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Highlights Photonic synapse capable of multispectral color discrimination is demonstrated. Strong excited-state dipoles enable remarkable discrimination intensity (0.05–40 mW cm-2). This approach is not restricted to a specific medium in the channel layer, and convolutional neural network with synapses array achieves over 94% inference accuracy for Canadian-Institute-For-Advanced-Research-10 images.

Immunoglobulin G4-related disease (IgG4-RD) is an immune-mediated condition characterized by mass-forming inflammation with a sclerosing pattern that can affect nearly any organ. However, involvement of the diaphragm in IgG4-RD is exceptionally rare. We present the case of a 62-year-old male patient with chest radiographic abnormalities. Further investigation with CT revealed an infiltrative mass in the right hemidiaphragm. This mass, composed of engorged feeding vessels, an atypical manifestation of IgG4-RD, was also associated with lymphadenopathy. Surgical excision confirmed the presence of IgG4-positive cell infiltration, solidifying the diagnosis of IgG4-RD. Notably, the patient remained asymptomatic and did not require any treatment postoperatively. This case highlights the uncommon presentation of IgG4-RD as an infiltrative diaphragmatic mass.

Abstract Within the breath lie numerous health indicators, encompassing respiratory patterns and biomarkers extending beyond respiratory conditions to cardiovascular health. Recently, the emergence of the SARS-CoV-2 pandemic has not only underscored the necessity of on-the-spot breath analysis but has also normalized the use of masks in everyday life. Simultaneously, the rapid evolution of wearable technology has given rise to innovative healthcare monitoring tools, with a specific emphasis on wearable breath sensors. This review explores current research trends in utilizing wearable breathing sensors to detect diverse respiratory biomarkers and monitor respiratory parameters, including airflow, temperature, and humidity. Additionally, it explores diverse applications, ranging from recognizing breathing patterns to swiftly detecting diseases. Integrating the Internet of Things and machine learning technologies into these applications highlights their potential to offer a personalized, accurate, and efficient healthcare solution.

Abstract Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model’s qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

Abstract An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.

Abstract This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

The demand for Li-ion batteries has increased because of their extensive use in vehicles and portable electronic devices. This increasing demand implies greater interaction between batteries and humans, making safety a paramount concern. Although traditional batteries are fabricated using Al, recent efforts to enhance safety have led to the adoption of AISI304. The strength and corrosion resistance of AISI304 are greater than those of Al; however, issues such as stress-induced phase transformation and low high-temperature strength have been observed during processing. Duplex stainless steel SAF2507, which is characterized by a dual-phase structure consisting of austenite and ferrite, exhibits excellent strength and corrosion resistance. Although SAF2507 demonstrated outstanding high-temperature strength up to 700 °C, it precipitated a secondary phase. The precipitation of this secondary phase, believed to be caused by the precipitation of the carbides of Cr and Mo, has been extensively studied. Research on the precipitation of the secondary phase near 1000 °C has been conducted owing to the annealing temperature (1100 °C) of the SAF2507 solution. The secondary phase precipitates at approximately 1000 °C because of slow cooling rates. However, few studies have been conducted on the precipitation of the secondary phase at approximately 700 °C. This study analyzed the precipitation behavior of the secondary phase at 700 °C when SAF2507 was applied and assessed its safety during heat generation in Li-ion batteries. The precipitation behavior was analyzed using field emission scanning electron microscopy for morphology, energy-dispersive X-ray spectroscopy for composition, and X-ray diffraction for phase identification.

Conventional multi-object tracking (MOT) methods often fail to track objects that repeatedly disappear and reappear in the scene due to occlusion or moving off the screen. Previous studies have used motion and appearance information to address these issues but have limitations in dealing with complex scenarios. To overcome these limitations, we propose a post-hoc framework for MOT, a so-called rethinking MOT. The proposed framework efficiently combines MOT and re-identification methods to handle tracking failures in conventional MOT methods. To this end, we proposed a track reliability checking method to determine unreliable tracks and performed re-identification to correct incorrect tracking results. For re-identification, we proposed appearance aspect-ratio matching that is practical and effective. In addition, we designed an online appearance model management for accurate and efficient re-identification. The proposed framework does not require any offline training stages, such as feature or metric learning, but it is very practical for comparing appearances in real time. In addition, it is a flexible and pluggable framework that can adopt any baseline methods for MOT and Re-id. To validate the proposed methods, we collected 12 new challenging indoor datasets. Unlike the existing benchmark MOT datasets, we consider complex indoor sequences where targets reappear many times in the scene. The experimental results reveal that the proposed methods are promising for MOT under various challenging sequences and outperform state-of-the-art methods. Compared to baseline of the proposed rethinking MOT, the $IDF_{1}$ score was increased by 36.9% in own dataset and 50.4% in the benchmark dataset.

Carbon steel is subjected to several pretreatments to enable its use in highly corrosive environments, such as marine structures. However, its surface treatment is problematic owing to various processes, and these problems can be solved by replacing it with super duplex stainless steel (SDSS), which exhibits remarkable strength and corrosion resistance owing to its austenite and ferrite phases. EN 1.4410 and EN 1.4501 are the most extensively used SDSS grades in marine structures, as they exhibit exceptional strength and corrosion resistance in seawater. This study subjected EN 1.4410 and EN 1.4501 samples to specific heat treatment after casting and observed their structural alterations through field emission scanning electron microscopy. Their passivation states, with or without the Cu and W layers, were determined by examining their corrosion properties through open-circuit potential measurements, electrostatic polarisation tests, electrochemical impedance spectroscopy (EIS), and critical pitting temperature (CPT) analysis. The inclusion of Cu significantly improved the uniform corrosion resistance within the passivation layers, whereas the addition of W enhanced the pitting resistance (Epit, CPT). Additionally, the EIS analysis confirmed a double-layer structure in the passivation layer of EN 1.4501. Moreover, Cu did not act as a strengthening element of the passivation layer, whereas W significantly reinforced it.

The importance of lung microbiome changes in developing chronic lung allograft dysfunction (CLAD) after lung transplantation is poorly understood. The lung microbiome–immune interaction may be critical in developing CLAD. In this context, examining alterations in the microbiome and immune cells of the lungs following CLAD, in comparison to the lung condition immediately after transplantation, can offer valuable insights. Four adult patients who underwent lung retransplantation between January 2019 and June 2020 were included in this study. Lung tissues were collected from the same four individuals at two different time points: at the time of the first transplant and at the time of the explantation of CLAD lungs at retransplantation due to CLAD. We analyzed whole-genome sequencing using the Kraken2 algorithm and quantified the cell fractionation from the bulk tissue gene expression profile for each lung tissue. Finally, we compared the differences in lung microbiome and immune cells between the lung tissues of these two time points. The median age of the recipients was 57 years, and most (75%) had undergone lung transplants for idiopathic pulmonary fibrosis. All patients were administered basiliximab for induction therapy and were maintained on three immunosuppressants. The median CLAD-free survival term was 693.5 days, and the median time to redo the lung transplant was 843.5 days. Bacterial diversity was significantly lower in the CLAD lungs than at transplantation. Bacterial diversity tended to decrease according to the severity of the CLAD. Aerococcus, Caldiericum, Croceibacter, Leptolyngbya, and Pulveribacter genera were uniquely identified in CLAD, whereas no taxa were identified in lungs at transplantation. In particular, six taxa, including Croceibacter atlanticus, Caldiserium exile, Dolichospermum compactum, Stappia sp. ES.058, Kinetoplastibacterium sorsogonicusi, and Pulveribacter suum were uniquely detected in CLAD. Among immune cells, CD8+ T cells were significantly increased, while neutrophils were decreased in the CLAD lung. In conclusion, unique changes in lung microbiome and immune cell composition were confirmed in lung tissue after CLAD compared to at transplantation.

Abstract Background Clinical impact of preoperative diaphragm dysfunction on lung transplantation has not been studied. We aimed to evaluate how preoperative diaphragm dysfunction affects clinical outcomes and ventilation function after transplantation. Methods We retrospectively enrolled 102 patients. Ultrasound for diagnosis of diaphragm dysfunction was performed on all patients both before and after lung transplantation. The primary outcome was to compare prolonged mechanical ventilation after transplantation according to the preoperative diaphragm dysfunction. Secondary outcomes compared global inhomogeneity index and lung volume after transplantation. Multivariate regression analysis were used to evaluate the association between preoperative diaphragm dysfunction and prolonged mechanical ventilation after transplantation. Results A total of 33 patients (32.4%) had preoperative diaphragm dysfunction, and half of them (n = 18) recovered their diaphragm function after transplantation. In contrast, 15 patients (45.5%) showed postoperative diaphragm dysfunction. The ratio of prolonged mechanical ventilation after transplantation was significantly higher in the preoperative diaphragm dysfunction group (p = 0.035). The postoperative durations of mechanical ventilation, intensive care unit and hospital stays were higher in the preoperative diaphragm dysfunction group, respectively (p

In this study, we propose an optimized AlGaN/GaN high-electron-mobility transistor (HEMT) with a considerably improved breakdown voltage. First, we matched the simulated data obtained from a basic T-gate HEMT with the measured data obtained from the fabricated device to ensure the reliability of the simulation. Thereafter, to improve the breakdown voltage, we suggested applying a gate-head extended structure. The gate-head-top and gate-head-bottom lengths of the basic T-gate HEMT were symmetrically extended by 0.2 μm steps up to 1.0 μm. The breakdown voltage of the 1.0 μm extended structure was 52% higher than that of the basic T-gate HEMT. However, the cutoff frequency (fT) and maximum frequency (fmax) degraded. To minimize the degradation of fT and fmax, we additionally introduced a gate-recessed structure to the 1.0 μm gate-head extended HEMT. The thickness of the 25 nm AlGaN barrier layer was thinned down to 13 nm in 3 nm steps, and the highest fT and fmax were obtained at a 6 nm recessed structure. The fT and fmax of the gate-recessed structure improved by 9% and 28%, respectively, with respect to those of the non-gate-recessed structure, and further improvement of the breakdown voltage by 35% was observed. Consequently, considering the trade-off relationship between the DC and RF characteristics, the 1.0 μm gate-head extended HEMT with the 6 nm gate-recessed structure was found to be the optimized AlGaN/GaN HEMT for high-power operations.

Objective Coronavirus disease 2019 (COVID-19) has notably altered the emergency department isolation protocol, imposing stricter requirements on probable infectious disease patients that enter the department. This has caused adverse effects, such as an increased rate of leave without being seen (LWBS). This study describes the effect of fever/respiratory symptoms as the main cause of isolation regarding LWBS after the COVID-19 pandemic. Methods We retrospectively analyzed emergency department visits before (March to July 2019) and after (March to July 2020) the COVID-19 pandemic. Patients were grouped based on existing fever or respiratory symptoms, with the LWBS rate as the primary outcome. Logistic regression analysis was used to identify the risk factors of LWBS. Logistic regression was performed using interaction terminology (fever/respiratory symptom patient [FRP]×post–COVID-19) to determine the interaction between patients with FRPs and the COVID-19 pandemic period. Results A total of 60,290 patients were included (34,492 in the pre–COVID-19, and 25,298 in the post–COVID-19 group). The proportion of FRPs decreased significantly after the pandemic (P

In this work, effect of sample size on mechanical properties in the net-shaped 316 L alloys produced by laser powder bed fusion (LPBF) was studied. Tensile specimens with four different geometries, having the same total cross-sectional areas but with different tension beam widths of 0.75, 1.5, 3 and 6 mm, were produced by the LPBF and subjected to the tests. The experimental results indicated that there is a strong influence of the sample size of the mechanical properties in the as-built state. The yield strength increased and the ductility decreased pronouncedly with decreasing the sample size. Fine grain structures near the surface of the net-shaped samples due to the additional laser contour scanning is believed to be responsible to the strong sample size effect. The experiments also revealed that the sample size effect can be effectively reduced with the annealing heat treatments. The local strength of the fine-grained area near the surface was quantitatively characterized by an inverse finite element analysis. The local strength near the surface was found stronger in the direction parallel to the LPBF building direction than in the direction parallel to the LPBF transverse direction, leading to more significant size effect along this direction.

The phase transformation behavior from metastable bcc-δ to fcc-γ in Fe–Mn–Si based shape memory alloy processed by laser powder bed fusion was investigated. Primary δ phase formed in this alloy due to the rapid cooling and fast solidification. Bcc-δ to fcc-γ phase transformation occurred when the volumetric energy density was high due to the process-inherent heat treatment effect. Transmission electron microscope studies confirmed Kurdjumov-Sachs orientation relationship between bcc-δ and fcc-γ phases. The analytical results revealed that the phase transformation occurred by a combined displacive-diffusional mechanisms.

This study investigates the operating characteristics of AlGaN/GaN high-electron-mobility transistors (HEMTs) by applying HfO2 as the passivation layer. Before analyzing HEMTs with various passivation structures, modeling parameters were derived from the measured data of fabricated HEMT with Si3N4 passivation to ensure the reliability of the simulation. Subsequently, we proposed new structures by dividing the single Si3N4 passivation into a bilayer (first and second) and applying HfO2 to the bilayer and first passivation layer only. Ultimately, we analyzed and compared the operational characteristics of the HEMTs considering the basic Si3N4, only HfO2, and HfO2/Si3N4 (hybrid) as passivation layers. The breakdown voltage of the AlGaN/GaN HEMT having only HfO2 passivation was improved by up to 19%, compared to the basic Si3N4 passivation structure, but the frequency characteristics deteriorated. In order to compensate for the degraded RF characteristics, we modified the second Si3N4 passivation thickness of the hybrid passivation structure from 150 nm to 450 nm. We confirmed that the hybrid passivation structure with 350-nm-thick second Si3N4 passivation not only improves the breakdown voltage by 15% but also secures RF performance. Consequently, Johnson’s figure-of-merit, which is commonly used to judge RF performance, was improved by up to 5% compared to the basic Si3N4 passivation structure.

Super duplex stainless steel (SDSS) is used for manufacturing large valves and pipes in offshore plants because of its excellent strength and corrosion resistance. Large valves and pipes are manufactured by forging after casting, and the outside and inside microstructures are different owing to the difference in the cooling rate caused by the thermal conductivity. This microstructural variation causes cracks during solution annealing, which breaks the materials. To study the corrosion resistance of the SDSS forged material, the influence of the microstructure according to the difference between the inside and outside cooling rates of the cast SDSS was evaluated. To analyze the effects of the secondary phase fraction before solution annealing on the solution and corrosion resistance, the corrosion resistance with and without solution annealing was measured using the potentiodynamic polarization test and critical temperature test after the precipitation of the secondary phase. In the potentiodynamic polarization test, the secondary phase decreased the activation polarization and increased the corrosion rate. The critical pitting temperature exhibited the effect of the secondary phase.

This paper presents a method for recognizing behaviors in videos based on the ensemble RGB-S deep neural network, which combines RGB images and skeleton features from an action recognition database built in intelligent home service robot environments. The ensemble model is designed using the three-stream approach. The first stream classifies behaviors in videos using a convolutional neural network (CNN) based on a pre-trained ResNet101 model, which uses two-dimensional (2D) sequence images of actions as its input, and training a long short-term memory (LSTM) neural network with the sequence (RGB 2D-CNN + LSTM). The second stream directly manages the video and uses a three-dimensional (3D) CNN to include both temporal and spatial information. The 3D CNN is based on a pre-trained R3D-18 model (RGB 3D-CNN). The last stream uses the pose evolution image (PEI) method, which converts the skeleton sequence into a single-color image. The converted images are used as the input for the CNN (Skeleton PEI-2D-CNN). This approach not only reflects the spatial and temporal features of the behaviors in videos, but also includes all characteristics of the 2D sequence images, 3D videos, and skeleton sequences. Finally, a large-scale database for behavior recognition in videos, known as ETRI-Activity3D, is used in this study to verify the performance of the proposed deep neural network. A recognition performance of 93.2% is achieved in a cross-subject experiment, verifying the superiority of this method over models from previous studies.

Blockchain technology, which provides digital security in a distributed manner, has evolved into a key technology that can build efficient and reliable decentralized applications (called DApps) beyond the function of cryptocurrency. The characteristics of blockchain such as immutability and openness, however, have made DApps more vulnerable to various security risks, and thus it has become of great significance to validate the integrity of DApps before they actually operate upon blockchain. Recently, research on vulnerability in smart contracts (a building block of DApps) has been actively conducted, and various vulnerabilities and their countermeasures were reported. However, the effectiveness of such countermeasures has not been studied well, and no appropriate methods have been proposed to evaluate them. In this paper, we propose a software tool that can easily perform comparative studies by adding existing/new countermeasures and labeled smart contract codes. The proposed tool demonstrates verification performance using various statistical indicators, which helps to identify the most effective countermeasures for each type of vulnerability. Using the proposed tool, we evaluated state-of-the-art countermeasures with 237 labeled benchmark codes. The results indicate that for certain types of vulnerabilities, some countermeasures show evenly good performance scores on various metrics. However, it is also observed that countermeasures that detect the largest number of vulnerable codes typically generate much more false positives, resulting in very low precision and accuracy. Consequently, under given constraints, different countermeasures may be recommended for detecting vulnerabilities of interest. We believe that the proposed tool could effectively be utilized for a future verification study of smart contract applications and contribute to the development of practical and secure smart contract applications.

Abstract Background Although the prevention of extracorporeal membrane oxygenation (ECMO) catheter-related infection is crucial, scientific evidence regarding best practices are still lacking. Methods We conducted an uncontrolled before-and-after study to test whether the introduction of disinfection with 2% chlorhexidine gluconate (CHG) and 70% isopropyl alcohol (IPA) of the exposed circuits and hub in patients treated with ECMO would affect the rate of blood stream infection (BSI) and microbial colonization of the ECMO catheter. We compared the microbiological and clinical data before and after the intervention. Results A total of 1740 ECMO catheter days in 192 patients were studied. These were divided into 855 ECMO catheter days in 96 patients before and 885 ECMO catheter days in 96 patients during the intervention. The rates of BSI were significantly decreased during the intervention period at 11.7/1000 ECMO catheter days before vs. 2.3/1000 ECMO catheter days during (difference 9.4, 95% confidence interval (CI) 1.5–17.3, p = 0.019). Furthermore, the colonization of the ECMO catheter was similarly significantly reduced during the intervention period at 10.5/1000 ECMO catheter days before vs. 2.3/1000 ECMO catheter days during intervention (difference 8.3, 95% CI 0.7–15.8, p = 0.032). Hospital mortality (41.7% vs. 24%, p = 0.009) and sepsis-related death (17.7% vs. 6.3%, p = 0.014) were also significantly decreased during intervention. Conclusion Extensive disinfection of exposed ECMO circuits and hub with 2% CHG/IPA was associated with a reduction in both BSI and microbial colonization of ECMO catheters. A further randomized controlled study is required to verify these results. Trial registration KCT 0004431

Several lung transplantation (LTx) patients develop end-stage renal disease (ESRD) and often need a kidney transplant. Recently, the number of multiorgan transplantation cases has increased; however, no successfully combined redo lung-kidney transplantation has been reported in Korea. We present the first case of combined second redo lung-kidney transplantation in a patient with ESRD after LTx. In November 2018, a 40-year-old man with dyspnea was admitted to our hospital. Seventeen years ago, he underwent right pneumonectomy owing to refractory extensive drug-resistant tuberculosis. Four years ago, he underwent left single-LTx due to chronic respiratory failure. He was diagnosed with chronic lung allograft dysfunction and ESRD (glomerular filtration rate,

Abstract Background As extracorporeal membrane oxygenation (ECMO) has been widely used, the patient quality of life following ECMO termination has become an important issue as same as the patient’s survival. To date, the factors affecting complete recovery of adult survivors from ECMO have not been investigated. Methods Data from adult patients in the Extracorporeal Life Support Organization registry who received veno-venous ECMO between 2012 and 2017 were analyzed. Multivariate logistic regression analyses were conducted. Results In total, 6536 patients with 242,183 days of veno-venous ECMO were reviewed. The overall survival to discharge rate after weaning from ECMO was 89.7% (n = 5861), and 10.3% (n = 675) of the patients died during hospitalization. The discharge location varied as follows: 33.7% (n = 1976) returned home, 23.4% (n = 1369) were transferred to a referral hospital, 41.8% (n = 2447) required hospital services, and 0.6% (n = 36) were discharged to other places. The patients were divided into two groups according to the discharge location: a complete recovery group (n = 1976) and a partial recovery group (n = 3885). In the multivariate analyses, age (≥ 65 years) (odds ratio (OR) 0.72, 95% confidence interval (CI) 0.59–0.87, p = 0.001), cardiac arrest before ECMO (OR 0.76, 95% CI 0.60–0.96, p = 0.021), vasopressor use (OR 0.73, 95% CI 0.64–0.83, p < 0.001), renal replacement therapy (OR 0.40, 95% CI 0.28–0.57, p < 0.001), ECMO-related complications (OR 0.76, 95% CI 0.67–0.85, p < 0.001), and long-term ECMO support (≥ 2 weeks) (OR 0.42, 95% CI 0.37–0.48, p < 0.001) were significantly associated with complete recovery. Conclusion Complete recovery after veno-venous ECMO support is associated with the patient’s baseline condition, ECMO duration, and ECMO-related complications. Respiratory ECMO should aim to increase both the survival and the quality of life after weaning from ECMO.

Background Postpartum hemorrhage is the leading cause of maternal mortality. Oxytocin being the most popular uterotonic agent, has been routinely administered after both vaginal delivery and cesarean section. Carbetocin is a newer uterotonic agent and provides the benefit of a longer duration of action without additional administration post-delivery. Methods We recruited 34 women undergoing elective cesarean section under spinal anesthesia. All patient was received spinal anesthesia using 0.5% hyperbaric Marcaine 8–10 mg in conjugation with fentanyl 20 μg in the left lateral decubitus position. Hartmann’s solution 10–15 ml/kg was administered before carbetocin. The operation started as soon as sensory block at level T4–T6 was confirmed. A non-invasive hemodynamic monitoring cuff (Finometer®) was attached to the patient’s finger soon after the induction of spinal anesthesia. Using the Finometer, we recorded the heart rate and mean arterial pressure at every 15 s, starting from 15 s before the administration of carbetocin to 5 min after. After the removal of the placenta, the bolus group was administered intravenous bolus injection of carbetocin 100 μg and the infusion group was administered carbetocin 100 μg diluted in 50 ml normal saline, over 5 min using an infusion pump. Results The demographic data showed no significant difference between the two groups. Furthermore, there were no significant hemodynamic differences between the two groups. Conclusions The method of administration of carbetocin does not influence its hemodynamic effects.

Background: As the average life expectancy increases, anesthesiologists confront unique challenges in the perioperative care of elderly patients who have significant comorbidities. In this study, we evaluated Elixhauser comorbidity measures-based risk factors associated with 30-day mortality in patients aged 66 years and older who underwent femur fracture surgery. Methods: We used the Medical Information Mart for Intensive Care III which contains the medical records of patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center in the United States between 2001 and 2012 to identify patients admitted to the ICU after femur fracture surgery (n=209). Patients who died within 30 days of admission (case group, n=49) were propensity score-matched to patients who did not (control group, n=98). The variables for matching were age, sex, race, anemia (hemoglobin ≤10 g/dl), and malignancy. We attempted to explain mortality via nine independent factors: hypertension, uncomplicated diabetes, complicated diabetes, congestive heart failure (CHF), cardiac arrhythmias, chronic pulmonary disease, renal failure, neurological disorders other than paralysis, and peripheral vascular disease. Results: Logistic regression identified three significant risk factors: CHF, arrhythmias, and neurological disorders other than paralysis. The odds ratio (OR) for the 30-day mortality of CHF was 4.99 (95% confidence interval [CI], 2.18 to 12.06). The equivalent ORs for cardiac arrhythmias and neurological disorders other than paralysis were 2.61 (95% CI, 1.14 to 6.21) and 2.40 (95% CI, 0.95 to 6.48), respectively. Conclusions: Identifying patients with these risk factors (CHF, arrhythmias, and neurological disorders other than paralysis) will assist clinicians with perioperative planning and provide caregivers with valuable information for decision-making.

Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

Organic/inorganic halide perovskite materials have attracted substantial attention in solar cells, and they have achieved significant improvements in recent years. In perovskite solar cells (PSCs), the engineering of interfacial properties between multilayers is an important determinant of performance and stability. Here, we designed a bilayer structure of hole transporting layer by inserting poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA), which can improve the interfacial properties between the perovskite and the nickel oxide (NiO) hole transport layer in p-i-n planar PSCs. We observed that the hole transporting PTAA/NiO bilayer leads to higher performance by optimizing the energy level and accelerating the extraction of charges from the interface. The power conversion efficiency of the PSC was improved from 17.29% to 19.05% when the PTAA/NiO bilayer was introduced instead of the NiO monolayer. Ultimately, we confirmed that this interface engineering provides thermal and air stability of PSCs.

This study investigates the operational characteristics of AlGaN/GaN high-electron-mobility transistors (HEMTs) by applying a slant-gate structure and drain-side extended field-plate (FP) for improved breakdown voltage. Prior to the analysis of slant-gate-based HEMT, simulation parameters were extracted from the measured data of fabricated basic T-gate HEMTs to secure the reliability of the results. We suggest three different types of slant-gate structures that connect the basic T-gate electrode boundary to the 1st and 2nd SiN passivation layers obliquely. To consider both the breakdown voltage and frequency characteristics, the DC and RF characteristics of various slant-gate structures including the self-heating effect were analyzed by TCAD simulation. We then applied a drain-side extended FP to further increase the breakdown voltage. The maximum breakdown voltage was achieved at the FP length of 0.4 μm. Finally, we conclude that the slant-gate structures can improve breakdown voltage by up to 66% without compromising the frequency characteristics of the HEMT. When the drain-side FP is applied to a slant-gate structure, the breakdown voltage is further improved by up to 108%, but the frequency characteristics deteriorate. Therefore, AlGaN/GaN HEMTs with an optimized slant-gate-based structure can ultimately be a promising candidate for high-power and high-frequency applications.

Abstract Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses.

Organic–inorganic metal halide perovskite solar cells have been under the spotlight but what happens in the working solar cell device at the nanoscale remains elusive. Here Kim et al. show that light and voltage bias can induce strain and structural variation which may cause J–V scan hysteresis.

Background/Aims: p21-activated Ser/Thr kinase 1 (PAK1) is essential for the genesis and development of many cancers. The purpose of this study was to investigate the role of the PAK1–cyclic AMP response element-binding (CREB) axis in non-small cell lung cancer (NSCLC) tumorigenesis and its related mechanisms. Methods: Western blot assay and immunohistochemical staining were employed to investigate the PAK1 and CREB expression in the tissue microarray of human squamous NSCLC. Co-immunoprecipitation and immunofluorescence confocal assays were performed to determine the link between PAK1 and CREB. NSCLC xenograft models were used to study oncogenic function of PAK1 in vivo. Results: We observed that PAK1 and CREB expression levels were significantly elevated in human squamous NSCLC-tissue specimens, compared with those in adjacent normal bronchial or bronchiolar epithelial-tissue specimens, as well as their phosphorylated forms, based on western blotting. We showed in vitro that PAK1 knockdown by small-interfering RNA (siRNA) blocked CREB phosphorylation, whereas plasmid-based PAK1 overexpression resulted in CREB phosphorylation at Ser133, based on western blotting. In addition, PAK1 interacted with CREB in co-immunoprecipitation assays. Additionally, our in vitro findings detected by flow cytometry revealed that PAK1 silencing attenuated cell cycle progression, inducing apoptosis. Inhibition of PAK1 expression reduced tumor sizes and masses by modulating CREB expression and activation in xenograft models. Conclusion: These results suggest a novel mechanism whereby the PAK1–CREB axis drives carcinogenesis of squamous-cell carcinomas, and have important implications in the development of targeted therapeutics for squamous-cell lung cancer.

New exercise technology such as the virtual reality (VR)–based exercise system is required to meet soaring demand for target participants in exercises and to alleviate the difficulties in personnel mobilization through an alternative approach to the exercise system. In a previous study, event tree methodologies were introduced in setting up an exercise scenario of a VR-based radiological exercise system. In the scenario, the locations at which major events occur are rephrased as nodes, routes as paths, and public response actions as protective actions or contents of an exercise at individual locations. In the study, a model for estimating effective doses to the participants is proposed to evaluate the exercise system, using the effective dose rates at particular times and locations derived from a computer program. The effective dose received by a student when she/he follows a successful route is about a half of the dose received when she/he does not follow the exercise guide directions. In addition, elapsed time to finish an exercise when following a successful route is less than one-third of the time spent to finish an exercise when following the guide's directions. Keywords: Effective Dose Rate, Event Tree Methodology, Exercise Evaluation Program, Public Response Action, Virtual Reality–Based Radiological Exercise System

In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of the elderly and young adults based on their physical conditions enables silver robots to provide customized services for the elderly to counter aging society problems, laying the groundwork for improving elderly welfare systems and automating elderly care systems. Accordingly, skeleton sequences modeling the changes of the human body are converted into pose evolution images (PEIs), and a convolutional neural network (CNN) is trained to classify the elderly and young adults for a single behavior. Then, a heatmap, which is a contributed portion of the inputs, is obtained using a gradient-weighted class activation map (Grad-CAM) for the classified results, and a skeleton-heatmap is obtained through a series of processes for the ease of analysis. Finally, the behavioral characteristics are derived through the difference matching analysis between the domains based on the skeleton-heatmap and RGB video matching analysis. In this study, we present the analysis of the behavioral characteristics of the elderly and young adults based on cognitive science using deep learning and discuss the examples of the analysis. Therefore, we have used the ETRI-Activity3D dataset, which is the largest of its kind among the datasets that have classified the behaviors of young adults and the elderly.

The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides action sequences in actual environments and helps to handle a network generalization issue due to the dataset shift. When the action recognition model is trained on the ETRI-Activity3D and KIST SynADL datasets and evaluated on the ETRI-Activity3D-LivingLab dataset, the performance can be severely degraded because the datasets were captured in different environments domains. To reduce this dataset shift between training and testing datasets, we propose a close-up of maximum activation, which magnifies the most activated part of a video input in detail. In addition, we present various experimental results and analysis that show the dataset shift and demonstrate the effectiveness of the proposed method.

Abstract Background The use of extracorporeal membrane oxygenation (ECMO) in cases of near-fatal asthma (NFA) has increased, but the benefits and potential complications of this therapy have yet to be fully investigated. Methods Cases were extracted from the Extracorporeal Life Support Organization Registry between March 1992 and March 2016. All patients with a diagnosis of asthma (according to the International Classification of Diseases 9th edition), who also received ECMO, were extracted. Exclusion criteria included patients who underwent multiple courses of ECMO; those who received ECMO for cardiopulmonary resuscitation or cardiac dysfunction; and those with another primary diagnosis, such as sepsis. We analyzed survival to hospital discharge, complications, and clinical factors associated with in-hospital mortality, in patients with severe life-threatening NFA requiring ECMO support. Results In total 272 patients were included. The mean time spent on ECMO was 176.4 hours. Ventilator settings, including rate, fraction of inspired oxygen (FiO2), peak inspiratory pressure (PIP), and mean airway pressure, significantly improved after ECMO initiation (rate (breaths/min), 19.0 vs. 11.3, p

Background The risk of bleeding during extracorporeal membrane oxygenation (ECMO) is a potential deterrent in performing tracheostomy at many centers. To evaluate the safety of surgical tracheostomy (ST) in critically ill patients supported by ECMO, we reviewed the clinical correlation between preoperative coagulation status and bleeding complication-related ST during ECMO. Methods From April 1, 2012 to March 31, 2016, ST was performed on 38 patients supported by ECMO. We retrospectively reviewed and analyzed the medical records including complications related to ST. Results Heparin was administered to 23 patients (60.5%) for anticoagulation during ECMO, but 15 patients (39.5%) underwent ECMO without anticoagulation. Of the 23 patients administered anticoagulation therapy, heparin infusion was briefly paused in 13 prior to ST. The median platelet count, international normalized ratio, and activated partial thromboplastin time before ST were 126 × 109/L (range, 46 to 434 × 109/L), 1.2 (range, 1 to 2.3) and 62 seconds (27 to 114.2 seconds), respectively. No peri-procedural clotting complications related to ECMO were observed. Two patients (5.3%) suffering from ST-related major bleeding required surgical hemostasis. Minor bleeding after ST occurred in two cases (5.3%). No significant difference was found according to anticoagulation management (P = 0.723). No fatality was attributable to ST. Conclusions The complication rates of ST in the patients supported by ECMO were low. Therefore, ST performed by an experienced operator, and with careful optimization of coagulation status, is a relatively safe procedure; the use of ST with ECMO should thus not be dismissed on account of the potential for bleeding caused by the administration of anticoagulants.

Background: Extracorporeal membrane oxygenation (ECMO) has been successfully used as a method for the interhospital transportation of critically ill patients. In South Korea, a well-established ECMO interhospital transport system is lacking due to limited resources. We developed a simplified ECMO transport system without mechanical ventilation for use by public emergency medical services. Methods: Eighteen patients utilized our ECMO transport system from December 2011 to September 2015. We retrospectively analyzed the indications for ECMO, the patient status during transport, and the patient outcomes. Results: All transport was conducted on the ground by ambulance. The distances covered ranged from 26 to 408 km (mean, 65.9±88.1 km) and the average transport time was 56.1±57.3 minutes (range, 30 to 280 minutes). All patients were transported without adverse events. After transport, 4 patients (22.2%) underwent lung transplantation because of interstitial lung disease. Eight patients who had severe acute respiratory distress syndrome showed recovery of heart and lung function after ECMO therapy. A total of 13 patients (70.6%) were successfully taken off ECMO, and 11 patients (61.1%) survived. Conclusion: Our ECMO transport system without mechanical ventilation can be considered a safe and useful method for interhospital transport and could be a good alternative option for ECMO transport in Korean hospitals with limited resources.

Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand–object interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand–object interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods.

18Ni-300 maraging steel produced by the selective laser melting (SLM) process has a unique microstructure that is different from that of the same alloy processed by conventional methods. In this paper, maraging steels were fabricated by the selective laser melting process and their microstructures and mechanical properties were investigated in terms of post heat treatment conditions. Moreover, the effect of different heat treatments on the mechanical anisotropy was studied in detail. The micro Vickers hardness in the as-built state was around 340 Hv and could be increased to approximately 600 Hv by aging heat treatments. It was found that the solution heat treatment was not necessary to obtain a fully hardened state. From tensile tests of the maraging steels heat treated with different conditions, it was found that the highest strength was achieved by aging and solution treatment (ST) temperatures lower than the commonly used temperatures. In the direction parallel to the laser scanning, the highest ultimate tensile strength was obtained when 450 °C aging was done without solution heat treatment. In the other two directions tested, i.e., directions normal to the building and 45 degrees to the laser scanning direction, the highest tensile strength was obtained when aging was done at 450 °C after 750 °C solution treatment.

A case study of monitoring and analysis of ground settlement caused by tunnelling of stacked twin tunnels for underground metro line construction through the densely populated area using the slurry pressure-balanced TBM is presented. Detailed ground settlement monitoring was carried out for the initial stage of down-track tunnelling in order to estimate trough width factor and volume losses including face, shield, and tail losses. In addition, using the gap model, prediction of volume loss and ground settlement was carried out with consideration of the ground condition, TBM configurations, and actual operation data. The predictions of the gap model were compared with the observed results, and adjustment factors were determined for volume loss estimation. The adjusted factors were applied to predict ground settlement of the up-track tunnel, and its results were compared with the field measurements.

[This corrects the article DOI: 10.1371/journal.pone.0182341.].

To investigate incidence and distribution of major adverse reproductive health problems related to various kinds of industries in Korea and to compare risks for major reproductive outcomes to assess maternal health in working and non-working women.We requested claim data from the Korean National Health Insurance. We defined reference groups as (1) non-working women and (2) workers in the education field. Women working in each industry were compared with reference groups regarding rates of miscarriage, threatened abortion, preterm labor, and intrauterine growth restriction. Logistic regression was used for multivariate analysis, and age and income adjustment was performed.The percentages of all adverse obstetric outcomes were higher in working women than in non-working women. Working women had higher and statistically significant adjusted odds ratios (ORs) for miscarriage in 18 of the 21 industries. The age and income-adjusted OR for miscarriage for all working women was 1.26 (95% confidence interval, 1.23-1.28). Business facilities management and business support services, manufacturing, human health and social work activities, wholesale and retail trade, and professional, scientific, and technical activities were major industries with higher adjusted ORs for adverse obstetric outcomes.We confirmed that compared to non-working women, working women have a higher risk for adverse pregnancy outcomes. Thus, adverse pregnancy outcomes such as threatened abortion, preterm labor, and intrauterine growth restriction may be associated with working status. This exploratory study identified several industries where in-depth studies are required in future to improve occupational safety in women of reproductive age.