Tongmyong University

This study aimed to evaluate the construct validity and applicability of the Korean-translated version of C-BARQ (S) as a measurement tool for assessing canine behavioral patterns in Korea.In total, 142 dog owners (78.2% female; average dog age 6.48 4.27 years) from across Korea responded to the survey, which included 42 C-BARQ (S) items and demographic questions.After excluding two items with >25% missing responses and five inappropriately loaded items, exploratory factor analysis was conducted on 35 items, extracting 10 factorsenergy level, social aggression, social fear, owner-directed aggression, sensitivity, chasing, inappropriate elimination, excitability, trainability, and attachment/ attention-seeking-that together explained 71% of the variance.Cronbach's alpha for internal reliability was 0.70 for all but "excitability" (0.659).Logistic regression revealed: dogs under 2 years were 64.605 times more likely to show high energy level (OR = 64.61,CI = 5.72-729.81),small dogs (<10 kg) showed pronounced attachment (OR = 7.74, CI = 1.60-37.32),owner-cognition of behavioral problems were strongly associated with social aggression (OR = 4.05, CI = 1.04-15.80).The findings support the construct validity of the Korean-translated C-BARQ (S) and provide preliminary evidence on behavioral patterns within the surveyed population.

Background: Tetrodotoxin (TTX) is a potent neurotoxin found in pufferfish that blocks voltage-gated sodium channels and leads to flaccid paralysis. Although its effects are typically limited to the peripheral nervous system, rare central neurological manifestations may be under-recognized. Case: A 70-year-old male presented to the emergency department approximately 10 h after consuming pufferfish stew. The initial symptoms included slurred speech, facial weakness, and general fatigue without respiratory compromise. Vital signs and laboratory evaluations, including cardiac enzyme levels, blood gas analysis, and toxicology screening, were within normal limits. Brain computed tomography and electrocardiogramrevealed no acute abnormalities. Shortly after arrival, the patient developed progressive involuntary side-to-side head movements and repetitive swaying of the upper body. Comprehensive toxicological screening revealed negative results for other neurotoxins. Supportive care was administered and no antidotes were available. Over the course of 12 to 24 h, the involuntary movements gradually subsided and fully resolved by 48 h post-ingestion, with complete neurological recovery. Conclusion: This case report describes a rare presentation of tetrodotoxin poisoning involving involuntary motor activity, suggesting potential central nervous system involvement. This challenges the conventional understanding that tetrodotoxin toxicity is confined to the peripheral nervous system. Clinicians should be aware of such atypical features to ensure a timely diagnosis and appropriate supportive care.

Clothing structure design is a key link in the clothing manufacturing process, and the traditional design methods rely on the experience and skills of designers. With the development and application of digital technology in the apparel industry, it has greatly promoted the innovation and development of apparel manufacturing. In this paper, we combine the depth estimation and vision controller methods to construct an image Jacobi matrix to realize the control of the hand-eye mapping relationship of the robot's visual servo, which represents the mapping relationship between the robot's joint speed to the end speed. Using Kalman filtering algorithm, the image Jacobi matrix to be estimated is used as the system state to estimate the system state, so as to achieve the control of the stitch and displacement of the garment sewing, and, at the same time, capture the visual information contained in the garment to optimize the design of the garment structure. For the optimized designed garments using the Jacobi matrix, 4# has the highest mean comfort rating of 4.5 and above. The mean value of satisfaction evaluation for ease of movement and overall comfort of the optimized garment went up to 4. It is evident that the overall comfort of the garment optimized by the image Jacobi matrix algorithm has been significantly improved.

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For sustainable urban environments and improved public health, it is essential to employ technologies that can comprehensively monitor multiple environmental factors, including air pollution, temperature and humidity variations, particulate matter, and noise. However, most existing environmental sensor systems are limited to single-parameter measurements or require high installation and maintenance costs, which restrict their scalability for large-scale and long-term monitoring. Furthermore, the lack of seamless integration among heterogeneous sensors hinders real-time analysis and effective decision-making. To address these challenges, this paper presents the design and implementation of an integrated environmental monitoring system capable of simultaneously measuring and analyzing diverse environmental parameters. The proposed system emphasizes air pollution monitoring, ambient noise analysis, and low-power operation. This is achieved by integrating multiple environmental sensors, noise measurement and analysis modules, and a time-of-flight (ToF) sensor. In addition, the system supports internet connectivity for cross-platform data collection and interoperability, while transmitting all sensor data to the cloud for efficient processing and analysis.

Abstract The aim of this paper is to find a method for tracking the resonance frequency of an ultrasonic transducer driven by a phase-shift PWM inverter operating in a ‘phase-shift+operating frequency control’ scheme, as well as to overcome the drawback of IZL(Integration Zero Loop) where optimal frequency tracking is impossible when the phase-shift PWM inverter outputs the maximum power. The minimum admittance tracking system(MATS) proposed in this paper consists of a matching circuit with parameters determined by MATLAB simulation and performs experiments with the desired power to determine the L F so that the phase-shift PWM inverter operates in a stable operating state where shock currents are not generated by a small increase in the parameter L F of the L-C matching circuit. The MATS, which is the optimal frequency tracking method we proposed, overcame the drawback of IZL, where the optimal frequency tracking was not possible when the phase-shift PWM inverter outputs the maximum power. Moreover, it prevents the generation of shock currents that occurred on the right-branch of the inverter even when power control is performed at the resonant frequency of the load and allows the inverter to operate in a steady state.

Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there remains considerable variability in speech intelligibility among CI users. As a result, speech produced by CI talkers often exhibits distinct acoustic characteristics compared to that of NH individuals. Methods: Speech data were obtained from eight cochlear-implant (CI) and eight normal-hearing (NH) talkers, while electroencephalogram (EEG) responses were recorded from 11 NH listeners exposed to the same speech stimuli. Support Vector Machine (SVM) classifiers employing 3-fold cross-validation were evaluated using classification accuracy as the performance metric. This study evaluated the efficacy of Support Vector Machine (SVM) algorithms using four kernel functions (Linear, Polynomial, Gaussian, and Radial Basis Function) to classify speech produced by NH and CI talkers. Six acoustic features—Log Energy, Zero-Crossing Rate (ZCR), Pitch, Linear Predictive Coefficients (LPC), Mel-Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Predictive Cepstral Coefficients (PLP-CC)—were extracted. These same features were also extracted from electroencephalogram (EEG) recordings of NH listeners who were exposed to the speech stimuli. The EEG analysis leveraged the assumption of quasi-stationarity over short time windows. Results: Classification of speech signals using SVMs yielded the highest accuracies of 100% and 94% for the Energy and MFCC features, respectively, using Gaussian and RBF kernels. EEG responses to speech achieved classification accuracies exceeding 70% for ZCR and Pitch features using the same kernels. Other features such as LPC and PLP-CC yielded moderate to low classification performance. Conclusions: The results indicate that both speech-derived and EEG-derived features can effectively differentiate between CI and NH talkers. Among the tested kernels, Gaussian and RBF provided superior performance, particularly when using Energy and MFCC features. These findings support the application of SVMs for multimodal classification in hearing research, with potential applications in improving CI speech processing and auditory rehabilitation.

This study presents an electro-thermal analysis of high-power lithium-ion battery modules for urban air mobility (UAM) applications, focusing on assessing the operational impact of installing a thermal barrier pad (TBP)—designed for thermal runaway delay—to ensure that the module maintains acceptable performance during normal operations. An integrated electro-thermal simulation model was developed and validated through single-cell experiments under step-load conditions, showing good agreement with measured voltage and temperature. In the baseline module without a TBP, higher discharge rates resulted in increased heat generation and cell temperatures, with approximately 42.5% of the electrical output dissipated as heat under the 5C condition. When the TBP was applied, the cooling performance of the heat sink decreased, leading to higher module temperatures and increased temperature differences between the cell and the heat sink, particularly as the TBP thickness increased. A simplified UAM flight scenario was simulated to evaluate temperature behavior throughout various operating phases. For the 1.5 mm TBP model, the maximum temperature (75.7 °C) remained within the design limit (80 °C). However, increasing the maximum take-off discharge rate to 6C or higher caused the module to reach its thermal limit or cut-off voltage before mission completion. These results indicate that TBP installation can be applied without unacceptable performance degradation under normal operation, provided that its thickness is optimized by considering cooling performance, thermal safety, and weight/volume constraints in UAM applications.

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value.