stations. To acquire the optimal location of a mobile, we propose a recursive solution for localization using an iteratively reweighted-recursive least squares (IR-RLS) algorithm. The proposed IR-RLS scheme can obtain the optimal solution with a fast computational speed when additional TDOA and/or AOA data is measured from base stations. Moreover, while the number of measured TDOA/AOA data was limited, the proposed IR-RLS scheme could obtain the precise location of a mobile. The performance of the proposed IR-RLS method is confirmed through some simulation results.

The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

To monitor large areas or simultaneously measure multiple points, multiple unmanned aerial vehicles (UAVs) must be flown in formation. To perform such flights, sensor information generated by each UAV should be shared via communications. Although a variety of studies have focused on the algorithms for formation flight, these studies have mainly demonstrated the performance of formation flight using numerical simulations or ground robots, which do not reflect the dynamic characteristics of UAVs. In this study, an onboard sensor information sharing system and formation flight algorithms for multiple UAVs are proposed. The communication delays of radiofrequency (RF) telemetry are analyzed to enable the implementation of the onboard sensor information sharing system. Using the sensor information sharing, the formation guidance law for multiple UAVs, which includes both a circular and close formation, is designed. The hardware system, which includes avionics and an airframe, is constructed for the proposed multi-UAV platform. A numerical simulation is performed to demonstrate the performance of the formation flight guidance and control system for multiple UAVs. Finally, a flight test is conducted to verify the proposed algorithm for the multi-UAV system.

In this paper, a heterodyne laser interferometer, which is used as a sensor for high-precision displacement measurement, is introduced to measure ground vibration and seismic waves as a seismometer. The seismic wave is measured precisely through the displacement variation obtained by the heterodyne laser interferometer. The earthquake magnitude is estimated using only the P-wave magnitudes for the first 3 s through the total noise enhanced optimization (TNEO) model. We use data from southern California to investigate the relationship between peak acceleration amplitude ( P d ) and the earthquake magnitude ( M g ). For precise prediction of the earthquake magnitude using only the P d value, the TNEO model derives the relation equation between P d and the magnitude, considering the noise present in each measured seismic data. The optimal solution is obtained from the TNEO model based objective function. We proved the performance of the proposed method through simulation and experimental results.

In this paper, we suggest a seismic signal measurement system that uses a laser interferometer. The heterodyne laser interferometer is used as a seismometer due to its high accuracy and robustness. Seismic data measured by the laser interferometer is used to analyze crucial earthquake characteristics. To measure P-S time more precisely, the short time Fourier transform and instantaneous frequency estimation methods are applied to the intensity signal ( I y ) of the laser interferometer. To estimate the epicenter location, the range difference of arrival algorithm is applied with the P-S time result. The linear matrix equation of the epicenter localization can be derived using P-S time data obtained from more than three observatories. We prove the performance of the proposed algorithm through simulation and experimental results.