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Abstract Notwithstanding the latest advancements in anticancer therapy, non-small cell lung cancer (NSCLC) remains a prominent contributor to cancer-associated mortality worldwide. Therefore, effective anti-cancer agents are required for the treatment of NSCLC. We previously demonstrated that the natural alkaloid evodiamine efficiently suppressed lung cancer cells and lung cancer stem-like cell populations by suppressing heat shock protein 70 (Hsp70). This finding inspired us to formulate evodiamine-based anti-cancer compounds against NSCLC. In this study, we synthesized a series of evodiamine derivatives with substitutions at the N14 position. EV206 was chosen for further study because it was the most effective among the 22 evodiamine derivatives at stopping H1299 cell growth. EV206 treatment efficiently suppressed cell viability and colony formation in both attached cells and in soft agar, even in those carrying drug resistance, by inducing apoptosis. The effectiveness of EV206 is approximately ten times greater than that of evodiamine. Normal cell viability was marginally affected by EV206 treatment. Additionally, EV206 efficiently decreased the cancer stem cell (CSC) population in the NSCLC cells. EV206 reduced the growth of H460 xenograft tumors without exhibiting toxic effects. These data implied that EV206 has the potential to be an effective Hsp70-targeting anticancer drug with low toxicity.

Abstract Introduction This study is a retrospective case series to compare the accuracy of the Barrett toric calculator using predicted posterior corneal astigmatism (PCA) and PCA measurements using swept-source optical coherence tomography (SS-OCT) and a Scheimpflug camera. This evaluation was conducted across different types of anterior and posterior astigmatism. Methods A total of 146 eyes from 146 patients implanted with toric intraocular lenses were included. Mean absolute prediction error, standard deviation of prediction error, and the percentage of eyes with prediction errors within ±0.50 diopters (D) were calculated using vector analysis. Biometric measurements were conducted using the IOLMaster 700 and Pentacam HR. A subgroup analysis was conducted based on the orientation of both anterior and posterior corneal astigmatism. Results The Barrett toric calculator with predicted PCA yielded the best results, with 78.1% having a prediction error ≤ 0.50 D, which was a significantly higher percentage than the Barrett formula with the two versions of measured PCA (P

Abstract Out of practicality, ambient air rather than oxygen is preferred as a fuel in electrochemical systems, but CO2 and H2O present in air cause severe irreversible reactions, such as the formation of carbonates and hydroxides, which typically degrades performance. Herein, we report on a Na-air battery enabled by a reversible carbonate reaction (Na2CO3·xH2O, x = 0 or 1) in Nasicon solid electrolyte (Na3Zr2Si2PO12) that delivers a much higher discharge potential of 3.4 V than other metal-air batteries resulting in high energy density and achieves > 86 % energy efficiency at 0.1 mA cm−2 over 100 cycles. This cell design takes advantage of moisture in ambient air to form an in-situ catholyte via the deliquescent property of NaOH. As a result, not only reversible electrochemical reaction of Na2CO3·xH2O is activated but also its kinetics is facilitated. Our results demonstrate the reversible use of free ambient air as a fuel, enabled by the reversible electrochemical reaction of carbonates with a solid electrolyte.

In this paper, we consider a distributed antenna system (DAS) with polarized antennas for the industrial internet-of-things, where distributed sensor nodes (SNs) with a single antenna are served by the closest distributed antenna (DA) port having multiple antennas. By using stochastic geometry, the interference in the polarized DAS is analyzed and the channel outage probability (OP) of the polarized DAS is also derived in a closed form. From the analytic results, the optimal vertically/horizontally polarized node densities are derived to minimize the channel OP. Furthermore, by investigating the effects of node density and polarization on the channel OP, we also develop a dual-polarized DAS, in which the effective density of co-polarized DA ports is the same with the density of all DA ports and, simultaneously, the interference from the active cross-polarized DA ports is effectively mitigated. By analyzing the channel OP for the dual-polarized DAS, the optimal node densities to minimize the overall channel OP and the effect of network design parameters on the channel OP is also investigated, which gives us a useful insight into the design of the polarized DAS.

Safeguarding personal identity in various surveillance videos, dashcams, and on-street videos is crucial. One way to do this is to detect faces and blur them, but a better solution is to replace them with non-existent ones to maintain the naturalness of the videos. While face swapping methods have already been used in the media industry with high-quality faces, it is challenging to apply them for identity protection to faces in-the-wild where faces are often occluded and of low-resolution. Therefore, we propose a new framework for face swapping specifically designed to work with face images taken in real-world scenarios, making it useful as a privacy protection method. To tackle the issue of low-resolution images, we introduce a Cross-Resolution Contrastive Loss (CRCL) technique, which allows our neural network model to be trained using triplets of varying resolutions. This enables the model to learn and use identity information across different resolutions, thereby improving its accuracy. We also propose a plug-and-play framework that can be easily applied to existing face swapping models to handle occlusions. By explicit swapping of facial features and filling of occluded regions, our framework provides a more seamless blend. To demonstrate the effectiveness of our method in handling faces in-the-wild, we create an occluded VGGFace2 dataset consisting of face images augmented with various facial masks and hand occlusions. Through quantitative and qualitative assessments on this dataset, our proposed method demonstrates robust performance under low-resolution or occluded scenarios. Significant improvements are made in the quality of swapped faces while preserving their identity and attributes, highlighting the effectiveness of our framework in advancing face swapping as a reliable privacy protection measure.

Early research focused on developing effective algorithms for Ventricular fibrillation (VF) detection; while most of the evaluations have been conducted offline with prefiltered data sets, practical application requires these tests to be performed in real time. Because there are many factors that may impact detection effectiveness, it is important to understand the impact of factors that improve detection accuracy. In this study, we developed an integrated simulated environment using IAR Embedded Workbench software to build an embedded system using a MSP430 microcontroller and Visual studio tool for S/W build; we then used this system to conduct real-time experiments for evaluating five lightweight VF detection algorithms and to examine factors that may impact their performance in terms of sensitivity, specificity, positive-predictivity, accuracy and computational time. The results were cross-validated using a prototype of a wearable Electrocardiogram (ECG) system developed by this study. The study showed that 1) the chosen detection algorithm, data filtering, and window size all have a significant impact on the performance of real-time VF detection; among these, the detection algorithm had the greatest impact so it must be carefully selected; 2) it is important to select the proper threshold value that affects tradeoffs in performance metrics. Among the five algorithms that this study evaluated, the Time Delay (TD) algorithm outperformed the others independent of window size or filtering method. Considering the tradeoff between robustness and efficiency, TD is preferable because detection accuracy and robustness are more critical.

Aberrant communication in alveolar epithelium is a major feature of inflammatory response for the airway remodeling leading to chronic obstructive pulmonary disease (COPD). In this study, we investigated the effect of protein transduction domains (PTD) conjugated Basic Fibroblast Growth Factor (FGF2) (PTD-FGF2) in response to cigarette smoke extract (CSE) in MLE-12 cells and porcine pancreatic elastase (PPE)-induced emphysematous mice. When PPE-induced mice were intraperitoneally treated with 0.1–0.5 mg/kg PTD-FGF2 or FGF2, the linear intercept, infiltration of inflammatory cells into alveoli and pro-inflammatory cytokines were significantly decreased. In western blot analysis, phosphorylated protein levels of c-Jun N-terminal Kinase 1/2 (JNK1/2), extracellular signal-regulated kinase (ERK1/2) and p38 mitogen-activated protein kinases (MAPK) were decreased in PPE-induced mice treated PTD-FGF2. In MLE-12 cells, PTD-FGF2 treatment decreased reactive oxygen species (ROS) production and further decreased Interleukin-6 (IL-6) and IL-1b cytokines in response to CSE. In addition, phosphorylated protein levels of ERK1/2, JNK1/2 and p38 MAPK were reduced. We next determined microRNA expression in the isolated exosomes of MLE-12 cells. In reverse transcription-polymerase chain reaction (RT-PCR) analysis, level of let-7c miRNA was significantly increased while levels of miR-9 and miR-155 were decreased in response to CSE. These data suggest that PTD-FGF2 treatment plays a protective role in regulation of let-7c, miR-9 and miR-155 miRNA expressions and MAPK signaling pathways in CSE-induced MLE-12 cells and PPE-induced emphysematous mice.

X-ray structural science is undergoing a revolution driven by the emergence of X-ray Free-electron Laser (XFEL) facilities. The structures of crystalline solids can now be studied on the picosecond time scale relevant to phonons, atomic vibrations which travel at acoustic velocities. In the work presented here, X-ray diffuse scattering is employed to characterize the time dependence of the liquid phase emerging from femtosecond laser-induced melting of polycrystalline gold thin films using an XFEL. In a previous analysis of Bragg peak profiles, we showed the supersonic disappearance of the solid phase and presented a model of pumped hot electrons carrying energy from the gold surface to scatter at internal grain boundaries. This generates melt fronts propagating relatively slowly into the crystal grains. By conversion of diffuse scattering to a partial X-ray pair distribution function, we demonstrate that it has the characteristic shape obtained by Fourier transformation of the measured F(Q). The diffuse signal fraction increases with a characteristic rise-time of 13 ps, roughly independent of the incident pump fluence and consequent final liquid fraction. This suggests the role of further melt-front nucleation processes beyond grain boundaries.

Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models.

The commercialisation of virtual reality (VR) headsets has made them more affordable and popular in gaming and entertainment. The natural interaction between the VR environment and users can maximise immersion and is crucial to VR gaming. Despite their growing popularity, educational VR games prioritise learning over immersion and require users to learn to interact with and play games using tutorials. Herein, we developed a game named Numverse with an accompanying tutorial. After selecting the tutorial content, we programmed the user interface and proposed a delivery method for the tutorial. We evaluated the user experience based on the effects of the presence or absence of the tutorial and its mode of delivery. The tutorials were of three types: no tutorial, instruction-screen tutorial, and context-sensitive tutorial, with the latter being the most preferred. The evaluation results show that presence, ability to learn controls, intrinsic motivation, and learning effectiveness are higher for the instruction-screen and context-sensitive tutorials than for no tutorial. On average, users experienced more motion sickness in the no-tutorial case, with a significant difference in nausea items. This study asserts the importance of tutorials in VR games, and its findings could improve user experience in future VR games.

Abstract Thus far, attempts to develop drugs that target corticotropin-releasing hormone receptor 1 (CRF1R), a drug target in stress-related therapy, have been unsuccessful. Studies have focused on using high-resolution G protein-coupled receptor (GPCR) structures to develop drugs. X-ray free-electron lasers (XFELs), which prevent radiation damage and provide access to high-resolution compositions, have helped accelerate GPCR structural studies. We elucidated the crystal structure of CRF1R complexed with a BMK-I-152 antagonist at 2.75 Å using fixed-target serial femtosecond crystallography. The results revealed that two unique hydrogen bonds are present in the hydrogen bond network, the stalk region forms an alpha helix and the hydrophobic network contains an antagonist binding site. We then developed two antagonists—BMK-C203 and BMK-C205—and determined the CRF1R/BMK-C203 and CRF1R/BMK-C205 complex structures at 2.6 and 2.2 Å, respectively. BMK-C205 exerted significant antidepressant effects in mice and, thus, may be utilized to effectively identify structure-based drugs against CRF1R.

Background: In previous studies, several asthma phenotypes were identified using clinical and demographic parameters. Transcriptional phenotypes were mainly identified using sputum and bronchial cells. Objective: We aimed to investigate asthma phenotypes via clustering analysis using clinical variables and compare the transcription levels among clusters using gene expression profiling of the blood. Methods: Clustering analysis was performed using 6 parameters: age of asthma onset, body mass index, pack-years of smoking, forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity, and blood eosinophil counts. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples and RNA was extracted from selected PBMCs. Transcriptional profiles were generated (Illumina NovaSeq 6000) and analyzed using the reference genome and gene annotation files (hg19.refGene.gft). Pathway enrichment analysis was conducted using GO, KEGG, and REACTOME databases. Results: In total, 355 patients with asthma were included in the analysis, of whom 72 (20.3%) had severe asthma. Clustering of the 6 parameters revealed 4 distinct subtypes. Cluster 1 (n = 63) had lower predicted FEV1 % and higher pack-years of smoking and neutrophils in sputum. Cluster 2 (n = 43) had a higher proportion and number of eosinophils in sputum and blood, and severe airflow limitation. Cluster 3 (n = 110) consisted of younger subjects with atopic features. Cluster 4 (n = 139) included features of late-onset mild asthma. Differentially expressed genes between clusters 1 and 2 were related to inflammatory responses and cell activation. Th17 cell differentiation and interferon gamma-mediated signaling pathways were related to neutrophilic inflammation in asthma. Conclusion: Four clinical clusters were differentiated based on clinical parameters and blood eosinophils in adult patients with asthma form the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) cohort. Gene expression profiling and molecular pathways are novel means of classifying asthma phenotypes.

Abstract Exposure to phthalates has been shown to impede the human endocrine system, resulting in deleterious effects on pregnant women and their children. Phthalates modify DNA methylation patterns in infant cord blood. We examined the association between prenatal phthalate exposure and DNA methylation patterns in cord blood in a Korean birth cohort. Phthalate levels were measured in 274 maternal urine samples obtained during late pregnancy and 102 neonatal urine samples obtained at birth, and DNA methylation levels were measured in cord blood samples. For each infant in the cohort, associations between CpG methylation and both maternal and neonate phthalate levels were analyzed using linear mixed models. The results were combined with those from a meta-analysis of the levels of phthalates in maternal and neonatal urine samples, which were also analyzed for MEOHP, MEHHP, MnBP, and DEHP. This meta-analysis revealed significant associations between the methylation levels of CpG sites near the CHN2 and CUL3 genes, which were also associated with MEOHP and MnBP in neonatal urine. When the data were stratified by the sex of the infant, MnBP concentration was found to be associated with one CpG site near the OR2A2 and MEGF11 genes in female infants. In contrast, the concentrations of the three maternal phthalates showed no significant association with CpG site methylation. Furthermore, the data identified distinct differentially methylated regions in maternal and neonatal urine samples following exposure to phthalates. The CpGs with methylation levels that were positively associated with phthalate levels (particularly MEOHP and MnBP) were found to be enriched genes and related pathways. These results indicate that prenatal phthalate exposure is significantly associated with DNA methylation at multiple CpG sites. These alterations in DNA methylation may serve as biomarkers of maternal exposure to phthalates in infants and are potential candidates for investigating the mechanisms by which phthalates impact maternal and neonatal health.

Freezing of the flight environment of an aircraft occasionally causes the congealing of oil in the oil coolers. Although decongealing tests and re-designs require longer durations, many studies have examined the cooling loss arising from this phenomenon. Numerical simulations for the decongealing process are computationally expensive, and experiments under typical operating scenarios are costly. In this paper, an efficient one-dimensional flow and thermal approach is proposed, with transient simulations under realistic decongealing scenarios involving a standard fuel cooled oil cooler. With the objective function being the maximization of the heat transfer and minimization of the pressure drop, different cooler configurations with various internal fins were examined for both design and off-design conditions. The proposed lumped model provides a similar accuracy with a lower calculation cost compared to an earlier model. It also ensures better stability and easier code modifications. The current study demonstrates an optimum heat transfer and pressure drop with improved total decongealing performance when offset-strip fins on both the oil and fuel sides (ΔP: 23.9 psi, Tout: 12.3 °C) or an offset-strip fin on the oil side and a wavy or pin fin on the fuel sides (ΔP: 34.2 psi, Tout: 11.4 °C) are applied.

Constructing a high dynamic range (HDR) image from multi-exposure low dynamic range (LDR) images is challenging mainly due to two major problems. One is a large misalignment between the LDR images taken at different moments due to camera and object motions. The other is missing content in over- or under-exposed areas, which can be very large in the LDR images with long or short exposure times. These problems lead to ghosting artifacts or saturated regions in the reconstructed HDR image. In this paper, we propose a method using convolutional neural networks (CNNs) to address these problems. Specifically, each LDR image is fed to a two-branch CNN that extracts two kinds of features separately, named content features and global features. Then, another CNN aligns and merges the features to create the HDR output. The content features are used to create spatial attention masks, which are used to align the features in the time domain with exposure-invariant attributes. Further, Fourier coefficient vectors of global features are utilized to modulate intermediate features in the frequency domain during the reconstruction process. Experimental results show that our method achieves state-of-the-art HDR reconstruction performances on several benchmarks. Our code is available at https://github.com/KeuntekLee/FDM-HDR.

In recent years, artificial intelligence (AI) has been applied in various fields, with rapid expansion of the scope of AI-human interactions. However, most AI technologies continue to exhibit black-box characteristics, i.e., their decisions and actions are not explainable, degrading user experience (UX). Recently, research on explainable AI (XAI), focusing on both AI performance and UX, has garnered significant attention. However, development of generalizable UX evaluation tools for AI and improvement of AI UX have not been investigated adequately. In this study, a UX evaluation tool is developed for AI based on a systematic literature review and verified using exploratory and confirmatory factor analyses. Subsequently, based on identified AI UX factors, UX of an AI defense system is upgraded. Based on user evaluation, there were significant improvements in eight factors (satisfaction, safety, controllability, trust, causality, efficiency, accountability, and explainability) with the exception of fairness. The proposed evaluation tool is expected to serve as a cornerstone for future evaluation of AI UX advancement.

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.

To tackle a Doppler sensitivity problem of orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) has been investigated, where information is carried over delay-Doppler domain. In this paper, to improve communication reliability in doubly dispersive channel, an auto-encoder (AE)-based OTFS modulation and detection scheme is developed, where the transmit OTFS waveform and its associated detection scheme at the receiver are jointly optimized in a deep learning framework. However, the conventional AE architecture which takes one-hot encoded input vector is hard to be reused in OTFS due to its enormous input dimensionality that increases exponentially on the number of grid points in delay-Doppler domain. To overcome it, we divide the delay-Doppler grid into multiple subblocks and associate the one-hot encoded vector with each subblock. Then, by concatenating them, one multi-hot vector is formed and exploited as the input vector for the proposed AE-based OTFS modulation and detection. We also develop a meta-learning scheme to effectively train the AE-based OTFS transceiver for newly updated channel profile.

In this paper, a compact and broadband electrostatic discharge (ESD) protection input/output (I/O) pad for high-speed interfaces is designed and miniaturized using a pad-stacked inductor in a 28-nm CMOS technology. A $\pi $ -diode with a single inductor is adopted to compensate for the parasitic capacitance and extend the bandwidth of ESD-protected I/O pad. To minimize the increase of the chip area by the inductor, the pad-stacked inductor is proposed, where the inductor is placed below I/O pad. The eddy current, then, is induced in the I/O pad by time-varying magnetic field by the inductor, degrading the performance of the inductor, such as the reduced inductance, increased resistance and capacitance. These effects are thoroughly investigated using the developed equivalent circuit model which is also utilized to design the pad-stacked inductor. The compact and broadband $\pi $ -diode with the reduced eddy current is then designed by the proper selection of the inductor layer and a patterned I/O pad. Measurement of the designed $\pi $ -diode with the pad-stacked inductor exhibits broadband impedance match and insertion loss, that is, a return loss better than 10 dB up to 26.5 GHz and a 3-dB bandwidth as large as 22.9 GHz. The chip area of the $\pi $ -diode remains the same as that of the I/O pad thanks to the proposed pad-stacked inductor. Therefore, the designed $\pi $ -diode can be applied for the compact high-speed I/O interface circuits.

Due to its short half-life, thoron gas has been assumed to have negligible health hazards on humans compared to radon. But, one of the decay products with a long half-life can make it to be transported to a long distance and to cause a severe internal dose through respiration. Since most commercial radon detectors can not discriminate thoron signals from radon signals, it is very common to overestimate radon doses which in turn result in biased estimation of lung cancer risk in epidemiological studies. Though some methods had been suggested to measure thoron and radon separately, they could not be used for real-time measurement because of CR-39 or LR-115. In this study, an effective method was suggested to measure radon and thoron separately from the free air. It was observed that the activity of thoron decreases exponentially due to delay time caused by a long pipe between two chambers. Therefore from two ion chambers apart in time, it was demonstrated that thoron and radon could be measured separately and simultaneously. We also developed a collimated alpha source and with this source and an SBD, we could convert the ion chamber reading to count rate in cps.

Aminoacyl-tRNA synthetases possess unique domains. In this study the structure of the vertebrate IARS1 and EARS1 complex reveals that vertebrate IARS1 protects the DNA repair factor BRCA1 from proteolytic degradation via its UBX-fold domain.

Objective Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies. Methods The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review. Results As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users. Conclusions This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.

Microcrystal delivery instruments are pivotal to performing serial femtosecond crystallography experiments at the XFEL facilities. We present a novel sample delivery technique based on a micro-tubing reeling system (MRS). Despite the tiny size of the micro-tubing, the MRS device has the advantage of operating without real-time position adjustment of the tube to match with the XFEL pulses. Moreover, the applicable repetition rate is more flexible than the previously reported chip-based one-dimensional fixed target system.

S1P5 is a sphingosine-1-phosphate (S1P) receptor implicated in immune and neurodegenerative disorders. Here, authors report a crystal structure of the S1P5 receptor in complex with a selective inverse agonist, revealing an allosteric subpocket and shedding light on inverse agonism in S1P receptors.

Abstract When micro‐Doppler (MD) radars are distributed, a federated learning strategy over wireless backhaul links is developed for motion classification. Specifically to identify the human motion, a common convolutional neural network (CNN) model is shared for all the distributed radars (i.e. clients) and it is trained through the federated learning strategy over wireless backhaul connected to the main server. In the proposed system, a main bottleneck is the estimation of local gradients for CNN training at the server, which are transferred from distributed radars over the wireless backhaul link. To overcome it, a deep learning (DL) aided gradient estimation algorithm is proposed, in which the deep neural networks (DNNs) for encoding local gradient vectors at the distributed radars and the DNN for decoding (i.e. estimating) them at the server are jointly trained in an end‐to‐end autoencoder‐based learning strategy. To avoid the inter‐client interference over the wireless backhaul link, the DNN structure for the gradient estimation algorithm with the orthogonal multiple access is proposed, in which the proposed DNN effectively learns the encoding/decoding at the transceiver over wireless backhaul. By exploiting the experimental data measured through the USRP‐based MD radars, the authors validate the motion classification performance of the proposed federated learning strategy and DL aided gradient estimation over the wireless backhaul link.

The functionalisation of unactivated azines has been restricted because of their intrinsic low reactivity. Here the authors show a transition-metal-free, radical relay π-extension approach to produce N-doped polycyclic aromatic compounds directly from simple azines and cyclic iodonium salts.

Transition metal-catalysed hydroacylation of alkynes using aldehydes is an atom-economical route to access α,β-unsaturated ketones, but typically requires aldehydes that bear chelating moieties. Here, the authors report a nickel-catalysed anti-Markovnikov selective coupling process to afford non-tethered E-enones from terminal alkynes and S-2-pyridyl thioesters.

In the autonomous car, perception with point cloud semantic segmentation helps obtain a wealth of information about the surrounding road environment. Despite the massive progress of recent researches, the existing machine learning networks are still insufficient for online applications of autonomous driving due to too subdivided classes, the lack of training data, and their heavy computing load. To solve these problems, we propose a fast and lite point cloud semantic segmentation network for autonomous driving, which utilizes LiDAR synthetic data to improve the performance by transfer learning. First, we modify the labeling classes and generate the LiDAR synthetic data-set for additional training to alleviate the lack of training data of the realistic data-set. Then, to lower the computing load, we adopt a projection-based method and apply a lightweight segmentation network to projected LiDAR images, which has drastically reduced computing. Finally, we verified and evaluated the proposed network in this paper through experiments. Experimental results show that the proposed network can perform the three-dimensional point cloud semantic segmentation in an online way, in which the inference speed overwhelms the existing algorithms.

We sought to understand how the perception of personal space is influenced by different levels of social density, spatial density, and type of window-view in South Korean and United Kingdom workplaces. We employed virtual reality to simulate shared and single occupancy offices. We obtained personal space estimations using a virtual disc around the participant which could be extended and retracted, inside the simulation, to indicate perceived amount of personal space, and compared this measure to questionnaire-based estimations. We found that in both cultures participants experienced greater perceived personal space (1) when in a sparse rather than dense office and (2) having a view of the city outside the office. However, British, but not Korean, participants had significantly higher personal space estimations in single occupancy offices than in shared offices. These results suggest subtle cross-cultural differences in workplace experience, that could only be investigated using virtual reality.

Abstract Photosystem I (PS I) has a symmetric structure with two highly similar branches of pigments at the center that are involved in electron transfer, but shows very different efficiency along the two branches. We have determined the structure of cyanobacterial PS I at room temperature (RT) using femtosecond X-ray pulses from an X-ray free electron laser (XFEL) that shows a clear expansion of the entire protein complex in the direction of the membrane plane, when compared to previous cryogenic structures. This trend was observed by complementary datasets taken at multiple XFEL beamlines. In the RT structure of PS I, we also observe conformational differences between the two branches in the reaction center around the secondary electron acceptors A1A and A1B. The π-stacked Phe residues are rotated with a more parallel orientation in the A-branch and an almost perpendicular confirmation in the B-branch, and the symmetry breaking PsaB-Trp673 is tilted and further away from A1A. These changes increase the asymmetry between the branches and may provide insights into the preferential directionality of electron transfer.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. Despite alveolar epithelial cells is crucial role in lung, its contribution and the associated biomarker remain unknown in the pathogenesis of IPF. Recently, environmental factors including stone dust, silica and cigarette smoking were found as risk factors involved in IPF. Receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin super family of cell surface receptors. It has been shown that interaction between RAGE and its ligands on immune cells mediates cellular migration and regulation of pro-inflammation. RAGE is highly expressed in the lung, in particular, alveolar epithelial cells. Therefore, we determined whether RAGE expression is associated with fibrosis-associated genes in patients with IPF and mice. Results When bleomycin (BLM) was intratracheally administered to C57BL/6 mice for 1, 2 weeks, macrophage and neutrophils were significantly increased. The fibrotic nodule formed and accumulation of collagen was determined after BLM injection in H&E- and Masson’s trichrome staining. Levels of elastin, Col1a1 and fibronectin were increased in quantitative real-time PCR and protein levels of α-SMA was increased in western blot analysis. In the lung tissues of 1 mg/kg BLM-induced mice, RAGE expression was gradually decreased in 1- and 2 weeks in immunohistochemistry and western blot analysis, and 3 mg/kg of BLM-induced mice exhibited decreased RAGE levels while α-SMA expression was increased. We next determined RAGE expression in the lungs of IPF patients using immunohistochemistry. As a result, RAGE expression was decreased, while α-SMA expression was increased compared with non-IPF subjects. Conclusions Our findings suggest that reduced RAGE was associated with increased fibrotic genes in BLM-induced mice and patients with IPF. Therefore, RAGE could be applied with a biomarker for prognosis and diagnosis in the pathogenesis of IPF.

Abstract Deep‐learning‐based radar imaging is developed with distributed frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radars in which a deep‐learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deep‐learning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning.

A numerical study was carried out to analyze the condensation phenomena in a vertical channel between two flat plates. To solve the three-dimensional governing equations including conservation of the mass, momentum, and energy, the Volume of Fluid model was adopted. For the phase change phenomena, the Lee model was used. The tuning for the model parameter was performed using existing experimental data. To obtain the heat transfer coefficients under desired conditions, a model based on machine learning using artificial neural network was developed, which shows good accuracy and the future possibility of using the model for the tuning and validation of the phase-change model. A super-hydrophilic (10°), hydrophilic (58°), and hydrophobic surface (130°) were considered together with the variation of the gap spacing and the inlet velocity. The different condensation behavior according to the various surfaces wettability conditions was clearly identified. The formation of a liquid bridge in the channel for the dropwise condensation was observed, and its effects on the thermal and pressure drop performance were assessed. Details on the its performance were summarized quantitatively. It is expected that the present result and numerical method could provide useful information for the fin-type heat exchanger design.

Serial femtosecond crystallography (SFX) using an X-ray free electron laser (XFEL) enables the determination of room-temperature structures without causing radiation damage. Using an optical pump-probe or mix-and-injection, SFX enables the intermediate state visualization of a molecular reaction. In SFX experiments, serial and stable microcrystal delivery to the X-ray interaction point is vital for reasonable data collection and efficient beam time. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) facility established SFX instruments at a nanocrystallography and coherent imaging (NCI) experimental station. Various sample delivery methods, including injection, fixed-target scanning, and hybrid methods, have been developed and applied to collect XFEL diffraction data. Herein, we report the currently available sample delivery methods for SFX at the NCI experimental station at the PAL-XFEL. This article will help PAL-XFEL users access the SFX system for their experiments.

AbstractTo resolve energy depletion issues in massive Internet of Things sensor networks, we developed a set of distributed energy beamforming methods with one‐bit feedback and clustering for multi‐node wireless energy transfer, where multiple single‐antenna distributed energy transmitters (Txs) transfer their energy to multiple nodes wirelessly. Unlike previous works focusing on distributed information beamforming using a single energy receiver (Rx) node, we developed a distributed energy beamforming method for multiple Rx nodes. Additionally, we propose two clustering methods in which each Tx node chooses a suitable Rx node. Furthermore, we propose a fast distributed beamforming method based on Tx sub‐clustering. Through computer simulations, we demonstrate that the proposed distributed beamforming method makes it possible to transfer wireless energy to massive numbers of sensors effectively and rapidly with small implementation complexity. We also analyze the energy harvesting outage probability of the proposed beamforming method, which provides insights into the design of wireless energy transfer networks with distributed beamforming.

Recently, the interest in the lower-limb exoskeleton robot is increasing within rehabilitation practice. While research on advancing the gait rehabilitation exoskeleton robot (ER) is being actively conducted from a technical point of view, studies evaluating usability and user value of ERs are insufficient. In this study, an evaluation tool was developed and verified, and an evaluation of an ER-based rehabilitation training program was conducted using the evaluation tool. A total of 102 rehabilitation patients (51 using ER and 51 under the existing training program [ETP] without robots) and 38 therapists participated in this experiment. The ER used in this study was Exowalk (HR-02, HMH Co. Ltd, South Korea). As a result of the study, the usability of the ERs consists of trust and overall satisfaction. The patient group using the ER showed statistically higher trust and overall satisfaction scores than the patient group with the ETP. On the other hand, the user value of the ER includes self-direction and security. The self-direction score of the patients using the ER was significantly higher than that of the patients with the ETP, but the security score showed the opposite tendency. This study is unique in that measures were developed to evaluate the usability and user value of the gait rehabilitation ER, especially for the Exowalk, verified by various user groups, and statistically analyzed to be used as a reference for the design of usability and user value in other gait rehabilitation ERs.

The uninterrupted operation of systems with artificial intelligence (AI) ensures high productivity and accuracy of the tasks performed. The physiological state of AI operators indicates a relationship with an AI system failure event and can be measured through electrodermal activity. This study aims to model the stress levels of system operators based on system trustworthiness and physiological responses during a correct AI operation and its failure. Two groups of 18 and 19 people participated in the experiments using two different types of software with elements of AI. The first group of participants used English proofreading software, and the second group used drawing software as the AI tool. During the tasks, the electrodermal activities of the participants as a stress level indicator were measured. Based on the results obtained, the users’ stress was determined and classified using logistic regression models with an accuracy of approximately 70%. The insights obtained can serve AI product developers in increasing the level of user trust and managing the anxiety and stress levels of AI operators.

In this paper, we propose one‐bit feedback‐based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet‐of‐Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one‐bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on one‐bit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral.

e-Learning has been widely used in educational settings especially in developing countries like Indonesia. The purpose of this study was to investigate e-learning readiness among Indonesian university students and to compare students’ perceptions of their workload (including mental demand, physical demand, temporal demand, performance, effort, frustration, and sleepiness) in e-learning and classical learning settings. A survey was conducted with students in an Indonesian university. The results show that students have a high level of e-learning readiness. Mental workload is significantly higher in e-learning than in face-to-face learning. No significant difference in sleepiness was found between e-learning and face-to-face learning. The correlation between e-learning readiness and the mental workload was not significant. Implications of the results are discussed.

Chlorine evolution reaction (CER) is a key electrochemical reaction for chemical, pulp, and paper industries, and water treatments. Here, the authors report that an atomically dispersed Pt−N4 site can catalyse CER with high activity and selectivity under a wide range of Cl– concentrations and pH.

When carbon-fiber-reinforced plastic (CFRP) and steel are joined using blind riveting and bolting, fastener inclination occurs due to the clearance between the fastener and hole. To this end, this study investigated the unavoidable occurrence of clearance when joining metal and composite materials using mechanical fastening. The effect of inclination on the lap shear strength (LSS) was quantitatively investigated under various conditions. In riveting, breakage occurred mainly in the rivet; the LSS between the CFRP and steel improved by approximately 33% when the clearance was filled with structural adhesive compared to that in the unfilled state. In bolting, a washer was essential since it not only reduced the force exerted on the bolt but also reduced the bending deformation of the steel plate where breakage occurred. The clearance-filling effect showed the same effect as using a washer even without using it. In addition, the LSS was improved by approximately 10% by filling the clearance with a structural adhesive in the case of bolting with washers. Additionally, the force distribution for the fastening segment was calculated under the application of an external force, and the results demonstrated that hybrid-bonded fastening using a clearance-filling during mechanical bonding is essential for strong fastening.

Owing to technological advancements, kiosks have become more prevalent in public places. When using such kiosks, elderly persons and people with disabilities face problems related to accessibility and usability, such as difficulties in kiosk operations such as menu selection and in accessing the kiosk space. Previous studies have usually included accessibility as a subset of usability. However, in this study, we aim to redefine the relationship between these two concepts with a focus on newly emerging kiosk devices. First, we performed a literature review to thoroughly analyze these concepts. Then, we conducted a focus group interview (FGI) targeting people with visual, hearing, and physical impairments to learn about the difficulties that these people face when using kiosks. Finally, we analyzed the characteristics of accessibility and usability related to kiosks and designed a diagram that illustrated the relationship between them. While accessibility and usability shared similarities regarding consistency and user control, they differed deeply regarding their subcategory items; many opinions on accessibility were related to essential functions, whereas many on usability were related to psychological factors such as additional functions or personal preferences. These results can be useful when creating laws and guidelines regarding the accessibility and usability of kiosks or when developing kiosk functions.

Idiopathic Pulmonary fibrosis (IPF), a chronic interstitial lung disease, has pulmonary manifestations clinically characterized by collagen deposition, epithelial cell injury, and a decline in lung function. L-carnosine, a dipeptide consisting of β-alanine and L-histidine, has demonstrated a therapeutic effect on various diseases because of its pivotal function. Despite the effect of L-carnosine in experimental IPF mice, its anti-oxidative effect and associated intercellular pathway, particularly alveolar epithelial cells, remain unknown. Therefore, we demonstrated the anti-fibrotic and anti-inflammatory effects of L-carnosine via Reactive oxygen species (ROS) regulation in bleomycin (BLM)-induced IPF mice. The mice were intratracheally injected with BLM (3 mg/kg) and L-carnosine (150 mg/kg) was orally administrated for 2 weeks. BLM exposure increased the protein level of Nox2, Nox4, p53, and Caspase-3, whereas L-carnosine treatment suppressed the protein level of Nox2, Nox4, p53, and Caspase-3 cleavage in mice. In addition, the total SOD activity and mRNA level of Sod2, catalase, and Nqo1 increased in mice treated with L-carnosine. At the cellular level, a human fibroblast (MRC-5) and mouse alveolar epithelial cell (MLE-12) were exposed to TGFβ1 following L-carnosine treatment to induce fibrogenesis. Moreover, MLE-12 cells were exposed to cigarette smoke extract (CSE). Consequently, L-carnosine treatment ameliorated fibrogenesis in fibroblasts and alveolar epithelial cells, and inflammation induced by ROS and CSE exposure was ameliorated. These results were associated with the inhibition of the NFκB pathway. Collectively, our data indicate that L-carnosine induces anti-inflammatory and anti-fibrotic effects on alveolar epithelial cells against the pathogenesis of IPF.

One of the aims of successful total knee arthroplasty (TKA) is to restore the natural range of motion of the infected joint. The operated leg motion highly depends on the coordinate systems that have been used to prepare the bone surfaces for an implant. Assigning a perfect coordinate system to the knee joint is a considerable challenge. Various commercially available knee arthroplasty devices use different methods to assign the coordinate system at the distal femur. Transepicondylar axis (TEA) and Whiteside’s line are commonly used anatomical axes for defining a femoral coordinate system (FCS). However, choosing a perfect TEA for FCS is trickier, even for experienced surgeons, and a small error in marking Whiteside’s line leads to a misaligned knee joint. This work proposes a modified Whiteside’s line method for the selection of TEA. The Whiteside’s line, along with the knee center and femur head center, define two independent central planes. Multiple prominent points on the lateral and medial sides of epicondyles are marked. Based on the lengths of perpendicular distances between the multiple points and central planes, the most prominent epicondyle points are chosen to define an optimal TEA. Compared to conventional techniques, the modified Whiteside’s line defines a repeatable TEA

Laser-matter interaction has been intensively studied in equilibrium states, but irreversible processes in a highly nonequilibrium state at nanoscales remains elusive due to experimental challenges. Here, Ihm et al. image heterogeneous melting of gold nanoparticles with nanometer and picosecond resolution.

Recently, the deep learning algorithm has received considerable attention and is influencing different fields including human-computer interaction (HCI). The purpose of this study is to maximize accuracy by applying deep learning to the classification of body movements. An experiment was performed to collect acceleration information on the wrist while performing seven workouts: pull up, row-barbell, bench press, dips, squat, deadlift, and military press. Participants were asked to perform each workout for ten sets repeated ten times per set. Experimental results confirm that one-dimensional convolutional neural network was the best among different algorithms including support vector machine, multi-layer perceptron, long short-term memory, and other deep convolutional neural networks. The accuracy was extremely high, 96%. The results of this experiment are applicable not only to the classification of fitness activities but also to the classification of different motions in numerous sporting events.

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) can be applied to determine the room-temperature structure of target molecules while minimizing radiation damage and visualizing molecular dynamics. In SFX, a sample delivery system is required to deliver microcrystals to the XFEL beam path in a serial manner. We recently developed a sample delivery method, the combined inject-and-transfer system (BITS), which is a hybrid method based on the injector and fixed-target scanning approach. In this study, we introduced recently upgraded hardware to move the injection needle in the direction of the XYZ-axis and a graphic user interface for user motion control. Furthermore, we report that the viscous solution containing 10% (w/v) PEG 3350 or PEG 6000 that is widely used for protein crystallization can be stably deposited on polyimide film with a hydrophobic surface without any special treatment. Moreover, the development of an inject-and-diffuse method for time-resolved studies with liquid applications in the BITS and its preliminary results are reported. This study provides up-to-date instrument information to SFX users using BITS and provides insights to instrument developers for SFX.

A crystal delivery system is essential in serial femtosecond crystallography experiments using an X-ray free-electron laser (XFEL). Investigating the XFEL-induced radiation damage to materials potentially applicable to sample delivery devices is vital for developing a sample delivery system. In this study, we investigated the radiation damage caused by an XFEL to polydimethylsiloxane (PDMS) and polyimide (PI), which are widely used as sample delivery materials in synchrotron X-rays. Upon XFEL exposure, the PDMS film produced irregularly shaped and sized holes, whereas the PI film produced relatively regular shaped and sized holes. When XFELs were exposed to the channel of the PDMS-based microfluidic device, holes were generated on the film by the radiation damage and the microfluidic device and the internal channel region were structurally destroyed. The PI-based microfluidic device experienced no structural destruction, except for the holes generated by the XFEL. However, as the XFELs were continuously exposed, bubbles generated from the solution due to radiation damage; the accumulation of these bubbles interfered with the path of the inner channel of the microfluidic device. Our results will not only help understand the phenomenon of radiation damage of PDMS and PI films by XFEL, but also provide insight into the directions to pursue in developing applications of PDMS and PI films in XFEL studies.