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As intercity buses are a mode that moves large scaled occupancy between regions, it accounts for the mode share-means for mid to long-distance movement in South Korea. However, the study on intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and random parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of the study discovered that the influencing factors that reflect heterogeneity with Random Parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle-pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae.

This paper experimentally examines different configurations of a multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of “micro-cameras” to jointly image across a large field-of-view (FOV) at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100×135mm2 FOV at approximately 20 µm resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83×123mm2 FOV with twofold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 µm resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.

Microelectrodes are widely used for neural signal analysis because they can record high-resolution signals. In general, the smaller the size of the microelectrode for obtaining a high-resolution signal, the higher the impedance and noise value of the electrodes. Therefore, to improve the signal-to-noise ratio (SNR) of neural signals, it is important to develop microelectrodes with low impedance and noise. In this research, an Au hierarchical nanostructure (AHN) was deposited to improve the electrochemical surface area (ECSA) of a microelectrode. Au nanostructures on different scales were deposited on the electrode surface in a hierarchical structure using an electrochemical deposition method. The AHN-modified microelectrode exhibited an average of 80% improvement in impedance compared to a bare microelectrode. Through electrochemical impedance spectroscopy analysis and impedance equivalent circuit modeling, the increase in the ECSA due to the AHN was confirmed. After evaluating the cell cytotoxicity of the AHN-modified microelectrode through an in vitro test, neural signals from rats were obtained in in vivo experiments. The AHN-modified microelectrode exhibited an approximate 9.79 dB improvement in SNR compared to the bare microelectrode. This surface modification technology is a post-treatment strategy used for existing fabricated electrodes, so it can be applied to microelectrode arrays and nerve electrodes made from various structures and materials.

A highly fluorinated alternating polymer, P(RFMi-St), possessing improved thermal properties and patterning capabilities over perfluoroalkyl polymethacrylates under high energy radiation was achieved with semi-perfluorododecyl maleimide (RFMi) and styrene (St). RFMi could be synthesised efficiently via a Mitsunobu reaction condition and copolymerised with St by free radical and reversible-deactivation radical polymerisation protocols. P(RFMi-St) showed a satisfactory glass-transition temperature (108 °C) and intermolecular cross-linking behaviour under electron-beam and commercially more important extreme UV (λ = 13.5 nm) irradiation. The exposed regions lost their solubility, resulting in the successful formation of mechanically non-deteriorated negative-tone images down to 50 nm. In addition, P(RFMi-St) could be solution-processed with chemically non-damaging fluorous liquids, which enabled the polymer to be applied effectively on top of an organic semiconductor layer as a dielectric material (dielectric constant 2.7) for the organic field-effect transistor fabrication.

The distinction between the role of systemic risk and the systematic risk remains unclear and is sometimes confusing. In this paper, we exploit three types of systemic risk measurements and examine their role as a systematic risk component in the equity market. We first construct the systemic risk factors by using the portfolio mimicking method. Then we analyse the systemic risk factors through the lens of the empirical asset pricing test to determine whether the factors are systematically priced in the equity market. As a result, we empirically find evidence that the systemic risk factors are systematically priced in the equity market, indicating that the systemic risk is compensated for the higher returns.

While there are many chip models that simulate the air-tissue interface of the respiratory system, only a few represent the upper respiratory system. These chips are restricted to unidirectional flow patterns that are not comparable to the highly dynamic and variable flow patterns found in the native nasal cavity. Here we describe the development of a tunable nose-on-chip device that mimics the air-mucosa interface and is coupled to an air delivery system that simulates natural breathing patterns through the generation of bi-directional air flow. Additionally, we employ computational modeling to demonstrate how the device design can be tuned to replicate desired mechanical characteristics within specific regions of the human nasal cavity. We also demonstrate how to culture human nasal epithelial cell line RPMI 2650 within the lab-on-chip (LOC) device. Lastly, Alcian Blue histological staining was performed to label mucin proteins, which play important roles in mucous secretion. Our results revealed that dynamic flow conditions can increase mucous secretion for RPMI 2650 cells, when compared to no flow, or stationary, conditions.

We present a new multi-camera array microscope (MCAM) that can capture images at single micrometer resolution over 54 cm2 fields of view, which contain up to 16 gigapixels, and associated software for seamless viewing and analysis.

We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-μm center full-pitch resolution.

Background: Topical sinus irrigation plays a critical role in the management of sinonasal diseases. Yet, the penetration of irrigant to targeted sinuses may be highly variable and difficult to predict. Here, we investigate the use of 3D printing as a planning tool to optimize outcomes. Methods: Eight post-operative models were 3D printed with a FormLabs Form3 printer based on individual CT scans. Irrigations were performed and video recorded with a squeeze bottle attached via silicon water-tight seal, in 4 head positions: 45° to-the-side, 90° to-the-side, 45° forward and 45° to-the-side, and 90° forward, with irrigation fluid entering the upper (conventional) or lower (backfill) nostrils. Results: Significant individual variations were observed in sinus penetration as a function of head position. In general, the maxillary sinus was the easiest to irrigate in most head positions ( P Conclusion: Variations in technique and position significantly impacted irrigation outcome. Backfill irrigation that pushes fluid against gravity to pool around the ostium, seems to provide overall better outcomes. This study demonstrates the advantage of 3D printing as a rapid planning tool to guide irrigation strategies.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

The dynamics of living organisms are organized across many spatial scales, yet existing, cost-effective imaging systems can measure only a subset of these scales at once. Here, we have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from cellular structures to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies near cellular resolution and 5 μm sensitivity over hundreds of square centimeters. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. The MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Further, we demonstrate the ability to acquire dual color fluorescence video of multiple freely moving zebrafish, recording neural activity via ratiometric calcium imaging. Overall, the MCAM provides a powerful platform for investigating cellular and behavioral processes across a wide range of spatial scales, but without the bottlenecks imposed by single-camera image acquisition systems.

Investigations to evaluate the extreme ultraviolet (EUV) lithographic performance of 160 nm thick poly(methyl methacrylate) with 13.5 nm wavelength EUV light were performed using a synchrotron radiation source at Pohang Light Source-II (PLS-II). The single system enabled the determination of the sensitivity, contrast, linear absorption coefficient, critical dimension, and line edge roughness of polymer thin films through tests and measurements. The experimental findings were also compared to theoretical results and those of previously reported studies. According to the results of the dose-to-clear test and transmission measurements, the critical dimension of a line and space pattern (50 nm)

BACKGROUND A large amount of healthcare data has been produced and accumulated in recent years: big data derived from hospital records, patient medical records, medical examinations, and other sources. These data require proper management and analysis to derive meaningful results to improve healthcare. For proper management, the data must be integrated, secured, and made interoperable between individuals, hospitals, and companies, while protecting patient privacy. OBJECTIVE The purpose of this research was to create a healthcare data marketplace using a blockchain-based business-to-consumer model that ensured the integration, security, and interoperability of healthcare data between individuals, hospitals, companies, and marketplaces. This marketplace should enable direct transactions of healthcare data between companies that need individual healthcare data and individuals who own the data. METHODS To investigate the essential elements of a healthcare data marketplace, we considered two aspects: technology and service. A healthcare data marketplace was created using MySQL 8.0, JavaScript library, and Node.js software. The marketplace system featured security of data, access for companies and individuals, and a data transaction monitor. Furthermore, anonymization and standardization of data were implemented. A web-based application was developed for implementation of the marketplace system, along with a mobile application system for use by individuals. Finally, we evaluated the performance of the data marketplace system in three scenarios: male and obese (BMI ≥30 kg/m2); male aged ≥40 years with abnormal liver function tests (aspartate aminotransferase ≥40 units/L and alanine aminotransferase ≥40 units/L); and female aged ≥60 years with high blood pressure (systolic pressure ≥130 mmHg and diastolic pressure ≥90 mmHg). RESULTS A web-based application was developed to implement the healthcare data marketplace; a mobile application was also developed for individuals who provide their healthcare data. The performance of the data marketplace was evaluated through the three scenarios by calculating the time taken for completion of each scenario. A data query was executed fully within about 1–2 s; approximately 9.5 healthcare data queries were processed per minute in each scenario. CONCLUSIONS We successfully created a prototype of a healthcare data marketplace that ensured the integrity and security of data through its honest broker design.

Fluorescence imaging is used throughout biological research to identify subcellular structures, detect neural activity, and differentiate cell types. Multi-channel fluorescence is a challenging subset of fluorescence imaging where multiple fluorescent modes are emitted simultaneously, allowing the detection of a multitude of elements within the specimen (for example, multiple types of neurons). In our work, we demonstrate a learned sensing approach to realize virtual multi-channel fluorescence, by jointly optimizing image illumination and a deep learning neural network that infers labels from brightfield images. We used our setup to demonstrate the influence of key design decisions, such as model architecture, choice of loss function, and amount of input images, on the final optical design. We expect that our work can provide a better understanding of building machine learning based imaging systems and demonstrate the scalability of our illumination optimization technique.