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Background There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. Methods We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. Results In single-pollutant models, we found that long-term 10 μg/m3 increments in PM2.5 (OR 2.83, 95% CI 2.18–3.67) and PM10 (OR 2.36, 95% CI 1.99–2.79) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2, O3, and CO) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-density population regions in contrast to the low-density population regions. Conclusions Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside.

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

This study proposes and implements an intelligent transportation system (ITS)-based system that collects road event information via a vehicle’s on-board unit (OBU) and roadside unit (RSU), integrates and processes information based on the database and the road network, and provides information to vehicles. This system enables the collection of road unit information without calculating the exact location of event information recognized by the vehicle and facilitates duplicate processing of information recognized from multiple sources. This information can then be provided to other vehicles or road operators via apps or the web, enabling immediate response to emergency situations or changes in road conditions. To verify the practical applicability of this system, we developed a prototype and validated its functions through experiments. Using this system and methods, general drivers, autonomous vehicles, and infrastructure can cooperate in an ITS environment to recognize and propagate various road situations, contributing to the creation of safer and more efficient roads.

Photolithography is the prevalent microfabrication technology. It needs to meet resolution and yield demands at a cost that makes it economically viable. However, conventional far-field photolithography has reached the diffraction limit, which imposes complex optics and short-wavelength beam source to achieve high resolution at the expense of cost efficiency. Here, we present a cost-effective near-field optical printing approach that uses metal patterns embedded in a flexible elastomer photomask with mechanical robustness. This technique generates sub-diffraction patterns that are smaller than 1/10th of the wavelength of the incoming light. It can be integrated into existing hardware and standard mercury lamp, and used for a variety of surfaces, such as curved, rough and defect surfaces. This method offers a higher resolution than common light-based printing systems, while enabling parallel-writing. We anticipate that it will be widely used in academic and industrial productions., Photolithography is an established microfabrication technique but commonly uses costly shortwavelength light sources to achieve high resolution. Here the authors use metal patterns embedded in a flexible elastomer photomask with mechanical robustness for generation of subdiffraction patterns as a cost effective near-field optical printing approach.

It is necessary to evaluate a multiple sensor probe methods according to the bubble shape for the measurement of local bubble parameters. For this, numerical simulation and air-water benchmark experiments were performed with the double-sensor and four-sensor probes. It was found that the velocity measurement errors of the probes were 5.3% and 4.2%, respectively. In terms of Sauter mean diameter (D 32), its measurement performance is affected by the type of the probe and bubble shape. The double-sensor probe showed reasonable measurement accurancy of the D 32 for the spherical shape bubble. However, as the bubble shape is distorted from the sphere, the D32 measurement error increases. The four-sensor probe has a relatively large error compared to that of the double sensor probe under the bubbly flow with small size. While, when the D 32 is larger than 2 mm, the four sensor probe shows excellent measurement performance regardless of the bubble shape.

This study reports an experiment to investigate the wetted wall fraction (WWF), which represents the shape of the continuous interface of the air–water stratified and wavy flow regimes, in horizontal and inclined pipes with an inner diameter of 40 mm. Using the experimental data, a semi-empirical model to predict the WWF was developed based on the energy balance for the liquid phase in separated flows. The model used a relationship between the WWF and the center of gravity of the liquid phase with a concave interface that can describe continuous changes to the flow regime from a stratified to an annular. The coefficients of the model were empirically determined based on a wide range of experimental data from the literature obtained in the air and various liquids covering a void fraction of greater than 0.79, a pipe with inner diameter in the range 24–150 mm, density differences varying from 812 to 1052 kg/m3, a range of liquid phase viscosity of 0.87–5.66 mPa s, surface tension ranging from 27.9 to 72.7 mN/m, a wide range of inclination angles from −27° for a downward flow to +3° for an upward flow, and gas and liquid Reynolds numbers based on a superficial velocity of up to 219,000 and 7500, respectively. This WWF model was tested using an extensive experimental database of the WWF and void fraction in the stratified and wavy flow regimes, and yielded the best agreement compared with existing models in the literature.

In the presence of non-condensable gases, the effects of thermal-hydraulic parameters on steam condensation on a tube-bundle heat exchanger differ from those for a single-tube because the geometry of the tube-bundle enhances shadow and suction effects. In this study, experiments were performed to investigate these effects on three types of tube-bundle heat exchangers with pitch-to-diameter ratios of 2.0, 2.5, and 3.0, and pressures, air mass fractions, and wall subcooling of 200–500 kPa, 0.2–0.7, and 20–55 K, respectively. The local bundle factor was defined as the difference in condensation heat transfer coefficients between a single-tube and tubes in a tube-bundle. Therefore, a local bundle factor correlation, which accounts for the shadow and suction effects, was developed. The applicable ranges of the local bundle factor correlation for local pressure loss, Bird's suction factor, and blockage fraction were 0.267–0.734, 1.12–1.98, and 0.044–0.304, respectively. An average bundle factor was derived from the local bundle factor. The developed correlation applying the average bundle factor predicted the heat transfer coefficients for the obtained experimental data within an error of 8.50 %.

Sensing the surroundings and processing the sensed data in a timely manner are critical as part of the perception process of autonomous vehicles that are getting closer to reality. This paper addresses the issues of managing the data streams generated from a variety of sensors embedded in a vehicle. To do so, we first adopt the real-time database structure that provides the clear programming abstraction over time-varying heterogeneous sensor data, and then exploit deep reinforcement learning (DRL) that can deal with highly dynamic data updates with underlying system restrictions such as limited in-vehicle network bandwidth. The experiments demonstrate that our DRL-based approach performs steadily against rapidly changing data and is able to efficiently suppress unnecessary data updates.

In this paper, we evaluated quantitative changes in the luminance and the color on the light-exit plane of an LED edge-lit backlight unit (BLU) by systematically controlling the optical properties of individual white LEDs. We placed a light absorbing film and red-green-blue filters to control the white intensity and the color of an individual LED string, respectively. Impacts of this ‘defect’ LED's optical properties were systematically monitored across the light-exit plane in terms of local distribution of the luminance and the color, overlaying prism films and diffusion film on a light guiding plate (LGP). The roles of prism films and diffusion film were also experimentally analyzed to assess their homogenizing capability for the luminance and the color with respect to the defect-like LEDs.

Recently, as LCD (Liquid Crystal Display) monitors have been widely used in public places such as banks, hospitals, counseling offices, wholesale marts and theaters, there is an increasing demand on their function of mutual communication. Since the conventional 2D displays can be observed only in front of the displaying side, two-way communication was achieved by spatial configuration of two or more monitors. This is not an efficient way in aspects of power consumption, space occupation and costs. Therefore, in this paper, the bidirectional LCD module with edge-lit BLU (Backlight Unit) is proposed by utilizing LGP (Light Guide Plate) patterning technology, with a single BLU for simultaneous display. In particular, 40 % reduction in thickness, 15 % decreased power consumption, and 13.8 % of lower production cost is achieved when compared with a conventional bidirectional display.

Collimation sheet for the edge-lit backlight has been designed, which is well specialized for the emission profile of one-side edge-lit backlight. Operation theory and design principles of the proposed sheet were presented in detail. Simulation study shows that emission angle of 12° is achievable using the designed sheet without additional sheets while conventional backlight has the angle of 50° with two sheets (micro lens sheet and prism sheet).

We proposed and demonstrated a polarized backlight unit (BLU) configuration with a new light-redirecting film (LRF) to improve the luminance gain and light transmittance in liquid crystal displays. We combined a very low birefringence triacetyl cellulose (TAC) base layer with UV-cured prismatic patterns in a mass-producible process to demonstrate a polarization-preserving LRF. Detailed analyses of the states of polarization (SOP) through the LRF were reported. We also fabricated a 7-in. edge-lit BLU using the new LRF. We found that the light directing capability of the new LRF was equivalent to that of a conventional prism film whose base layer was a poly(ethylene terephthalate) (PET) film, and that the new LRF successfully suppressed random polarization changes of the transmitted light. Utilizing these new advantages, we obtained 68% transmittance through the polarizer over the BLU with the new LRF, which was about 1.3-fold that for the BLU with the conventional prism film (54%). We also obtained 127% luminance gain using the new LRF, which was equivalent to that of the conventional prism film. Here, the luminance gain is referred to as the amount of increase in luminance on the surface normal of the light-guiding plate without any film.

We propose and demonstrate a new polarized backlight unit (BLU) configuration to enhance light transmission in a twisted nematic (TN) mode liquid crystal (LC) panel. By adding an optimized phase retardation film (PRF) to the BLU, we efficiently controlled the polarization state of the light from the light guide plate (LGP), enabling it to be aligned along the transmission axis of the bottom polarizer of the LC panel. We designed and fabricated a 7 in. edge-lit BLU with a PRF. To obtain a uniform positional distribution in both light intensity and degree of polarization (DOP), the LGP was optimized with linear groove patterns. To maximize BLU light transmission through the bottom polarizer in a TN-mode LC panel, the slow axis of the PRF was aligned at 22.5° with respect to the LED array axis. We improved the transmittance from 45 to 72%.

Distortion to the angular light distribution of LED within a LED Backlight unit can result in a decline of luminance and color uniformity. Effects of components within a LED package on angular light distribution uniformity were studied in detail by utilizing the ‘LightTools®’, a commercialized software, to derive optimal optical solution for a LED backlight unit.