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Parkinson's disease (PD) is a neurodegenerative disorder that involves the loss of dopaminergic neurons in the substantia nigra (SN). Matrix metalloproteinases-8 (MMP-8), neutrophil collagenase, is a functional player in the progressive pathology of various inflammatory disorders. In this study, we administered an MMP-8 inhibitor (MMP-8i) in Leucine-rich repeat kinase 2 (LRRK2) G2019S transgenic mice, to determine the effects of MMP-8i on PD pathology. We observed a significant increase of ionized calcium- binding adapter molecule 1 (Iba1)-positive activated microglia in the striatum of LRRK2 G2019S mice compared to normal control mice, indicating enhanced neuro-inflammatory responses. The increased number of Iba1-positive activated microglia in LRRK2 G2019S PD mice was down-regulated by systemic administration of MMP-8i. Interestingly, this LRRK2 G2019S PD mice showed significantly reduced size of cell body area of tyrosine hydroxylase (TH) positive neurons in SN region and MMP-8i significantly recovered cellular atrophy shown in PD model indicating distinct neuro-protective effects of MMP-8i. Furthermore, MMP-8i administration markedly improved behavioral abnormalities of motor balancing coordination in rota-rod test in LRRK2 G2019S mice. These data suggest that MMP-8i attenuates the pathological symptoms of PD through anti-inflammatory processes.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a novel virus that causes coronavirus disease 2019 (COVID-19). To understand the identity, functional characteristics and therapeutic targets of the virus and the diseases, appropriate infection models that recapitulate the in vivo pathophysiology of the viral infection are necessary. This article reviews the various infection models, including Vero cells, human cell lines, organoids, and animal models, and discusses their advantages and disadvantages. This knowledge will be helpful for establishing an efficient system for defense against emerging infectious diseases.

This article serves as a review of the existing research on Asian family firms, synthesizing the literature using the goals, governance, and resources (GGR) framework, and suggesting future research directions. The article intends to take stock of the knowledge on Asian family firms and compare it with the literature on Western firms to enhance our understanding of family firms.

Treatments for epilepsy include pharmacotherapy or surgery. Recently, focused ultrasound stimulation has been investigated as a promising non-invasive neuromodulation tool for neurological disorders, including epilepsy. To investigate the neuronal dynamics in epilepsy, we acutely stimulated 29 Sprague–Dawley rats to low-intensity focused ultrasound stimulation (LIFUS) three times for 3 minutes. Pentylenetetrazol (PTZ) was injected into the abdominal cavity of the anesthetized rats to induce epilepsy. During the anesthesia, the electroencephalography (EEG) signal was measured and analyzed for 1 h in groups of animals untreated (sham) and treated with LIFUS. The EEG signal was quantitatively processed to show the different characteristics of the frequency change over time and of the band power between sham and treated groups. Histological analyses (Nissl, Iba1, c-Fos, and GAD65) measured the degree of staining of expression factors to confirm the effect of the stimulation on seizure suppression. These results suggest that repetitive LIFUS can effectively reduce epileptic seizure activity by attenuating theta and beta-band oscillation in a PTZ-induced rat model. LIFUS can potentially facilitate hippocampal and cortical cellular recovery by augmenting GABAergic inhibitory neurons via its anti-seizure effect.

Training an object instance detector where only a few training object images are available is a challenging task. One solution is a cut-and-paste method that generates a training dataset by cutting object areas out of training images and pasting them onto other background images. A detector trained on a dataset generated with a cut-and-paste method suffers from the conventional domain shift problem, which stems from a discrepancy between the source domain (generated training dataset) and the target domain (real test dataset). Though state-of-the-art domain adaptation methods are able to reduce this gap, it is limited because they do not consider the difference of domain gaps of foreground and background. In this study, we present that the conventional domain gap can be divided into two sub-domain gaps for foreground and background. Then, we show that the original cut-and-paste approach suffers from a new domain gap problem, an unbalanced domain gaps, because it has two separate source domains for foreground and background, unlike the conventional domain shift problem. Then, we introduce an advanced cut-and-paste method to balance the unbalanced domain gaps by diversifying the foreground with GAN (generative adversarial network)-generated seed images and simplifying the background using image processing techniques. Experimental results show that our method is effective for balancing domain gaps and improving the accuracy of object instance detection in a cluttered indoor environment using only a few seed images. Furthermore, we show that balancing domain gaps can improve the detection accuracy of state-of-the-art domain adaptation methods., Comment: 12 pages, 7 figures

To introduce the printed circuit steam generator (PCSG), the design methods were developed, and the CFD analysis was performed. The design considerations were divided into three parts: thermal sizing, structural integrity evaluation, and specific design for water channels. The thermal sizing was performed by a 1D heat transfer analysis and the channel length was increased until it satisfied the target thermal power. The structural integrity was evaluated based on the ASME code and the calculated stresses were compared to the allowable stress. The flow instability, flow maldistribution, and fouling have negative effects on the performance. In order to prevent such phenomena, the mixing channel and orifice were designed. The CFD analysis on the final design of the PCSG was performed to evaluate the steam state at the outlet and flow distribution. Although a flow maldistribution (∼12 %) was observed, the steam state at the outlet was satisfied with the operating conditions.

This article presents a 5.2-Mpixel, 12-in wafer-scale CMOS X-ray detector that consists of lithographically stitched 169 sub-chips. The detector employs a 3T pixel with a voltage-controlled storage capacitor to achieve both a low dark random noise (RN) and a large well capacity, and the pixel outputs are read out by column-parallel continuous-time (CT) incremental delta–sigma ( $\Delta \Sigma $ ) analog-to-digital converters (ADCs). The use of a CT incremental $\Delta \Sigma $ ADC enables high resolution and low energy consumption while securing uniformity and robustness over the 12-in wafer. This work is fabricated in a 1P4M 65-nm CMOS technology. The 16-bit ADC implemented within a 45- $\mu \text{m}$ pitch achieves a differential nonlinearity (DNL) of +0.79/−0.65 LSB, an integral nonlinearity (INL) of +6.85/−6.15 LSB, and a peak signal-to-noise ratio (SNR) of 88.5 dB with a conversion time of $12.6~\mu \text{s}$ . This detector achieves a CFPN of $181~\mu \text {V}_{\text {rms}}$ , a dark RN of $267~\mu \text {V}_{\text {rms}}$ , and a DR of 88.4 dB while consuming 3.9 W at 30 frames/s. Compared with the state of the arts, this work achieves $3\times $ larger spatial resolution, $1.8\times $ higher pixel rate, $1.9\times $ higher energy-efficiency, and 17 dB higher DR, simultaneously.

Most of existing deep learning-based depth and optical flow estimation methods require the supervision of a lot of ground truth data, and hardly generalize to video frames, resulting in temporal inconsistency. In this letter, we propose a joint framework that estimates disparity and optical flow of stereo videos and generalizes across various video frames by considering the spatiotemporal relation between the disparity and flow without supervision. To improve both accuracy and consistency, we propose a loop consistency loss which enforces the spatiotemporal consistency of the estimated disparity and optical flow. Furthermore, we introduce a video-based training scheme using the c-LSTM to reinforce the temporal consistency. Extensive experiments show our proposed methods not only estimate disparity and optical flow accurately but also further improve spatiotemporal consistency. Our framework outperforms the state-of-the-art unsupervised depth and optical flow estimation models on the KITTI benchmark dataset.

We describe a small lipid nanoparticle (SLNP)-based nanovaccine platform and a new combination treatment regimen. Tumor antigen-displaying, CpG adjuvant-embedded SLNPs (OVAPEP -SLNP@CpG) were prepared from biocompatible phospholipids and a cationic cholesterol derivative. The resulting nanovaccine showed highly potent antitumor efficacy in both prophylactic and therapeutic E.G7 tumor models. However, this vaccine induced T cell exhaustion by elevating PD-L1 expression, leading to tumor recurrence. Thus, the nanovaccine was combined with simultaneous anti-PD-1 antibody treatment, but the therapeutic efficacy of this regimen was comparable to that of the nanovaccine alone. Finally, mice that showed a good therapeutic response after the first cycle of immunization with the nanovaccine underwent a second cycle together with anti-PD-1 therapy, resulting in suppression of tumor relapse. This suggests that the antitumor efficacy of combinations of nanovaccines with immune checkpoint blockade therapy is dependent on treatment sequence and the timing of each modality.

Following the previous article, here we describe the first field demonstration of the ELVIS system, performed at Newport Beach, CA. We examined ocean water to detect microorganisms using the combined holographic and light-field fluorescence microscope and successfully detected both eukaryotes and prokaryotes. The shared field of view provided simultaneous bright-field (amplitude), phase, and fluorescence information from both chlorophyll autofluorescence and acridine orange staining. The entire process was performed in a nearly autonomous manner using a specifically designed sample processing unit (SPU) and custom acquisition software. We also discuss improvements to the system made after the field test that will make it more broadly useful to other types of fluorophores and samples.

The buildability of cementitious structure for 3D printing was investigated in terms of ettringite formation and hardening behaviour with packing density of OPC-CSA blends. The packing density of t...

A hurdle to the commercialization of thermoelectrochemical cells (TECs) based on the redox reaction of hexacyanoferrate (HCF) to convert low-grade waste heat into electricity is the high manufactur...

Summary We report the implantation of patient-derived midbrain dopaminergic progenitor cells, differentiated in vitro from autologous induced pluripotent stem cells (iPSCs), in a patient with idiop...

Recent advancements in robots and deep learning have led to active research in human-robot interaction. However, non-physical interaction using visual devices such as laser pointers has gained less attention than physical interaction using complex robots such as humanoids. Such vision-based interaction has high potential for use in recent human-robot collaboration environments such as assembly guidance, even with a minimum amount of configuration. In this paper, we introduce a simple robotic laser pointer device that follows an arbitrary planar path and is designed to be a visual instructional aid. We also propose an image-based automatic path generation method using reinforcement learning and a sequential pattern reduction technique. However, such vision-based human-robot interaction is generally performed in a dynamic environment, and it can frequently be necessary to calibrate the devices more than once. In this paper, we avoid the need for this re-calibration process through episodic randomization learning and improved learning efficiency. In particular, contrary to previous approaches, the agent controls the curriculum difficulty in a self-directed manner to determine the optimal curriculum. To our knowledge, this is the first study of curriculum learning that incorporates an explicit learning environment control signal initiated by the agent itself. Through quantitative and qualitative analyses, we show that the proposed self-directed curriculum learning method outperforms ordinary episodic randomization and curriculum learning. We hope that the proposed method can be extended to a general reinforcement learning framework.

Background Fat fraction values obtained from magnetic resonance imaging (MRI) can be used to obtain an accurate diagnosis of fatty liver diseases. However, MRI is expensive and cannot be performed for everyone. Objective In this study, we aim to develop multi-view ultrasound image–based convolutional deep learning models to detect fatty liver disease and yield fat fraction values. Methods We extracted 90 ultrasound images of the right intercostal view and 90 ultrasound images of the right intercostal view containing the right renal cortex from 39 cases of fatty liver (MRI–proton density fat fraction [MRI–PDFF] ≥ 5%) and 51 normal subjects (MRI–PDFF < 5%), with MRI–PDFF values obtained from Good Gang-An Hospital. We obtained combined liver and kidney-liver (CLKL) images to train the deep learning models and developed classification and regression models based on the VGG19 model to classify fatty liver disease and yield fat fraction values. We employed the data augmentation techniques such as flip and rotation to prevent the deep learning model from overfitting. We determined the deep learning model with performance metrics such as accuracy, sensitivity, specificity, and coefficient of determination (R2). Results In demographic information, all metrics such as age and sex were similar between the two groups—fatty liver disease and normal subjects. In classification, the model trained on CLKL images achieved 80.1% accuracy, 86.2% precision, and 80.5% specificity to detect fatty liver disease. In regression, the predicted fat fraction values of the regression model trained on CLKL images correlated with MRI–PDFF values (R2=0.633), indicating that the predicted fat fraction values were moderately estimated. Conclusions With deep learning techniques and multi-view ultrasound images, it is potentially possible to replace MRI–PDFF values with deep learning predictions for detecting fatty liver disease and estimating fat fraction values.

Most of the recent studies of reinforcement learning and robotics basically employ computer simulation due to the advantages of time and cost. For this reason, users have to spare time for investigation in order to choose optimal environment for their purposes. This paper presents a survey result that can be a guidance in user’s choice for simulation environments. The investigation result includes features, brief historical backgrounds, license policies and formats for robot and object description of the eight most popular environments in robot RL studies. We also propose a quantitative evaluation method for those simulation environments considering the features and a pragmatic point of view.

Gujin Shilin 古今释林 is a multilingual dictionary, published in 1789 by Yi Ui-Pong (李义凤). The initial motivation for publishing the book was to supplement the incompleteness of other commentary books on Zhuzi Yulei 朱子语类, including Yi Hwang’s (李滉) Yulujie 语录解. At last, Yi Ui-Pong completed his project as an unabridged encyclopedia of East Asian languages based on more than 1500 Jingshiziji 经史子集 (classics, history, philosophy and collections of belle letters) of China and Korea. The book has been understudied because of its enormous number of entries, its inaccessibility due to the many languages used within, and its lack of readability due to typos and variant letters. Therefore, the goal of the present paper is to analyze the background of the publication of Gujin Shilin, and its structure, content and research value. Gujin Shilin contains vocabulary from Mongolian, Manchu, Japanese, Vietnamese, Thai as well as Korean and Chinese from different historical periods. These can be used as invaluable data for the study of international studies involved in each field, including the phonological changes that the Asian languages have gone through, the interrelation among Asian languages and their mutual influences, the linguistic life, culture and custom shared by the ‘Chinese character cultural sphere’ countries and the study of the variant Chinese characters.

This study examines the impact of advertising to determine whether advertising expenditure after economic shocks is associated with hospitality firm performance. Using the ordinary least squares (O...

A holey graphene film emitter, introduced here as a planar emitter, does not have the problems of screening and emitter height leveling that traditional planar emitters suffer from. Therefore, the emitter exhibits outstanding performance in terms of output intensity and device lifetime. Furthermore, the flexible nature of the device would allow its use as a high power electron beam source.

OBJECTIVE To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms. SETTINGS AND POPULATION A total of 499 pairs of hand-wrist radiographs and lateral cephalograms of 455 orthodontic patients aged 6-18 years were used for developing the prediction model for hand-wrist skeletal maturation stages. MATERIALS AND METHODS The hand-wrist radiographs and the lateral cephalograms were collected from two university hospitals and a paediatric dental clinic. After identifying the 13 anatomic landmarks of the CV, the width-height ratio, width-perpendicular height ratio and concavity ratio of the CV were used as the morphometric features of the CV. Patients' chronological age and sex were also included as input data. The ground truth data were the Fishman SMI based on the hand-wrist radiographs. Three specialists determined the ground truth SMI. An ensemble machine learning methods were used to predict the Fishman SMI. Five-fold cross-validation was performed. The mean absolute error (MAE), round MAE and root mean square error (RMSE) values were used to assess the performance of the final ensemble model. RESULTS The final ensemble model consisted of eight machine learning models. The MAE, round MAE and RMSE were 0.90, 0.87 and 1.20, respectively. CONCLUSION Prediction of hand-wrist SMI based on CV images is possible using machine learning methods. Chronological age and sex increased the prediction accuracy. An automated diagnosis of the skeletal maturation may aid as a decision-supporting tool for evaluating the optimal treatment timing for growing patients.

In this paper, a new full-bridge (FB) converter with asymmetric duty cycle control for low common mode (CM) noise is proposed in order to achieve high power density. The conventional phase shifted full-bridge converter (PSFB) has a large CM noise, which increases the volume of the CM choke. The proposed converter uses a coupled inductor and an asymmetric duty cycle control to generate the similar voltage variations of the transformer at the primary and secondary side. A CM noise generated from the transformer can be reduced because dv/dt characteristic of the primary side is similar to it of the secondary side. As a result, the proposed converter can achieve high power density by reducing the volume of the CM choke. The performance of the proposed converter is verified by the experimental results with a 56 V/13.4 A 750 W prototype converter.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

Background Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.

This paper deals with a methodology for displaying the geomagnetic direction of cameras installed in various parts of a city on a map. Since the normal camera does not have a sensor that can measure the geomagnetic direction, it does not know the direction. So, it’s not possible to draw the direction and the region being viewed on the map unlike its position. For this reason, this paper propose a methodology for acquiring the direction with operator’s feedback. The camera is set with several pan, tilt, zoom value and the operator directs which area in the map belong to the scenery from the camera. This paper established a camera environment model for parameter acquisition and presented the results of testing this model in a laboratory environment.

The authors have requested that this preprint be removed from Research Square.

Background Dust exposure has been reported as a risk factor of pulmonary disease, leading to alterations of segmental airways and parenchymal lungs. This study aims to investigate alterations of quantitative computed tomography (QCT)-based airway structural and functional metrics due to cement-dust exposure. Methods To reduce confounding factors, subjects with normal spirometry without fibrosis, asthma and pneumonia histories were only selected, and a propensity score matching was applied to match age, sex, height, smoking status, and pack-years. Thus, from a larger data set (N = 609), only 41 cement dust-exposed subjects were compared with 164 non-cement dust-exposed subjects. QCT imaging metrics of airway hydraulic diameter (Dh), wall thickness (WT), and bifurcation angle (θ) were extracted at total lung capacity (TLC) and functional residual capacity (FRC), along with their deformation ratios between TLC and FRC. Results In TLC scan, dust-exposed subjects showed a decrease of Dh (airway narrowing) especially at lower-lobes (p p θ at most of the central airways (p p θ at the right main bronchi and left main bronchi (p Conclusions Dust-exposed subjects with normal spirometry demonstrated airway narrowing at lower-lobes, wall thickening at all segmental airways, a different bifurcation angle at central airways, and a loss of airway wall elasticity at lower-lobes. The airway structural alterations may indicate different airway pathophysiology due to cement dusts.

White-light-emitting materials have received significant attention because of their potential application in lighting, displays, and sensors. However, it is a challenge to obtain white light from one phosphor, because the basic requirement of the white light emission spectrum is that it should be wide enough to cover the entire visible light region. In this study, we have designed and demonstrated a white-light-emitting PMMA–CBS-127/PVP–coumarin 6/PAN–rhodamine B (PSCR) fibrous membrane, which was prepared through a triphasic electrospinning method. Three luminescent organic dyes, CBS-127 (4.77 wt%, blue), coumarin 6 (0.1 wt%, green), and rhodamine B (0.42 wt%, red), were elaborately selected and doped into PMMA, PVP, and PAN, respectively. The resulting flexible PSCR membranes show white light emission (cover the entire visible-light region from 382 to 700 nm) with Commission Internationale de L'Eclairage (CIE) coordinates of (0.31, 0.32), which is very close to ideal white light with CIE coordinates (0.33, 0.33). In addition, the PSCR membranes maintained high-quality white light emission after about 10 weeks of storage. The PSCR membranes can be used as the phosphor converting layer in white light-emitting diodes (WLEDs) through a remote membrane packaging method. A bright white emission is achieved at an applied voltage of 9 V. Therefore, the results indicate that PSCR membranes are potentially attractive candidates for application in WLEDs and displays.

This is the first of two articles on the Extant Life Volumetric Imaging System (ELVIS) describing a combined digital holographic microscope (DHM) and a fluorescence light-field microscope (FLFM). The instrument is modular and robust enough for field use. Each mode uses its own illumination source and camera, but both microscopes share a common objective lens and sample viewing chamber. This allows correlative volumetric imaging in amplitude, quantitative phase, and fluorescence modes. A detailed schematic and parts list is presented, as well as links to open-source software packages for data acquisition and analysis that permits interested researchers to duplicate the design. Instrument performance is quantified using test targets and beads. In the second article on ELVIS, to be published in the next issue of Microscopy Today, analysis of data from field tests and images of microorganisms will be presented.

A promising way to search for microbial life in our solar system's Ocean Worlds is to make use of 3-d microscopes, as these can provide single-image inventories of the complete contents of liquid sample volumes. Two applicable 3d microscopy techniques are digital holographic microscopy and light-field fluorescence microscopy. The former can provide high-resolution imaging information on cellular morphology, structure, index of refraction, and motility, while the latter can identify and locate targeted molecule families, such as lipids and nucleic acids. The combination of this pair of 3-d techniques thus provides a powerful suite of diagnostic tools. We have recently combined both types of microscope into an integrated dual-mode microscope prototype aimed at demonstrating its utility at terrestrial field sites, and the combined instrument has already been taken on an initial foray into the field to assess its performance and shortcomings. Here we describe the design and capabilities of our dual-mode microscope, as well as initial performance measurements obtained during its first field trip to the local seashore.

CMOS X-ray detectors used in industrial and medical equipment should provide a full image depth even for a specific region of interest, and require high resolution, low noise, and wide DR in a wafer-scale detector [1], [4]. To achieve a wide DR, a large integration capacitor is required within the pixel to prevent its saturation at high dose, but this degrades image quality at low dose. To facilitate wide DR (>70dB), a conventional detector uses a column-parallel readout with a programmable gain amplifier (PGA) and an ADC [3]. However, the PGA consumes substantially more power and area than the ADC, and its gain control requires multiple X-ray exposures. The use of switched-capacitor (SC) ΔΣ ADC provides wide DR with an improved noise performance [2], [5]. However, its SC input draws high peak current that must be supplied by pixels and reference drivers, and its complex clock distribution also requires high power consumption.

Integration of dark-field microscopy into compressed ultrafast photography enables 1 THz real-time imaging of transparent objects., With the growing interest in the optical imaging of ultrafast phenomena in transparent objects, from shock wave to neuronal action potentials, high contrast imaging at high frame rates has become desirable. While phase sensitivity provides the contrast, the frame rates and sequence depths are highly limited by the detectors. Here, we present phase-sensitive compressed ultrafast photography (pCUP) for single-shot real-time ultrafast imaging of transparent objects by combining the contrast of dark-field imaging with the speed and the sequence depth of CUP. By imaging the optical Kerr effect and shock wave propagation, we demonstrate that pCUP can image light-speed phase signals in a single shot with up to 350 frames captured at up to 1 trillion frames per second. We expect pCUP to be broadly used for a vast range of fundamental and applied sciences.

The surveillance video management system is rapidly expanding its scope of application at the request of citizens and the development of related technologies. In addition, as Cloud Computing and 5G network are applied with AI, scope and function of surveillance systems are being enhanced to intelligent CCTV beyond simple monitoring. However, intelligent CCTV systems with Mobile Edge Computing and 5G, which have the risk of privacy infringement. Accordingly, it is necessary to identify various types of security threats that can be occurred through the cloud based surveillance system and to eliminate the risk of privacy and personal information breaches. So, in this paper, we propose a hierarchical cloud based video surveillance system considering security on the 5G Network.

The ongoing expedition to harvest ambient renewable energies from the environment by wearable fabric-based nanogenerators is a promising route to sustainably drive the small electronics with unprecedented opportunities in next-generation self-powered devices. Here, we report a simple method to fabricate a washable, breathable and wearable triboelectric nanogenerator that harvests the energy of triboelectricity through an enhanced friction surface area made of the gold nanodot-pattern crafted by electron-beam sputtering on an inexpensive polyurethane surface. The gold deposition which crops-up as regular small islands, under oxygen plasma is subsequently, etched into nanodot-pattern on a polyurethane surface to convert mechanical energy into an electrical signal via in-plane sliding mode with a maximum output of ~2 μW. The nanodot engineering plays an important role to improve the active sliding frictional area, as well as the corresponding output-performance of the triboelectric nanogenerator. To demonstrate the potential applications of our approach, we designed a self-powered wearable device integrated with clothes to harvest different kinds of mechanical energies from the human motion. To elevate the power output-performance, we fabricated waterproof fiber with flutter membrane and quantified triboelectric charge against airflow speed. At mild wind speed, the fabricated triboelectric nanogenerator shows a maximum output of 70 µW. Besides, as an example of practical application, the nanogenerator constructed can produce an improved capacitor charge voltage to drive dozens of light-emitting diodes and apply them to low power consumption devices. This technology is produced in a simple and cost-effective manner and reports an easy way to produce an energy harvesting system based on triboelectric effects using a sustainable and renewable energy source of body motions and air flows. This system is expected to be one of the best green energy sources for portable and wearable electronic devices in the near future.

Carbon dioxide separation has drawn much interest because of the concern over global warming. A simple and yet effective way of removing CO2 is highly desirable for the separation involving combustion effluents, consisting mainly of CO2 and N2, hydrogen production involving CO2 and H2 mixtures, and natural gas sweetening involving mainly CO2 and CH4. Here, we present water membrane for CO2 separation. The water membrane is simply a water layer formed on a hydrophobic support membrane to prevent the water from permeating through the support layer. The membrane exploits the fact that the solubility of CO2 in water is almost two orders of magnitude higher than the solubility of the gases of N2, H2, and CH4. Both the permeance and the selectivity increases with decreasing water layer thickness, contrasting the traditional tradeoff between permeance and selectivity. The carbon dioxide selectivity with respect to nitrogen, methane, and hydrogen is 86, 66, and 74, respectively. The permeance of 1.1 GPU reached with 5 mm thick water layer should improve in due time with further work on reducing the thickness.

Purpose This paper aims to examine the merit of an indirect payment system for audit fees, a system where an intermediary collects fees from the auditee and then pays this audit fee to the auditor. Design/methodology/approach Big 4 auditors and professional analysts in South Korea participated in an experiment and survey to investigate whether the change in the payment channel (from direct to indirect) of audit fees positively impacts auditors’ decision-making. Findings The authors find evidence that the indirect payment of audit fees is positively associated with professional skepticism. Research limitations/implications This paper, by highlighting the potential for alternate auditor payment channels to improve the quality of auditor judgments, motivates future research in this area. Practical implications Qualified by the need for further research, the potential merit in an indirect payment system may have implications for audit regulators. Social implications An indirect payment channel has the potential to improve public perceptions of the audit function, thereby elevating society’s confidence in auditor opinions and improving the effectiveness and efficiency with which scarce resources are distributed within society. Originality/value This study is one of the first that looks into a systematic change in audit fee payment channel and how an indirect payment system of audit fees impacts professional skepticism.

Buildings feature a prominent role in electric grid loading, as they use about 75% of the total electricity generated in the United States and are main drivers of electric peak demand in the summer due to electrically driven air conditioning systems. Energy storage is a key technology that can increase energy cost savings, and add flexibility to the grid. However, cost is an important factor to consider. This study proposes a rapid approach that allows for visualization of potential cost savings by introducing energy storage as a peak load control for residential buildings in California. A combination of EnergyPlus load data generation, Matlab post-processing, and Google Fusion Tables data presentation analyses the potential cost savings when energy storage is implemented and TOU rates are applied. The study presents potential annual cost savings of $420 per home with storage capacities of 24 kWh.

Recently, interest in the development of alternative water resources has been increasing rapidly due to environmental pollution and depletion of water resources. In particular, seawater desalination has been attracting the most attention as alternative water resources. As seawater desalination consumes a large amount of energy due to high operating pressure, many researches have been conducted to improve energy efficiency such as energy recovery device (ERD). Consequently, this study aims to compare the energy efficiency of RO process according to ERD of isobaric type which is applied in scientific control pilot plant process of each 100 ㎥/day scale based on actual RO product water. As a result, it was confirmed that efficiency, mixing rate, and permeate conductivity were different depending on the size of the apparatus even though the same principle of the ERD was applied. It is believed that this is caused by the difference in cross-sectional area of the contacted portion for pressure transfer inside the ERD. Therefore, further study is needed to confirm the optimum conditions what is applicable to the actual process considering the correlation with other factors as well as the factors obtained from the previous experiments.

A clear solution for efficient thermal management of light emitting diodes (LEDs) is elusive, which is critical for general purpose lighting. Here, a hierarchical porous structure of reduced graphene oxide through ion-mediation (IMA-rGO) is developed for efficient heat dissipation. Compared to existing copper materials, IMA-rGO yielded the highest cooling efficiency of 36.5% through the simple and easily scalable ion-mediation process. This highly efficient heat dissipater lowered the operating temperature of a high power commercial LED from 75 °C to 59 °C, leading to a reduction in the required energy and an increase in device lifetime. With the features of a simple, scalable process and excellent heat dissipation performance, the IMA-rGO introduced here could bring LEDs closer to our daily lives.

Objective: Adrenal nodules are often incidentally detected on abdominal computed tomography (CT) scans due to their asymptomatic nature. We aimed to develop an automatic detection program for adrenal nodules on abdominal CT scans using deep learning algorithms. Methods: We retrospectively analyzed abdominal CT scans performed at two university-affiliated hospitals (n = 483 and n = 514, respectively) from 2006 to 2019. This dataset was randomly divided into training set (181 CTs without adrenal nodule and 362 CTs with adrenal nodule) and test set (291 CTs without adrenal nodule and 163 CTs with adrenal nodule). All CT scans were contrast-enhanced and the phase with the highest contrast between adrenal gland and adjacent normal tissues was selected for multi-phase CT. The core algorithm of our deep learning algorithm for adrenal nodule (DLAAN) was MULAN (Multitask Universal Lesion Analysis Network) algorithm whose backbone was a convolutional neural network. DLAAN was composed of two stages. The first stage was to detect the CT slice where normal adrenal gland or adrenal nodule were located. The second stage was for fine localization of adrenal nodule on the corresponding CT slice. The performance of DLAAN was evaluated using the area under the receiver operating characteristic curve (AUROC) for patient-level classification and free-response ROC for nodule-level localization. The figure of merit for free-response ROC was calculated as an average sensitivity when 0.5, 1, 2, and 4 false positives per slice were allowed. Results: The AUROC of DLAAN was 0.927 (95% confidence interval: 0.900–0.955). With a threshold probability of 0.9, the sensitivity and specificity were 86.5% and 89.0%, respectively. When left and right adrenal nodules were analyzed separately, the AUROC was 0.910 for left adrenal nodule and 0.957 for right adrenal nodule, respectively. The accuracy of DLAAN according to the size of adrenal nodule was 0.890, 0.734, 0.981, 1.00 and 1.00 for no adrenal nodule, adrenal nodule sized 1–2 cm, 2–3 cm, 3–4 cm and > 4 cm, respectively. The performance of DLAAN for the localization of adrenal nodule which was estimated by average sensitivity was 0.812. The number of CTs with at least one false positive nodule was 93/454 (20.5%). Conclusion: Our proof of concept study of deep learning-based automatic detection of adrenal nodule on contrast-enhanced abdominal CT scans showed high accuracy for both the classification of patients with or without adrenal nodule and the localization of adrenal nodule, although the performance of the algorithm decreased for small sized adrenal nodules. External validation with different CT settings and patient population is needed to assess the generalizability of our algorithm.

Nowadays, the need for research on an intelligent video monitoring system is increasing worldwide. Among the object detection methods, the core technology of the intelligent video monitoring system, or object detection using a deep learning-based convolutional neural network, is used widely due to its proven performance. Nonetheless, deep learning-based object detection requires many hardware resources because it decodes the videos to analyze. Therefore, this article suggests an advanced object recognition technique by conducting compressed video stream-based object detection in order to reduce consumption of resources for object detection as well as improve performance and confirms via the performance evaluation that speed and recognition rate improved compared to existing algorithms such as YOLO, SSD, and Faster RCNN.

Motion imitation is a fundamental communication skill for a robot; especially, as a nonverbal interaction with a human. Owing to kinematic configuration differences between the human and the robot, it is challenging to determine the appropriate mapping between the two pose domains. Moreover, technical limitations while extracting 3D motion details, such as wrist joint movements from human motion videos, results in significant challenges in motion retargeting. Explicit mapping over different motion domains indicates a considerably inefficient solution. To solve these problems, we propose a three-phase reinforcement learning scheme to enable a NAO robot to learn motions from human pose skeletons extracted from video inputs. Our learning scheme consists of three phases: (i) phase one for learning preparation, (ii) phase two for a simulation-based reinforcement learning, and (iii) phase three for a human-in-the-loop-based reinforcement learning. In phase one, embeddings of the motions of a human skeleton and robot are learned by an autoencoder. In phase two, the NAO robot learns a rough imitation skill using reinforcement learning that translates the learned embeddings. In the last phase, the robot learns motion details that were not considered in the previous phases by interactively setting rewards based on direct teaching instead of the method used in the previous phase. Especially, it is to be noted that a relatively smaller number of interactive inputs are required for motion details in phase three when compared to the large volume of training sets required for overall imitation in phase two. The experimental results demonstrate that the proposed method improves the imitation skills efficiently for hand waving and saluting motions obtained from NTU-DB.

When applying deep reinforcement learning to the motion mimicry problem between teacher and student robots, this paper reports the initial results of how various hyper-parameter configurations affect performance of learning processes and quality of generated motions. The hyperparameters considered in this study include the structure of policies such as convolutional and fully connected networks, the type of activation functions such as ReLU and hyperbolic tangent, and the number of input sequences such as one, four and eight. Under these deep neural network configurations, PPO reinforcement learning algorithm has been applied for learning. In the simulator environment, the teacher NAO robot demonstrates a target action repeatedly, and the learner NAO robot tries to learn that action. The target actions include handshaking and two-arm raising. Our experimental results show that fully connected networks outperform the convolutional counterparts both in training statistics and motion quality. For activation functions, however, we found an interesting mismatch between training and evaluation quality: for example, a configuration with higher rewards does not guarantee less motion discrepancy, which may suggest a new research direction to design better loss and reward functions for robotic motion mimicry.

Air pollution is one of the most serious problems facing mankind because of its impact on ecosystems and human beings. Although particulate matter (PM) consists of both filterable PM (FPM) and condensable PM (CPM), most research has focused on eliminating only FPM. In this work, we introduce a filter system that removes both FPM and CPM from pollution source with high efficiency. The system consists of two reduced graphene oxide (rGO) filters and a condenser between them that can remove the usual FPM and at the same time CPM-induced FPM that typically leaves the pollution source unabated. The filters, quite effective in removing the PM with their three-dimensional structure, retain the removal capability even at high temperature and in acidic condition that prevail at the pollution source. The proposed rGO system could provide a complete solution for removal of both FPM and CPM from the pollution source.