Search Results (6)

There are global concerns regarding the transmission of antimicrobial-resistant pathogens from animals to humans. Especially, companion animals are increasingly recognized as a potential source due to their close interactions with people, despite a limited number of reported cases. Although, social demands regarding comprehensive surveillance for antimicrobial resistance (AMR) among companion animals are highlighted, there is a lack of a relevant system in South Korea. In this regard, we conducted preliminary investigation on antimicrobial use (AMU) among small animal clinics, along with veterinary practitioner’s knowledge and attitude regarding this issue in South Korea. We collected data on 684,153 antimicrobial prescription visits for canine and feline patients from 2019 to 2022 at 100 veterinary facilities in South Korea, using electronic medical records. To evaluate antimicrobial use (AMU) and facilitate comparisons across institutions and time periods, we developed the Defined Daily Dose for Animals (DDDA) and the Defined Animal Daily Dosages per 1000 Animal-Days (DAPD). In addition, we conducted an online survey of 362 veterinary practitioners, which included questions on their perceptions, attitudes, and practices regarding antimicrobial prescriptions. Simple frequency analyses were performed to examine temporal trends, regional differences and variations by facility size in AMU, and to summarize survey responses. Descriptive analysis using data from 100 veterinary clinics revealed a rising trend in AMU between 2019 and 2022, with higher usage observed in larger clinics and non-capital regions. DDDA values for dogs were generally higher than for cats. Survey results highlighted that, while veterinarians exhibited high awareness of AMR, prescribing practices were significantly influenced by clinical judgments and owner demands, often deviating from established guidelines. The adoption of an electronic veterinary prescription management system (e-Vet) was proposed to enhance antimicrobial stewardship. However, concerns regarding the system’s efficiency and administrative burden were prominent. To our best knowledge, this study provided DDDA for companion animals for the first time in South Korea. Although the indicator should be improved with more comprehensive data and expert opinion, our study showed that it enables reasonable situation analysis regarding AMU in companion animals. The identified factors that affect veterinarians’ prescription practices can also be used to design an effective strategy for promoting appropriate antimicrobial usage. Full article

Infrared (IR) radiation curing technology has a high potential to improve the curing process of thermosetting polymers. To investigate the IR curing reaction mechanism, the present study explores the curing kinetics of glycidyl methacrylate (GMA)/dodecanedioic acid (DDDA) powder coatings subjected to IR radiation. Fourier-transformed infrared (FT-IR) spectroscopy is employed to record the concentration of epoxide groups with respect to time under different temperature conditions, with the reaction conducted under IR radiation. The resulting data are then fitted by the Levenberg–Marquardt algorithm using MATLAB software to obtain the kinetic parameters, namely the rate constant (k), catalytic constants (n, m), manifestation activation energy (E), and the pre-exponential factor (A) of the curing reaction. Additionally, this study proposes a new concept: the ‘photo-thermal synergistic effect’ of infrared curing and its evaluation criteria using a dimensionless quantity. Incredibly, this index integrates the impact of IR curing technology on two aspects: the curing process and the properties of the cured product. Overall, this study deepens our understanding of the IR curing reaction mechanism and provides a reference for the application of this technology in practical engineering. Full article

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments. Full article

The presented paper introduces a new methodology of experimental testing procedures required by the complex systems of electric vehicles (EV). This methodology is based on real-time connection of test setups and platforms, which may be situated in different geographical locations, belong to various cyber-physical domains, and are united in a global X-in-the-loop (XIL) experimental environment. The proposed concept, called XILforEV, allows exploring interdependencies between various physical processes that can be identified or investigated in the process of EV development. The paper discusses the following relevant topics: global XILforEV architecture; realization of required high-confidence models using dynamic data driven application systems (DDDAS) and multi fidelity models (MFM) approaches; and formulation of case studies to illustrate XILforEV applications. Full article

Partially acetylated chitosan oligosaccharides (COS), which consists of N-acetylglucosamine (GlcNAc) and glucosamine (GlcN) residues, is a structurally complex biopolymer with a variety of biological activities. Therefore, it is challenging to elucidate acetylation patterns and the molecular structure-function relationship of COS. Herein, the detailed deacetylation pattern of chitin deacetylase from Saccharomyces cerevisiae, ScCDA₂, was studied. Which solves the randomization of acetylation patterns during COS produced by chemical. ScCDA₂ also exhibits about 8% and 20% deacetylation activity on crystalline chitin and colloid chitin, respectively. Besides, a method for separating and detecting partially acetylated chitosan oligosaccharides by high performance liquid chromatography and electrospray ionization mass spectrometry (HPLC-ESI-MS) system has been developed, which is fast and convenient, and can be monitored online. Mass spectrometry sequencing revealed that ScCDA₂ produced COS with specific acetylation patterns of DAAA, ADAA, AADA, DDAA, DADA, ADDA and DDDA, respectively. ScCDA₂ does not deacetylate the GlcNAc unit that is closest to the reducing end of the oligomer furthermore ScCDA₂ has a multiple-attack deacetylation mechanism on chitin oligosaccharides. This specific mode of action significantly enriches the existing limited library of chitin deacetylase deacetylation patterns. This fully defined COS may be used in the study of COS structure and function. Full article

In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills. Full article