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The COVID-19 pandemic has significantly disrupted global economies, posing unprecedented business challenges worldwide. This study examines the influence of Environmental, Social, and Governance (ESG) factors on corporate value in the context of the pandemic, focusing on China and South Korea. Using empirical analysis and data from Chinese A-share and South Korean KOSPI-listed companies, our research reveals complex dynamics. ESG had a negative impact on corporate value during the pandemic. However, firms with higher ESG scores demonstrated an insurance effect, mitigating risk. This effect held in both China and South Korea, highlighting the importance of ESG principles for resilience and sustainable growth in contemporary business practices. This research provides implications for ESG-focused businesses during crises.

Abstract Pathologic α-synuclein (α-syn) spreads from cell-to-cell, in part, through binding to the lymphocyte-activation gene 3 (Lag3). Here we report that amyloid β precursor-like protein 1 (Aplp1) interacts with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic α-syn. Deletion of both Aplp1 and Lag3 eliminates the loss of dopaminergic neurons and the accompanying behavioral deficits induced by α-syn preformed fibrils (PFF). Anti-Lag3 prevents the internalization of α-syn PFF by disrupting the interaction of Aplp1 and Lag3, and blocks the neurodegeneration induced by α-syn PFF in vivo. The identification of Aplp1 and the interplay with Lag3 for α-syn PFF induced pathology deepens our insight about molecular mechanisms of cell-to-cell transmission of pathologic α-syn and provides additional targets for therapeutic strategies aimed at preventing neurodegeneration in Parkinson’s disease and related α-synucleinopathies.

In this study, we investigate what drives the MAX effect in the South Korean stock market. We find that the MAX effect is significant only for overpriced stocks categorized by the composite mispricing index. Our results suggest that investors' demand for the lottery and the arbitrage risk effect of MAX may overlap and negate each other. Furthermore, MAX itself has independent information apart from idiosyncratic volatility (IVOL), which assures that the high positive correlation between IVOL and MAX does not directly cause our empirical findings. Finally, by analyzing the direct trading behavior of investors, our results suggest that investors' buying pressure for lottery-like stocks is concentrated among overpriced stocks.

Abstract Mitophagy induction upon mitochondrial stress is critical for maintaining mitochondrial homeostasis and cellular function. Here, we found that Mst1/2 (Stk3/4), key regulators of the Hippo pathway, are required for the induction of mitophagy under various mitochondrial stress conditions. Knockdown of Mst1/2 or pharmacological inhibition by XMU-MP-1 treatment led to impaired mitophagy induction upon CCCP and DFP treatment. Mechanistically, Mst1/2 induces mitophagy independently of the PINK1-Parkin pathway and the canonical Hippo pathway. Moreover, our results suggest the essential involvement of BNIP3 in Mst1/2-mediated mitophagy induction upon mitochondrial stress. Notably, Mst1/2 knockdown diminishes mitophagy induction, exacerbates mitochondrial dysfunction, and reduces cellular survival upon neurotoxic stress in both SH-SY5Y cells and Drosophila models. Conversely, Mst1 and Mst2 expression enhances mitophagy induction and cell survival. In addition, AAV-mediated Mst1 expression reduced the loss of TH-positive neurons, ameliorated behavioral deficits, and improved mitochondrial function in an MPTP-induced Parkinson’s disease mouse model. Our findings reveal the Mst1/2-BNIP3 regulatory axis as a novel mediator of mitophagy induction under conditions of mitochondrial stress and suggest that Mst1/2 play a pivotal role in maintaining mitochondrial function and neuronal viability in response to neurotoxic treatment.

Abstract Electric fields have been highlighted as a smart reagent in nature’s enzymatic machinery, as they can directly trigger or accelerate chemical processes with stereo- and regio-specificity. In enzymatic catalysis, controlled mass transport of chemical species is also key in facilitating the availability of reactants in the active reaction site. However, recent progress in developing a clean catalysis that profits from oriented electric fields is limited to theoretical and experimental studies at the single molecule level, where both the control over mass transport and scalability cannot be tested. Here, we quantify the electrostatic catalysis of a prototypical Huisgen cycloaddition in a large-area electrode surface and directly compare its performance to the conventional Cu(I) catalysis. Our custom-built microfluidic cell enhances reagent transport towards the electrified reactive interface. This continuous-flow microfluidic electrostatic reactor is an example of an electric-field driven platform where clean large-scale electrostatic catalytic processes can be efficiently implemented and regulated.

Abstract Background Cognitive impairment, a characteristic and prior stage of dementia, is a serious public health concern in Korea a country with rapidly aging population. In a neurovisceral integration model, cognitive ability is connected to emotional and autonomic regulation via an interconnection in the brain, which may be associated with health-related quality of life (HRQoL). Methods This study investigated the association between the HRQoL and the autonomic nervous system (ANS) via EuroQoL-5 Dimension (EQ-5D) and heart rate variability (HRV) among 417 patients who visited the Neurology Department in Veterans Health Service Medical Center, Seoul, South Korea. Results The mean age of 275 patients in the cognitive impairment group (CIG) was higher than that of 142 patients in the normal cognition group (NCG) (74.85 years vs. 72.96 years, p

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.

Background: Studies investigating the immediate effects of a single intervention to correct forward head posture are rare. Objects: This study aimed to compare the changes in treatment effects in patients with forward head posture and neck pain after manual and self-exercise therapy over a 1-hour period. Methods: Twenty-eight participants were randomly divided into manual and self-exercise therapy groups. Following the initial evaluation, manual or self-exercise therapy was applied to each group for 30 minutes each in the prone, supine, and sitting positions. The variables measured were the craniovertebral angle (CVA), stress level, pain level, and sternocleidomastoid (SCM) stiffness. After the intervention, re-evaluation was conducted immediately, 30 minutes later, and 1 hour later. Two-way analysis of variance (ANOVA) was used to compare the maintenance of treatment effects between the two groups. Results: Based on the two-way mixed ANOVA variance, there was no interaction between the groups and time for all variables, and no main effects were found between the groups. However, a significant effect of time was observed (p < 0.05). Post hoc tests using Bonferroni's correction revealed that in both groups, the CVA, pain, and stress showed significant improvements immediately after the intervention compared with before the intervention, and these treatment effects were maintained for up to 1 hour after the treatment (p < 0.0083) in the manual therapy group. However, the stress level was maintained until 30 minutes later (p < 0.0083) in the self-exercise group. There was no significant decrease in right SCM stiffness before and after the intervention; however, left SCM stiffness significantly decreased after the self-exercise intervention (p < 0.0083). Conclusion: Both manual and self-exercise therapy for 30 minutes were effective in reducing forward head posture related to the CVA, pain, and stress levels. These effects persisted for at least 30 minutes.

The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make image authentication extremely difficult. The aim of this paper is to establish a binary classification method for determining copyright images from copyright-free ones. A deep hashing model is introduced for an image authentication system, which uses deep learning-based perceptual hashing. Hash codes from a deep hashing model trained with a copyright image dataset are used to identify images. The deep learning model is able to learn features that represent the implicit meaning or structural information of an image. The copyright dataset, which lacks class labels, is trained with deep hashing models with self-supervision. The proposed model is based on an autoencoder or variational autoencoder model and is improved by including convolutional filters, residual blocks, and vision transformer blocks. Experimental results show that the proposed model performs a one-to-one mapping with most stored images and can retrieve related images using image features in hash collisions. The model can find the query image among the top 5 images with comparable hash codes. The results indicate that the proposed deep hashing approach is robust and applicable.

Abstract Impurities or boundaries often impose nontrivial boundary conditions on a gapless bulk, resulting in distinct boundary universality classes for a given bulk, phase transitions, and non-Fermi liquids in diverse systems. The underlying boundary states however remain largely unexplored. This is related with a fundamental issue how a Kondo cloud spatially forms to screen a magnetic impurity in a metal. Here we predict the quantum-coherent spatial and energy structure of multichannel Kondo clouds, representative boundary states involving competing non-Fermi liquids, by studying quantum entanglement between the impurity and the channels. Entanglement shells of distinct non-Fermi liquids coexist in the structure, depending on the channels. As temperature increases, the shells become suppressed one by one from the outside, and the remaining outermost shell determines the thermal phase of each channel. Detection of the entanglement shells is experimentally feasible. Our findings suggest a guide to studying other boundary states and boundary-bulk entanglement.

Abstract The study demonstrates high‐quality pulsed‐laser‐deposited (PLD) barium titanate (BTO) thin‐films on a magnesium oxide substrate. The frequency response of the relative permittivity (dielectric constant) and the linear electro‐optical coefficient (Pockels coefficient) are measured. At 0.2 GHz, the Pockels coefficient is fitted to be r42 ≈ 1030 pm V−1. It decreases to ≈390 pm V−1 at 10 GHz after which it remains constant up to 70 GHz. The unbiased BTO permittivity is measured to be εa ≈ 7600 at 0.2 GHz, dropping to ≈1100 at 67 GHz, while the biased BTO had a permittivity εa ≈ 2000 at 0.2 GHz, dropping to ≈500 at 67 GHz. These results fill an important experimental characterization gap for high‐speed BTO applications and show the high quality of PLD‐grown BTO films. Lastly, the material's crystalline quality is characterized and the domain distribution is imaged. The findings enable the design and fabrication of a new generation of BTO‐based components for sensing and communications.

Abstract Background A link between sarcopenia and cognitive function has been proposed and is supported by several investigations. Nevertheless, the sex-linked relationship between these two diseases has been scarcely investigated. This cross-sectional study investigated sex differences in the association between sarcopenia and mild cognitive impairment. Methods We included all 286 participants aged 60 years or older with MCI who visited the Department of Neurology at Veterans Health Service Medical Center in South Korea from January to December 2021. The diagnosis of MCI was confirmed by two neurologists based on the participants’ neuropsychological test scores. Diagnosis of sarcopenia was based on the algorithm of Asian Working Group for Sarcopenia (AWGS) 2019 including bioelectrical impedance analysis and handgrip strength, and cognitive function was assessed using Seoul Neuropsychological Screening Battery Core (SNSB-C) test. Results Among the 286 participants, 171 and 112 were men and women. After adjustment for potential covariates including APOE genotype, in women participants, there were significant associations between diagnosis of sarcopenia and MCI (OR = 4.72, 95%CI [1.39–15.97]), while there was no significant relationship in men participants. In eight subdomains of SNSB-C, we also found that women participants with sarcopenia demonstrated a significant memory decline (OR = 3.21, 95%CI [1.01–10.19]) as compared with the reference women group without sarcopenia after adjusting all covariates mentioned above. No significant association between any SNSB-C subdomain and MCI was demonstrated in men participants. Conclusions We demonstrated that there was a different relationship between sarcopenia and MCI by sex and that sarcopenia may affect the cognitive subdomain differently by sex. These results imply that, with regard to cognitive function, maintaining muscle function and muscle mass might be more crucial for women than for men.

Abstract Functional oxide nanoparticles are extensively utilized in the last decades for biomedical purposes due to their unique functional properties. Nevertheless, their biodegradation mechanism by biological species, particularly by proteins at oxide/protein interfaces, still remains limited. Here, a systematic approaches is provided to investigate electrochemical behavior, electronic properties, and biodegradation mechanism of cobalt ferrite (CFO) and cobalt ferrite‐bismuth ferrite (CFO‐BFO) core‐shell nanoparticles in apoferritin‐containing media. Scanning Kelvin probe force microscopy results indicate that the presence of a thin shell (≈5 nm) of BFO on CFO causes a significant increase in surface potential. The potentiodynamic polarization measurements in different solutions showed higher anodic current densities for both samples when decreasing pH and increasing apoferritin concentration. Notably, CFO‐BFO exhibits lower anodic current densities than CFO due to a slightly higher flat band potential and lower donor density distribution on CFO‐BFO than on CFO, which results in lower electrochemical activity. Long‐term monitoring reveals that biodegradation of both nanoparticles is accelerated by high apoferritin concentrations and acidic media, resulting in the decrease of electrochemical potentials and impedance values, and enhancement of metal ion release. Thus, this systematic biodegradation study can help to predict the lifespan and toxicity of these functional nanoparticles in biological environments.

Shape-memory materials are promising actuation sources for small-scale machines. The authors demonstrate that domain switching in twisted ferroic nanocomposites enables a giant shape-memory effect and superelasticity in the nanoscale structure.

Technology advancements have led to the use of millions of IoT devices. However, IoT devices are being exploited as an entry point due to security flaws by resource constraints. IoT malware is being discovered in a variety of types. The purpose of this study is to investigate whether IoT malware can be detected from benign and whether various malware family types can be classified. We propose fixed-length and low-dimensional features using opcode category information on ML models. The binary IoT dataset for this study is converted into opcode to create features. The opcodes are categorized into 6 or 11 according to their functionality. Features are created using a sequence of opcode categories and the entropy values of opcode categories. These features can be visualized by using a 2D image in order to observe patterns. We evaluate our proposed features on various ML models (5-NN, SVM, Decision Tree, and Random Forest) and MLP with various performance metrics, such as Accuracy, Precision, Recall, F1-score, MCC, AUC-ROC, and AUC-PR. The performance results for malware detection and classification have an accuracy over 98.0%. The experiments have demonstrated that the features we’ve proposed are effective and robust for identifying different types of IoT malware and benign.

Background: Pilates exercises are used for body shape correction because they can achieve correct posture alignment through spinal stabilization.Objects: This study aimed to determine whether the use of reformers increases the effective-ness of Pilates core exercises on body alignment in standing.Methods: The study included 30 women without known diagnoses of musculoskeletal and neurological disorders or cancer. Those who had taken more than 10 Pilates lessons were ex-cluded. The participants were randomly assigned to either the reformer exercise group or the mat exercise group, and interventional Pilates exercises were performed for 60 minutes a day, three times a week, for a total of 8 weeks. Ten movements of the reformer and mat Pilates core exercise programs were included. Exbody® 9100 MOMI musculoskeletal analysis equipment (Exbody Inc.) was used to assess the alignment of the standing posture in the frontal plane.Results: As a result of comparing the differences within and between the groups before and after the intervention using the two-way mixed analysis of variance test, height differences in the head, pelvis, left and right, shoulders, scapulas, knees, and ankles in the frontal plane after the intervention were found in both groups. For example, the left-right symmetry of the body alignment in the standing posture was significantly improved within each group (p < 0.05). However, no significant difference was found between the groups (p > 0.05).Conclusion: Both the reformer and mat Pilates core exercises were effective for standing posture alignment, which has clinical significance. If an exercise program is developed based on the analysis of movements necessary for posture improvement and the target muscles to be strengthened, the same effect can be achieved only with mat exercise without using the reformer equipment at the beginner stage.

Covalent modification is an essential chemical method for altering the physicochemical properties of material interfaces. Here, the authors show that the no-slip conditions in microfluidic devices grant spatiotemporal control over molecular grafting.

Abstract Background Klotho deficiency is a significant predictor of cardiovascular disease (CVD)-related mortality and morbidity. However, research assessing the association between klotho and individual risk factors of CVD is limited. This study aimed to explore the association between circulating serum klotho levels and risk factors for CVD in adults. Methods We used the 2007–2016 National Health and Nutrition Examination Survey and included 13,154 participants for whom serum klotho levels were available. Body mass index (BMI), exercise, smoking status, alcohol consumption, hypertension, dyslipidemia, serum lipid parameters, and blood pressure were considered as CVD risk factors. Results Circulating klotho levels were negatively associated with being overweight (beta coefficient: − 22.609, p = 0.0025), obesity (beta coefficient: − 23.716, p = 0.0011), current smoking (beta coefficient: − 46.412, p

The conventional von Neumann computing architecture is ill suited to data intensive tasks as data must be repeated moved between the separated processing and memory units. Here, Seo et al propose a CMOS compatible, highly linear gate injection field-effect transistor where data can be both stored and processed.

BackgroundFunctional limitations and disabilities have been associated with a decrease in cognitive function due to increasing age. Gait performance and cognitive function have been associated with gait variability in executive function, the phase domain in memory, and gait abnormalities in cognitive decline. ObjectiveOur study aimed to investigate whether gait harmony was associated with cognitive function in the older adult population. Moreover, we aimed to investigate whether gait harmony was associated with cognitive function and explore each cognitive function in a specific harmonic state. MethodsThe study population included 510 adults aged ≥60 years who visited the Department of Neurology at the Veterans Health Service Medical Center, Seoul, South Korea. Gait data were collected using a 3D motion capture device with a wireless inertial measurement unit system. For cognitive function assessments, we used the Seoul Neuropsychological Screening Battery-Core test, which evaluates the level of cognitive function or impairment in 5 cognitive domains. ResultsIn general, the association between the Seoul Neuropsychological Screening Battery-Core tests and the stance-to-swing ratio in the >1.63 ratio group yielded lower β coefficients than those in the 1.50-1.63 ratio group. After adjustment for confounders, the odds ratio (OR) for the Digit Symbol Coding test (adjusted OR 0.42, 95% CI 0.20-0.88) and the Korean version of the Color Word Stroop Test: 60 seconds (adjusted OR 0.51, 95% CI 0.29-0.89) for frontal and executive function were significantly lower for the >1.63 ratio group than the reference group. ConclusionsOur findings suggest that the gait phase ratio is a valuable indicator of walking deficits and may also be associated with cognitive impairment in older adults.

Background and objectives: This study investigates the effects of chest mobilization and breathing exercises on respiratory function, trunk stability, and endurance in chronic stroke patients who have contracted coronavirus disease (COVID-19). Materials and Methods: Thirty inpatients of a tertiary hospital in South Korea, who had a history of COVID-19 and were diagnosed with stroke within the last 6 months, were randomly assigned to either chest mobilization exercise with breathing exercise (CMEBE) or conservative physical therapy with breathing exercise (CPTBE) groups. The respiratory function, trunk stability, and endurance were measured at baseline and 6 weeks after the interventions. Results: Both CMEBE and CPTBE groups showed improvements in respiratory function, trunk stability, and endurance after the intervention (p < 0.05). However, the CMEBE group showed significantly greater improvements in forced expiratory volume in 1 s (p < 0.05), trunk stability (p < 0.05), and endurance (p < 0.05) than the CPTBE group. No significant intergroup difference was observed in forced vital capacity and peak expiratory flow. Conclusions: The combination of chest mobilization and breathing exercises improved respiratory muscle mobility and endurance, stabilized the trunk, and enhanced balance and the transfer of weight. The findings suggest that this intervention could be beneficial in improving respiratory function and endurance in stroke patients.

Abstract Alzheimer’s disease (AD) is the most common cause of age-related dementia. Increasing evidence suggests that neuroinflammation mediated by microglia and astrocytes contributes to disease progression and severity in AD and other neurodegenerative disorders. During AD progression, resident microglia undergo proinflammatory activation, resulting in an increased capacity to convert resting astrocytes to reactive astrocytes. Therefore, microglia are a major therapeutic target for AD and blocking microglia-astrocyte activation could limit neurodegeneration in AD. Here we report that NLY01, an engineered exedin-4, glucagon-like peptide-1 receptor (GLP-1R) agonist, selectively blocks β-amyloid (Aβ)-induced activation of microglia through GLP-1R activation and inhibits the formation of reactive astrocytes as well as preserves neurons in AD models. In two transgenic AD mouse models (5xFAD and 3xTg-AD), repeated subcutaneous administration of NLY01 blocked microglia-mediated reactive astrocyte conversion and preserved neuronal viability, resulting in improved spatial learning and memory. Our study indicates that the GLP-1 pathway plays a critical role in microglia-reactive astrocyte associated neuroinflammation in AD and the effects of NLY01 are primarily mediated through a direct action on Aβ-induced GLP-1R+ microglia, contributing to the inhibition of astrocyte reactivity. These results show that targeting upregulated GLP-1R in microglia is a viable therapy for AD and other neurodegenerative disorders.

Background/Aims Human adenovirus type 55 (HAdV-55), an emerging epidemic strain, has caused several large outbreaks in the Korean military since 2014, and HAdV-associated acute respiratory illness (HAdV-ARI) has been continuously reported thereafter. Methods To evaluate the epidemiologic characteristics of HAdV-ARI in the Korean military, we analyzed respiratory virus polymerase chain reaction (RV-PCR) results, pneumonia surveillance results, and severe HAdV cases from all 14 Korean military hospitals from January 2013 to May 2018 and compared these data with nationwide RV surveillance data for the civilian population. Results A total of 14,630 RV-PCRs was performed at military hospitals. HAdV (45.4%) was the most frequently detected RV, followed by human rhinovirus (12.3%) and influenza virus (6.3%). The percentage of the military positive for HAdV was significantly greater than the percentage of civilians positive for HAdV throughout the study period, with a large outbreak occurring during the winter to spring of 2014 to 2015. The outbreak continued until the end of the study, and non-seasonal detections increased over time. The reported number of pneumonia patients also increased during the outbreak. Case fatality rate was 0.075% overall but 15.6% in patients with respiratory failure. The proportion of severe patients did not change significantly during the study period. Conclusions A large HAdV outbreak is currently ongoing in the Korean military, with a trend away from seasonality, and HAdV-55 is likely the predominant strain. Persistent efforts to control the outbreak, HAdV type-specific surveillance, and vaccine development are required.

Malicious binary files are a serious threat to industrial information systems. Because of their large number, an automatic assistant tool becomes essential for analysis, and finding similar files would be a great help. In this paper, we present a fast, scalable, and multifaceted search scheme to find similar binary malware files. We use a content-defined chunking algorithm to convert a file into a feature set for the first time. The proposed scheme uses MinHash to reduce any feature set of any file to a fixed size, which significantly improves search accuracy, processing speed, and space utilization. We theoretically prove that the new scheme returns similar files in jaccard index order. Through implementation and experiments with 12 million malicious files, we confirm that the search speed is increased by 600%, space is reduced by 90%, and the accuracy is increased by 400% at least, compared with the state-of-the-art of Elasticsearch.

The number of malware has steadily increased as malware spread and evasion techniques have advanced. Machine learning has contributed to making malware analysis more efficient by detecting various behavioral and evasion patterns. However, when analyzing large-scale malware datasets, malware analysis through learning models has both high temporal and spatial complexity. In order to address these problems, this work proposes a low-dimensional feature using histogram entropy and a prototype selection algorithm using hyperrectangles. The low-dimensional feature forms an $L \times 256$ map according to the preselected parameter $L$ . The prototype selection algorithm divides the input space into overlapping subspaces where each subspace is decided by its hyperrectangle that becomes a prototype in the same class. A set cover optimization algorithm is employed to select a small number of prototypes that construct a new training dataset. A set of prototypes selected by the prototype selection algorithm is used to classify malware families. The experiment compares the performance of machine learning models for the histogram entropy feature using both the BIG 2015 dataset and the collected dataset. The integrated approach is evaluated using learning algorithms, such as Decision Tree, Random Forest, XGBoost, and CNN. The experimental results indicate that learning models perform competitively when compared to the entire dataset, while the proposed selection approach benefits from smaller datasets and lower time complexity.

Si-based electrolyte-gated transistors (EGTs) have been investigated to achieve high sensitivity and a low detection limit (LOD). Here, a lumped-capacitive model was applied to analyze the influence of receptor–target conjugates on the sensing performance. The voltage-related sensitivity is mainly determined by the effective dipole potential ( $V_{DP,EFF}$ ), the effective capacitance of the functionalization layer ( $C_{FN,EFF}$ ), and the total charge density in the channel ( $Q_{S}$ ) related to the operating regime. In an n-type EGT, the sensitivity is enhanced as the device is operated in the subthreshold regime with a positive $V_{DP,EFF}$ and a small $C_{FN,EFF}$ . The influence of the $V_{DP,EFF}$ and $C_{FN,EFF}$ was experimentally evaluated by detecting the peanut allergen (PA) using the fabricated n-type Si-EGTs. A higher sensitivity and a lower LOD could be achieved in the subthreshold regimes than in the linear regime. These results suggest that the proposed model is very useful to optimize the sensing characteristics of Si-based EGT for biosensing applications.

Advances in machine learning algorithms have improved the performance of malware detection systems for the last decade. However, there are still some challenges such as processing a large amount of malware, learning high-dimensional vectors, high storage usage, and low scalability in learning. This paper proposes low-dimensional but effective features for a malware detection system and analyzes them with tree-base ensemble models. Expert knowledge and frequency analysis are adapted for relevant feature selection from the collected data set, which contributes to fast low-dimensional feature preparation, low storage usage, and fast learning. We extract the five types of malware features represented from binary or disassembly files. Specifically, the novel WEM (Window Entropy Map) image is designed to represent malware with variable length, and the set of frequently used APIs is analyzed to shorten the processing time. To validate the effectiveness of the selected features, we compare the performance of tree-based ensemble models such as AdaBoost, XGBoost, random forest, extra trees, and rotation trees. The proposed feature can reduce the original feature dimensionality by several tens to hundreds of times and decrease the training time of ensemble models without degrading the malware detection rate when compared to the performance of the whole set of malware features. In accuracy and AUC-PRC evaluation, XGBoost is the highest in rank.

The big.LITTLE architecture has been extensively integrated into smart mobile devices for better performance and higher energy efficiency. However, the desired energy savings obtained by the big.LITTLE architecture is not sufficiently achieved because the LITTLE cores are not fully utilized while running real-time user applications. In this study, an energy efficient big.LITTLE core assignment algorithm is proposed to reduce the energy consumption of the mobile device by utilizing the LITTLE core as much as possible while guaranteeing the real-time performance of the mobile application. By applying the proposed multi-core assignment technique on a real test-bed of an off-the-shelf smartphone, we prove that the proposed technique can improve the energy saving effect while guaranteeing real-time performance. The energy efficiency of the proposed scheme is compared with that of the legacy scheduler in various environments. In addition, we propose a machine learning-based method to predict the expected processing time more accurately for a task before assigning to one of multi-cores. The presented prediction method is expected to reduce the chances of missing a deadline when employed on the proposed multi-core assignment scheme.

This paper proposes a novel artificial intelligence vision-based monitoring system (AVMS) for ship berthing. To dock a ship, it is necessary to accurately estimate the relative distance between the quay wall and the ship. However, maneuvering large ships near a port is a complicated and difficult procedure. Thus, tugboats push the ship and dock it at the berth under the supervision of a pilot, who receives distance information from a berthing aid system (BAS). The conventional BAS based on laser distance sensors, which is the most widely used approach, is high-priced and limited by the size of the ship. Additionally, if there is an obstacle between the ship and the berth, the distance cannot be measured, since it obscures the laser signal. To address this problem, we develop an AVMS sensor module composed of a low-priced camera, a differential global positioning system (DGPS) receiver, and an inertial measurement unit (IMU) with an algorithm to estimate the distance between ship and berth. To evaluate the performance of the proposed AVMS, field tests are performed at Ulsan port in Korea, and the results are compared with a conventional BAS. From the field test results, the AVMS provides highly accurate estimates and shows robust performance in poor weather conditions compared to the conventional BAS. The AVMS can measure the distance between ship and berth regardless of the size of the ship, since it has a wide field of view. In addition, it provides the pilot with real-time image information of the ship approaching the berth to obtain safe ship berthing.

Reports detailing the clinical characteristics, viral load, and outcomes of patients with normal initial chest CT findings are lacking. We sought to compare the differences in clinical findings, viral loads, and outcomes between patients with confirmed COVID-19 who initially tested negative on chest CT (CT negative) with patients who tested initially positive on chest CT (CT positive). The clinical data, viral loads, and outcomes of initial CT-positive and CT-negative patients examined between January 2020 and April 2020 were retrospectively compared. The efficacy of viral load (cyclic threshold value [Ct value]) in predicting pneumonia was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). In total, 128 patients underwent initial chest CT (mean age, 54.3 ± 19.0 years, 50% male). Of those, 36 were initially CT negative, and 92 were CT positive. The CT-positive patients were significantly older (P < .001) than the CT-negative patients. Only age was significantly associated with the initial presence of pneumonia (odds ratio, 1.060; confidence interval (CI), 1.020-1-102; P = .003). In addition, age (OR, 1.062; CI, 1.014–1.112; P = .011), fever at diagnosis (OR, 6.689; CI, 1.715–26.096; P = .006), and CRP level (OR, 1.393; CI, 1.150–1.687; P = .001) were significantly associated with the need for O2 therapy. Viral load was significantly higher in the CT-positive group than in the CT-negative group (P = .017). The cutoff Ct value for predicting the presence of pneumonia was 27.71. Outcomes including the mean hospital stay, intensive care unit admission, and O2 therapy were significantly worse in the CT-positive group than in the CT-negative group (all P < .05). In conclusion, initially CT-negative patients showed better outcomes than initially CT-positive patients. Age was significantly associated with the initial presence of pneumonia, and viral load may help in predicting the initial presence of pneumonia.

This paper proposes a system for the forecasting and automated inspection of rice Bakanae disease (RBD) infection rates via drone imagery. The proposed system synthesizes camera calibrations and area calculations in the optimal data domain to detect infected bunches and classify infected rice culm numbers. Optimal heights and angles for identification were examined via linear discriminant analysis and gradient magnitude by targeting the morphological features of RBD in drone imagery. Camera calibration and area calculation enabled distortion correction and simultaneous calculation of image area using a perspective transform matrix. For infection detection, a two-step configuration was used to recognize the infected culms through deep learning classifiers. The YOLOv3 and RestNETV2 101 models were used for detection of infected bunches and classification of the infected culm numbers, respectively. Accordingly, 3 m drone height and 0° angle to the ground were found to be optimal, yielding an infected bunches detection rate with a mean average precision of 90.49. The classification of number of infected culms in the infected bunch matched with an 80.36% accuracy. The RBD detection system that we propose can be used to minimize confusion and inefficiency during rice field inspection.

Among various sensors used to recognize obstacles in marine environments, vision sensors are the most basic. Vision sensors are significantly affected by the surrounding environment and cannot recognize distant objects. However, despite these drawbacks, they can detect objects that radars cannot detect in nearby regions. They can also recognize small obstacles such as boats that are not equipped with an automatic identification system (AIS) or buoys. Thus, vision sensors and radar can be used in a complementary manner. This paper proposes a vision sensor-based model, called Skip-ENet, for recognizing obstacles in real time. Compared with ENet, the amount of computation is not significantly higher. Further, Skip-ENet can segment complex marine obstacles effectively by increasing the values for the class accuracy and mean Intersection of Union (mIoU). Moreover, this model enables even low-cost embedded systems to compute 10 or more frames per second (fps). The superiority of the proposed model was verified by comparing its performance with that of the conventional segmentation models, MobileNet, ENet, and DeeplabV3+.

Vascular risk factors (e.g., obesity and hypertension) are associated with cerebral small vessel disease, Alzheimer’s disease (AD) pathology, and dementia. Reduced perfusion may reflect the impaired ability of blood vessels to regulate blood flow in reaction to varying circumstances such as hypercapnia (increased end-tidal partial pressures of CO2). It has been shown that cerebrovascular reactivity (CVR) measured with blood-oxygen-level-dependent (BOLD) MRI is correlated with cognitive performance and alterations of CVR may be an indicator of vascular disfunction leading to cognitive decline. However, the underlying mechanism of CVR alterations in BOLD signal may not be straight-forward because BOLD signal is affected by multiple physiological parameters, such as cerebral blood flow (CBF), cerebral blood volume, and oxygen metabolism. Arterial spin labeling (ASL) MRI quantitatively measures blood flow in the brain providing images of local CBF. Therefore, in this study, we measured CBF and its changes using a dynamic ASL technique during a hypercapnia challenge and tested if CBF or CVR was related to cognitive performance using the Mini-mental state examination (MMSE) score. Seventy-eight participants underwent cognitive testing and MRI including ASL during a hypercapnia challenge with a RespirAct computer-controlled gas blender, targeting 10 mmHg higher end-tidal CO2 level than the baseline while end-tidal O2 level was maintained. Pseudo-continuous ASL (PCASL) was collected during a 2-min baseline and a 2-min hypercapnic period. CVR was obtained by calculating a percent change of CBF per the end-tidal CO2 elevation in mmHg between the baseline and the hypercapnic challenge. Multivariate regression analyses demonstrated that baseline resting CBF has no significant relationship with MMSE, while lower CVR in the whole brain gray matter (β = 0.689, p = 0.005) and white matter (β = 0.578, p = 0.016) are related to lower MMSE score. In addition, region of interest (ROI) based analysis showed positive relationships between MMSE score and CVR in 26 out of 122 gray matter ROIs.

Most of the spherical unmanned aerial vehicles (SUAVs) use control surfaces, which are functions of aileron and an elevator, to generate control torque. The work proposes a new conceptual design of an SUAV system controlled through center-of-gravity (CG) variations with its path-tracking control law designed for the system. Compared to the one using control surfaces, the concept suggested is beneficial in the aspect of the expandability of building lighter and smaller SUAVs, especially. A CG variation principle by actuating a pendulum type of a moving part is considered as a methodology for both translational and rotational motion control of an SUAV. Since variations of the moment-of-inertia (MOI) elements which resulted from the motion of the moving part affect the performance of the suggested method, the variations of MOI analysis are performed for all angular ranges of the moving part. As a result, certain angular ranges for the moving part to prevent the degradation of the path-tracking performance by the effect of the MOI changes are found. By considering the findings, numerical studies are performed for hovering, ascent, descent, and horizontal tracking missions. The applicability of the proposed SUAV system and the corresponding controller to achieve the path-tracking missions is demonstrated through the numerical simulation.

The highly sensitive detection of peanut allergens (PAs) using silicon-based electrolyte-gated transistors (Si-EGTs) was demonstrated. The Si-EGT was made using a top-down technique. The fabricated Si-EGT showed excellent intrinsic electrical characteristics, including a low threshold voltage of 0.7 V, low subthreshold swing of SV) showed a constant behavior from the subthreshold regime to the linear regime. The current-related sensitivity (SI) was high in the subthreshold regime and then significantly decreased as the drain current increased. The limit of detection (LOD) was calculated to be as low as 25 pg/mL based on SI characteristics, which is the lowest value reported to date in the literature for various sensor methodologies. The Si-EGT showed selective detection of PA through a non-specific control test. These results confirm that Si-EGT is a high-sensitivity and low-power biosensor for PA detection.

In this research, we investigated the effects of reduction atmospheres on the creation of the Mo-Si-B intermetallic compounds (IMC) during the heat treatments. For outstanding anti-oxidation and elevated mechanical strength at the ultrahigh temperature, we fabricated the uniformly dispersed IMC powders such as Mo5SiB2 (T2) and Mo3Si (A15) phases using the two steps of chemical reactions. Especially, in the second procedure, we studied the influence of the atmospheres (e.g. vacuum, argon, and hydrogen) on the synthesis of IMCs during the reduction. Furthermore, the newly produced IMCs were observed by SEM, XRD, and EDS to identify the phase of the compounds. We also calculated an amount of IMCs in the reduced powders depending on the atmosphere using the Reitveld refinement method. Consequently, it is found that hydrogen atmosphere was suitable for fabrication of IMC without other IMC phases.

Abstract Heterozygous mutations in glucocerebrosidase 1 (GBA1) are a major genetic risk factor for Parkinson’s disease and Dementia with Lewy bodies. Mutations in GBA1 leads to GBA1 enzyme deficiency, and GBA1-associated parkinsonism has an earlier age of onset and more progressive parkinsonism. To investigate a potential influence of GBA1 deficiency caused by mutations in GBA1 on the disease progression of PD, GBA1 mice carrying D409H knock-in mutation were crossbred with the human A53T (hA53T) α-synuclein transgenic mice. Here, we show that GBA1 enzyme activity plays a significant role in the hA53T α-synuclein induced α-synucleinopathy. The expression of D409H GBA1 markedly shortens the lifespan of hA53T α-synuclein transgenic mice. Moreover, D409H GBA1 expression exacerbates the formation of insoluble aggregates of α-synuclein, glial activation, neuronal degeneration, and motor abnormalities in the hA53T α-synuclein transgenic mice. Interestingly, the expression of D409H GBA1 results in the loss of dopaminergic neurons in the substantia nigra pars compacta of hA53T transgenic mice. Taken together, these results indicate that GBA1 deficiency due to D409H mutation affects the disease onset and course in hA53T α-synuclein transgenic mice. Therefore, strategies aimed to maintain GBA1 enzyme activity could be employed to develop an effective novel therapy for GBA1 linked-PD and related α-synucleinopathies.

Abstract Background Mutations in glucocerebrosidase (GBA) cause Gaucher disease (GD) and increase the risk of developing Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB). Since both genetic and environmental factors contribute to the pathogenesis of sporadic PD, we investigated the susceptibility of nigrostriatal dopamine (DA) neurons in L444P GBA heterozygous knock-in (GBA +/L444P ) mice to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a selective dopaminergic mitochondrial neurotoxin. Method We used GBA +/L444P mice, α-synuclein knockout (SNCA −/− ) mice at 8 months of age, and adeno-associated virus (AAV)-human GBA overexpression to investigate the rescue effect of DA neuronal loss and susceptibility by MPTP. Mitochondrial morphology and functional assay were used to identify mitochondrial defects in GBA +/L444P mice. Motor behavioral test, immunohistochemistry, and HPLC were performed to measure dopaminergic degeneration by MPTP and investigate the relationship between GBA mutation and α-synuclein. Mitochondrial immunostaining, qPCR, and Western blot were also used to study the effects of α-synuclein knockout or GBA overexpression on MPTP-induced mitochondrial defects and susceptibility. Results L444P GBA heterozygous mutation reduced GBA protein levels, enzymatic activity and a concomitant accumulation of α-synuclein in the midbrain of GBA +/L444P mice. Furthermore, the deficiency resulted in defects in mitochondria of cortical neurons cultured from GBA +/L444P mice. Notably, treatment with MPTP resulted in a significant loss of dopaminergic neurons and striatal dopaminergic fibers in GBA +/L444P mice compared to wild type (WT) mice. Levels of striatal DA and its metabolites were more depleted in the striatum of GBA +/L444P mice. Behavioral deficits, neuroinflammation, and mitochondrial defects were more exacerbated in GBA +/L444P mice after MPTP treatment. Importantly, MPTP induced PD-like symptoms were significantly improved by knockout of α-synuclein or augmentation of GBA via AAV5-hGBA injection in both WT and GBA +/L444P mice. Intriguingly, the degree of reduction in MPTP induced PD-like symptoms in GBA +/L444P α-synuclein (SNCA) −/− mice was nearly equal to that in SNCA −/− mice after MPTP treatment. Conclusion Our results suggest that GBA deficiency due to L444P GBA heterozygous mutation and the accompanying accumulation of α-synuclein render DA neurons more susceptible to MPTP intoxication. Thus, GBA and α-synuclein play dual physiological roles in the survival of DA neurons in response to the mitochondrial dopaminergic neurotoxin, MPTP.

An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.

With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues.

We present the fabrication and electrical characteristics of nanonet-channel (NET) low-temperature polysilicon channel (LTPS) thin-film transistors (TFTs) using a nanosphere-assisted patterning (NAP) technique. The NAP technique is introduced to form a nanonet-channel instead of the electron beam lithography (EBL) or conventional photolithography method. The size and space of the holes in the nanonet structure are well controlled by oxygen plasma treatment and a metal lift-off process. The nanonet-channel TFTs show improved electrical characteristics in terms of the ION/IOFF, threshold voltage, and subthreshold swing compared with conventional planar devices. The nanonet-channel devices also show a high immunity to hot-carrier injection and a lower variation of electrical characteristics. The standard deviation of VTH (σVTH) is reduced by 33% for a nanonet-channel device with a gate length of 3 μm, which is mainly attributed to the reduction of the grain boundary traps and enhanced gate controllability. These results suggest that the cost-effective NAP technique is promising for manufacturing high-performance nanonet-channel LTPS TFTs with lower electrical variations.

Recently, owing to changes in weather conditions, cyanobacterial blooms, also known as harmful algal blooms (HABs), have caused serious damage to the ecosystems of rivers and lakes by producing cyanotoxins. In this paper, for the removal of HABs, an algal bloom removal robotic system (ARROS) is proposed. The ARROS has been designed with a catamaran-type unmanned surface vehicle (USV) and an algae-removal device attached below. In addition, electrical control systems and a guidance, navigation, and control (GNC) system are implemented on the ARROS to remove the algal bloom autonomously. Moreover, to increase the efficiency of the work, an unmanned aerial vehicle (UAV) is utilized and the system detects algal blooms with an image-based detection algorithm, which is known as a local binary pattern. The overall mission begins with a command from a server when the UAV detects an algal bloom, and the USV follows the given path autonomously generated by a coverage path planning algorithm. Subsequently, with an electrocoagulation and floatation reactor under the USV, HABs are removed. The performance of the algal bloom detection and HABs removal is verified through outdoor field tests in Daecheong Dam, South Korea.

The development of implantable neuroelectrodes is advancing rapidly as these tools are becoming increasingly ubiquitous in clinical practice, especially for the treatment of traumatic and neurodegenerative disorders. Electrodes have been exploited in a wide number of neural interface devices, such as deep brain stimulation, which is one of the most successful therapies with proven efficacy in the treatment of diseases like Parkinson or epilepsy. However, one of the main caveats related to the clinical application of electrodes is the nervous tissue response at the injury site, characterized by a cascade of inflammatory events, which culminate in chronic inflammation, and, in turn, result in the failure of the implant over extended periods of time. To overcome current limitations of the most widespread macroelectrode based systems, new design strategies and the development of innovative materials with superior biocompatibility characteristics are currently being investigated. This review describes the current state of the art of in vitro, ex vivo, and in vivo models available for the study of neural tissue response to implantable microelectrodes. We particularly highlight new models with increased complexity that closely mimic in vivo scenarios and that can serve as promising alternatives to animal studies for investigation of microelectrodes in neural tissues. Additionally, we also express our view on the impact of the progress in the field of neural tissue engineering on neural implant research.

Recent studies concerning graphene quantum dots (GQDs) focus extensively on their application in biomedicine, exploiting their modifiable optical properties and ability to complex with various molecules via π–π or covalent interactions. Among these nascent findings, the potential therapeutic efficacy of GQDs was reported against Parkinson’s disease, which has to date remained incurable. Herein, we present an environmentally friendly approach for synthesizing GQDs through a waste-to-treasure method, specifically from coffee waste to nanodrug. Consistent with the previous findings with carbon fiber-derived GQDs, the inhibitory effects of coffee bean-derived GQDs demonstrated similar effectiveness against abnormal α-synuclein fibrillation and the protection of neurons from relevant subcellular damages. The fact that a GQDs-based nanodrug can be prepared from a non-reusable yet edible source illustrates a potential approach to convert such waste materials into novel therapeutic agents with minimal psychological rejection by patients.

In engineering disciplines, many important problems are to be formed as boundary value problems (BVPs) that have conditions that are specified at the extremes. To handle such problems, the conventional shooting method that transforms BVPs into initial value problems has been extensively used, but it does not always guarantee solving the problem due to the possible failure of finding a proper initial guess. This paper proposes a universal initial guess finder that is composed of Bézier functions. Various dimensional problems that include Lambert’s problem for several orbits around the spherically symmetric Earth are studied to validate the efficacy of the proposed approach, and the results are compared.

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

In the event of a control failure on an axis of a spacecraft, a target attitude can be achieved by several sequential rotations around the remaining control axes. For a spacecraft actuating with wheels, the form of each submaneuver should be a pure single axis rotation since the failed axis should not be perturbed. The rotation path length in sequential submaneuvers, however, increases extremely but is short under normal conditions. In this work, it is shown that the path length is reduced dramatically by finding a proper number of sequential submaneuvers, especially for the target attitude rotation around the failed axis. A numerical optimization is suggested to obtain the shortest path length and the relevant number of maneuvers. Optimal solutions using the sequential rotation approach are confirmed by numerical simulations.