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Abstract Acute kidney injury (AKI) is a common complication of bites by Viperidae snakes. The main venomous snakes in South Korea are members of the Viperidae family, but there is limited information on snake-bite induced AKI in South Korea. Therefore, we investigated snake envenomation-related AKI and the performance of surrogate baseline creatinine to diagnose true AKI. This retrospective, case-control study divided 71 patients with baseline serum creatinine into two groups according to the development of envenomation-related AKI determined based on Kidney Disease Improving Global Outcome Classification. The incidence of AKI was 14.1% with 10% having stage-3 AKI. During hospitalization, the AKI group did not develop venom-induced consumption coagulopathy more frequently than did the non-AKI group. Only one patient needed renal replacement therapy, and no patients died. The serum creatinine concentration at presentation underestimated the incidence of AKI, while the lowest creatinine concentration during hospitalization overestimated the incidence of AKI. After discharge, the incidence of chronic kidney disease did not increase in the AKI group. Snake envenomation-related AKI is characterized by a relatively lower incidence and severity, has no correlation with coagulation disturbance, and has favorable short- and long-term outcomes in South Korea. Based on this study, it is recommended that future envenomation-related AKI studies would utilize the baseline creatinine for diagnosing AKI. If not, envenomation-related AKI studies should include the definition of baseline creatinine used to diagnose AKI and report bias if a baseline creatinine surrogate is used.

The main purpose of this study is to optimize engine performance and emission characteristics of off-road engines with retarded spark timing compared to MBT by repurposing the existing passenger engine. This study uses a one-dimensional (1D)-simulation to develop a non-road gasoline MPI turbo engine. The SI turbulent flame model of the GT-suite, an operational performance predictable program, presents turbocharger matching and optimal operation design points. To optimize the engine performance, the SI turbulent model uses three operation parameters: spark timing, intake valve overlap, and boost pressure. Spark timing determines the initial state of combustion and thermal efficiency, and is the main variable of the engine. The maximum brake torque (MBT) point can be identified for spark timing, and abnormal combustion phenomena, such as knocking, can be identified. Spark timing is related to engine performance, and emissions of exhaust pollutants are predictable. If the spark timing is set to variables, the engine performance and emissions can be confirmed and predicted. The intake valve overlap can predict the performance and exhaust gas by controlling the airflow and combustion chamber flow, and can control the performance of the engine by controlling the flow in the cylinder. In addition, a criterion can be set to consider the optimum operating point of the non-road vehicle while investigating the performance and exhaust gas emissions accompanying changes in boost pressure With these parameters, the design of experiment (DoE) of the 1D-simulation is performed, and the driving performance and knocking phenomenon for each RPM are predicted during the wide open throttle (WOT) of the gasoline MPI Turbo SI engine. The multi-objective Pareto technique is also used to optimize engine performance and exhaust gas emissions, and to present optimized design points for the target engine, the downsized gasoline MPI Turbo SI engine. The results of the Pareto optimal solution showed a maximum torque increase of 12.78% and a NOx decrease of 54.31%.

When Japan invaded the Philippines, two missionary dentists (Dr. McAnlis and Dr. Boots) who were forced to leave Korea were captured and interned in the Santo Thomas camp in Manila. Japan continued to bombard and plunder the Philippines in the wake of the Pacific War following the Great East Asia policy, leading to serious inflation and material deficiency. More than 4,000 Allied citizens held in Santo Thomas camp without basic food and shelter. Santo Thomas Camp was equipped with the systems of the Japanese military medical officers and Western doctors of captivity based on the Geneva Conventions(1929). However, it was an unsanitary environment in a dense space, so it could not prevent endemic diseases such as dysentery and dengue fever. With the expansion of the war in Japan, prisoners in the Shanghai and Philippine prisons were not provided with medicines, cures and food for healing diseases. In May 1944, the Japanese military ordered the prisoners to reduce their ration. The war starting in September 1944, internees received 1000 kcal of food per day, and since January 1945, they received less than 800 kcal of food. This was the lowest level of food rationing in Japan’s civilian prison camps. They suffered beriberi from malnutrition, and other endemic diseases. An averaged 24 kg was lost by adult men due to food shortages, and 10 percent of the 390 deaths were directly attributable to starvation. The doctors demanded food increases. The Japanese Military forced the prisoner to worship the emperor and doctors not to record malnourishment as the cause of death. During the period, the prisoners suffered from psychosomatic symptoms such as headache, diarrhea, acute inflammation, excessive smoking, and alcoholism also occurred. Thus, the San Thomas camp had many difficulties in terms of nutrition, hygiene and medical care. The Japanese military had unethical and careless medical practices in the absence of medicines. Dr. McAnlis and missionary doctors handled a lot of patients focusing mainly on examination, emergency treatment and provided the medical services needed by Philippines and foreigners as well as prisoners. Through out the war in the Great East Asia, the prisoners of Santo Thomas camp died of disease and starvation due to inhumane Japanese Policy. Appropriate dietary prescriptions and nutritional supplements are areas of medical care that treat patients’ malnutrition and disease. It is also necessary to continue research because it is a responsibility related to the professionalism and ethics of medical professionals to urge them to observe the Geneva Convention.

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

In this study, we aimed to enhance the reactivity of polycarbonate (PC) with the epoxy group of styrene-co-glycidyl methacrylate copolymer (SGMA) by introducing aqueous solutions of sodium hydroxide during the melt processing of PC. This approach led to the generation of hydroxyl groups at the ends of PC molecules, facilitating the formation of PC-SGMA copolymer. The effectiveness of this method in improving the compatibility of PC and styrene-acrylonitrile (SAN) blends was successfully confirmed through blending experiments with SAN and SGMA. Additionally, we investigated the impact of alkali-promoted hydrolysis on the rheological properties of PC, apart from the functionalization of PC end groups. Our findings revealed that an increase in the concentration of sodium hydroxide resulted in a decrease in the complex viscosity of PC. However, beyond a certain concentration level, the viscosity exhibited an upward trend. Furthermore, when constructing van Gurp-Palmen plots for the modified PC, we observed a significant deviation in curve shape compared to pure PC or water-modified PC. This observation suggests that changes in the microstructure occur during the degradation process due to the interaction between PC molecules and sodium ions.

This study presents a method for constructing machine learning-based reduced order models (ROMs) that accurately simulate nonlinear contact problems while quantifying epistemic uncertainty. These purely non-intrusive ROMs significantly lower computational costs compared to traditional full order models (FOMs). The technique utilizes adversarial training combined with an ensemble of Barlow twins reduced order models (BT-ROMs) to maximize the information content of the nonlinear reduced manifolds. These lower-dimensional manifolds are equipped with Gaussian error estimates, allowing for quantifying epistemic uncertainty in the ROM predictions. The effectiveness of these ROMs, referred to as UQ-BT-ROMs, is demonstrated in the context of contact between a rigid indenter and a hyperelastic substrate under finite deformations. The ensemble of BT-ROMs improves accuracy and computational efficiency compared to existing alternatives. The relative error between the UQ-BT-ROM and FOM solutions ranges from approximately 3% to 8% across all benchmarks. Remarkably, this high level of accuracy is achieved at a significantly reduced computational cost compared to FOMs. For instance, the online phase of the UQ-BT-ROM takes only 0.001 seconds, while a single FOM evaluation requires 63 seconds. Furthermore, the error estimate produced by the UQ-BT-ROMs reasonably captures the errors in the ROMs, with increasing accuracy as training data increases. The ensemble approach improves accuracy and computational efficiency compared to existing alternatives. The UQ-BT-ROMs provide a cost-effective solution with significantly reduced computational times while maintaining a high level of accuracy.

Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

When performing remote tasks using robots in nuclear power plants, a 3D shape measurement system is advantageous in improving the efficiency of remote operations by easily identifying the current state of the target object for example, size, shape, and distance information. Nuclear power plants have high-radiation and underwater environments therefore the electronic parts that comprise 3D shape measurement systems are prone to degradation and thus cannot be used for a long period of time. Also, given the refraction caused by a medium change in the underwater environment, optical design constraints and calibration methods for them are required. The present study proposed a method for developing an underwater 3D shape measurement system with improved radiation tolerance, which is composed of commercial electric parts and a stereo camera while being capable of easily and readily correcting underwater refraction. In an effort to improve its radiation tolerance, the number of parts that are exposed to a radiation environment was minimized to include only necessary components, such as a line beam laser, a motor to rotate the line beam laser, and a stereo camera. Given that a signal processing circuit and control circuit of the camera is susceptible to radiation, an image sensor and lens of the camera were separated from its main body to improve radiation tolerance. The prototype developed in the present study was made of commercial electric parts, and thus it was possible to improve the overall radiation tolerance at a relatively low cost. Also, it was easy to manufacture because there are few constraints for optical design.

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

The reliability and performance of a steam generator (SG) is one of the serious concerns in the operation of pressurized water nuclear power plants. Because of high levels of radiation, robotic systems have been used to inspect and repair SG tubes. In this paper, we present a mobile robotic system that positions the inspection and repair tools while hanging down from the tube sheets where the tubes are fixed. All of the driving mechanisms of the mobile robot are actuated by electric motors to start its works, providing that the electric power is prepared without the additional need for an on-site air services. A special tube-holding mechanism with a high holding force has been developed to prevent falling from the tube sheets, even in the case of an electric power failure. We have also developed a quick installation guide device that guides the mobile robot to desired initial positions in the tube sheet exactly and quickly, which helps to reduce the radiation exposure of human workers during the installation work. This paper also provides on-site experimental results and lessons learned.