Search

The high-resolution, inkjet-printed zinc oxide (ZnO) microdot arrays with suppressed coffee-ring effect was demonstrated by investigating the correlation between drying and solidification processes. The internal microfluidic behavior of an ejected droplet and the associated drying process is geometry-dependent, which can be controlled by the temperature and surface energy of the substrate. During evaporation, droplets in contact with a wettable surface exhibit a dominant outward convective flow, resulting in a pronounced coffee-ring effect. In contrast, for droplets with minimal contact area on a substrate with low surface energy, the surface energy gradient along the relatively long thermal conducting path length reinforces the Marangoni flow, which retards the pinning of the contact line, resulting in tiny microdots with suppressed coffee-ring effect. The controlled ZnO microdots exhibit a diameter of approximately 3 μm with a thickness of 50 nm, which is one of the smallest microstructures produced by the inkjet printing method. Additionally, the integration of the ZnO microdot arrays into organic solar cells aimed to alter the light path length, leading to enhanced internal absorption. The P3HT:PCBM, PTB7-Th:PCBM, and PM6:Y6 devices with ZnO microdot arrays exhibit higher power conversion efficiencies of 3.54%, 9.04%, and 15.61% compared to reference devices of 3.38%, 8.85%, and 15.25% respectively.

Background: Hominis placental (HPP) extract has been approved by the Ministry of Food and Drug Safety in Korea for treating chronic liver diseases and postmenopausal syndrome. However, its efficacy and safety for treating chronic temporomandibular disorder (TMD) remains unclear. We aimed to assess the effectiveness and safety of HPP for treating chronic TMD compared with physical therapy (PT). Methods: This study is a 2-arm parallel, multi-center, randomized controlled trial. We enrolled 82 chronic TMD patients from 2 Korean medicine hospitals between December 2019 and January 2021. We included patients with chronic TMD and randomly assigned them to undergo HPP or PT. The primary outcome was the difference in the scores for temporomandibular joint (TMJ) pain at baseline and week 6. The secondary outcomes were the scores for TMJ pain and bothersomeness, TMJ range of motion, the Korean version of Beck's depression index-Ⅱ, jaw functional limitation scale (JFLS) score, patient global impression of change (PGIC) scores, EuroQoL 5-dimension 5-level score, and short form-12 health survey (SF-12) scores. Results: Compared with PT, HPP showed significantly superior effects on TMJ pain and bothersomeness, protrusive movement pain, JFLS (verbal, emotional, and global), SF-12, and PGIC scores at week 6 (P < 0.05). Compared with the PT group, the HPP group showed a significantly higher recovery rate (≥50 % reduction in the scores for TMJ pain at the 24-week follow-up). Conclusion: HPP was more effective than PT managing pain and improving function and quality of life. Our findings demonstrate the effectiveness and safety of HPP for TMD treatment. Trial registration: This study has been registered at clinicalTrials.gov (NCT04087005), Clinical Research Information Service (CRIS) (KCT0004437), and Ministry of Food and Drug Safety (No. 31886).

This paper introduces Scenario-Risk Net, a novel approach to integrate risk assessment into the autonomous driving system segmentation process. The method incorporates an attention-based layer into an existing segmentation network, enabling the classification of driving scenes based on their risk levels. The experimental results show significant improvements in segmentation accuracy, risk classification, and situational awareness. Key contributions include an enhanced model architecture that improves Intersection over Union (IoU) scores, a detailed dataset with comprehensive annotations, and robust graph construction that effectively models real-world relationships. The results demonstrate the model’s precision in assessing risk levels and focusing on critical elements, while also emphasizing the system’s ability to refine segmentation results accurately. The research highlights the integration of risk assessment as a means to enhance the system’s ability to navigate complex environments and respond to varying levels of risk. This work lays the foundation for future advancements in autonomous driving technology, offering a path to safer and more efficient transportation solutions. Future research directions include further refinement of the dataset, optimization of the system, and extensive real-world testing to validate and improve the approach.

This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based on driver’s cognition (DCSeg-Net), our approach optimizes the graph construction process. The SE-GNN module enhances the extraction of critical features and relations within diverse driving scenarios, elevating both accuracy and the system’s adaptability to complex driving scenarios. The introduced graph re-match module refines classification discrepancies, significantly boosting segmentation accuracy and refining the understanding of autonomous driving scenes. Beyond technological enhancements, this work outlines the expansion and diversification of our original dataset, strengthening the learning capabilities of our model by including specific classification labels and incorporating a broader range of driving scenarios. The utility of the improved SBS-Net is demonstrated through superior performance in Graph Construction Accuracy and Intersection over Union (IoU) measures, as highlighted in our evaluation metrics. These advances underscore the practical applicability of scenario-based segmentation in real-world autonomous driving scenarios, enhancing overall scene comprehension capabilities. The developments presented signal substantial progress in the field of autonomous driving technology.

Gas sensors based on semiconductor metal oxides (SMOs) have gained widespread attention for Internet of Things applications; however, high operating temperatures and low gas selectivity limit their applications. Recently, metal–organic frameworks (MOFs) have demonstrated potential in enhancing gas selectivity through the physical filtration of gas molecules based on their kinetic diameters. However, their application has been predominantly limited to simplistic nanostructured sensors. These sensors exhibit inherently inferior gas sensor performance compared to three‐dimensional nanostructure gas sensors. In this study, a highly periodic, 3D hierarchical ZnO/ZIF‐8 nanostructure is fabricated for photoactivated gas sensing at room temperature. Under UV illumination, the gas sensor exhibited a 17‐fold enhancement in gas response toward 0.1 ppm NO2 compared to pristine ZnO. In addition, the ZIF‐8 coating selectively increased the NO2 gas response compared to ethanol, acetone, and toluene gases, thereby improving the gas selectivity. The gas response improvement by the ZIF‐8 layer coating, which has not been achieved by previous studies, is based on enhanced photoactivation by the solid interaction between ZIF‐8 and ZnO. These results provide a systematic background for controlling the layer thickness of SMO‐MOF nanostructures and the catalytic role of MOF in photoactivated gas sensing.

This study aimed to investigate the effects of ultrasound-guided high-dose Shinbaro 2 pharmacopuncture on the pain, dysfunction, and quality of life in patients with low back pain and radiating pain due to an acute herniated intervertebral disc (HIVD). A 39-year-old male patient with low back pain and radiating pain caused by an acute HIVD was treated with Korean and Western integrative medicine, with a focus on ultrasound-guided high-dose Shinbaro 2 pharmacopuncture at Kambin’s triangle. The treatment lasted 16 weeks, including a 12-day hospitalization. The low back pain and radiating pain were evaluated using the numeric rating scale (NRS). The lumbar function and quality of life were assessed using the Oswestry disability index (ODI) and the EuroQol five-dimension index (EQ5D). Satisfaction was gauged using the patient global impression of change (PGIC). After treatment, the NRS score decreased from 10 to 1, whereas the ODI and EQ5D scores improved from 84.44 to 28.89 and from 0.303 to 0.871, respectively. The PGIC was rated as 1, indicating considerable improvement. Notably, the changes observed during hospitalization were significant. This report suggests that ultrasound-guided high-dose Shinbaro 2 pharmacopuncture at Kambin’s triangle significantly improves the pain, dysfunction, and quality of life in patients with an acute HIVD, demonstrating its potential usefulness among Korean medicine practitioners.

Recently, autonomous driving technologies require robust perception performance through deep learning with huge data and annotations. To guarantee performance, perception accuracy should be robust even in nighttime. However, lots of perception technologies perform poorly with nighttime data. It is because most current datasets with annotation are composed of daytime scenes and there are few datasets for adverse conditions especially in nighttime. A massive cost of human resources and time is required to collect large amounts of data with annotation. To deal with the upper problem, many image translation methods by Generative Adversarial Networks (GANs) are proposed to generate realistic synthetic data. However, there is a significant limitation in traditional image translation methods. It is that generated images are inconsistent on semantic information to their original images. To handle this limitation, we propose an image translation with applying transfer learning to keep semantic information. There are two steps to train the proposed network. First, the segmentation network is trained on the source domain, i.e., daytime. After that, we transfer the pretrained segmentation weights to the encoder of generator and retrain only the decoder of GANs for day-to-night image translation. Experimental results show that the proposed method can generate more semantic consistent nighttime images than traditional studies.

Abstract Understanding the nature of molecular excitons in low-dimensional molecular solids is of paramount importance in fundamental photophysics and various applications such as energy harvesting, switching electronics and display devices. Despite this, the spatial evolution of molecular excitons and their transition dipoles have not been captured in the precision of molecular length scales. Here we show in-plane and out-of-plane excitonic evolution in quasilayered two-dimensional (2D) perylene-3, 4, 9, 10-tetracarboxylic dianhydride (PTCDA) crystals assembly-grown on hexagonal boron nitride (hBN) crystals. Complete lattice constants with orientations of two herringbone-configured basis molecules are determined with polarization-resolved spectroscopy and electron diffraction methods. In the truly 2D limit of single layers, two Frenkel emissions Davydov-split by Kasha-type intralayer coupling exhibit energy inversion with decreasing temperature, which enhances excitonic coherence. As the thickness increases, the transition dipole moments of newly emerging charge transfer excitons are reoriented because of mixing with the Frenkel states. The current spatial anatomy of 2D molecular excitons will inspire a deeper understanding and groundbreaking applications of low-dimensional molecular systems.

We conducted simulations to analyze the effects of a hard-kill-type underwater defense system that defends friendly warships against an enemy wake-homing torpedo. Assuming that the enemy torpedo is a wake-homing torpedo, our surface warship detours to the prespecified evasive course by firing a hard-kill-type system, which is modeled as a passive acoustic homing-torpedo, to attack the enemy torpedo. We analyzed the effectiveness of a warship’s survival probability via Monte Carlo simulation, given the probabilistic angles of the launched torpedoes, to compare two cases where one used only evasive maneuvering and the other used the hard-kill-type underwater defense system with evasion at the same time. By changing the maximum torpedo detection range of a warship and the torpedo’s initial location, we observed that the resulting survival probability of a warship was above 61% with a hard-kill-type defense system, whereas it remained at 34% without a hard-kill defense system, the necessity of a hard-kill underwater defense system, especially against wake-homing torpedoes.

Although various fabrication methods for metal–oxide nanostructures have been well developed for enlarged surface area, numerous efforts to further enhance the effective surface area for their chemical sensor applications are still being studied. Herein, a high‐power laser is irradiated on the existing metal–oxide nanostructures to expose the hidden inner surface of the nanostructures for full participation in the surface gas‐sensing reactions, resulting in extraordinary gas‐sensing performance. In addition, noble metal catalyst decoration at both the inner and outer surfaces of the nanostructures records extremely high gas response and selectivity to volatile organic compounds. The numerical simulation and experimental verification of the effects of high‐power laser irradiation for morphological evolution of the metal–oxide nanostructures can provide a new perspective toward the time‐efficient development of nanostructure‐based electronic devices.

Recently, research on autonomous driving has focused on the advent of various deep learning algorithms. The main sensors for autonomous driving include cameras, LiDAR, and radar, but these algorithms primarily focus on image and LiDAR data. This is because radar data is limited compared to image and LiDAR data. To address the lack of data problem, GAN-based translation methods have been proposed. However, these methods also focus only on image and LiDAR data, such as day-to-night translation or sunny-to-adverse weather translation. Since radar data differs depending on radar sensors and radar points are too sparse to learn patterns compared to LiDAR, translation with radar data is a challenging task. Radar is usually utilized as a sensor that is nearly unaffected by the weather. However, it has been confirmed through JARI data collected by us that rain has a negative effect. CycleGAN is useful for data translation in traffic scenes where pair data is difficult to acquire, since CycleGAN is a network specialized in style translation. KP-Convolution is a module specialized in feature extraction of points while maintaining location information. Therefore, we propose a radar translation network between sunny and rainy domains by combining KP-Convolution and CycleGAN. In this process, we address the adverse effects of radar data by rain, establishing the training format of radar data, KP-Convolution which can learn patterns despite a small number of points, and CycleGAN which is the basis of the translation method.

Specialized accelerators are becoming a standard way to achieve both high-performance and efficient computation. We see this trend extending to all areas of computing, from low-power edge-computing systems to high-performance processors in datacenters. Reconfigurable architectures, such as Coarse-Grained Reconfigurable Arrays (CGRAs), attempt to find a balance between performance and energy efficiency by trading off dynamism, flexibility, and programmability. Our goal in this work is to find a new solution that provides the flexibility of traditional CPUs, with the parallelism of a CGRA, to improve overall performance and energy efficiency. Our design, the Dynamic Data-Driven Reconfigurable Architecture (3DRA), is unique, in that it targets both low-latency and high-throughput workloads. This architecture implements a dynamic dataflow execution model that resolves data dependencies at run-time and utilizes non-blocking broadcast communication that reduces transmission latency to a single cycle to achieve high performance and energy efficiency. By employing a dynamic model, 3DRA eliminates costly mapping algorithms during compilation and improves the flexibility and compilation time of traditional CGRAs. The 3DRA architecture achieves up to 731MIPS/mW, and it improves performance by up to 4.43x compared to the current state-of-the-art CGRA-based accelerators.

Efforts to commercialize organic solar cells (OSCs) by developing roll-to-roll compatible modules have encountered challenges in optimizing printing processes to attain laboratory-level performance in fully printable OSC architectures. In this study, we present efficient OSC modules fabricated solely through printing methods. We systematically evaluated the impact of processing solvents on the morphology of crucial layers, such as the hole transport, photoactive, and electron transport layers, applied using the doctor blade coating method, with a particular focus on processability. Notably, deposition of charge transport layer using printing techniques is still a challenging task, mainly due to the hydrophobic characteristic of the organic photoactive layer. To overcome this issue, we investigated the solvent effect of a well-studied cathode interlayer, bathocuproine (BCP). We were able to form a uniform thin BCP film (∼10 nm) on a non-fullerene based organic photoactive layer using the doctor bladed coating method. Our results showed that the use of volatile alcohols in the BCP processing required a delicate balance between wettability and vaporization, which contrasted with the results for spin-coated films. These findings provide important insights into improving the efficiency of printing techniques for depositing charge transport layers. The fully printed OSC modules, featuring uniform and continuous BCP layer formation, achieved an impressive power conversion efficiency of 10.8% with a total area of 10.0 cm2 and a geometrical fill factor of 86.5%.

A 28 GHz digitally controlled 6-bit phase shifter with a precision calibration technique in GaN high-electron mobility transistor (HEMT) technology is presented for Ka-band phased-array systems and applications. It comprises six stages, in which stages 1 and 2 for 5.625° and 11.25° are designed in the form of a switched-line circuit, and stages 3, 4, and 5 for 22.5°, 45°, and 90° are designed in the form of a switched-filter circuit. The final stage 6 for 180° is designed in a single-to-differential balun followed by a single-pole double-throw (SPDT) switch for achieving an efficient phase inversion. A novel continuous tuning calibration technique is proposed to improve the phase accuracy. It controls the gate bias voltage of off-state HEMTs at the stage 6 SPDT switch for fine calibration of the output phase. Fabricated in a 0.15 μm GaN HEMT process using a die size of 1.75 mm2, the circuit produces 64 phase states at 28 GHz with a 5.625° step. The experimental results show that the Root-Mean-Square (RMS) phase error is significantly improved from 8.56° before calibration to 1.08° after calibration. It is also found that the calibration does not induce significant changes for other performances such as the insertion loss, RMS amplitude error, and input-referred P1dB. This work successfully demonstrates that the GaN technology can be applied to millimeter-wave high-power phased-array transceiver systems.

Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in training deep-learning-based perception models for environments with adverse conditions (e.g., rainy and nighttime). In this paper, we propose an unsupervised network designed for the translation of images from day-to-night to solve the ill-posed problem of learning the mapping between domains with unpaired data. The proposed method involves extracting both semantic and geometric information from input images in the form of attention maps. We assume that the multi-task network can extract semantic and geometric information during the estimation of semantic segmentation and depth maps, respectively. The image-to-image translation network integrates the two distinct types of extracted information, employing them as spatial attention maps. We compare our method with related works both qualitatively and quantitatively. The proposed method shows both qualitative and qualitative improvements in visual presentation over related work.

In autonomous vehicles, the LiDAR and radar sensors are indispensable components for measuring distances to objects. While deep-learning-based algorithms for LiDAR sensors have been extensively proposed, the same cannot be said for radar sensors. LiDAR and radar share the commonality of measuring distances, but they are used in different environments. LiDAR tends to produce less noisy data and provides precise distance measurements, but it is highly affected by environmental factors like rain and fog. In contrast, radar is less impacted by environmental conditions but tends to generate noisier data. To reduce noise in radar data and enhance radar data augmentation, we propose a LiDAR-to-Radar translation method with a voxel feature extraction module, leveraging the fact that both sensors acquire data in a point-based manner. Because of the translation of high-quality LiDAR data into radar data, this becomes achievable. We demonstrate the superiority of our proposed method by acquiring and using data from both LiDAR and radar sensors in the same environment for validation.

We investigated Ti-doped ZrO _2 deposition using a cyclopentadienyl tris(dimethylamino) zirconium (CpZr(NMe _2 ) _3 ) precursor and a titanium tetraisopropoxide (TTIP) precursor using an O _3 thermal atomic layer deposition process. In addition, the effect of Ti doping concentration on the chemical bonding and electrical properties of the Ti-doped ZrO _2 thin films was studied. O _3 was used at a high concentration of 400 g m ^−3 . We varied the Ti doping concentration by controlling the rate of the supercycle process in the ZrO _2 process window of 200 °C–300 °C. As a result, the highest dielectric constant was observed at a Ti doping concentration of 2.5% because it enhances the crystallinity of ZrO. Excessive Ti doping hinders crystal formation.

The capsule vehicle is a new transportation system that travels in a sub-vacuum tunnel at a speed of about 1000 km/h, and adopts the electrodynamic suspension type levitation system. Accordingly, although it differs according to the driving speed, the vibration of capsule vehicle is expected to be greater than that of the wheel/rail type high-speed rail vehicles. Therefore, a method of applying a semi-active control technique using an MR damper as a secondary suspension of a capsule vehicle to improve passenger comfort is being studied. When performing semi-active control using an MR damper, the most important thing is to determine the current for the MR damper. However, it is difficult to determine the appropriate current for the MR damper because of the hysteretic characteristics of the MR damper. In this study, a practical model named the Bi-exponential model is presented for the application of semi-active control using MR dampers on a capsule vehicle. This Bi-exponential model well reflects the hysteretic characteristics of the MR damper, which makes it easy to obtain the inverse model for determining the current supplied to the MR damper. The semi-active control performance of the Bi-exponential model was evaluated by simulation and a HILS experiment, taking into account the vertical motion of a 1/4-car capsule vehicle. Also, the Bi-exponential model has been validated against the performance of the most commonly used Bouc-Wen model. As a result, the Bi-exponential model showed better hysteretic characteristics of MR dampers compared to the Bouc-Wen model. Furthermore, despite the simpler model compared to the Bouc-Wen model, the semi-active control performance of the Bi-exponential model was found to be better.

The current standard for the pharmacological management of lumbar disk herniation (LDH), involving non-steroidal anti-inflammatory drugs, muscle relaxants, and opioid analgesics, often carries a risk of adverse events. The search for alternative therapeutic options remains a vital objective, given the high prevalence of LDH and the critical impact on the quality of life. Shinbaro 2 is a clinically effective herbal acupuncture against inflammation and various musculoskeletal disorders. Therefore, we explored whether Shinbaro 2 exerts protective effects in an LDH rat model. The results showed that Shinbaro 2 suppressed pro-inflammatory cytokines, interleukin-1 beta, tumor necrosis factor-alpha, disk degeneration-related factors, matrix metalloproteinase-1,−3,−9, and ADAMTS-5 in LDH rats. Shinbaro 2 administration reinstated a behavioral activity to a normal level in the windmill test. The results indicated that Shinbaro 2 administration restored spinal cord morphology and functions in the LDH model. Therefore, Shinbaro 2 exerted a protective effect in LDH via actions on inflammatory responses and disk degeneration, indicating that future research is warranted to assess the action mechanisms further and validate its effects.

Abstract We report a Spectroscopic Imaging Scanning Tunneling Microscopy (SI-STM) study of a DyBa2Cu3O7-δ (DBCO) thin film (T c ~ 79 K) synthesized by the molecular beam epitaxy (MBE). We observed an unusual transfer of spectral weight in the local density of states (LDOS) spectra occurring only within the superconducting gap. By a systematic control of the tip-sample distance and the junction resistance, we demonstrate that the spectral weight transfer can be switched at a nano-meter length scale. These results suggest that an interaction between the STM tip and the sample alters the electronic configurations in the film. This probably originates from a combination of an intrinsic band bending at the interface between the surface and the bulk, and a tip-induced band bending. These results may open a new avenue for band engineering and applications of thin films of high-T c cuprates.

It is expected that secondary exposure to e-cigarette aerosol (passive vaping) will soon become an issue of public health. Passive vaping inhales e-cigarette aerosol containing similar harmful substances as active vaping. However, parallel studies on passive vaping are minimal. Therefore, there is a need for passive vaping-related health risk studies to assess the impact of vaping on public health. This research conducted a series of experiments in a room using a puffing machine and the Mobile Aerosol Lung Deposition Apparatus (MALDA) to study e-cigarette aerosol respiratory deposition through passive vaping. The experimental data acquired were applied to estimate the deposited mass and health risks caused by toxic metals contained in e-cigarette aerosol. Five popular e-cigarette products were used in this study to generate e-cigarette aerosol for deposition experiments. In addition, size-segregated e-cigarette aerosol samples were collected, and metal compositions in the e-cigarette aerosol were analyzed. Results obtained showed that estimated non-cancer risks were all acceptable, with hazard quotient and hazard index all less than 1.0. The calculated cancer risks were also found acceptable, with lifetime excess cancer risk generally less than 1E-6. Therefore, the e-cigarettes tested and the passive vaping exposure scenarios studied do not seem to induce any potential for metal-related respiratory health effects.

Abstract. Background:. Neck pain and functional impairment are common complications of traffic accidents (TAs); however, the effects of manual therapy on these symptoms have rarely been studied in the literature. Thus, this randomized controlled trial aims to assess the effectiveness and safety of non-resistance manual therapy (NRT)—a treatment combining mobilization and pressure release techniques—on acute neck pain caused by TA. Method:. This study will use a two-armed, parallel, assessor-blinded randomized controlled trial design and will be conducted in the Daejeon Jaseng Hospital of Korean Medicine in South Korea. One hundred twenty patients will be recruited and randomized into an integrative Korean medicine treatment (IMKT) + NRT group and IMKT group in a 1:1 ratio. The primary outcome is a change in the numeric rating scale for neck pain immediately after treatment on hospital day 5 compared to those at baseline. The secondary outcomes are numeric rating scale for radiating arm pain, visual analogue scale for neck pain and radiating arm pain, cervical active range of motion, neck disability index, Patient Global Impression of Change, Short Form-12 Health Survey, and Posttraumatic Stress Disorder Checklist for DSM-5. Discussion:. The findings of this study on the effectiveness and safety of NRT will be helpful for patients with TA-induced neck pain in clinical practice and will provide evidence for developing relevant healthcare-related policies. Trial registration:. This protocol has been registered at Clinicaltrials.gov (NCT04660175).

Summary: Anxiety is characteristic comorbidity of noise-induced hearing loss (NIHL), which causes physiological changes within the dentate gyrus (DG), a subfield of the hippocampus that modulates anxiety. However, which DG circuit underlies hearing loss-induced anxiety remains unknown. We utilize an NIHL mouse model to investigate short- and long-term synaptic plasticity in DG networks. The recently discovered longitudinal DG-DG network is a collateral of DG neurons synaptically connected with neighboring DG neurons and displays robust synaptic efficacy and plasticity. Furthermore, animals with NIHL demonstrate increased anxiety-like behaviors similar to a response to chronic restraint stress. These behaviors are concurrent with enhanced synaptic responsiveness and suppressed short- and long-term synaptic plasticity in the longitudinal DG-DG network but not in the transverse DG-CA3 connection. These findings suggest that DG-related anxiety is typified by synaptic alteration in the longitudinal DG-DG network.

Abstract An experimental investigation into the nonlinear optical properties of rhenium diselenide (ReSe2) was conducted at a wavelength of 1.9 μm using the open-aperture and closed-aperture Z-scan techniques for the nonlinear optical coefficient (β) and nonlinear refractive index (n 2) of ReSe2, respectively. β and n 2 measured at 1.9 μm were ~ − 11.3 × 103 cm/GW and ~ − 6.2 × 10–2 cm2/GW, respectively, which to the best of our knowledge, are the first reported measurements for ReSe2 in the 1.9-μm spectral region. The electronic band structures of both ReSe2 and its defective structures were also calculated via the Perdew–Becke–Erzenhof functional to better understand their absorption properties. A saturable absorber (SA) was subsequently fabricated to demonstrate the usefulness of ReSe2 for implementing a practical nonlinear optical device at 1.9 μm. The 1.9-μm SA exhibited a modulation depth of ~ 8% and saturation intensity of ~ 11.4 MW/cm2. The successful use of the ReSe2-based SA for mode-locking of a thulium–holmium (Tm–Ho) co-doped fiber ring cavity was achieved with output pulses of ~ 840 fs at 1927 nm. We believe that the mode-locking was achieved through a hybrid mechanism of saturable absorption and nonlinear polarization rotation.

Abstract We evaluated the reliability and accuracy of the noncontact CT-1P tonopachymeter (Topcon, Japan) in terms of intraocular pressure (IOP) and central corneal thickness (CCT). One hundred sixty-three healthy participants and 33 patients with open angle glaucoma were enrolled. IOPs were measured by CT-1P (T-IOP) and Goldmann applanation tonometer (G-IOP), and CCTs were measured by the CT-1P (T-CCT) and an ultrasound pachymeter (US-CCT). Both CCT instrument-adjusted (T-IOP-C) and unadjusted T-IOPs (T-IOP-NC) were included. Pearson correlation coefficients and biases assessed with Bland–Altman analysis with 95% confidence interval (CI) were calculated for reliability evaluation. Intrasession repeatability was excellent for both T-IOP (intraclass correlation coefficient [ICC] 0.91) and T-CCT (ICC 0.98). Intersession reproducibility was also excellent for T-CCT (ICC 0.94). T-IOP-NC and T-IOP-C both showed significant correlations with G-IOP (r = 0.801, P < 0.001 and r = 0.658, P

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources’ uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA’s own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

AbstractThe Joint Video Exploration Team (JVET) has studied future video coding (FVC) technologies with a potential compression capacity that significantly exceeds that of the high‐efficiency video coding (HEVC) standard. The joint exploration test model (JEM), a common platform for the exploration of FVC technologies in the JVET, employs quadtree plus binary tree block partitioning, which enhances the flexibility of coding unit partitioning. Despite significant improvement in coding efficiency for chrominance achieved by separating luminance and chrominance tree structures in I slices, this approach has intrinsic drawbacks that result in the redundancy of block partitioning data. In this paper, an adaptive tree structure correlating luminance and chrominance of single and dual trees is presented. Our proposed method resulted in an average reduction of −0.24% in the Y Bjontegaard Delta rate relative to the intracoding of JEM 6.0 common test conditions.

This study aimed to compare the cost-effectiveness of manual therapy and usual care for patients with chronic neck pain. A cost-utility analysis alongside a pragmatic randomized controlled trial was conducted in five South Korean hospitals. Data were procured from surveys and nationally representative data. Participants were 108 patients aged between 19 and 60 years, with chronic neck pain persisting for at least 3 months and a pain intensity score of ≥5 on the numerical rating scale in the last 3 days. The study was conducted for 1 year, including 5 weeks of intervention and additional observational periods. Participants were divided into a manual therapy (Chuna) group and a usual care group, and quality-adjusted life-years, costs, and the incremental cost-effectiveness ratio were calculated. The quality-adjusted life-years of the manual therapy group were 0.024 higher than that of the usual care group. From the societal perspective, manual therapy incurred a lower cost—at $2,131—and was, therefore, the more cost-effective intervention. From a healthcare system perspective, the cost of manual therapy was higher, with an incremental cost-effectiveness ratio amount of $11,217. Manual therapy is more cost-effective for non-specific chronic neck pain management from both a healthcare system and societal perspective.

Background The purpose of this study was to investigate the clinical differences between open reduction and plate fixation via a deltopectoral approach with allogenous fibular bone graft and a minimally invasive plate osteosynthesis (MIPO), in Neer's classification two-, three- part proximal humeral fractures. Methods In this retrospective study, 77 patients with two-, three-part proximal humeral fractures were treated at two different institutions. Clinical and radiological evaluations were performed in 39 patients, who underwent MIPO at one institution (group A), and 38 patients, who underwent a deltopectoral approach with allo-fibular bone graft (group B) at another institution. The results between the groups were compared. Results The MIPO technique was significantly less time consuming and caused less bleeding than the deltopectoral approach with allo-fibular bone graft (P

Abstract Background Osteoarthritis (OA) holds significance as a highly prevalent disorder in elderly populations. Various studies have been conducted on the association between alcohol consumption and OA, but the results have often been conflicting. The aim of this study was to investigate the relationship between alcohol consumption and OA in a large-scale sample representative of the Korean population. Methods Among the 25,534 participants surveyed in the fifth Korean National Health and Nutrition Examination Survey (2010–2012), 7165 individuals aged ≥50 who responded to drinking-related items were analyzed. The Alcohol Use Disorders Identification Test (AUDIT) grade was calculated, and radiologic examination analysis included the Kellgren-Lawrence (KL) grade of the lumbar spine, hip, and knee joints. Logistic regression analysis was performed to evaluate the association between AUDIT grades and OA through estimation of odds ratios (ORs). Results In crude analyses, OA (KL grade ≥ 2) of the lumbar spine and knee was more prevalent towards Zone I, but following adjustment, knee OA prevalence significantly increased in Zone III and IV compared to Zone I (Zone III: OR 1.464, 95% confidence interval (CI) 1.027–2.088; Zone IV: OR 1.543, 95% CI 1.028–2.317, respectively). Meanwhile, adjusted hip and lumbar OA values showed positive associations towards Zone IV, but did not reach statistical significance. Additional analyses of the association between alcohol consumption and pain severity of knee OA patients were nonsignificant. Conclusions These results imply that radiological knee OA, rather than symptomatic knee OA, is associated with alcohol consumption.

Abstract We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomography (SD-OCT) scans for 431 eyes of 259 participants (183 eyes of 114 healthy controls, 68 eyes of 46 glaucoma suspects, and 180 eyes of 99 glaucoma patients) were enrolled. An HDLM was built by combining a pre-trained deep learning network and support vector machine. The correlation coefficient and mean absolute error (MAE) between the predicted and measured mGCIPL thicknesses were calculated. The measured (OCT-based) and predicted (HDLM-based) average mGCIPL thicknesses were 73.96 ± 8.81 µm and 73.92 ± 7.36 µm, respectively (P = 0.844). The predicted mGCIPL thickness showed a strong correlation and good agreement with the measured mGCIPL thickness (Correlation coefficient r = 0.739; P

This is a pragmatic, two-armed, parallel, single-center, randomized controlled clinical trial for comparative evaluation between the effectiveness of integrated Korean medicine (IKM) and herbal medicine treatment with that of IKM monotherapy (control) for post-accident syndrome persistent after the acute phase. Participants were randomized into Herbal Medicine (HM, n = 20) and Control groups (n = 20) to receive the allocated treatment of 1–3 sessions/week for 4 weeks. Intention-to-treat analysis was conducted. The Difference of Numeric Rating Scale (NRS) change of overall post-accident syndromes from baseline to week 5 for the two groups was 1.78 (95% CI: 1.08–2.48; p < 0.001). Regarding secondary outcomes, a significant decrease compared to the baseline values was confirmed for NRS of musculoskeletal, neurological, psychiatric complaints and general symptoms of post-accident syndromes. In a survival analysis based on the recovery criteria of “patients with a reduction in the NRS of overall post-accident syndromes of ≥50%,” the HM group showed a shorter time to recovery than the control group during the 17-week study period (p < 0.001 by the log-rank test). IKM combined with herbal medicine treatment significantly improved the quality of life by relieving somatic pain and alleviating the overall post-accident syndrome persistent after the acute phase; this effect was maintained for at least 17 weeks.

Ca1−xLaxFeAs2 (CLFA112) belongs to a new family of Fe-based superconductors (FeSCs) and has a unique crystal structure featuring an arsenic zigzag chain layer, which has been proposed to be a possible two-dimensional topological insulator. This suggests that CLFA112 is a potential topological superconductor—a platform to realize Majorana fermions. Up to now, even a clear superconducting (SC) gap in CLFA112 has never been observed, and the SC properties of CLFA112 remain largely elusive. In this letter, we report the results of an atomic-scale investigation of the electronic structure of CLFA112 crystals using low-temperature scanning tunneling microscopy (STM). We revealed four different types of surfaces exhibiting distinct electronic properties, with all surfaces displaying dominating 2 × 1 surface reconstructions. On a Ca/La layer on top of an FeAs layer, a clear SC gap of ~12 mV was observed only at the crevices (vacancies) where the FeAs layer can be directly accessed. Remarkably, the FeAs termination layer displayed a dispersing nematic modulation both in real and q space. We also present peculiar zero-bias conductance peaks for the very As chain layer that is believed to exhibit a topological edge state as well as the influence of La dopants on the As chain layer.

Recently, emerging third-generation photovoltaic technologies have shown rapid progress in device performance; the power conversion efficiencies (PCEs) of organic bulk heterojunction (BHJ) and perovskite solar cells (PSCs) are now surpassing 19% and 25%, respectively. Despite this dramatic enhancement, their efficiencies are theoretically limited based on the detailed balance model which accounts for inevitable loss mechanisms under operational conditions. Integrated solar cells, formed by monolithically integrating two photoactive layers of perovskite and BHJ with complementary absorption, provide a promising platform for further improvement in solar cell efficiency. In perovskite/BHJ integrated solar cells (POISCs), high bandgap perovskite offers high open-circuit voltage with minimal losses while low bandgap organic BHJ extends absorption bandwidth by covering the near-infrared region, resulting in additional photocurrent gain. Different from conventional tandem solar cells, integrated solar cells contain merged photoactive layers without the need for complicated recombination layers, which greatly simplifies fabrication processes. In this review, we summarize the recent progress in POISCs, including operational mechanism and structural development, and remaining challenges on the road toward efficient devices.

Recently, the uniformity in the wafer edge area that is normally abandoned in the fabrication process has become important for improving the process yield. The wafer edge structure normally has a difference of height between wafer and electrode, which can result in a sheath bend, distorting important parameters of the etch, such as ionic properties, resulting in nonuniform etching. This problem nowadays is resolved by introducing the supplemented structure called a focus ring on the periphery of the wafer. However, the focus ring is known to be easily eroded by the bombardment of high-energy ions, resulting in etch nonuniformity again, so that the focus ring is a consumable part and must be replaced periodically. Because of this issue, there are many simulation studies being conducted on the correlation between the sheath structural characteristics and materials of focus rings to find the replacement period, but the experimental data and an analysis based on this are not sufficient yet. In this study, in order to experimentally investigate the etching characteristics of the wafer edge area according to the sheath structure of the wafer edge, the etching was performed by increasing the wafer height (thickness) in the wafer edge area. The result shows that the degree of tilt in the etch profile at the wafer edge and the area where the tilt is observed severely are increased with the height difference between the wafer and electrode. This study is expected to provide a database for the characteristics of the etching at the wafer edge and useful information regarding the tolerance of the height difference for untilted etch profile and the replacement period of the etch ring.

We applied Deep Q-Network with a Convolutional Neural Network function approximator, which takes stock chart images as input for making global stock market predictions. Our model not only yields profit in the stock market of the country whose data was used for training our model but also generally yields profit in global stock markets. We trained our model only on US stock market data and tested it on the stock market data of 31 different countries over 12 years. The portfolios constructed based on our model's output generally yield about 0.1 to 1.0 percent return per transaction prior to transaction costs in the stock markets of 31 countries. The results show that some patterns in stock chart images indicate the same stock price movements across global stock markets. Moreover, the results show that future stock prices can be predicted even if the model is trained and tested on data from different countries. The model can be trained on the data of relatively large and liquid markets (e.g., US) and tested on the data of small markets. The results demonstrate that artificial intelligence based stock price forecasting models can be used in relatively small markets (emerging countries) even though small markets do not have a sufficient amount of data for training.

Providing a high quality video streaming experience in a mobile data network via the ubiquitous HTTP Adaptive Streaming (HAS) protocol is challenging. This is largely because HAS traffic arrives as regular Internet Protocol (IP) packets, indistinguishable from those of other data services. This paper presents real-time network-based Machine Learning (ML) classifiers incurring low overhead and capable of (a) detecting the service type of different flows including HAS, and (b)detecting the player status for users with HAS flows. We utilize random forests, an ensemble classifier, relying only upon standard unencrypted packet headers. By applying the ML classifier outputs to derive scheduling metrics, we show how existing LTE base-station schedulers can improve video Quality-of-Experience (QoE) while incurring minimal overhead. For a simulated LTE cellular network, we present quantitative performance results that include misclassification errors. Our classification and scheduling framework is shown to provide an improved video QoE with tolerable impact on other non-video best effort services. These design insights can be applied to optimize video delivery in current and future wireless networks.

Milk was co-fermented with Bacillus subtilis HA and Lactiplantibacillus plantarum EJ2014 to produce a dairy ingredient enriched with poly-γ-glutamic acid (γ-PGA) and γ-aminobutyric acid (GABA). The first fermentation of milk with B. subtilis HA resulted in a viscous broth with pH 6.56, 0.26% acidity, 1.40 mg/g tyrosine equivalent, and 17.21 U/g protease activity. The viable cell counts of B. subtilis indicated 8.74 log CFU/mL, and the consistency index of the alkaline fermented milk was 1.82 Pa·sn. In addition, 4.65% mucilage was produced with 35.93% γ-PGA content. The milk co-fermented by L. plantarum indicated 1.34% acidity and pH 4.91. The viable bacterial counts of B. subtilis decreased to 4.44 log CFU/mL, whereas those of L. plantarum increased to 9.42 log CFU/mL. Monosodium glutamate (MSG) as a precursor was effectively converted into γ-PGA by B. subtilis, and then residual MSG was completely converted into GABA by L. plantarum with a yield of 26.15 mg/g. Furthermore, the co-fermented milk produced volatiles, including hexanoic acid, 2,3-butanediol, and acetoin, which may be responsible for its aged cheese-like aroma.

As the analysis of complicated reaction chemistry in bulk plasma has become more important, especially in plasma processing, quantifying radical density is now in focus. For this work, appearance potential mass spectrometry (APMS) is widely used; however, the original APMS can produce large errors depending on the fitting process, as the fitting range is not exactly defined. In this research, to reduce errors resulting from the fitting process of the original method, a new APMS approach that eliminates the fitting process is suggested. Comparing the neutral densities in He plasma between the conventional method and the new method, along with the real neutral density obtained using the ideal gas equation, confirmed that the proposed quantification approach can provide more accurate results. This research will contribute to improving the precision of plasma diagnosis and help elucidate the plasma etching process.

Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.

The need for unmanned underwater vehicles (UUVs) has received significant attention given the current security situation in the Korean Peninsula. UUVs are used in the military to perform five key missions: intelligence, surveillance, and reconnaissance (ISR), mine countermeasures, anti-submarine warfare, inspection/identification, and oceanography. In this study, we analyze the standards of evaluation of the characteristics and capabilities of UUVs that are necessary for ISR missions, which are most important from among the above mentioned ones. First, we establish 5 main criteria and 15 sub-criteria by consulting a group of experts. Next, we use the analytic hierarchy process (AHP) for these criteria. It computes the relative weights of the criteria based on pairwise comparisons, in order to evaluate the relative importance and priorities of the criteria we established, by selecting another group of 51 experts for the evaluation of the pairwise comparisons among the criteria. Consequentially, the importance associated with performance and ability of the ISR tends to be relatively high while the one associated with cost and economy for operating the UUVs appears to be low. These results may be useful in analyzing required capabilities in a more objective and concrete manner while procuring UUVs in the future.

Abstract One of the well‐known strategies for achieving high‐performance light‐activated gas sensors is to design a nanostructure for effective surface responses with its geometric advances. However, no study has gone beyond the benefits of the large surface area and provided fundamental strategies to offer a rational structure for increasing their optical and chemical performances. Here, a new class of UV‐activated sensing nanoarchitecture made of highly periodic 3D TiO2, which facilitates 55 times enhanced light absorption by confining the incident light in the nanostructure, is prepared as an active gas channel. The key parameters, such as the total 3D TiO2 film and thin‐shell thicknesses, are precisely optimized by finite element analysis. Collectively, this fundamental design leads to ultrahigh chemoresistive response to NO2 with a theoretical detection limit of ≈200 ppt. The demonstration of high responses with visible light illumination proposes a future perspective for light‐activated gas sensors based on semiconducting oxides.

A suspension system is one of the integral parts of a hyperloop capsule train, which is used to isolate the car-body from bogie vibration to provide a safer and comfortable service. A semi-active suspension system is one of the best candidates for its advantageous features. The performance of a semi-active suspension system relies greatly on the control strategy applied. In this article, Skyhook (SH) and mixed Skyhook-Acceleration Driven Damper (SH-ADD) controlling algorithms are adopted for a nonlinear quarter-car model of a capsule with semi-active magnetorheological damper. The nonlinear vertical dynamic response and performance of the proposed control algorithms are evaluated under MATLAB Simulink environment and hardware-in-loop-system (HILS) environment. The SH controlled semi-active suspension system performance is found to be better at the first resonance frequency and worse at the second resonance frequency than the passive MR damper, but the mixed SH-ADD controlled semi-active suspension system performs better than the passive at all frequency domains. Taking the root-mean-square (RMS) value of sprung mass vertical displacement as an evaluation criterion, the response is reduced by 58.49% with mixed SH-ADD controller and by 54.49% with the SH controller compared to the passive MR damper suspension.

This study analyzed factors influencing clinical symptoms and treatment of patients with traffic accident injuries. It used a retrospective chart review and questionnaire survey obtained from 560 patients (266 men and 294 women). It also conducted follow-up observations of progress after car insurance settlements and investigated the usefulness of and patient satisfaction with integrative Korean medicine treatment for traffic accident injuries. Retrospective data of patients admitted for traffic accident injury were obtained. A questionnaire survey was conducted to collect data regarding the degree of traffic accident damage, severity of pain at settlement, any treatment after settlement and duration and cost of such treatment, and patient satisfaction with car insurance services and Korean medicine treatment for traffic accident injury. The results showed no significant association between pain and the degree of damage to the car at the time of traffic accident (P = 0.662), although the degree of damage to the car was more significantly associated with time to reach a car insurance settlement than severity of pain in the patient (P = 0.003). There was no significant association between the degree of damage to the car in a traffic accident and pain after a traffic accident. Greater severity of pain at the time of the car insurance settlement was associated with greater cost and longer time spent in treatment after the car insurance settlement.

Objectives: The present study was undertaken to investigate the relationship between acupuncture therapy and surgery rate.Design: Matched, retrospective cohort study.Materials and Methods: From nationwide health insurance data (2002–2013 cohort data published by the National Health Insurance Service of Korea), patients with new cases of knee osteoarthritis that occurred between January 1, 2004 and December 31, 2010 were analyzed. Patients were divided into an acupuncture group (AG) and a control group (CG), based on records of acupuncture therapy. Propensity scores were calculated by using gender, age, income level, and Charlson comorbidity index (CCI), with the groups matched at a ratio of 1:3 (AG:CG). The final analysis period was 2 years after the first acupuncture therapy for AG and 2 years after initial diagnosis for CG; surgery rates were compared between the two groups. Stratified analyses were performed based on age, gender, and income level; sensitivity analyses were performed based on the frequency and duration of acupuncture therapy.Results: Propensity score-matched AG and CG included 8,605 and 25,815 subjects, respectively. Post-matching surgery rates were 0.26 and 0.93% in AG and CG, respectively. For all age groups, AG showed a lower surgery rate than CG. In the analysis based on gender, the female group showed a significantly lower hazard ratio of 0.225. In analysis based on income level, the results of the entire group were significant, with the lower income group showing the lowest hazard ratio. In sensitivity analyses, AG tended to show a lower surgery rate than CG.Conclusions: The present study demonstrated that acupuncture therapy is associated with a low rate of surgery for knee osteoarthritis. Additional studies are needed to support this conclusion.

In this research, novel genetic algorithm (nGA) is proposed for Bouc-Wen modle parameters esstimation for magnetorheological (MR) fluid dampers. The optimization efficiency is improved by modifying the crossover and mutation steps of a GA. In the crossover stage, the probability of reproducing offspring from the same parent (same mother and father chromosome) is done to be zero, which may happen in the standard GA, and the probability of a chromosome to be selected for mating is based on error rank weighting of the chromosomes. Additional fitness evaluation of chromosomes will take place in between the crossover and mutation steps to save the best chromosome found so far, which is not implemented in the standard genetic algorithm (GA). The model is validated by comparing its simulation output force ( F sim ) with experimentally generated MR damper force ( F exp ). The mean absolute error, standard deviation and number of generations for convergence are taken as a criterias for performance evaluation. With these ctriterias, the proposed novel GA outperform better than the other researches. The accuracy is improved by 46.67% compared to standard GA. The proposed novel GA for Bouc-Wen model parameter identification can be used for any MR damper control system with better accuracy.

We propose a novel method for training neural networks to predict the future prices of stock indexes. Unlike previous works, we do not use target stock index data for training neural networks for index prediction. Instead, we use only the data of individual companies to obtain sufficient amount of data for training neural networks for stock index prediction. As a result, our method can avoid various problems due to training complex machine learning models on a small amount of data. We performed numerous types of experiments to test methods designed for predicting the future price of the S&P 500 which is one of the most commonly traded stock indexes. Our experiments show that neural networks trained using our method outperform neural networks trained on stock index data. Compared with other state-of-the-art methods, our method is conceptually simpler and easier to apply, and achieves better results. We obtained approximately a 5-16% annual return before transaction costs during the test period (2006-2018).