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The present study focuses on the development of an equipment qualification (EQ) testing facility for Class 1E equipment in nuclear power plants (NPPs), emphasizing the need to ensure safety functions under design basis events (DBEs). The Republic of Korea (ROK) has implemented international safety standards to support large NPP projects, necessitating the development of domestic EQ testing facilities to reduce the dependence on foreign facilities. To address this need, herein, a specialized facility capable of simulating harsh DBE conditions that may occur in NPPs in ROK was constructed. Through exhaustive research, target temperature and pressure profiles were developed for pressurized water reactors, and a superheated steam injection technique was devised for rapid temperature and pressure changes. The present study contributes to the advancement of domestic EQ testing capabilities, achieving up to 230 °C and 700 kPa within 30 s.

Highly automated driving requires the use of multiple sensors for reliable tracking functionality. In response to the requirement, the proposed method modifies the conventional Interacting Multiple Model (IMM) filter to fuse multi-sensor data by utilizing the independence of observations. In addition, the proposed IMM is integrated with a Centralized Kalman Filter (CKF) that ensures track continuity against sensor failures, providing optimal state estimates. When tracking objects in a moving reference frame, such as in autonomous vehicles, onboard sensor measurements represent relative values, making it challenging to estimate the actual motion of objects. While transforming states to a global coordinates is a solution, the solution can arise another problem where the tracking results depends on the status and performance of the localization. Therefore, to tackle the problem, a track compensation algorithm utilizing a hybrid coordinate system is proposed. The actual motions of objects are estimated based on errors between the track state and the associated measurement. The performance of the proposed algorithms is demonstrated using experimental scenario data conducted with an actual vehicle.

Nuclear power plants play a pivotal role in the global energy infrastructure, fulfilling a substantial share of the world's energy requirements in a sustainable way. The management of these facilities, especially the handling of spent nuclear fuel (SNF), necessitates meticulous inspections to guarantee operational safety and efficiency. However, the prevailing inspection methodologies lean heavily on human operators, which presents challenges due to the potential hazards of the SNF environment. This study introduces the design of a novel Pre-Batched Inspection Strategy (PBIS) that integrates robotic automation and image processing techniques to bolster the inspection process. This methodology deploys robotics to undertake tasks that could be perilous or time-intensive for humans, while image processing techniques are used for precise identification of SNF targets and regulating the robotic system. The implementation of PBIS holds considerable promise in minimizing inspection time and enhancing worker safety. This paper elaborates on the structure, capabilities, and application of PBIS, underlining its potential implications for the future of nuclear energy inspections.

JWST observations of galaxies at z ≳ 8 suggest that they are more luminous and clumpier than predicted by most models, prompting several proposals on the physics of star formation and feedback in the first galaxies. In this paper, we focus on the role of ultraviolet (UV) radiation in regulating star formation by performing a set of cosmological radiation hydrodynamics simulations of one galaxy at subparsec resolution with different radiative feedback models. We find that the suppression of cooling by far-UV (FUV) radiation (i.e., H _2 dissociating radiation) from Population II stars is the main physical process triggering the formation of compact and massive star clusters and is responsible for the bursty star formation observed in metal-poor galaxies at z ≳ 10. Indeed, artificially suppressing FUV radiation leads to a less intense continuous mode of star formation distributed into numerous but low-mass open star clusters. Due to the intense FUV field, low-metallicity clouds remain warm (∼10 ^4 K) until they reach a relatively high density (≳10 ^3 cm ^−3 ), before becoming self-shielded and transitioning to a colder (∼100 K), partially molecular phase. As a result, star formation is delayed until the clouds accumulate enough mass to become gravitationally unstable. At this point, the clouds undergo rapid star formation, converting gas into stars with high efficiency. We therefore observe exceptionally bright galaxies (10 times brighter than for continuous star formation) and subsequent quenched “dead” galaxies that did not form stars for tens of Myr.

Self-driving cars are undergoing extensive road tests and should enter the market within the decade, but consumers continue to worry about the safety of autonomous vehicles—even though most traffic accidents are caused by human errors that are avoidable with automation technology. Four experiments investigated how a vehicle’s automation level affects its perceived safety, why excessive safety concerns prevail, and how they can be mitigated. In all experiments, participants read descriptions of full-automation (Level 5) and high-automation (Level 4) self-driving cars: Participants consistently perceived Level 5 (vs. Level 4) vehicles as less safe. The effect persisted when objective safety information was available. A mediation analysis and a mediation-by-moderation approach suggest that the negative effect of the automation level on perceived safety is driven by the perceived lack of control over driving. Finally, the effect disappeared when participants imagined themselves as passengers rather than drivers, offering a practical implication for managers.

Objectives Length of in-hospital stay (LOS) is often regarded as a surrogate marker of efficiency in medical care. A shorter stay can redistribute medical resources to more patients if patient outcomes would not be worsened. However, the adequate LOS remains largely understudied for a complex head and neck cancer (HNC) surgery and free flap reconstruction. Methods Active management of LOS (14-day LOS program) included detailed preoperative surgical planning, intensive wound care, postoperative early ambulation and positive psychological encouragement. It was applied to 43 patients undergoing HNC surgery and free flap reconstruction. Outcomes such as noninferior oncological results, rates of timely adjuvant treatments and complications were compared with those of 125 patients without active management of LOS. In addition, the medical costs of shortened LOS were compared with those of the control group. Cases undergoing HNC surgery as a salvage treatment were excluded from both groups for analyses. Results Active management of LOS resulted in less in-hospital period compared to the control group (15.0 vs. 21.0 days, P=0.001), and reduced medical costs significantly. Incidence of postoperative complications was comparable between the two groups. Oncological outcomes did not differ significantly according to LOS. In all patients in both groups, initial high T status (T3–4) and occurrence of postoperative complications were independent risk factors for long LOS (>30 days). Conclusion In patients undergoing HNC surgery with free flap reconstruction as an initial treatment, a 14-day LOS could be safe in terms of comparable oncological outcomes and postoperative complications. To achieve this goal safely, careful management for T3–4 tumors and prevention of postoperative complications seem to be necessary.

Due to the recent pandemic caused by coronavirus disease 2019 (COVID-19), the lateral flow immunoassay used for its rapid antigen test is more popular than ever before. However, the history of the lateral flow immunoassay is about 60 years old, and its original purpose of use, such as a COVID-19 rapid antigen test or a pregnancy test, was the qualitative detection of a target analyte. Recently, the demand for quantitative analysis of lateral flow immunoassays is increasing in various fields. Lateral flow immunoassays for quantitative detection using various materials and sensor technologies are being introduced, and readers for analyzing them are being developed. Quantitative analysis readers are highly anticipated for their future development in line with technological advancements such as optical, magnetic field, photothermal, and electrochemical sensors and trends such as weight reduction, miniaturization, and cost reduction of systems. In addition, the sensing, processing, and communication functions of portable personal devices such as smartphones can be used as tools for the quantitative analysis of lateral flow immunoassays. As a result, lateral flow immunoassays can efficiently achieve the goal of rapid diagnosis by point-of-care testing. Readers used for the quantification of lateral flow immunoassays were classified according to the adopted sensor technology, and the research trends in each were reviewed in this paper. The development of a quantitative analysis system was often carried out in the assay aspect, so not only the readers but also the assay development cases were reviewed if necessary. In addition, systems for quantitative analysis of COVID-19, which have recently been gaining importance, were introduced as a separate section.

Pinellia ternata Breitenbach (PTB) is a widely used herbal medicine in China, Japan, and South Korea. It has antiemetic, anti-inflammatory, antitussive, and sedative properties. The raw material is toxic, but can be made safer using alum solution or by boiling it for a long time. In addition, PTB seems to be effective for gastrointestinal motility disorders (GMDs), but this is yet to be conclusively proven. Herein, PTB compounds, targets, and related diseases were investigated using the traditional Chinese medical systems pharmacology database and an analysis platform. Information on target genes was confirmed using the UniProt database. Using Cytoscape 3.8.2, a network was established and GMD-related genes were searched using the Cytoscape stringApp. The effects of the PTB extract on the pacemaker potential of interstitial cells of Cajal and GMD mouse models were investigated. In total, 12 compounds were found to target 13 GMD-related genes. In animal experiments, PTB was found to better regulate pacemaker potential in vitro and inhibit GMD signs compared to control groups in vivo. Animal studies showed that the mechanism underlying the effects of PTB is closely related to gastrointestinal motility. The results obtained demonstrated that PTB offers a potential means to treat GMDs, and we suggested that the medicinal mechanism of GMDs can be explained by the relationship between 12 major components of PTB, including oleic acid, and 13 GMD-related genes.

A glucose sensor is used as an essential tool for diagnosing and treating diabetic patients and controlling processes during cell culture. Since the development of an electrochemical-based glucose sensor, an optical glucose sensor has been devised to overcome its shortcomings, but this also poses a problem because it requires a complicated manufacturing process. This study aimed to develop an optical glucose sensor film that could be fabricated with a simple process using commercial pressure sensitive paints. The sensor manufacturing technology developed in this work could simplify the complex production process of the existing electrochemical or optical glucose sensors. In addition, a photometric method for glucose concentration analysis was developed using the color image of the sensor. By developing this sensor and analysis technology, the basis for glucose measurement was established that enables two-dimensional, online, and continuous measurement. The proposed sensor showed good linearity at 0–4 mM glucose in an aqueous sample solution, its limit of detection was 0.37 mM, and the response time was 2 min.

A series of poly(pyridinium salt)s-fluorene main-chain ionic polymers with various organic counterions were synthesized by using ring-transmutation polymerization and metathesis reactions. Their chemical structures were characterized by Fourier Transform Infrared (FTIR), proton (1H), and fluorine 19 (19F) nuclear magnetic resonance (NMR) spectrometers. These polymers showed a number-average molecular weight (Mns) between 96.5 and 107.8 kg/mol and polydispersity index (PDI) in the range of 1.12–1.88. They exhibited fully-grown lyotropic phases in polar protic and aprotic solvents at different critical concentrations. Small-angle X-ray scattering for one polymer example indicates lyotropic structure formation for 60–80% solvent fraction. A lyotropic smectic phase contains 10 nm polymer platelets connected by tie molecules. The structure also incorporates a square packing motif within platelets. Thermal properties of polymers were affected by the size of counterions as determined by differential scanning calorimetry and thermogravimetric analysis measurements. Their ultraviolet-visible (UV-Vis) absorption spectra in different organic solvents were essentially identical, indicating that the closely spaced π-π* transitions occurred in their conjugated polymer structures. In contrast, the emission spectra of polymers exhibited a positive solvatochromism on changing the polarity of solvents. They emitted green lights in both polar and nonpolar organic solvents and showed blue light in the film-states, but their λem peaks were dependent on the size of the counterions. They formed aggregates in polar aprotic and protic solvents with the addition of water (v/v, 0–90%), and their λem peaks were blue shifted.

Utilizing a simple fluidic structure, we demonstrate the improved performance of oxidase-based enzymatic biosensors. Electrolysis of water is utilized to generate bubbles to manipulate the oxygen microenvironment close to the biosensor in a fluidic channel. For the proper enzyme reactions to occur, a simple mechanical procedure of manipulating bubbles was developed to maximize the oxygen level while minimizing the pH change after electrolysis. The sensors show improved sensitivities based on the oxygen dependency of enzyme reaction. In addition, this oxygen-rich operation minimizes the ratio of electrochemical interference signal by ascorbic acid during sensor operation (i.e., amperometric detection of hydrogen peroxide). Although creatinine sensors have been used as the model system in this study, this method is applicable to many other biosensors that can use oxidase enzymes (e.g., glucose, alcohol, phenol, etc.) to implement a viable component for in-line fluidic sensor systems.

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.

Despite its broad penetration of various markets, the quantitative lateral flow immunoassay (LFIA) suffers from sensitivity issues in some cases. To solve this problem, an optimized colorimetric readout method for LFIA quantification is proposed in this study. An assay reader device utilizing a color camera and an analysis method using a Bayer filtered image were developed. Spectrometric measurements of the assay test line were performed to determine the color channel that contains the test line information and effectively minimizes noise. The change in the intensity ratio with increasing concentration of the target substance in the sample was largest in the green channel. The linear range of the output curve ranged from 0 to 10 ng/mL, and the detection limit was 2 ng/mL. The suggested instrumentation and analysis methods are expected to effectively resolve the low-sensitivity problems of the former LFIA systems and to offer other prospective functionalities for LFIA quantification.