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The Directed Energy Deposition (DED) method, which utilizes metal powder and a high-powder laser directed at specific locations on a substrate, is employed across various fields for repairing and refurbishing existing parts. In this study, two types of multi-materials were fabricated by depositing materials similar in alloy composition, and dissimilar, respectively, on an SCM440 substrate using the DED method. Unlike similar material, depositing the dissimilar material resulted in a recrystallized fine microstructure in the interlayer region, which enhanced the tensile properties of the multi-layer specimens. The yield strength, tensile strength, and elongation of the multi-layer specimen with similar material were 562 MPa, 787 MPa, and 14%, respectively, while those of the specimen with dissimilar material were significantly higher at 934 MPa, 1016 MPa, and 30%. These results demonstrate that using dissimilar materials, rather than identical or similar compositions, can improve the tensile properties when repairing and refurbishing parts made of SCM440 material.

This paper considers the distributed cooperative control problem for a linear stochastic multi-agent system (MAS). The optimal cooperative control design for each agent is challenging due to the limited neighboring information, contingent upon the MAS network topology. A synthesized distributed control-estimation framework is proposed to address the computationally tractable suboptimal solution. In particular, a distributed estimator extends MAS information beyond neighboring agents, allowing interactions with non-neighboring agents. The proposed control-estimation law is theoretically verified and further validated using numerical simulations.

This study presents the synthesis and characterization of hexadecyl-modified SiO2 (HD-SiO2) nanoparticles and their application in the fabrication of a multilayered elastomer hybrid sheet to enhance water resistance in implantable biomedical devices. The surface modification of SiO2 nanoparticles was confirmed via FT-IR and TGA analyses, showing the successful grafting of hydrophobic hexadecyl groups. FE-SEM and DLS analyses revealed spherical HD-SiO2 nanoparticles with an average size of 360 nm. A multilayered elastomer hybrid sheet, consisting of a PDMS-based circuit-protecting body, a water resistance layer of HD-SiO2, a planarization layer, and a biocompatible layer of polydopamine, was fabricated and characterized. The water resistance layer exhibited superhydrophobic properties, with a water contact angle of 154.7° and a 27% reduction in water vapor transmission rate (WVTR) compared to the circuit-protecting body alone. The device packaged with both the circuit-protecting body and water resistance layer demonstrated a tenfold increase in operational lifespan in water medium compared to the device without the water resistance layer. Cytotoxicity and cell proliferation tests on human dermal fibroblast cells (HDFn) confirmed the biocompatibility of the multilayered sheet, with no significant cytotoxicity observed over 48 h.

The function of peripheral nociceptors, the neurons that relay pain signals to the brain, are frequently tuned by local and systemic modulator substances. In this context, neurohormonal effects are emerging as an important modulatory mechanism, but many aspects remain to be elucidated. Here we report that gonadotropin-releasing hormone (GnRH), a brain-specific neurohormone, can aggravate pain by acting on nociceptors in mice. GnRH and GnRHR, the receptor for GnRH, are expressed in a nociceptor subpopulation. Administration of GnRH and its analogue, localized for selectively affecting the peripheral neurons, deteriorated mechanical pain, which was reproducible in neuropathic conditions. Nociceptor function was promoted by GnRH treatment in vitro, which appears to involve specific sensory transient receptor potential ion channels. These data suggest that peripheral GnRH can positively modulate nociceptor activities in its receptor-specific manner, contributing to pain exacerbation. Our study indicates that GnRH plays an important role in neurohormonal pain modulation via a peripheral mechanism.

Abstract We verify the global Poincaré invariance of the Hamiltonian mechanics of gravitating binary dynamics at the second post Minkowskian (2PM) order. For spinless point particles, based on the known 2PM Hamiltonian in the center of momentum frame, we compute the general 2PM Hamiltonian valid in an arbitrary reference frame. An off-shell extension of the 1PM Hamiltonian, which contributes at the 2PM order through an iteration process, plays a crucial role. We then construct the 2PM boost generator that uniquely satisfies all the conditions imposed by the Poincaré algebra.

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

Abstract We study the formalism of Kosower-Maybee-O’Connell (KMOC) to extract classical impulse from quantum amplitude in the context of the partial wave expansion of a 2-to-2 elastic scattering. We take two complementary approaches to establish the connection. The first one takes advantage of Clebsch-Gordan relations for the base amplitudes of the partial wave expansion. The second one is a novel adaptation of the traditional saddle point approximation in the semi-classical limit. In the former, an interference between the S-matrix and its conjugate leads to a large degree of cancellation such that the saddle point approximation to handle a rapidly oscillating integral is no longer needed. As an example with a non-orbital angular momentum, we apply our methods to the charge-monopole scattering problem in the probe limit and reproduce both of the two angles characterizing the classical scattering. A spinor basis for the partial wave expansion, a non-relativistic avatar of the spinor-helicity variables, plays a crucial role throughout our computations.

Abstract Soft robotics systems are currently under development using ionic electroactive polymers (i‐EAP) as soft actuators for the human‐machine interface. However, this endeavor has been impeded by the dilemma of reconciling the competing demands of force and strain in i‐EAP actuators. Here, the authors present a novel design called “ions‐silica percolated ionic dielectric elastomer (i‐SPIDER)”, which exhibits ionic liquid‐confined silica microstructures that effectively resolve the chronic issue of conventional i‐EAP actuators. The i‐SPIDER actuator demonstrates remarkable electromechanical conversion capacity at low voltage, thanks to improved ion accumulation facilitated by interpreting electrode polarization at the electrolyte‐electrode interface. This approach concurrently enhances both strain (by approximately 1.52%) and force (by roughly 1.06 mN) even at low Young's modulus (merely 5.9 MPa). Additionally, by demonstrating arachnid‐inspired soft robots endowed with user‐desired tasks through control of various form factors, the development of soft robots using the i‐SPIDER that can concomitantly enhance strain and force holds promise as a compelling avenue for ushering in the next generation of miniaturized, low‐powered soft robotics.

Abstract Dissolved organic carbon (DOC) in throughfall plays a vital role in providing carbon and energy to organisms in forests. We utilized carbon isotope analysis to allocate the sources of throughfall DOC, including leaching from plant tissues, PM2.5 deposition on plant foliage, and precipitation. Rainwater, PM2.5, and throughfall samples were collected from pine and oak forests between March and November 2021. The mean concentration of throughfall DOC was 7.9 mg L−1, approximately six times higher than that of rainfall. The mean Δ14C of throughfall DOC was −38.2‰, ∼200‰ higher than that of rainwater or PM2.5. Mass balance estimates revealed that canopy leaching contributed to ∼83% of throughfall DOC, while desorption of PM2.5 and rainwater accounted for only ∼3% and ∼14% of throughfall DOC, respectively. These results clearly highlight canopy leaching as the primary source of carbon input to the forest floor, with a relatively minor contribution from PM2.5 desorption on leaves.

Ultrahigh-resolution patterning with high-throughput and high-fidelity is highly in demand for expanding the potential of OLEDs. Here, the authors report that silicone-incorporated organic light-emitting semiconductors can achieve anisotropic lithography via reactive ion etching-coupled photolithography, for ultrahigh-density RGB OLED arrays.

Abstract A broadband metasurface flat lens is proposed as a polarization-independent wideband superstrate for wave focusing and gain enhancement at Ka-band. The proposed metasurface structure consists of four metal layers and is designed with diagonally symmetric unit cells to accommodate both the vertical and horizontal polarizations. The focusing ability of the proposed metasurface flat lens is validated via simulation and measurement, where normally incident plane waves are shown to be enhanced by up to 11 dB as a result of wave focusing. Also, the radiation gain enhancement due to the proposed metasurface flat lens is demonstrated via simulation and measurement, where a gain enhancement of up to 10.5 dB is achieved. The results show that the proposed structure maintains the wave focusing and gain enhancement characteristics over a bandwidth of 28–32 GHz. Furthermore, to demonstrate the utility of the proposed metasurface for circular polarization (CP), the gain enhancement of a CP patch antenna as a result of implementing the proposed metasurface as a superstrate is demonstrated via simulation and measurement. It is shown that the proposed metasurface superstrate provides a CP gain enhancement of nearly 10 dB.

This study presents a novel microgrid optimal scheduling strategy, which considers the degradation of distributed generators (DGs). DGs affected by degradation exhibit reduced generation efficiency and capacity. Operating the microgrid without consideration of these impacts increases operating and management costs and reduces reliability and stability. To overcome this problem, we developed a new optimal scheduling strategy that considers the degradation of DGs. This study focuses on a permanent magnet synchronous generator, which is widely used as DGs recently, and on the degradation of stator insulation, which is one of the most frequently occurring forms of degradation. The reduction in performance caused by insulation degradation is analyzed using finite element analysis and is integrated into the optimal scheduling strategy. We demonstrate that the proposed strategy operates a microgrid more economically through a reduction of total operating costs, achieved by adaptively accounting for the increase in operating costs and the reduction of capacity arising from degradation. In addition, the sudden shutdown of a DG can be prevented by predicting its remaining useful life and incorporating it into the optimal scheduling strategy. The effectiveness and feasibility of the proposed strategy are confirmed using various case studies.

A retrodirective beamforming system (BFS) based on a Rotman lens is proposed for far-field wireless power transfer at Ka-band. The true-time-delay property of the Rotman lens allows for a wideband operation covering 28–38 GHz. The designed BFS comprises a Rotman lens with nine beam ports connected to a nine-element linear Vivaldi array. The proposed BFS is implemented using PCB technology for ease of manufacturing and low-cost processing. The simulated and measured results demonstrate that the proposed BFS can generate nine discrete beams over a scan range of ±45° with a wide impedance bandwidth.

We use iterative numerical procedures combined with analytical methods due to Rapach and Wohar (2009) to solve for the dynamic asset allocation strategy for optimal portfolio demand. We compare different optimal portfolio demands when investors in each country have different access to overseas and domestic investment opportunities. The optimal dynamic asset allocation strategy without foreign investment opportunities leads domestic investors in Korea, Hong Kong, and Singapore to allocate more funds to domestic bonds than to domestic stocks. However, the U.S. investors allocate more wealth to domestic stocks than to domestic bonds. Investors in all countries short bills at a low level of risk aversion. Next, we investigate dynamic asset allocation strategy when domestic investors in Korea have access to foreign markets. The optimal portfolio demand leads investors in Korea to allocate most resources to domestic bonds and foreign stocks. On the other hand, the portfolio weights on foreign bonds and domestic stocks are relatively low. We also analyze dynamic asset allocation strategy for the investors in the U.S., Hong Kong, and Singapore when they have access to the Korean markets as overseas investment opportunities. Compared to the results when the investors only have access to domestic markets, the investors in the U.S. and Singapore increase the portfolio weights on domestic stocks in spite of the overseas investment opportunities in the Korean markets. The investors in the U.S., Hong Kong, and Singapore short domestic bills to invest more than initial funds in risky assets with a varying degree of relative risk aversion coefficients without exception.

Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a “soft fault state”, which is a transient state that eventually becomes a permanent fault —open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estimating fault severity to diagnose early-stage faults. The proposed diagnosis method comprised a novelty detection and severity estimation network. The novelty detection part is specially designed to deal with varying operating conditions of industrial applications. First, an autoencoder calculates anomaly scores to detect faults using three-phase currents. If a fault is detected, a fault severity estimation network, wherein long short-term memory and attention mechanisms are integrated, estimates the fault severity based on the time-dependent information of the input. Accordingly, no additional equipment, such as voltage sensors and signal generators, is required. The conducted experiments demonstrated that the proposed method successfully distinguishes seven different soft fault degrees.

Abstract Metasurfaces allow the rapid development of compact and flat electromagnetic devices owing to their capability in manipulating the wavefront of electromagnetic waves. Particularly, with respect to the metasurface lenses, wide operational bandwidth and wide incident angle behavior are critically required for practical applications. Herein, a single-layer phase gradient metasurface lens is presented to achieve millimeter-wave focusing at a focal point of 13 mm regardless of the incident angle. The proposed metasurface lens is fabricated by constructing subwavelength-thick (

Objective To investigate the epidemiologic and demographic characteristics of patients with spinal cord injury (SCI) who were admitted to a department of rehabilitation of a university hospital. Methods This was a descriptive cross-sectional study. Medical records including sex, age at injury, type of disability, traumatic or non-traumatic etiology and presence of ossification of posterior longitudinal ligament (OPLL) of patients with SCI who were admitted to the department of rehabilitation between 2012 and 2018 were reviewed. Results Of the 221 cases of SCI, 161 were traumatic and 60 were non-traumatic. The mean age at injury was 52.8 years. People aged 40–49 years showed highest proportion among overall SCI patients (19.0%). The proportion of male patients was higher in traumatic SCI at 4.96:1 than in non-traumatic SCI at 1.30:1. The most common cause of traumatic SCI was falling off (37.3%), followed by motor vehicle crash (35.4%) and tripping over (19.3%). Meanwhile, the most common cause of non-traumatic SCI was neoplasm (35.0%). Tripping over was the leading cause of traumatic SCI in patients aged ≥60 years (42.6%). A high proportion of traumatic SCI patients were found to have underlying OPLL (26.1%), particularly those who were injured by tripping over (64.5%). Conclusion The mean age of SCI patients was higher than that of previous studies. Falls was the single most common cause of traumatic SCI, and tripping over was the most common cause of injury in the elderly patients. OPLL was prevalent in patients who were injured from tripping over.

This paper presents algebraic connectivity invariance of a network-of-networks derived from a graph product. The network is modeled as an interconnected system of several kinds of similar subsystems via a certain structure, where several vertices of each subsystem are attached to the structure. The algebraic connectivity of this network can be characterized by using an appropriate network having homogeneous subsystems under a certain condition, which is investigated in this paper. In particular, a necessary and sufficient condition for adding links (edges) to subsystems which preserve the algebraic connectivity is clarified.

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

With the increased demand for permanent magnet synchronous machines (PMSMs) in various industrial fields, interturn short fault (ITSF) diagnosis of PMSMs is under the limelight. In particular, to prevent accidents caused by PMSM malfunctions, it is difficult and greatly necessary to diagnose slight ITSF, which is a stage before the ITSF becomes severe. In this paper, we propose a novel fault indicator based on the magnitude and phase of the current. The proposed fault indicator was developed using analysis of positive-sequence current (PSC) and negative-sequence current (NSC), which means the degree of the asymmetry of the three-phase currents by ITSF. According to the analysis, as ITSF increases, the phase difference between PSC and NSC decreases and the magnitude of NSC increases. Therefore, the novel fault indicator is suggested as a product of the cosine value of the phase indicator and the magnitude indicator. The magnitude indicator is the magnitude of NSC, and the phase indicator means the phase difference between the PSC and the NSC. The suggested fault indicator diagnoses the degree of ITSF as well as slight ITSFs under various conditions by only measured three-phase currents. Experimental results demonstrate the effectiveness of our proposed method under various torque and speeds.

This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.

With the growth of factory automation, deep learning-based methods have become popular diagnostic tools because they can extract features automatically and diagnose faults under various fault conditions. Among these methods, a novelty detection approach is useful if the fault dataset is imbalanced and impossible reproduce perfectly in a laboratory. This study proposes a novelty detection-based soft fault-diagnosis method for control cables using only currents flowing through the cables. The proposed algorithm uses three-phase currents to calculate the sum and ratios of currents, which are used as inputs to the diagnosis network to detect novelties caused by soft faults. Autoencoder architecture is adopted to detect novelties and calculate anomaly scores for the inputs. Applying a moving average filter to anomaly scores, a threshold is defined, by which soft faults can be properly diagnosed under environmental disturbances. The proposed method is evaluated in 11 fault scenarios. The datasets for each scenario are collected when an industrial robot is working. To induce soft fault conditions, the conductor and its insulator in the cable are damaged gradually according to the scenarios. Experiments demonstrate that the proposed method accurately diagnoses soft faults under various operating conditions and degrees of fault severity.

The Unfolded Protein Response (UPR) plays a key role in the adaptive response to loss of protein homeostasis within the endoplasmic reticulum (ER). The UPR has an adaptive function in protein homeostasis, however, sustained activation of the UPR due to hypoxia, nutrient deprivation, and increased demand for protein synthesis, alters the UPR program such that additional perturbation of ER homeostasis activates a pro-apoptotic program. Since ubiquitination followed by proteasomal degradation of misfolded proteins within the ER is a central mechanism for restoration of ER homeostasis, inhibitors of this pathway have proven to be valuable anti-cancer therapeutics. Ubiquitin activating enzyme 1(UAE1), activates ubiquitin for transfer to target proteins for proteasomal degradation in conjunction with E2 and E3 enzymes. Inhibition of UAE1 activity in response to TAK-243, leads to an accumulation of misfolded proteins within the ER, thereby aggravating ER stress, leading to DNA damage and arrest of cells in the G2/M phase of the cell cycle. Persistent drug treatment mediates a robust induction of apoptosis following a transient cell cycle arrest. These biological effects of TAK-243 were recapitulated in mouse models of PDAC demonstrating antitumor activity at a dose and schedule that did not exhibit obvious normal tissue toxicity. In vitro as well as studies in mouse models failed to show enhanced efficacy when TAK-243 was combined with ionizing radiation or gemcitabine, providing an impetus for future studies to identify agents that synergize with this class of agents for improved tumor control in PDAC. Significance: The UAE1 inhibitor TAK-243, mediates activation of the unfolded protein response, accumulation of DNA breaks and apoptosis, providing a rationale for the use as a safe and efficacious anti-cancer therapeutic for PDAC.

This paper proposes a robust tracking control method for swing-up and stabilization of a rotational inverted pendulum system by applying equivalent input disturbance (EID) rejection. The mathematical model of the system was developed by using a Lagrangian equation. Then, the EID, including external disturbances and parameter uncertainties, was defined; and the EID observer was designed to estimate EID using the state observer dynamics and a low-pass filter. For robustness, the linear-quadratic regulator method is used with EID rejection. The closed-loop stability is proven herein using the Lyapunov theory and input-to-state stability. The performance of the proposed method is validated and verified via experimental results.

We introduce a new approach for online and offline soft fault diagnosis in motor power cables, utilizing periodic burst injection and nonintrusive capacitive coupling. We focus on diagnosing soft faults because local cable modifications or soft faults that occur without any indication while the cable is still operational can eventually develop into hard faults; furthermore, advance diagnosis of soft faults is more beneficial than the later diagnosis of hard faults, with respect to preventing catastrophic production stoppages. Both online and offline diagnoses with on-site diagnostic ability are needed because the equipment in the automated lines operates for 24 h per day, except during scheduled maintenance. A 1D CNN model was utilized to learn high-level features. The advantages of the proposed method are that (1) it is suitable for wiring harness cables in automated factories, where the installed cables are extremely short; (2) it can be simply and identically applied for both online and offline diagnoses and to a variety of cable types; and (3) the diagnosis model can be directly established from the raw signal, without manual feature extraction and prior domain knowledge. Experiments conducted with various fault scenarios demonstrate that this method can be applied to practical cable faults.

In this paper, we propose a novel voltage programmed pixel circuit based on amorphous-indium-gallium-zinc-oxide thin-film transistors (a-IGZO TFTs) for active-matrix organic light-emitting displays. Through the extensive simulation work based on a-IGZO TFT and OLED models, we confirm that the proposed pixel circuit can compensate for threshold voltage variations of TFTs and OLED degradation over wide dynamic range (~104) of OLED current as well as achieve a high pixel aperture ratio with the suppressed OLED current error rate below 9%.

To use implantable biomedical devices such as electrocardiograms and neurostimulators in the human body, it is necessary to package them with biocompatible materials that protect the internal electronic circuits from the body’s internal electrolytes and moisture without causing foreign body reactions. Herein, we describe a hydrogel surface-modified polyurethane copolymer film with concurrent water permeation resistance and biocompatibility properties for application to an implantable biomedical device. To achieve this, hydrophobic polyurethane copolymers comprising hydrogenated poly(ethylene-co-butylene) (HPEB) and aliphatic poly(carbonate) (PC) were synthesized and their hydrophobicity degree and mechanical properties were adjusted by controlling the copolymer composition ratio. When 10 wt% PC was introduced, the polyurethane copolymer exhibited hydrophobicity and water permeation resistance similar to those of HPEB; however, with improved mechanical properties. Subsequently, a hydrophilic poly(vinyl pyrrolidone) (PVP) hydrogel layer was formed on the surface of the polyurethane copolymer film by Fenton reaction using an initiator and crosslinking agent and the effect of the initiator and crosslinking agent immobilization time, PVP concentration and crosslinking agent concentration on the hydrogel properties were investigated. Finally, MTT assay showed that the hydrogel surface-modified polyurethane copolymer film displays excellent biocompatibility.

This study was conducted to identify the characteristics and mobility of debris flows and analyze the performance of a berm as a debris flow mitigation measure. The debris flow velocity, flow depth, Froude number, flow resistance coefficients, and mobility ratio were accordingly determined using the results of flume tests. To analyze the influence of the berm, the results for a straight channel test without a berm were compared with those for a single-berm channel test. The debris flow velocity was observed to increase with increasing channel slope and decreasing volumetric concentration of sediment, whereas the mobility ratio was observed to increase with increasing channel slope and volumetric concentration of sediment. In addition, it was confirmed that the installation of a berm significantly decreased the debris flow velocity and mobility ratio. This indicates that a berm is an effective method for reducing damage to areas downstream of a debris flow by decreasing its potential mobility. By identifying the effects of berms on debris flow characteristics according to the channel slope and volumetric concentration of sediment, this study supports the development of berms to serve as debris flow damage mitigation measures.

We propose a novel array antenna with high aperture efficiency by employing an array element operating in a high-order mode to improve the transmission efficiency of wireless power transmission applications. The proposed array element, operating at a frequency of 5.8 GHz, consists of a square patch with four metallic shorting pins, an extended cavity, and a high dielectric superstrate. The four metallic shorting pins derive the high-order operation, which can enhance the bore-sight gain, resulting in the high aperture efficiency of the array. The extended cavity structure can help further improvement the aperture efficiency and minimize the mutual coupling strength between adjacent elements. The proposed antenna element is extended to 2 × 2 and 4 × 4 arrays, resulting in aperture efficiencies of 74.2% and 80.5%, respectively. For the 2 × 2 array, maximum measured gains of 19.9 dBi in the zx-plane and 18.8 dBi in the zy-plane are observed. For the 4 × 4 array, the maximum measured gains of 24.3 dBi in the zx-plane and 24.5 dBi in the zy-plane are obtained in a full-anechoic chamber. Finally, the transmission efficiency of 6% with a distance of 0.8 m between the transmitter (Tx) and the receiver (Rx) is measured when setting up the wireless power transfer system, which consists of the Tx with the proposed 4 × 4 array and the Rx with a single element.

The purpose of this study is to evaluate the behaviour and mechanism of debris flow on various slopes through numerical simulation. The numerical simulation was performed using the Finite Difference Element Method based on the equation of mass conservation and momentum conservation. In order to measure the behaviour of the debris flow, the debris flow of the straight rectangular channel slope and that of the one-stepped channel slope were compared. Firstly the water flow discharge, the water flow depth, and the sediment volume concentration at the slope downstream of the channel, depending on the different inflow water flow discharges at the upstream of the channel, were analyzed. The smaller the supply from the upstream of the channel, the water flow discharge and the water flow depth surged only at the point after the debris flow reached the downstream of the channel, and showed a tendency to decrease thereon after. On the other hand, when the supply at the upstream of the channel increased, the curve of the water flow discharge and the water flow depth was unsteadily high. Through the Root Mean Square ratio (RMS) comparison, the water flow discharge and water flow depth of the one-stepped channel slope was lower than that of the straight rectangular channel slope. Secondly, the water flow discharge, water flow depth, and sediment volume concentration depending on the change in the slope of the one-stepped channel slope were analyzed. The larger the slope, the larger the fluctuation in amplitude of the curve and this resulted in a higher water flow discharge distribution as well as in a wider fluctuation bandwidth. In the results of the study at each point, in the case of the straight rectangular channel, the water flow discharge and depth increased as it went downstream. This is because more erosion than deposition occurs when debris flow occurs at the upstream of the channel.

Metamaterial Sensors show significant potential for applications ranging from hazardous chemical detection to biochemical analysis with high-quality sensing properties. However, they require additional measurement systems to analyze the resonance spectrum in real time, making it difficult to use them as a compact and portable sensor system. Herein, we present a novel wireless-powered chemical sensing system by using energy-harvesting metamaterials at microwave frequencies. In contrast to previous studies, the proposed metamaterial sensor utilizes its harvested energy as an intuitive sensing indicator without complicated measurement systems. As the spectral energy-harvesting rate of the proposed metamaterial sensor can be varied by changing the chemical components and their mixtures, we can directly distinguish the chemical species by analyzing the resulting output power levels. Moreover, by using a 2.4 GHz Wi-Fi source, we experimentally realize a prototype chemical sensor system that wirelessly harvests the energy varying from 0 mW up to 7 mW depending on the chemical concentration of the water-based binary mixtures.

Despite development of medicine, cardiovascular diseases (CVDs) are still the leading cause of mortality and morbidity worldwide. Over the past 10 years, various stem cells have been utilized in therapeutic strategies for the treatment of CVDs. CVDs are characterized by a broad range of pathological reactions including inflammation, necrosis, hyperplasia, and hypertrophy. However, the causes of CVDs are still unclear. While there is a limit to the currently available target-dependent treatments, the therapeutic potential of stem cells is very attractive for the treatment of CVDs because of their paracrine effects, anti-inflammatory activity, and immunomodulatory capacity. Various studies have recently reported increased therapeutic potential of transplantation of microRNA- (miRNA-) overexpressing stem cells or small-molecule-treated cells. In addition to treatment with drugs or overexpressed miRNA in stem cells, stem cell-derived extracellular vesicles also have therapeutic potential because they can deliver the stem cell-specific RNA and protein into the host cell, thereby improving cell viability. Here, we reported the state of stem cell-based therapy for the treatment of CVDs and the potential for cell-free based therapy.

We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH(1,1) model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH(1,1) volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH(1,1) model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.

We compare the out-of-sample forecasting performance of volatility models using daily exchange rate for the KRW/USD during the period from 1992 to 2008. For various forecasting horizons, historical volatility models with a long memory tend to make more accurate forecasts. Especially, we carefully observe the difference between the EWMA and the GARCH(1,1) model. Our empirical finding that the GARCH model puts too much weight on recent observations relative to those in the past is consistent with prior evidence showing that asset market volatility has a long memory, such as Ding and Granger (1996). The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the EWMA model in which the forecast volatility for the coming period is a weighted average of recent squared return with exponentially declining weights. In terms of forecast accuracy, it clearly dominates the widely accepted GARCH and rolling window GARCH models. We also present a multiple comparison of the out-of-sample forecasting performance of volatility using the stationary bootstrap of Politis and Romano (1994). We find that the White's reality check for the GARCH(1,1) expanding window model and the FIGARCH(1,1) expanding window model clearly reject the null hypothesis and there exists a better model than the two benchmark models. On the other hand, when the EWMA model is the benchmark, the White's for all forecasting horizons are very high, which indicates the null hypothesis may not be rejected. The Hansen's report the same results. The GARCH(1,1) expanding window model and the FIGARCH(1,1) expanding window model are dominated by the best competing model in most of the forecasting horizons. In contrast, the RiskMetrics model seems to be the most preferred. We also consider combining the forecasts generated by averaging the six raw forecasts and a trimmed set of forecasts which calculate the mean of the four forecasts after disregarding the highest and lowest forecasts from the six models. This experiment confirms that the forecast combinations always outperform forecasts from a single model.