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Aims The outcomes of mitral valve replacement/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning. Methods and results Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834–0.925), with mortality risk increased strongly with left atrial dimension >55 mm, and LV end-systolic diameter >45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034). Conclusion A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.

Background Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. Results The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. Conclusion Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.

ObjectivesPatients with mitral regurgitation (MR) may be heterogeneous with different risk profiles. We aimed to identify distinct phenogroups of patients with severe primary MR and investigate their long-term prognosis after mitral valve (MV) surgery.MethodsThe retrospective cohort of patients with severe primary MR undergoing MV surgery (derivation, n=1629; validation, n=692) was analysed. Latent class analysis was used to classify patients into subgroups using 15 variables. The primary outcome was all-cause mortality after MV surgery.ResultsDuring follow-up (median 6.0 years), 149 patients (9.1%) died in the derivation cohort. In the univariable Cox analysis, age, female, atrial fibrillation, left ventricular (LV) end-systolic dimension/volumes, LV ejection fraction, left atrial dimension and tricuspid regurgitation peak velocity were significant predictors of mortality following MV surgery. Five distinct phenogroups were identified, three younger groups (group 1–3) and two older groups (group 4–5): group 1, least comorbidities; group 2, men with LV enlargement; group 3, predominantly women with rheumatic MR; group 4, low-risk older patients; and group 5, high-risk older patients. Cumulative survival was the lowest in group 5, followed by groups 3 and 4 (5-year survival for groups 1–5: 98.5%, 96.0%, 91.7%, 95.6% and 83.4%; pConclusionFive phenogroups of patients with severe primary MR with different risk profiles and outcomes were identified. This phenogrouping strategy may improve risk stratification when optimising the timing and type of interventions for severe MR.

Inverse reinforcement learning (IRL) is a technique for automatic reward acquisition, however, it is difficult to apply to high-dimensional problems with unknown dynamics. This article proposes an efficient way to solve the IRL problem based on the sparse Gaussian process (GP) prediction with $l_1$ -regularization only using a highly limited number of expert demonstrations. A GP model is proposed to be trained to predict a reward function using trajectory-reward pair data generated by deep reinforcement learning with different reward functions. The trained GP successfully predicts the reward functions of human experts from their collected demonstration trajectory datasets. To demonstrate our approach, the proposed approach is applied to the obstacle avoidance navigation of the mobile robot. The experimental results clearly show that the robots can clone the experts’ optimality in navigation trajectories avoiding obstacles using only with a very small number of expert demonstration datasets (e.g., $\leq 6$ ). Therefore, the proposed approach shows great potential to be applied to complex real-world applications in an expert data-efficient manner.

In this paper, we generated intelligent self-driving policies that minimize the injury severity in unexpected traffic signal violation scenarios at an intersection using the deep reinforcement learning. We provided guidance on reward engineering in terms of the multiplicity of objective function. We used a deep deterministic policy gradient method in the simulated environment to train self-driving agents. We designed two agents, one with a single-objective reward function of collision avoidance and the other with a multi-objective reward function of both collision avoidance and goal-approaching. We evaluated their performances by comparing the percentages of collision avoidance and the average injury severity against those of human drivers and an autonomous emergency braking (AEB) system. The percentage of collision avoidance of our agents were 78.89% higher than human drivers and 84.70% higher than the AEB system. The average injury severity score of our agents were only 8.92% of human drivers and 6.25% of the AEB system.

The emission characteristics of mobile air pollution sources belonging to the non-road sector have yet to be thoroughly investigated, especially when compared to those of on-road vehicles. Hence, the present study employed an engine dynamometer to evaluate the emissions of a diesel engine complying with Tier 3 standards during various driving modes (KC1-8, NRSC, and NRTC) and a portable emissions measurement system (PEMS) to assess those of two excavators, one complying with Tier 3 and the other with Tier 4 standards, during real-world working and driving conditions. The characteristics and chemical composition of the emitted regulated pollutants (carbon monoxide (CO), non-methane hydrocarbon (NMHC), nitrogen oxide (NOx), and particulate matter (PM)) and hazardous air pollutants (HAPs), e.g., volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), were then determined. For the regulated pollutants, NRTC mode generated the highest emissions owing to the rapid changes in engine load and speed during transitions. As a result of the after-treatment systems, which utilized a diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR), CO, NMHC, and NOx emissions from the Tier 4-compliant excavator were lower than those from the Tier 3-compliant diesel engine and excavator. The operating conditions of the diesel engine and construction equipment as well as the after-treatment systems significantly affected the composition of the non-methane volatile organic compound (NMVOC) emissions, which were dominated by alkanes that comprised 23 detected species. Sampling the particle-phase PAHs, we found phenanthrene, fluoranthene, and naphthalene; furthermore, we estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. Formaldehyde, which contributed the largest fraction of the aldehydes, was more abundant than the other species. Thus, emissions from non-road sources, including construction equipment, must be controlled because of the considerable health risks from pollutants such as formaldehyde and benzene. Our results will aid in the establishment of a national emission inventory for HAPs.

Long-term exposure to fine particles (PM

In this study, a basic study was performed to analyze the seasonal temperature status of a research room in the Global Environment Research Building, where ceiling-embedded indoor units are installed to study the room temperature status of the building, as well as to improve its thermal environment. In addition, a direction for improvement of the indoor thermal environment in the winter was proposed through a CFD (computational fluid dynamics) simulation and was proven by an additional experiment. Through the results of this study, it appeared that if the ceiling-embedded indoor unit was installed in the small indoor space without considering the thermal vulnerability of its perimeter boundary, the air temperature of the upper part was greatly different from that of the bottom part in the winter. Based on the PMV measurement result, the case that used both FCUs and convectors showed 1.33, the biggest maximum and minimum difference, and the case that used all FCUs, convectors and circulating fans showed 0.68, the smallest maximum and minimum difference. Therefore, it was considered that the operating method suggested in the room used in this study would improve not only the temperature stratification but also the thermal comfort. Hence, in this study, as a means to improve the stratification, convectors were installed to minimize the effect of the external thermal environment, and angle-controllable air flowing fans were installed to mitigate the stratification distribution. With such a result, it was intended to present essential data for the improvement of the thermal environment, as well as the conservation of heating energy in the winter, by reviewing the use of the ceiling-embedded indoor units in the future.

Studies on the characteristics of hazardous air pollutants (HAPs) in the emissions of medium-duty diesel trucks are significantly insufficient compared to those on heavy-duty trucks. This study investigated the characteristics of regulated pollutants and HAPs, such as volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), and estimated non-methane hydrocarbon (NMHC) speciation in the emissions of medium-duty diesel trucks. Ten medium-duty diesel trucks conforming to Euros 5 and 6 were tested for four various driving cycles (WLTC, NEDC, CVS-75, and NIER-9) using a chassis dynamometer. In an urban area such as Seoul, CO and NMHC emissions were increased because of its longer low-speed driving time. NOx emissions were the highest in the high-speed phase owing to the influence of thermal NOx. PM emissions were almost not emitted because of the DPF installation. Alkanes dominated non-methane volatile organic compound (NMVOC) emissions, 36–63% of which resulted from the low reaction of the diesel oxidation catalyst. Formaldehyde emissions were the highest for 35–53% among aldehydes irrespective of driving cycles. By sampling the particle-phase of PAHs, we detected benzo(k)fluoranthene and benzo(a)pyrene and estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. In the particle portion, benzo(k)fluoranthene and benzo(a)pyrene were over 69% and over 91%, respectively. The toxic equivalency quantities of benzo(k)fluoranthene and benzo(a)pyrene from NIER-9 (cold) for both Euro 5 and Euro 6 vehicles were more than five times higher than those of NIER (hot) and NEDC. In the case of NMHC speciation, formaldehyde emissions were the highest for 10–45% in all the driving cycles. Formaldehyde and benzene must be controlled in the emissions of medium-duty diesel trucks to reduce their health threats. The results of this study will aid in establishing a national emission inventory system for HAPs of mobile sources in Korea.

In 2015, air pollutant emissions in the Republic of Korea were 792,776 metric tons of CO, 1,157,728 metric tons of NOx, 352,292 metric tons of SOx, 604,243 metric tons of TSP, 233,177 metric tons of PM10, 98,806 tons of PM2.5, 15,934 metric tons of BC, 1,010,771 metric tons of VOCs, and 297,167 metric tons of NH3. Among major emission source categories, the main emission sources and the contributions to emissions, by pollutant, were as follows: road transport (31.0%), biomass burning (29.3%), and non-road transport (17.1%) for CO; road transport (31.9%), non-road transport (26.3%), and manufacturing industry (14.6%) for NOx; industrial processes (29.9%), energy production (25.9%), and manufacturing industry (24.2%) for SOx; fugitive dust (67.6%) manufacturing industry (20.1%) for TSP; fugitive dust (47.0%) and manufacturing industry (30.4%) for PM10; manufacturing industry (36.8%), fugitive dust (17.5%), and non-road transport (14.3%) for PM2.5; road transport (42.0%) and non-road transport (39.6%) for BC; solvent use (54.9%) and industrial processes (18.1%) for VOCs; and agriculture (77.8%) and industrial processes (13.3%) for NH3. The data we calculate can be used as official national emissions data for the establishment, implementation, and assessment of air quality-related policy, such as measures to deal with particulate matter, as well as for related modeling and other research.

Learning the uncertain dynamical environments for online learning and prediction from noisy sensory measurement streams is essential for various tasks in robotics. Recently, Gaussian process (GP) online learning such as an infinite-horizon Gaussian process (IHGP) has shown effectiveness to cope with non-stationary dynamical random processes in learning hyperparameters online by reducing the computational cost. However, the IHGP was originally proposed to deal with only a single-output. Therefore, to tackle complex real-world problems, we propose a multi-output infinite-horizon Gaussian process (MOIHGP) that generalizes the single-output IHGP to deal with multiple outputs for better prediction. Our approach allows us to consider correlations between multiple outputs for better prediction, even with occlusions in a Bayesian way. Finally, we successfully demonstrate the effectiveness of our approach by benchmark and experimental results. For simulated benchmark experiments with high noise levels, our approach reduced 16.6% of the averaged RMSE value achieved by the single-output IHGP.

Intramammary bacterial infection, the most common cause of mastitis, is the most costly disease in dairy cattle in the US and reason for antibiotic usage. Ceftiofur, a third-generation cephalosporin, is generally used to treat such disease, but it has a high treatment failure rate. Though the reason is not known clearly, it is hypothesized that multiple factors are associated with the treatment failure. In this study, we analyzed 169 milk samples from cows with mastitis in two independent dairy farms (Farm A and B) in which 19.4% (Farm A) and 14.3% (Farm B) of the antibiotic treated cows were not cured. The prevalence of cephalosporin-resistant bacteria (CRB) in milk was 72.0% and 42.1% in Farm A and B, respectively. Nineteen and nine bacterial genera were identified in Farm A and B respectively, with the most abundant genus being Staphylococcus (27.1%; Farm A) and Bacillus (63.5%; Farm B). However, no strong relationship between the treatment failure rate and the CRB prevalence was observed. Furthermore, the metagenomic analysis showed no significant differences in the α- and β-diversities of microbiota in milk samples from cured and uncured cows, suggesting that antibiotic-resistant bacteria were not the sole reason for the antibiotic treatment failure. KEY POINTS: • The mastitic milk samples had high prevalence of cephalosporin-resistant bacteria (CRB). • The CRB identified belong to diversified species. • Antibiotic treatment failure was not solely caused by the abundance of CRB.

A letter entitled “Gaussian Process Online Learning With a Sparse Data Stream” suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) to deal with a sparse data stream. We point out that there is an error in differentiating the discrete algebraic Riccati equation (DARE), which significantly changes the results of the benchmarking study in a sense that the proposed approach using the solution of the Lyapunov equation does not show outperformance against the original IHGP. In this letter, we provide a correction with details and its consequential implication.

Emission characterization of particle number as well as particle mass from three diesel passenger cars equipped with diesel particulate filter (DPF), diesel oxidation catalyst (DOC) and exhaust gas recirculation (EGR) under the vehicle driving cycles and regulatory cycle. Total particle number emissions (PNEs) decreased gradually during speed-up of vehicle from 17.3 to 97.3km/hr. As the average vehicle speed increases, the size-segregated peak of particle number concentration shifts to smaller size ranges of particles. The correlation analysis with various particulate components such as particle number concentration (PNC), ultrafine particle number concentration (UFPNC) and particulate matter (PM) mass was conducted to compare gaseous compounds (CO, CO2, HC and NOx). The UFPNC and PM were not only emitted highly in Seoul during severe traffic jam conditions, but also have good correlation with hydrocarbons and NOx influencing high potential on secondary aerosol generation. The effect of the dilution temperature on total PNC under the New European Driving Cycle (NEDC), was slightly higher than the dilution ratio. In addition, the nuclei mode (DP: ≤13nm) was confirmed to be more sensitive to the dilution temperature rather than other particle size ranges. Comparison with particle composition between vehicle speed cycles and regulatory cycle showed that sulfate was slightly increased at regulatory cycle, while other components were relatively similar. During cold start test, semivolatile nucleation particles were increased due to effect of cold environment. Research on particle formation dependent on dilution conditions of diesel passenger cars under the NEDC is important to verify impact on vehicular traffic and secondary aerosol formation in Seoul.

Size-segregated ultrafine particles (UFPs) in diesel exhaust were investigated to characterize carbonaceous substances, metals, and organic compounds originating from a medium-duty diesel engine dynamometer using the 13 driving mode. Organic carbon (OC) and elemental carbon (EC) peaked at 330–550 nm, but the OC/EC ratio showed two peaks in the ultrafine and accumulation modes. The distribution trend of metal elements was opposite to that of the size-segregated OC/EC ratio. The amounts of toxic Pb, As, and Cd were less than 0.03–2.5% in diesel exhaust particles (DEPs), but their cumulative fractions in the ultrafine mode exceeded 50%. Most organic compounds (76.6%) and alkanes (67.0%) were emitted in the accumulation mode (170–1000 nm). More than 70% of the identified polycyclic aromatic hydrocarbons (PAHs) were emitted in the accumulation mode (94–1000 nm), with phenanthrene being the most abundant. Two significant size ranges of toxicity equivalent quantity peaks in the ultrafine (34–66 nm) and accumulation (170–330 nm) modes were observed for the size-segregated DEPs. Contrary to the trends for PAHs and organic compounds, the identifiable nitrogen-containing polycyclic aromatic compounds were more abundant in the ultrafine mode. Overall toxicity was high as UFPs can be deposited with high efficiency throughout the human respiratory tract.

We present a highly configurable, low-power, low-area, low-EVM, SAW-less transmitter (TX) architecture that is based on a dynamically biased power mixer. All FDD/TDD bands from 0.7 to 2.7 GHz for 4G LTE Rel-11 and 3G $\bf{HSPA} + $ are supported in addition to 2G quad bands. The power-mixer bias current is dynamically adjusted based on the instantaneous baseband signal swing using a fully-differential hybrid full-wave rectifier/envelope-detector circuit. Dynamic biasing leads to greater than 50% current savings when compared to fixed-biasing while providing a higher output power with better linearity. Implemented in 28 nm CMOS technology, the TX shows better than $- \bf{157}\;\bf{dBc}/\bf{Hz}$ RX-band noise emission and $- \bf{41}\;\bf{dBc}$ ACLR for output powers up-to $+ \bf{4}\;\bf{dBm}$ across all 3G/4G bands, while demonstrating above 80 dB of gain control range. In addition, the TX can be configured to provide better than $- \bf{65}\;\bf{dBc}$ CIM3, allowing it to meet stringent spurious emission specifications when transmitting 1 RB 4G LTE signals in B13/B26/B1.

This study aimed to investigate the comparative effects of oxygenates such as ethanol (EA), methyl tertiary-butyl ether (MTBE), and ethyl tertiary butyl ether (ETBE) by fixing the oxygen contents as 0.82 wt% 1.65 wt%, and 2.74 wt% of the fuels on the regulated (CO, NMHC and NOx) and unregulated (formaldehyde, acetaldehyde and BTEX) exhaust emissions in gasoline-powered vehicles. The most widely used type of vehicles (light-duty, medium-duty, heavy-duty) in Korea were tested on a chassis dynamometer under the CVS-75 Cycle. When EA, MTBE and ETBE percentage increased, the CO and NMHC concentration decreased. The NOx emission decreased at 1.65 wt% and 2.74 wt% oxygen content of MTBE and ETBE. The emissions of CO decreased by 0.363 g/km, 0.266 g/km and 0.356 g/km for light-duty vehicle when EA, MTBE and ETBE oxygenates blending ratio increased. Increased EA, MTBE and ETBE oxygenates blending ratio demonstrated no specific reducing effect on CO emissions from low-mileage vehicle, but NMHC emissions decreased by 0.011 g/km (medium-duty), 0.015 g/km (light-duty) and 0.018 g/km (heavy-duty). More CO was emitted from MTBE among three oxygenates at same oxygen content. The emitted concentrations of NMHC from three oxygenates at same oxygen content were almost similar, but reduced NOx emissions from EA (10%) to MTBE (20.4%) and ETBE (23.6%) were observed at 2.74 wt% oxygen content. Reducing effect on CO emissions was order of EA > ETBE > MTBE. Formaldehyde emissions increased up to 54.3% as MTBE ratio increased. When oxygen content of ETBE, EA, and MTBE increased from 0.82 wt% to 2.74 wt%, the acetaldehyde emissions increased up to 177.4%, 39.5% and 31.0%, respectively. There was significant formaldehyde concentration difference between high emission vehicle type (light-duty and medium-duty) and low emission vehicle type (heavy-duty and low-mileage) for three oxygenates. Reduction effect of MTBE and ETBE on BTEX was the order of toluene > benzene > ethylbenzene > xylene, and MTBE showed more reduction effect than ETBE at same oxygen content.

BACKGROUND Ultraviolet (UV) rays are electromagnetic waves that account for about 5% of the solar light, and when overexposed, they pose malevolent effects on the human skin and health. However, with recent reports on the beneficial effects of some wavelength bands of UV rays, people’s interest in UV information has increased. This has resulted in requiring, not just simple information such as the amount of UV or UV index (UVI), but detailed UV information that directly affects health, such as EUVB(Erythemally weighted UVB). However, calculating EUVB, which can be done by applying the erythemal weighted function on the intensity value in wavelength, requires specialized optical measurement devices, which cannot be easily accessed by the general public; furthermore, public institutions UV information services do not offer EUVB information for individuals. OBJECTIVE Therefore, the present study proposes a UVI sensor-based portable device, with which the general public can have easy access to UV-related information. METHODS The proposed device comprises a UVI sensor that can measure the intensity of erythemal UV radiation, a BLE module that supports communication, and a MCU for key operations. In addition, it applies the ratio of EUVB by month/time, resulting from the actual analysis of natural light to calculate the EUVB and provides the amount of UVI and EUVB to check if they meet conditions required for outdoor activities through the device and smartphone applications. RESULTS The applicability of the proposed device was verified by the measurement performance comparison test with the standard device, a spectrometer (CAS 140 CT), which showed an average error of 0.06 for UVI and 0.0017 W/m2. CONCLUSIONS The proposed device’s offering of UV-related information such as UVI and EUVB to user is expected to prevent potential damage due to the exposure to UV and to support healthy outdoor activities.

For decades lysophosphatidylcholine (LPC, lysolecithin) has been used to induce demyelination, without a clear understanding of its mechanisms. LPC is an endogenous lysophospholipid so it may cause demyelination in certain diseases. We investigated whether known receptor systems, inflammation or nonspecific lipid disruption mediates LPC-demyelination in mice. We found that LPC nonspecifically disrupted myelin lipids. LPC integrated into cellular membranes and rapidly induced cell membrane permeability; in mice, LPC injury was phenocopied by other lipid disrupting agents. Interestingly, following its injection into white matter, LPC was cleared within 24 hr but by five days there was an elevation of endogenous LPC that was not associated with damage. This elevation of LPC in the absence of injury raises the possibility that the brain has mechanisms to buffer LPC. In support, LPC injury in culture was significantly ameliorated by albumin buffering. These results shed light on the mechanisms of LPC injury and homeostasis.

The double-base number system has been used in digital signal processing systems for over a decade because of its fast inner product operation and low hardware complexity. This letter proposes an innovative multiplier architecture using hybrid operands. The multiplier can easily be linked to flash analog-to-digital converters or digital systems through a double-base number encoder (DBNE) for realtime signal processing. The design of the DBNE and the multiplier enable faster digital signal processing and require less hardware resources compared to the binary processing method.

While increased use of motor cycles in the recent years for various demands could worsen air quality, only few studies have been conducted on estimation of emission factors and characterization of emissions from motorcycle. In this study, emissions from selected six models of motorcycle based on largest market share and production rate were investigated. To investigate gaseous and carbonaceous air pollutants, such as carbon monoxide (CO), total hydrocarbon (THC), nitrogen oxide (), elemental carbon (EC) and organic carbon (OC), total 124 motorcycles between 2003 and 2007 model year were tested with regulatory driving conditions, such as CVS-40 and CVS-47 mode. These motorcycles were further sub-categorized based on their displacement ( 150 cc), type of stroke (2- and 4 strokes) and model year (2003~2005 and 2006~2007). Tested motorcycles with recent model year (2006~2007) exhibited less emissions of regulatory gaseous and carbonaceous air pollutants compared to old model year (2003~2005). Chemical analysis showed that CO present in highest concentration followed by THC and for all tested motorcycles. Interestingly, two strokes motorcycle produced higher THC emission but less CO and than those of four strokes. For all types of displacement and stroke, emission factors (gram per kilometer) of THC and CO except with recent model year (2006~2007) showed decreased trend compared to old model year (2003~2005). In addition to this, due to mixed combustion between gasoline fuel and lubricant, two strokes motorcycle showed OC > EC emission trend.