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Radiation possesses inherent physical characteristics, such as penetrability and radionuclide energy, which enable its widespread applicability in fields such as medicine, industry, environment, security, and research. Advancements in scintillator-based radiation detection technology have led to revolutionary changes by ensuring the safe use and precise measurement of radiation. Nevertheless, certain fields require higher scintillation yields to obtain more refined and detailed results. Therefore, in this study, we explored inorganic scintillators coated with perovskite nanomaterials to detect gamma rays with high light yields. By mixing perovskite with a polymer, we improved the intrinsic characteristics of quantum dots, which otherwise failed to maintain their performance over time. On this basis, we investigated the interactions among inorganic scintillators and a mixed material (CsPbBr3 + PMMA) and confirmed an increase in the scintillation yield and measurement trends. Furthermore, optimized scintillation yield measurement experiments facilitated gamma spectroscopy, demonstrating the validity of our approach through the analysis of the peak channel increases in the energy spectra of various gamma sources in relation to the increased scintillation yield.

We fabricated a 15 m long position-sensitive plastic scintillation optical fiber (PSOF) detector consisting of a PSOF, two photomultiplier tubes, four fast amplifiers, and a digitizer. A single PSOF was used as a sensing part to estimate the gamma-ray source position, and 137Cs, an uncollimated solid-disk-type radioactive isotope, was used as a gamma-ray emitter. To improve the sensitivity, accuracy, and measurement time of a PSOF detector compared to those of previous studies, the performance of the amplifier was optimized, and the digital signal processing (DSP) was newly designed in this study. Moreover, we could measure very low dose rates of gamma-rays with high sensitivity and accuracy in a very short time using our proposed PSOF detector. The results of this study indicate that it is possible to accurately and quickly locate the position of a very low dose rate gamma-ray source in a wide range of contaminated areas using the proposed position-sensitive PSOF detector.

The electrical-impedance method has been widely used for void-fraction measurement in two-phase flow due to its many favorable features. In the impedance method, the response characteristics of the electrical signal heavily depend upon flow pattern, as well as phasic volume. Thus, information on the flow pattern should be given for reliable void-fraction measurement. This study proposes an improved electrical-conductance sensor composed of a three-electrode set of adjacent and opposite electrodes. In the proposed sensor, conductance readings are directly converted into the flow pattern through a specified criterion and are consecutively used to estimate the corresponding void fraction. Since the flow pattern and the void fraction are evaluated by reading conductance measurements, complexity of data processing can be significantly reduced and real-time information provided. Before actual applications, several numerical calculations are performed to optimize electrode and insulator sizes, and optimal design is verified by static experiments. Finally, the proposed sensor is applied for air-water two-phase flow in a horizontal loop with a 40-mm inner diameter and a 5-m length, and its measurement results are compared with those of a wire-mesh sensor.

A simple Couette flow velocity profile with an appropriate correlation for the free terminal rise velocity of a single bubble in a quiescent liquid can produce reliable results for the trajectories of small spherical air bubbles in a low-viscosity liquid (water) provided the liquid remains under uniform shear flow. Comparison of the model adopted in this paper with published results has been accomplished. Based on this study it has also been found that the lift coefficient in water is higher than its typical value in a high-viscosity liquid and therefore a modified correlation for the lift coefficient in a uniform shear flow of water within the regime of the Eötvös number 0.305≤Eo≤1.22 is also presented.

An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

This paper presents an experimental investigation on the enhancement of the heat transfer rate of steam condensation on the external surfaces of a vertical tube with annular fins. A cylindrical condenser tube, which is 40 mm in outer diameter and 1000 mm in length, with annular disks of uniform cross-sectional area is fabricated in the manner of ensuring perfect contact between the base surface and fins. A total of 13 annular fins of 80 mm diameter were installed along the tube height in order to increase the effective heat transfer area by 85%. Through a series of condensation tests for the air-steam mixture under natural convection conditions, the heat transfer data was measured in the pressure range of between 2 and 5 bar, and the air mass fraction from 0.3 to 0.7. The rates of heat transfer of the finned tube are compared to those that are measured on a bare tube to demonstrate the enhanced performance by extended surfaces. In addition, based on the experimental results and the characteristics of steam condensation, the applicability of finned tubes to a large condenser system with a bundle layout is evaluated.

In this study, a new and improved electrical conductance sensor is proposed for application not only to a horizontal pipe, but also an inclined one. The conductance sensor was designed to have a dual layer, each consisting of a three-electrode set to obtain two instantaneous conductance signals in turns, so that the area-averaged void fraction and structure velocity could be measured simultaneously. The optimum configuration of the electrodes was determined through numerical analysis, and the calibration curves for stratified and annular flow were obtained through a series of static experiments. The fabricated conductance sensor was applied to a 45 mm inner diameter U-shaped downward inclined pipe with an inclination angle of 3° under adiabatic air-water flow conditions. In the tests, the superficial velocities ranged from 0.1 to 3.0 m/s for water and from 0.1 to 18 m/s for air. The obtained mean void fraction and the structure velocity from the conductance sensor were validated against the measurement by the wire-mesh sensor and the cross-correlation technique for the visualized images, respectively. The results of the flow regime classification and the corresponding time series of the void fraction at a variety of flow velocities were also discussed.

This paper introduces an autonomous self-propelled Napa cabbage harvester, designed to significantly improve the efficiency and effectiveness of the traditionally labor-intensive harvesting process. The harvester integrates three key modules: a cutting, an attitude control, and a loading module. The cutting module is equipped with an attitude control module that ensures precise severance of the Napa cabbage stems, minimizing damage to the crop and maintaining product quality. The attitude control module employs a backstepping-based force control that continuously adjusts the cutting angle and height to ensure consistent cutting precision, even on uneven terrain, thereby optimizing the quality of the Napa cabbages. The loading module automates the collection and transfer of harvested Napa cabbages into storage, significantly reducing the physical burden on workers and improving operational efficiency. Field experiments demonstrated improvements, including a 42–66% reduction in task time compared to manual harvesting, as well as a 37% increase in cutting accuracy through the use of autonomous control. The proposed system presents a comprehensive solution for enhancing productivity, reducing labor demands, and maintaining high crop quality in Napa cabbage harvesting, offering a practical approach to modernizing agricultural practices.

Abstract The coronavirus pandemic accentuated the need for molecular diagnostic tests. A technique highly used to this end is the Polymerase Chain Reaction (PCR)—a sensitive and specific technique commonly used as the gold standard for molecular diagnostics. However, it demands highly trained personnel and high-maintenance equipment and is relatively time-consuming. An alternative is the Loop-Mediated Isothermal Amplification (LAMP) technique, which doesn’t need sample purification or expensive equipment, and is similar to PCR when compared in sensitivity and specificity. In this paper, we developed an optimized colorimetric Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) Point-of-Care test using a portable device to diagnose COVID-19. Variables such as concentration of primers, magnesium sulfate, betaine, hydrochloride guanidine, Bst, and temperature of the reactions were tested. We also created a pipetting quality control system—using a combination of dyes—to avoid false negatives due to a lack of samples added to the reaction test tube. Mineral oil was incorporated in the composition of the RT-LAMP reactions to avoid evaporation when a heating lid isn't available. The final RT-LAMP test is tenfold more sensitive when compared to the WarmStart Colorimetric Master mix from New England Biolabs with a sensitivity of 5 copies per μL.

Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients. However, one of the main challenges for personalized FL is the heavy reliance on clients’ computing resources to calculate higher-order gradients since client data is segregated from the server to ensure privacy. To resolve this, we focus on a problem setting where the server may possess data independent of clients’ data – a prevalent problem setting in various applications, yet relatively unexplored in the existing literature. Specifically, we propose FedSIM, a new method for personalized FL that actively utilizes such server data to improve meta-gradient calculation in the server for increased personalization performance. Experimentally, we demonstrate through various benchmarks and ablations that FedSIM is superior to existing methods in terms of accuracy, more computationally efficient by calculating the full meta-gradients in the server, and converges up to 34.2% faster.

Adaptive front-lighting systems (AFSs) have been widely adopted to automotive industries for providing higher driver’s safety. As their light sources, multi-string light-emitting diodes (LED) arrays have been widely adopted because of their simpler driver controls. Recently, micro-structured AFSs ( $\mu $ AFSs) with a micro-LED ( $\mu $ LED) array are highly demanded for their controllability of individual LEDs. However, the integration of a $\mu $ LED array and its high-power active-matrix driver are not available on the market. Moreover, a high-power driver causes not only a significant variation in driving current, but also a higher power density requiring over-temperature protection (OTP). In this paper, the average current through each $\mu $ LED is adaptively controlled with pulse width modulation (PWM) in conjunction with an additional PWM control for temperature calibration. Experimental results with a 16 $\times 16\,\,\mu $ LED array placed on top of the proposed driver show that a 5-bit PWM signal controls the average current through each $\mu $ LED cell up to 11 mA. The maximum current error of 4.11% at 100 °C is reduced to 0.23%. When OTP is enabled, the amount of average pixel current reduction depends on the given temperature. The maximum power efficiency of the proposed $\mu $ AFSs driver is as high as 92.3%.

Abstract The simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R2 = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R2 = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry.

Abstract Background The associations between long‐term changes in body mass composition and decline in lung function in healthy adults are unknown. Methods Using a well‐defined health check‐up database, we first assessed individual longitudinal changes in muscle mass (MM) and fat mass (FM) measured via bioelectrical impedance analyses. Then we classified the enrolled individuals into five body composition groups according to their MM index (MMI) [MM (kg)/height (m)2] or FM index (FMI) [FM (kg)/height (m)2] change rate quartiles. Linear mixed models adjusted for age, smoking status, height, and body mass index were used to analyse the rate of forced expiratory volume in 1 s (FEV1) decline and body composition groups. Results A total of 15 476 middle‐aged individuals (6088 women [mean age ± standard deviation: 50.74 ± 7.44] and 9388 men [mean age ± standard deviation: 49.36 ± 6.99]) were enrolled. The mean number of measurements was 6.96 (interquartile range [IQR]: 5–9) over an average follow‐up period of 8.95 years (IQR: 6.73–11.10). Decrease in MMI was significantly associated with accelerated FEV1 decline in men only (P = 1.7 × 10−9), while increase in FMI was significantly associated with accelerated FEV1 decline in both women and men (P = 7.9 × 10−10 and P

Objective The objective of this study was to estimate the genetic correlation (rpc) of growth performance between purebred (Duroc and Korean native) and synthetic (WooriHeukDon) pigs using a single-step method. Methods Phenotypes of 15,902 pigs with genotyped data from 1,792 pigs from a nucleus farm were used for this study. We estimated the rpc of several performance traits between WooriHeukDon and purebred pigs: day of target weight (DAY), backfat thickness (BF), feed conversion rate (FCR), and residual feed intake (RFI). The variances and covariances of the studied traits were estimated by an animal multi-trait model that applied the Bayesian inference. Results rpc within traits was lower than 0.1 for DAY and BF, but high for FCR and RFI; in particular, rpc for RFI between Duroc and WooriHeukDon pigs was nearly 1. Comparison between different traits revealed that RFI in Duroc pigs was associated with different traits in WooriHeukDon pigs. However, the most of rpc between different traits were estimated with low or with high standard deviation. Conclusion The results indicated that there were substantial differences in rpc of traits in the synthetic WooriHeukDon pigs, which could be caused by these pigs having a more complex origin than other crossbred pigs. RFI was strongly correlated between Duroc and WooriHeukDon pigs, and these breeds might have similar single nucleotide polymorphism effects that control RFI. RFI is more essential for metabolism than other growth traits and these metabolic characteristics in purebred pigs, such as nutrient utilization, could significantly affect those in synthetic pigs. The findings of this study can be used to elucidate the genetic architecture of crossbred pigs and help develop new breeds with target traits.

Abstract We performed a retrospective cohort study of 19,237 individuals who underwent at least three health screenings with follow-up periods of over 5 years to find a routinely checked serum marker that predicts lung function decline. Using linear regression models to analyze associations between the rate of decline in the forced expiratory volume in 1 s (FEV1) and the level of 10 serum markers (calcium, phosphorus, uric acid, total cholesterol, total protein, total bilirubin, alkaline phosphatase, aspartate aminotransferase, creatinine, and C-reactive protein) measured at two different times (at the first and third health screenings), we found that an increased uric acid level was significantly associated with an accelerated FEV1 decline (P = 0.0014 and P = 0.037, respectively) and reduced FEV1 predicted % (P = 0.0074 and P = 8.64 × 10–7, respectively) at both visits only in non-smoking individuals. In addition, we confirmed that accelerated forced vital capacity (FVC) and FEV1/FVC ratio declines were observed in non-smoking individuals with increased serum uric acid levels using linear mixed models. The serum uric acid level thus potentially predicts an acceleration in lung function decline in a non-smoking general population.

Piezoelectric energy harvesters (PEHs) are widely used to convert energy from a piezoelectric transducer into a stable DC form, which enables low-power IoT devices to have an unlimited operating life without using batteries. Under weak excitation conditions, however, the power-extraction efficiency of conventional PEHs is too low to provide power even to low-power IoT devices that requires low operation voltages less than 2 V. This paper proposes an asymmetric synchronous electric charge extraction (ASECE) scheme that improves the extraction efficiency of PEHs at low output voltages under weak excitation. The proposed ASECE is implemented using 0.18 μm CMOS technology. The figure-of-merits (FOMs) of the proposed ASECE while operating under 2 V of output voltage are 7.14 and 6.24 at weak and strong excitations, respectively. The maximum FOM for various different excitation levels is observed to be as high as 7.7. The proposed ASECE is superior to prior art with respect to FOM, by at least 1.15×, 1.63×, and 2.21× under 2 V, 1 V, and 0.5 V outputs, respectively, under strong excitation.

Time Slotted Channel Hopping (TSCH) is a link layer protocol defined in the IEEE 802.15.4 standard. Although it is designed to provide highly reliable and efficient service targeting industrial automation systems, scheduling TSCH transmissions in the time and frequency dimensions is left to the implementers. We evaluate the performance of existing autonomous scheduling approaches for TSCH on various traffic patterns and network configurations. We thoroughly investigate the pros and cons of each scheme; moreover, we propose the use of node based channel allocation to improve the performance of the best scheme, and demonstrate its practicality and reliability, with up to 6 percentage points better packet delivery ratio than the second best option while retaining a similar radio duty cycle. Finally, based on our extensive performance evaluation, we provide some guidelines on how to select a scheduler for a given network.

Direct AC LED drivers have advantages over conventional LED drivers in size and cost because of no need for bulky and expensive inductive components. However, none of conventional direct AC LED drivers researched so far support triode AC switch (TRIAC) compatibility at low percent flicker. This paper achieves both TRIAC dimmer compatibility and low percent flicker by correlating a phase-cut ratio to luminance and using always-on multi-arrays, respectively, without the need of a dedicated IC. The proposed LED driver consists of a phase-cut/DC converter, a switch controller, a current regulator, and three LED arrays. The experimental results based on commercial 15W LEDs show that the percent flicker of 18.6% @ 120 Hz is achieved with an efficiency of 84.7% and a power factor of 97.3% @ 120 V.

A Korean synthetic pig breed, Woori-Heukdon (WRH; F3), was developed by crossing parental breeds (Korean native pig [KNP] and Korean Duroc [DUC]) with their crossbred populations (F1 and F2). This study in genome-wide assessed a total of 2,074 pigs which include the crossbred and the parental populations using the Illumina PorcineSNP60 BeadChip. After quality control of the initial datasets, we performed population structure, genetic diversity, and runs of homozygosity (ROH) analyses. Population structure analyses showed that crossbred populations were genetically influenced by the parental breeds according to their generation stage in the crossbreeding scheme. Moreover, principal component analysis showed the dispersed cluster of WRH, which might reflect introducing a new breeding group into the previous one. Expected heterozygosity values, which were used to assess genetic diversity, were .365, .349, .336, .330, and .211 for WRH, F2, F1, DUC, and KNP, respectively. The inbreeding coefficient based on ROH was the highest in KNP (.409), followed by WRH (.186), DUC (.178), F2 (.107), and F1 (.035). Moreover, the frequency of short ROH decreased according to the crossing stage (from F1 to WRH). Alternatively, the frequency of medium and long ROH increased, which indicated recent inbreeding in F2 and WRH. Furthermore, gene annotation of the ROH islands in WRH that might be inherited from their parental breeds revealed several interesting candidate genes that may be associated with adaptation, meat quality, production, and reproduction traits in pigs.

Abstract To utilize thermally activated delayed fluorescence (TADF) technology for future displays, it is necessary to develop host materials which harness the full potential of blue TADF emitters. However, no publication has reported such hosts yet. Although the most popular host for blue TADF, bis[2‐(diphenylphosphino)phenyl]ether oxide (DPEPO) guarantees high‐maximum external quantum efficiency (EQEmax) TADF devices, they exhibit very short operational lifetimes. In contrast, long‐lifespan blue TADF devices employing stable hosts such as 3′,5‐di(9H‐carbazol‐9‐yl)‐[1,1′‐biphenyl]‐3‐carbonitrile (mCBP‐CN) exhibit much lower EQEmax than the DPEPO‐employed devices. Here, an elaborative approach for designing host molecules is suggested to achieve simultaneously stable and efficient blue TADF devices. The approach is based on engineering the molecular geometry, ground‐ and excited‐state dipole moments of host molecules. The engineered hosts significantly enhance delayed fluorescence quantum yields of TADF emitters, as stabilizing the charge‐transfer excited states of the TADF emitters and suppressing exciton quenching, and improve the charge balance. Moreover, they exhibit both photochemical and electrochemical stabilities. The best device employing one of the engineered hosts exhibits 79% increase in EQEmax compared to the mCBP‐CN‐employed device, together with 140% and 92‐fold increases in operational lifetime compared to the respective mCBP‐CN‐ and the DPEPO‐based devices.

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP. [ABSTRACT FROM AUTHOR]

One of the most important and recurrent tasks in managing a store is to provide accurate information of products to customers by maintaining up-to-date price tags of every item. Till today, however, updating price tags is done manually in most markets, which is labor-intensive, time-consuming, and error-prone. Adopting wirelessly reconfigurable electronic price tags could be of significant benefit to this problem. A key requirement of such a system, then, is to establish a robust, reliable, and scalable wireless communication network while spending minimal energy on thousands of densely-deployed and battery-powered price tags. In this work, we present a comprehensive study of electronic shelf labeling (ESL) system including its design and real-world deployment experiences. While the overall design turns out to be relatively straightforward, our preliminary study using 1000 real commercial e-tags reveals technical challenges in reliable and low-power wireless communication with ultra-high node density in scatter-rich indoor environments. To address the problem, we adopt multi-radio and multi-channel diversity techniques which need to modify only the gateways but significantly improve overall system performance. Our evaluation from a real-world deployment of 550 tags at an actual convenience store shows that our proposal achieves 98.51% network connectivity, 590 ms average latency for price updates, and over 5 years of e-tag lifetime. ESL system is part of the upcoming smart market era, and we see this work as a practical industrial application case study of Internet of Things technologies.

Low-power wireless network for the emerging Internet of Things (IoT) should be reliable enough to satisfy the application requirements, and also energy-efficient for embedded devices to remain battery powered. Synchronized communication methods such as Time Slotted Channel Hopping (TSCH) have shown promising results for these purposes, achieving end-to-end reliability over 99% with low duty-cycles. However, they lack one thing: flexibility to support a wide variety of applications and services with unpredictable traffic load and routing topology due to “fixed” slotframe sizes. To this end, we propose TESLA, a traffic-aware elastic slotframe adjustment scheme for TSCH networks which enables each node to dynamically self-adjust its slotframe size at run time. TESLA aims to minimize its energy consumption without sacrificing reliable packet delivery by utilizing incoming traffic load to estimate channel contention level experienced by each neighbor. We extensively evaluate the effectiveness of TESLA on large-scale 110-node and 79-node testbeds, demonstrating that it achieves up to 70.2% energy saving compared to Orchestra (the de facto TSCH scheduling mechanism) while maintaining 99% reliability.

A small angle x-ray scattering simulator has been developed for metrology and inspection applications based on the first Born approximation considering non-ideal equipment-related factors. The simulator shows good reproducibility in various device structures and measurement environments, including slit-configurations and incident angles. In addition, a Q-space adaptive weighting method is proposed to enhance about 300% accuracy, especially on non-single repeated pitch structures such as VNAND with complex x-ray diffraction patterns.

Background. Quinoline-3-carboxamides have been used to treat autoimmune/inflammatory diseases in humans because they inhibit the functions of S100 calcium-binding protein A9 (S100A9), which participates in the development of neutrophilic inflammation in asthmatics and in an animal model of neutrophilic asthma. However, the therapeutic effects of these chemicals have not been evaluated in asthma. The purpose of this study was to evaluate the effect of paquinimod, one of the quinoline-3-carboxamides, on a murine model of neutrophilic asthma. Methods. Paquinimod was orally administered to 6-week-old C57BL/6 mice sensitized and challenged with ovalbumin (OVA)/complete Freund’s adjuvant (CFA) and OVA. Lung inflammation and remodeling were evaluated using bronchoalveolar lavage (BAL) and histologic findings including goblet cell count. S100A9, caspase-1, IL-1β, MPO, IL-17, IFN-γ, and TNF-α were measured in lung lysates using western blotting. Results. Paquinimod restored the enhancement of airway resistance and the increases in numbers of neutrophils and macrophages of BAL fluids and those of goblet cells in OVA/CFA mice toward the levels of sham-treated mice in a dose-dependent manner (0.1, 1, 10, and 25 mg/kg/day, p.o.). Concomitantly, p20 activated caspase-1, IL-1β, IL-17, TNF-α, and IFN-γ levels were markedly attenuated. Conclusion. These data indicate that paquinimod effectively inhibits neutrophilic inflammation and remodeling in the murine model of neutrophilic asthma, possibly via downregulation of IL-17, IFN-γ, and IL-1β.

Background and Objectives: Traumatic duodenal injury is a rare disease with limited evidence. We aimed to evaluate the risk factors for postoperative leakage and outcomes of pyloric exclusion after duodenal grade 2 and 3 injury. Materials and Methods: We reviewed a prospectively collected trauma database for the period January 2004–December 2020. Patients with grade 2 and 3 traumatic duodenal injury were included. To identify the risk factors for postoperative leakage, we used a stepwise multivariable logistic regression model and a least absolute shrinkage and selection operator (LASSO) logistic model. We constructed a receiver operator characteristic (ROC) curve to predict risk factors for postoperative leakage. Results: During the 17-year period, 179,887 trauma patients were admitted to a regional trauma center in Korea. Of these patients, 74 (0.04%) had duodenal injuries. A total of 49 consecutive patients had grade 2 and 3 traumatic duodenal injuries and underwent laparotomy. The incidence of postoperative leakage was 32.6% (16/49). Overall mortality was 18.4% (9/49). A stepwise multivariable logistic regression and LASSO logistic regression model showed that time from injury to initial operation was the sole statistically significant risk factor. The ROC curve at the optimal threshold of 15.77 h showed the following: area under ROC curve, 0.782; sensitivity, 68.8%; specificity, 87.9%; positive predictive value, 73.3%; and negative predictive value, 85.3%. There was no significant difference in outcomes between primary repair alone and pyloric exclusion. Conclusions: Time from injury to initial operation may be the sole significant risk factor for postoperative duodenal leakage. Pyloric exclusion may not be able to prevent postoperative leakage.

Thermal and mechanical properties of poly(ionic liquid)s (PILs), an epoxidized ionic liquid-amine network, are studied via molecular dynamics simulations. The poly(ionic liquid)s are designed with two different ionic liquid monomers, 3-[2-(Oxiran-2-yl)ethyl]-1-{4-[(2-oxiran-2-yl)ethoxy]phenyl}imidazolium (EIM2) and 1-{4-[2-(Oxiran-2-yl)ethyl]phenyl}-3-{4-[2-(oxiran-2-yl)ethoxy]benzyl}imidazolium (EIM1), each of which is networked with tris(2-aminoethyl)amine, paired with different anions, bis(trifluoromethanesulfonyl)imide (TFSI−) and chloride (Cl−). We investigate how ionic liquid monomers with high ionic strength affect structures of the cross-linked polymer networks and their thermomechanical properties such as glass transition temperature (Tg) and elastic moduli, varying the degree of cross-linking. Strong electrostatic interactions between the cationic polymer backbone and anions build up their strong structures of which the strength depends on their molecular structures and anion size. As the anion size decreases from TFSI− to Cl−, both Tg and elastic moduli of the PIL increase due to stronger electrostatic interactions present between their ionic moieties, making it favorable for the PIL to organize with stronger bindings. Compared to the EIM2 monomer, the EIM1 monomers and TFSI− ions generate a PIL with higher Tg and elastic moduli. This attributes to the less flexible structure of the EIM1 monomer for the chain rotation, in which steric hindrance by ring moieties in the EIM1-based PIL enhances their structural rigidity. The π-π stacking structures between the rings are found to increase in EIM1-based PIL compared to the EIM2-based one, which becomes stronger with smaller Cl− ion rather than TFSI−. The effect of the degree of the cross-linking on thermal and mechanical properties is also examined. As the degree of cross-linking decreases from 100% to 60%, Tg also decreases by a factor of 10–20%, where the difference among the given PILs becomes decreased with a lower degree of cross-linking. Both the Young’s (E) and shear (G) moduli of all the PILs decrease with degree of cross-linking, which the reduction is more significant for the PIL generated with EIM2 monomers. Transport properties of anions in PILs are also studied. Anions are almost immobilized globally with very small structural fluctuations, in which Cl− presents lower diffusivity by a factor of ~2 compared to TFSI− due to their stronger binding to the cationic polymer backbone.

Conventional fractional open-circuit voltage (FOCV) methods in maximum power point tracking (MPPT) are widely adopted for their simple structure and low computing power requirements. However, under mismatch and environmental changing conditions, the FOCV methods introduce a large amount of energy loss due to their maximum power point being fixed at the initial setup. To reduce energy loss, the intermittent FOCV MPPT proposed in this paper regularly refreshes all the parameters for each condition in time by using an I-V curve tracer. The proposed intermittent FOCV consists of two phases: the scan and set phases. In scan phase, the I-V curve of a photovoltaic (PV) cell is scanned and its power is calculated. In set phase, the global MPP of the PV cell is extracted and set by controlling the 8-bit capacitance array. Simulation and calculation based on experimental results with a single PV cell show that the energy loss of the proposed intermittent FOCV under daily temperature and illuminance distributions decreased by up to 99.9% compared to that of the conventional FOCV.

As smart grids develop rapidly, low-cost monitoring systems for pole-mounted transformers increase in demand. Even though battery-powered wireless monitoring systems appear to provide optimal solutions, they consume large amounts of energy for continuous sampling and data transmission. Operation and maintenance costs then increase owing to reduced battery lifetime and battery replacement. To overcome this problem, this paper presents an event-driven battery-powered wireless monitoring system that monitors abnormalities of a transformer and transmits data only if an abnormality occurs. When the proposed event controller detects an abnormality, it enables a root mean square (RMS) converter and a peak detector for sampling and transmitting the maximum RMS value of the abnormal signal and then falls into sleep mode until the next event to save energy. Simulation and experimental results show that the proposed system enhances battery lifetime by up to two orders of magnitude compared to a conventional battery-powered wireless monitoring system.