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Paralytic shellfish toxins (PSTs) are potent neurotoxins produced by certain microalgae, particularly dinoflagellates, and they can accumulate in shellfish in coastal seawater and thus pose significant health risks to humans. To explore the relationship between toxicity and PST profiles in seawater and mussels, the spatiotemporal variations in PST concentrations and profiles were investigated along the southern coast of Korea under peak PST levels during spring. Seawater and mussel samples were collected biweekly from multiple stations, and the toxin concentrations in the samples were measured. Moreover, the dinoflagellate community composition was analyzed using next-generation sequencing to identify potential PST-producing species. The PST concentrations and toxin profiles showed substantial spatiotemporal variability, with GTX1 and GTX4 representing the dominant toxins in both samples, and C1/2 tending to be higher in seawater. Alexandrium species were identified as the primary sources of PSTs. Environmental factors such as water temperature and salinity influenced PST production. This study demonstrates that variability in the amount and composition of PSTs is due to intricate ecological interactions. To mitigate shellfish poisoning, continuous monitoring must be conducted to gain a deeper understanding of these interactions.

The clinical anteroposterior (AP) chest images taken with a mobile radiography system were analyzed in this study to utilize the clinical exposure index (EI) as a patient dose-monitoring tool. The digital imaging and communications in medicine header of 6048 data points exposed under the 90 kVp and 2.5 mAs were extracted using Python for identifying the distribution of clinical EI. Even under the same exposure conditions, the clinical EI distribution was 137.82–4924.38. To determine the cause, the effect of a patient's body shape on EI was confirmed using actual clinical chest AP image data binarized into 0 and 255-pixel values using Python. As a result, the relationship between the direct X-ray area of the chest AP image, the higher the clinical EI, the larger the rate of the direct X-ray area. A conversion equation was also derived to infer entrance surface dose through clinical EI based on the patient thickness. This confirmed the possibility of directly monitoring patient dose through EI without a dosimeter in real-time. Therefore, to use the clinical EI of the mobile radiography system as a patient dose-monitoring tool, the derivation method of clinical EI considers several factors, such as the relationship between patient factors.

The spring phytoplankton bloom is a critical event in temperate oceans typically associated with the highest productivity levels throughout the year. To investigate the bloom process in the Yellow Sea, daily data on physical, chemical, and phytoplankton taxonomic group biomass, calculated via the chemotaxonomic approach, were collected from late March or early April to late May between 2018 and 2020 at the Socheongcho Ocean Research Station. During early spring (late March to mid-April), phytoplankton biomass increased, accompanied by a decrease in nutrient levels, with Bacillariophyceae and Cryptophyceae being the dominant groups. As water temperature increased, a pycnocline began to develop in late April, leading to a peak of the phytoplankton bloom dominated by chlorophytes and Cryptophyceae. Network analysis suggested that this phytoplankton bloom was caused by the onset of vertical stratification induced by increased sea surface temperature. The chlorophyte peak induced phosphate limitation above the pycnocline, resulting in succession to Prymnesiophyceae and Dinophyceae. Following pycnocline formation, phytoplankton biomass below the pycnocline was dominated by Bacillariophyceae and Cryptophyceae, with decreasing or fluctuating trends depending on phosphate concentration. Apart from these general patterns, 2019 and 2020 both had distinctive traits. The 2019 data revealed lower phosphate concentrations than the other 2 years, leading to a smaller chlorophyte peak at the surface compared to 2018 and extreme phosphate limitation above the pycnocline. This limitation resulted in decreased biomass of late successional groups, including Prymnesiophyceae and Dinophyceae. Pycnocline formation was delayed in year 2020, and stratification was significantly weaker compared to the previous 2 years. Due to the pycnocline delay, the surface chlorophyte peak did not develop and no succession to late successional groups was observed. Instead, high levels of Bacillariophyceae and Cryptophyceae biomass were observed throughout the water column with no surface bloom. Thus, among various environmental factors, increasing surface water temperature and phosphate concentrations play pivotal roles in shaping phytoplankton bloom dynamics. Distinct yearly variation points to the broader impacts of climate shifts, emphasizing the need for continued marine monitoring.

An advanced nuclear reactor based on molten salts including a molten salt reactor and pyroprocessing needs a sensitive monitoring system suitable for operation in harsh environments with limited access. Multi-element detection is challenging with the conventional technologies that are compatible with the in-situ operation; hence laser-induced breakdown spectroscopy (LIBS) has been investigated as a potential alternative. However, limited precision is a chronic problem with LIBS. We increased the precision of LIBS under high temperature by protecting optics using a gas protective layer and correcting for shot-to-shot variance and lens-to-sample distance using a laser-induced acoustic signal. This study investigates cerium as a surrogate for uranium and corrosion products for simulating corrosive environments in LiCl–KCl. While the un-corrected limit of detection (LOD) range is 425–513 ppm, the acoustic-corrected LOD range is 360–397 ppm. The typical cerium concentrations in pyroprocessing are about two orders of magnitude higher than the LOD found in this study. A LIBS monitoring system that adopts these methods could have a significant impact on the ability to monitor and provide early detection of the transient behavior of salt composition in advanced molten salt-based nuclear reactors.

A microelectrode is useful to determine electrochemical parameters such as diffusion coefficient, exchange current density, and Tafel slope without the limitations of ion diffusion. This study aims to develop a new microelectrode for use in high-temperature molten salts, which has been evaluated by determining the parameters of the Sm(III)/Sm(II) reaction in a LiCl–KCl eutectic molten salt. The electrochemical and kinetic properties of the samarium ion in the temperature range 723–823 K were calculated using the limiting current from cyclic voltammograms and the Allen–Hickling equation. The results imply that kinetic parameters (exchange current density and charge transfer coefficient) critical for designing pyroprocesses can be obtained using the developed microelectrode.

In this study, the effects of Ca addition on the microstructure, thermal properties, and mechanical properties of a Mg-6.0Zn-1.2Y-0.7Zr (ZWK611) alloy at room temperature and 150 °C were investigated. With an increase in the Ca content, the ignition resistance of the ZWK611 alloy improved and the grains became finer. The as-cast ZWK611 alloy consisted mainly of the dendritic α-Mg matrix and I-phase (Mg3Zn6Y) at the grain boundaries. On the other hand, the τ- (Ca2Mg6Zn3) and I-phases were formed in the Ca-added ZWK611 alloy. The fraction of the τ-phase increased with an increase in the Ca content. After the solid-solution treatment, these phases remained partially at the grain boundaries of the Ca-added ZWK611 alloys and an additional W-phase (Mg3Zn3Y) was observed. The phases remaining at the grain boundaries restricted the grain growth through the grain boundary pinning effect. The aging treatment resulted in the formation of MgZn' precipitates in the α-Mg matrix of the alloys. These precipitates were more uniformly distributed in the Ca-added alloys than in the alloys without Ca. Thus, the heat treatment-induced precipitation improved the tensile and creep properties of the T6-treated alloys. The T6-treated ZWK611+0.7Ca alloy exhibited the best mechanical properties at room temperature and 150 °C among all the tested alloys.

To regulate the safety protocols in nuclear facilities, radioactive aerosols have been extensively researched to understand their health impacts. However, most measured particle-size distributions remain at low resolutions, with the particle sizes ranging from nanometer to micrometer. This study combines the high-resolution detection of 500 size classes, ranging from 6 nm to 10 μm, for aerodynamic diameter distributions, with a regional lung deposition calculation. We applied the new approach to characterize particle-size distributions of aerosols generated during the plasma arc cutting of simulated non-radioactive steel alloy wastes. The high-resolution measured data were used to calculate the deposition ratios of the aerosols in different lung regions. The deposition ratios in the alveolar sacs contained the dominant particle sizes ranging from 0.01 to 0.1 μm. We determined the distribution of various metals using different vapor pressures of the alloying components and analyzed the uncertainties of lung deposition calculations using the low-resolution aerodynamic diameter data simultaneously. In high-resolution data, the changes in aerosols that can penetrate the blood system were better captured, correcting their potential risks by a maximum of 42%. The combined calculations can aid the enhancement of high-resolution measuring equipment to effectively manage radiation safety in nuclear facilities.

Radioactive aerosols are produced during the cutting of contaminated and activated metals. They must be collected and removed by a high-performing filtration system before releasing to the environment from the decommissioning workplace. The filtration system requires regular replacement to ensure the sufficient removal of radioactive aerosols because its filtration efficiency gradually decreases. This study evaluates the efficiency and lifetime of filters while cutting metals by using a plasma arc cutter. Particularly, this study considers the aerodynamic diameter distribution of number and mass concentrations for aerosols from 6 nm to 10 μm when evaluating the performance of filters. After 20 time reuses for cutting operation performed in a cutting chamber, the removal efficiency is reduced from over 99 to below 93% at 2 μm. The results are used to analyze the lifetime of filters, the frequencies of their replacements, and impact on internal radiation dose. Keywords: Radioactive aerosols, HEPA filter, Aerodynamic diameter, Metal cutting, Nuclear power plant decommissioning

A brain-machine interface (BMI) is a technology that controls machines via brainwaves. In BMI, the performance of brainwave analysis is very important for achieving machine control that reflects the user's intention. One of the main obstacles in this analysis is an insufficient amount of data points because long-term brain signal experiments tend to reduce data quality. Data augmentation methods can be used to overcome this limitation. Recently, several neural network-based data augmentation methods have been developed. However, those methods have several limitations; first, they require considerable computation time because a very large number of parameters must be obtained. Moreover, the neural network based method can suffer from unstable training, which results in quality degradation of artificial data. To address these problems, this paper introduces a method that generates an artificial dataset which has correlation of feature similar to the original dataset. Specifically, after decomposing the covariance matrix for the features into a lower triangular matrix, an artificial dataset can be generated by multiplying the lower triangular matrix by random variables. This method is computationally fast, and the augmentation is stable. When the brainwave data were augmented using this method, classification performance was improved by 1.08%-6.72%. This method focuses on mean, correlation, and not taking into account the other statistical parameters. Since it rapidly generates a large dataset, it can also be useful in other applications.

The disposal of mine tailings into the marine environment is considered an essential option to secure the economic efficiency of deep-sea mining, but it might adversely affects the ecosystem. To examine the potential impacts of tailing disposal from polymetallic nodules and polymetallic sulfide mines on phytoplankton communities, addition experiments of crushed fine particles into surface seawater were conducted in the open Indian Ocean and changes in chlorophyll a fluorescence and community composition were analyzed. The addition of tailings had serious adverse effects on phytoplankton fluorescence and photosynthetic activity, regardless of mine type. The adverse effects seemed to mainly be due to the physical properties of the mine tailings. These also showed discriminatory effects on phytoplankton, resulting in great changes in community composition. The results suggest that mine tailings could have significant adverse impacts on phytoplankton assemblages, but the degree of impact greatly varies depending on the phytoplankton groups. The discriminatory impacts would cause changes in biomass, community structure, and thus ecological function.

The aim of this study was to develop a new Al–Mg–Si–Zr alloy with a high magnesium content to achieve a wide range of mechanical properties using heat treatment and at a lower cost. Additive manufacturing was conducted using a powder bed fusion process with various scan speeds to change the volumetric energy density and establish optimal process conditions. In addition, mechanical properties were evaluated using heat treatment under various conditions. The characterization of the microstructure was conducted by scanning electron microscopy with electron backscatter diffraction and transmission electron microscopy. The mechanical properties were determined by tensile tests. The as-built specimen showed a yield strength of 447.9 ± 3.6 MPa, a tensile strength of 493.4 ± 6.7 MPa, and an elongation of 9.6 ± 1.1%. Moreover, the mechanical properties could be adjusted according to various heat treatment conditions. Specifically, under the HT1 (low-temperature artificial aging) condition, the ultimate tensile strength increased to 503.2 ± 1.1 MPa, and under the HT2 (high-temperature artificial aging) condition, the yield strength increased to 467 ± 1.3 MPa. It was confirmed that the maximum elongation (14.3 ± 0.8%) was exhibited with the HT3 (soft annealing) heat treatment.

When a wearable device such as a smartwatch is paired with a host device, e.g., a smartphone, it is inevitable that a certain amount of data stored in the host device will be copied to the wearable device for initialization and personalization purposes. This incident may frequently occur with the Internet of Things. However, it is not well known, particularly among users, what type of data and how much of it is actually being copied or retained as a result of device pairing. Thus, it is unclear whether users are properly managing smartphones and smartwatches based on their stored data. In this regard, we coined a new term called data transfusion to describe the phenomenon in which a user experiences data transfer while pairing but without having any knowledge regarding the data. To the best of our knowledge, there are no previous studies that deal with how much sensitive data are transfused regardless of user consent, and how users perceive and behave toward such a phenomenon for smartwatches. As this is a significant issue with the Internet of Things, we tackle this problem in two ways. We first conduct an experimental study of data extraction from commodity devices such as in Android Wear, watchOS, and Tizen platforms, followed by a survey of 205 smartwatch users. The experimental study reveals that large amounts of sensitive data are being transfused without sufficient user notification. The survey demonstrates that users have lower risk perceptions for smartwatches than for smartphones in terms of security and privacy, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation measures that involve volatile transfusion for securing the Internet of Things.

The acquisition of a large-volume brainwave database is challenging because of the stressful experiments that are required; however, data synthesis techniques can be used to address this limitation. Covariance matrix decomposition (CMD), a widely used data synthesis approach, generates artificial data using the correlation between features and random noise. However, previous CMD methods constrain the stochastic characteristics of artificial datasets because the random noise used follows a standard distribution. Therefore, this study has improved the performance of CMD by releasing such constraints. Specifically, a generalized normal distribution (GND) was used as it can alter the kurtosis and skewness of the random noise, affecting the distribution of the artificial data. For the validation of GND performance, a motor imagery brainwave classification was conducted on the artificial dataset generated by GND. The GND-based data synthesis increased the classification accuracy obtained with the original data by approximately 8%.

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.

This study presents a method to predict the flow velocity in a fluid-conveying pipe using vibration signals from the pipe surface. The flexural vibration of a fluid pipe is investigated through wave propagation. The wavenumbers and mode shapes of the pipe are determined based on its mechanical properties and flow velocities. The transient components of wavenumbers at low frequencies vary and converge on all values at higher frequencies as the flow velocity is increased. While the stationary fluid pipe exhibits symmetrical mode shapes, pipes with increasing flow velocities exhibit an asymmetric mode shape distribution skewed on one side of the axis. The resonant frequencies shift to the low frequency side as the flow velocity increases. The analytical results of the vibration analysis are used in the transfer function method to predict the flow velocities. To validate the accuracy of the prediction method, numerical vibration signals simulated by the finite element model are used. The actual input flow velocity is compared with the numerical results regarding the same to gauge the accuracy of the prediction method. This method can be used to monitor the flow rate without using flow meters, and thus protect pipelines from sudden malfunction.

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.

The structural behavior of the nuclear rod bundles that consisted of cylindrical beams was predicted using the spectral element method (SEM) while considering the interaction with the surrounding fluid. Viscous fluid behavior was utilized in order to calculate the forces acting on the nuclear rod bundles from the incident pressure waves. The added mass and fluid coupling on the nuclear rod bundles were determined for the position patterns and gaps of each of the cylindrical beams. The pressure field from propagating waves in the surrounding fluid was calculated with respect to the boundary conditions of the surface of the vibrating structures. With the increasing number of nuclear rods and decreasing pressure wavelengths, the structural vibration of the nuclear rod bundles that were induced by the propagating forces affected the scattering events of the pressure field. The frequency response of the nuclear rod bundles from the pressure waves in the water exhibited smaller damping, because the incident pressure wave travels without fluid coupling due to the longer wavelength when compared with distance between rods. The proposed numerical method can be utilized for the detailed design for effective parameters of a supporting system to reduce the vibration of nuclear fuel rod bundles for safety control.