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Despite a large number of existing studies about direct carbon fuel cells (DCFCs), sufficient power generation has remained a major technical challenge for the commercialization of DCFCs. This study was designed to implement the benefits of a carbon-filled porous anode developed in our recent studies in a unit cell. First, we developed a new tubular cell assembly comprising an anode, a thin matrix, and a tubular cathode with a certain number of holes in its surface. By employing a reference electrode, we measured the resistance and

In case of pneumonia often accompanied by serious complications, sometimes lead to death, early diagnosis and continuous monitoring can greatly reduce the dangerousness. Moreover, the COVID-19 pandemic has demonstrated the need for new diagnostic tools that can minimize medical personnel engagement while avoiding equipment being exposed to afflicted patients. In this study, we developed cough monitoring algorithm by detecting the vibrations of human body. The acceleration response at each part of body was measured to determine propagation characteristics of vibration when cough occurs. and it was confirmed that the monitoring accuracy was improved when use the vibration signal compared to the case of using only acoustic signal. After that, we analyzed the cough sounds in terms of psych-acoustical and sound-energy aspects. For the characteristic features derived by quantifying the results of analysis, the data augmentation process was applied, and finally AI-based pneumonia diagnosis algorithm was constructed. To estimate the performance of algorithm, the accuracy of pneumonia determination in new cough cases was verified. It showed the higher value than the accuracy of pulmonologists with only cough sounds. Therefore, developed algorithm that perform continuous cough monitoring and reliable pneumonia diagnosis can be used as an effective supplementary tool for early diagnosis and prognosis of pneumonia. © INTER-NOISE 2021 .All right reserved.

In this study, we attempted to evaluate relative significance of the factors that have been proposed to affect the electrochemical reaction of coals in a direct carbon fuel cell (DCFC). In the pres...

The sensitivity of molecular diagnostics could be affected by nucleotide variants in pathogen genes, and the sites affected by such variants should be monitored. We report a single-nucleotide variant (SNV) in the nucleocapsid (N) gene of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), i.e., G29179T, which impairs the diagnostic sensitivity of the Xpert Xpress SARS-CoV-2 assay (Cepheid, Sunnyvale, CA, USA). We observed significant differences between the threshold cycle (Ct) values for envelope (E) and N genes and confirmed the SNV as the cause of the differences using Sanger sequencing. This SNV, G29179T, is the most prevalent in Korea and is associated with the B.1.497 virus lineage, which is dominant in Korea. Clinical laboratories should be aware of the various SNVs in the SARS-CoV-2 genome and consider their potential effects on the diagnosis of coronavirus disease 2019.

A single-particle mass spectrometer (SPMS) with laser ionization was constructed to determine the chemical composition of single particles in real time. The technique was evaluated using various polystyrene latex particles with different sizes (125 nm, 300 nm, 700 nm, and 1000 nm), NaCl, KCl, MgCO3, CaCO3, and Al2O3 particles with different chemical compositions, an internal mixture of NaCl and KCl, and an internal mixture of NaCl, KCl, and MgCl2 with different mixing states. The results show that the SPMS can be useful for the determination of chemical characteristics and mixing states of single particles in real time. The SPMS was then applied to obtain the chemical signatures of various combustion aerosols (diesel engine exhaust, biomass burning (rice straw), coal burning, and cooking (pork)) based on their single-particle mass spectra. Elemental carbon (EC)-rich and EC-organic carbon (OC) particles were the predominant particle types identified in diesel engine exhaust, while K-rich and EC-OC-K particles were observed among rice straw burning emissions. Only one particle type (ash-rich particles) was detected among coal burning emissions. EC-rich and EC-OC particles were observed among pork burning particles. The single-particle mass spectra of the EC or OC types of particles differed among various combustion sources. The observed chemical signatures could be useful for rapidly identifying sources of atmospheric fine particles. In addition, the detected chemical signatures of the fine particles may be used to estimate their toxicity and to better understand their effects on human health.

A particle-reinforced composite material is a matrix with thermally conductive particles that has a diverse range of applications from electronics to energy harvesting/storage systems. In the engineering design of a particle-reinforced composite material for application, it is crucial to accurately and practically predict its effective thermal conductivity. Here, we report the development of a simple analytical model for predictions with improved accuracy and applicability. Comprehensive evaluation of existing models was first conducted to clarify their limitations in prediction accuracy and applicability to various experimental conditions. To overcome the challenges of the existing models, our new model was derived to consider the effect of shape, particle aggregation, and mutual interaction of particles on effective thermal conductivity. Lattice Boltzmann simulations were conducted to obtain a quasi-universal coefficient representing interactions of particles, whereas a shape coefficient characterizing microstructures of aggregated particles was obtained from experimental data available from literature. As a result, our model prediction outperformed the existing models in its prediction accuracy, and it could be applicable to any experimental circumstances where previous model predictions are inappropriate to use.

This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT), which is funded by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 10067182).

Recently, a number of events have begun to be created and shared as event based social network becomes more active. Accordingly, methods for providing events that are suited to individual’s interests are being studied through analysis of participation and sharing of events by users. In this paper, we propose a new personalized event recommendation method based on user preference analysis in event based social networks. The proposed method manages the recent preferences of users by taking into account information about the recent event participations and the circumstances of the users. Our method uses relationship analysis and collaborative filtering to predict values of user properties that cannot be evaluated otherwise. The proposed method suggests events only to users who are expected to join when new events occur, thus avoiding unwanted suggestions. A performance evaluation was conducted to show the superiority of the proposed event recommendation method. As a result of the performance evaluation, it was confirmed that the proposed method has precision and recall rates that are higher than those of the existing methods by 10–30%.

Despite technological advances in biomolecule detections, evaluation of molecular interactions via potentiometric devices under ion-enriched solutions has remained a long-standing problem. To avoid severe performance degradation of bioelectronics by ionic screening effects, we cover probe surfaces of field effect transistors (FETs) with a single film of the supported lipid bilayer (SLB), and realize outstanding potentiometric signals from receptor-ligand bindings irrespective of ionic strength of bulky solutions by placing an ion-free water layer underneath the SLB. High-energy X-ray reflectometry together with the circuit analysis discovered biochemical findings that effective electrical signals dominantly originated from the sub-nanoscale conformational change of lipids in the course of receptor-ligand bindings. Beyond thorough analysis on the underlying mechanism at the molecular level, the proposed SLB-FET platform ensures the world-record level of sensitivity in molecular detection with excellent reproducibility regardless of molecular charges and environmental ionic conditions.

The growing Asian population in the United States has resulted in a need for behavior analysts who have the necessary knowledge and skills in tailoring applied behavior analysis (ABA) treatments to the unique cultural variations of Asian American clients and families. The purpose of this chapter is to provide ABA clinicians with strategies that will be useful in working with Asian American clients and their families in delivering ABA treatment. Specifically, this chapter will: (1) discuss the need for culturally sensitive behavior analysts, (2) provide a brief overview of the unique barriers and obstacles to receiving ABA treatments experienced by Asian American individuals with disabilities and their families, (3) discuss the literature on models or approaches to cultural adaptation of treatments, and (4) offer specific strategies for use by behavior analysts in delivering culturally tailored ABA treatment services to Asian American clients and families.

Nanocomposites with aligned nanofillers have received significant attention because of their excellent mechanical properties. However, because of the difficulty in identifying interfacial interactions in mechanical responses, the anisotropic dynamic mechanical properties of aligned nanocomposites, especially those with high reinforcement contents, are not fully understood. In this study, aligned aramid nanofiber/poly(vinyl alcohol) (ANF/PVA) nanocomposites with a nanofiller volume fraction of 48% are fabricated by using the swelling-assisted stretching method. This method enabled high degree of alignment of the ANF nanofibers with a sheath of PVA matrix. Enhancement of mechanical strength and stiffness is attributed to improvements of interfacial interactions between aligned ANFs and PVA via effective stress transfer. The damping properties are varied by activation of different dissipation modes, such as the stick–slip and reversible matrix-tearing at the interfaces, that are dependent to the loading angle relative to the nanofiber alignment direction. Resultant viscoelastic properties evaluated by the weight-adjusted viscoelastic figure of merits, the combination of stiffness and damping properties, exceed the conventional limit line up to three folds in magnitude. Deepening the understanding of anisotropic dynamic mechanical responses is required for designing aligned nanocomposite materials for mechanical and electronic applications.

Nanoparticle deposit has various microstructures from multiple dendrites to mesoporous compact structures depending on deposition conditions, and the required microstructures are different depending on its applications. There have been recent reports that the overall porosity of deposit can be predicted by a function of diffusive Knudsen number ( K n D ) and dimensionless translational energy ( χ F ) of the particle. Unlike compact structures, however, dendritic structures consisting of multiple dendrites cannot be characterized solely by overall porosity and their formation mechanism is not known. In this study, various structures of deposit were produced in a wide range of deposition conditions ( K n D : 10−1.5–101.5; χ F : 10−3–103) using off-lattice Monte Carlo simulation, and the unique characteristics of the deposit could be visualized by presenting local fractional accumulations of pores as color-filled contours. Furthermore, a spatial autocorrelation was calculated for the contour plot in order to find a quantitative footprint of structural features of nanoparticle deposits, in compact vs dendritic structures. As a result, we successfully identified the dendritic-to-compact structural transition in a quantitative manner, for the first time. Lastly considering the deposition behavior of particles, we developed a new model capable of a priori prediction of the specific deposit structure among compact, dendritic, and transition structures, given the deposition condition in terms of K n D and χ F .

The goal of this study was to achieve a significant improvement in the size-selection performance of DMA, by combining unipolar charging and the condensational method of growing nanoparticles. We developed a size-independent unipolar charger consisting of a saturator, a condenser, a corona charger, and an evaporator. Starting with a two-fluid mixing design, a porous-alumina-lined saturator was tested in terms of the size uniformity of particles after their condensational growth in a subsequent condenser. 20-nm Ag and 65-nm NaCl polydisperse nanoparticles were successfully grown into droplets of few micrometers with a geometric standard deviation of 1.20 or less. A simple model was also developed to explain the heat and mass transfer occurring in the saturator and condenser, resulting in predictions of particle growth that agreed with experimental results. The entire charging system was experimentally evaluated in terms of size dependencies of charging efficiency and charge numbers. The results revealed that ~52% of the nanoparticles were uniformly charged and released with a charge number of + 32, irrespective of the particle sizes. A regular nano DMA, using the proposed size-independent charging system in place of a bipolar charger, was finally tested to determine its productivity when size-classifying particles. It was found that the proposed charger, when combined with the DMA, was eight times more efficient compared to the bipolar charger, and became more advantageous as the target particle size decreased. These promising results were reconfirmed by TEM image analysis of the produced monodisperse particles.

Focusing on the possibility of using ammonia as a hydrogen carrier, a combustion strategy for pure ammonia as an engine fuel to convert the stored energy into a usable form has been proposed, which uses ammonia itself as a combustion promotor. Under this strategy, the conditions of sufficiently high temperature and pressure to cause the spray combustion of ammonia are obtained by the auto-ignition of a mixture of pilot-injected ammonia and air. To confirm the feasibility and the operating characteristics of an engine with the proposed ammonia combustion strategy, engine modeling has been conducted, the combustion strategy has been simulated, and a parametric study has been performed. To analyze the NO production mechanisms in an engine with the proposed combustion strategy, the NO production process has been classified into four phases, and the NO production in each phase has been analyzed under various start of injection timing and fuel amounts.

Although high-pressure gas atomization has been widely used for the large-scale production of fine metal powders, high operating costs remain one of its biggest issues. The key to efficiency lies in how to strengthen the gas/molten metal interaction. Despite this, most of the extensive previous studies have focused on evaluating various nozzles by flow visualization and the size measurement of the resulting powder. It is known that strong shock waves are inevitably produced by a pressure mismatch through the nozzle, however the effect of those shock waves on gas flow and breakup behavior is still unclear. The purpose of this numerical simulation study was to determine whether any efficient paths for maximizing the gas-melt interaction exist, and to elucidate possible effects of the shock waves. Two types of supersonic nozzles were employed for the simulations: an annular slit nozzle, versus an isentropic plug nozzle working in shock-free mode. Single particle analysis was performed by injecting a single coarse droplet at different locations near the nozzle exit and then monitoring the local gas velocity and breakup characteristics along its path. The same path-resolved analysis was repeated for continuous droplet injection under various gas-to-melt ratios. The results indicate that more efficient paths exist for the maximal use of gas kinetic energy, and that shock waves are detrimental to producing smaller sized powders.

This paper describes a novel model developed for a priori prediction of the maximal spread of a liquid drop on a surface. As a first step, a series of experiments were conducted under precise control of the initial drop diameter, its falling height, roughness, and wettability of dry surfaces. The transient liquid spreading was recorded by a high-speed camera to obtain its maximum spreading under various conditions. Eight preexisting models were tested for accurate prediction of the maximum spread; however, most of the model predictions were not satisfactory except one, in comparison with our experimental data. A comparative scaling analysis of the literature models was conducted to elucidate the condition-dependent prediction characteristics of the models. The conditioned bias in the predictions was mainly attributed to the inappropriate formulations of viscous dissipation or interfacial energy of liquid on the surface. Hence, a novel model based on energy balance during liquid impact was developed to overcome the limitations of the previous models. As a result, the present model was quite successful in predicting the liquid spread in all the conditions.

The public health impact of particulate matter in ambient air with a size of 2.5 μm or smaller (PM2.5) has been of great concern. It is well known that PM2.5 is much more harmful to human health than coarse particles. To remove PM2.5, most air purifiers on the market have been equipped with a high efficiency particulate air (HEPA) filter. Under the circumstances that generate PM2.5 aerosols at high concentrations such as during indoor cooking or in work places, HEPA-grade filters are neither durable nor applicable because of their high replacement cost. Thus, a large number of cooks and workers are exposed to intensive emissions of PM2.5 without proper filtration. In this paper, we introduce a novel concept to remove PM2.5 without HEPA filters. A key idea is to use the condensational growth of particles. Once the particles have grown to a few micron, they are much easier to remove because of their increased inertia. Based on this, we developed the first prototype of a filter-free particle filtration unit consisting of an air saturator (equipped with water spray nozzles), a condenser in which humid air is cooled down to a supersaturation state and thereby allows particles to grow by condensation, and a multi-nozzle-impactor assembly for collecting the grown particles downstream of the condenser. We started with a small-scale model, and then demonstrated that a large-scale prototype could remove organic, inorganic, and metallic ultrafine particles with a collection efficiency of larger than 80% at a volume flow rate of 50–92 L/s.

We have studied the variational matrix product state (MPS) algorithm to find the ground state of many-body systems. For that, we discuss how to represent the many-body quantum states with use of the MPS formalism and investigate its structure related to the ground state energy of the system. Also, we show a few specific examples to disassemble the operator into the class of the matrices in matrix product operator representation. After programming an algorithm for the matrix simulation, we test the accuracy of its algorithm for s = 1/2 one-dimensional Heisenberg model as calculating the energy difference between variational MPS method and DMRG method. Using the method, we simulate the ground energy for one-dimensional XX model and the Majumdar-Ghosh model.

Discrimination and quantification of trace amounts of steroid hormones in biological specimens are needed to elucidate their changing expression because their biological functions are responsible for the development and prevention of endocrine disorders. Although mass-spectrometry-based assays are most commonly recommended, development of a new type of highly sensitive and selective detection methods in clinical practices is needed. Here, we introduce a label-free type of terahertz molecule sensor capable of sensing and identifying progesterone and 17α-OH-progesterone selectively. Nanoslot-array-based sensing chips were used as launching pads for absorption cross-section enhancement of molecules at a reliable terahertz frequency. With use of nanoslots with resonances at 1.17 THz corresponding to intrinsic THz absorption resonance mode for progesterone and at 1.51 THz for 17α-OH-progesterone, respectively, each steroid shows prominent transmittance change in terms of its amount. In particular, the sensing performance has been much improved by controlling evaporation speed, in turn resulting in an efficient, homogeneous distribution of the molecules onto a sensing hot spot.

Concrete structures that employed general and multicomponent blended binders were constructed using a low-carbon, ecofriendly microwave heat curing technology with the aim of accelerating t...

Niche differentiation enables ecologically similar species to coexist by lessening competition over food and/or shelters and may be critical for reproductive isolation between closely related species in close proximity. Because no extra traits need to evolve, spatial and temporal differentiation may readily take place to complement other isolating mechanisms. Two closely related treefrog species occur together in Korea: the endangered Hyla suweonensis and the widespread Hyla japonica. Advertisement calls are differentiated, but it is unclear whether call difference is sufficient for reproductive isolation. We tracked individuals of both species to study fine-scale differentiation in microhabitat use in the diel cycle of the breeding season using a harmonic direction finder. tracking male movement patterns of both species revealed spatial and temporal differentiation in microhabitat use for calling and resting during the breeding season. Males of both H. suweonensis and H. japonica occurred in all 5 microhabitats identified in this study: rice paddy, ground, buried, grass and bush. Both treefrog species showed general similarities in calling from rice paddies and resting in grass and bush. However, H. suweonensis moved into rice paddies and produced advertisement calls 3 h earlier than H. japonica. These differences likely minimize contact between the species and provide an additional isolating mechanism. In addition, the activity of H. suweonensis may be contributing to the decline of this species, as resting in grass would increase dangers from predatory birds and habitat disturbance.

The CO2 gasification kinetics of various carbonaceous samples of high-and low-rank coal and a biomass were determined under CO2 flow with increasing temperature in a Thermogravimetric analysis (TGA) coupled with Fourier transform infrared spectroscopy (FTIR). We utilized four different types of fuels and their chars with significant differences in their physico-chemical properties that are being most widely used in Korea. As a result, fuels with larger surface area and more catalytic components in ash were preferred for increasing the intrinsic reactivity of CO2 gasification particularly for low-rank coals and biomass, respectively. It was postulated that the catalytic effect of ash components could compensate for the lack of active sites in the biomass samples. From the practical point of view, the utilization of the low-rank coal with the porous char structure with blending the biomass is recommended for a remarkable increase of the gasification rate.