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The electrocatalytic CO2 reduction reaction (CO2RR) to formic acid has gained significant attention as a potential environmentally friendly approach to reducing CO2 emissions and producing carbon-neutral liquid fuels. However, several challenges must be addressed to achieve the production of high-purity and high-concentration formic acid through CO2RR. One major challenge is the formation of a formate mixture instead of pure formic acid in conventional reactors. This requires costly downstream purification and concentration processes to obtain pure formic acid. To overcome this problem, a three-compartment reactor design has been proposed where a solid-state electrolyte (SSE) is inserted between the anode and cathode compartments to recover pure formic acid directly. This reactor design involves the use of an anion exchange membrane (AEM) and a cation exchange membrane (CEM) to separate the anode and cathode compartments, and a center compartment filled with high-conductivity SSE to minimize ohmic resistance. Several studies have implemented this reactor design for continuous CO2RR and have reported remarkable improvements in the concentration and purity of the formic acid product. In this review, we summarize the recent progress of the SSE reactor design for CO2RR to produce pure formic acid (HCOOH) and propose further research to scale up this technology for industrial-scale applications in the future. [ABSTRACT FROM AUTHOR]

Compared to the existing noninvasive methods, temporal interference (TI) current stimulation is an emerging noninvasive neuromodulation technique that can improve the ability to focus an electrical field on a target nerve. Induced TI field distribution depends on the anatomical structure of individual neurons, and thus the electrode and current optimization to enhance the field focus must reflect these factors. The current study presents a TI field optimization framework for focusing the stimulation energy on the target tibial nerve through extensive electrical simulations, factoring in individual anatomical differences. We conducted large-scale in-silico experiments using realistic models based on magnetic resonance images of human subjects to evaluate the effectiveness of the proposed methods for tibial nerve stimulation considering overactive bladder (OAB) treatment. The electrode position and current intensity were optimized for each subject using an automated algorithm, and the field-focusing performance was evaluated based on the maximum intensity of the electric fields induced at the target nerve compared with the electric fields in the neighboring tissues. Using the proposed optimization framework, the focusing ability increased by 12% when optimizing the electrode position. When optimizing both the electrode position and current, this capability increased by 11% relative to electrode position optimization alone. These results suggest the significance of optimizing the electrode position and current intensity for focusing TI fields at the target nerve. Our electrical simulation-based TI optimization framework can be extended to enable personalized peripheral nerve stimulation therapy to modulate peripheral nerves. [ABSTRACT FROM AUTHOR]

Flavanones in Citrus unshiu peel (CUP) have been used as therapeutic agents to reduce intestinal inflammation; however, the anti-inflammatory effects of their biometabolites remain ambiguous. Here, we identified aglycone-type flavanones, such as hesperetin and naringenin, which were more abundant in the bioconversion of the CUP than in the ethanol extracts of the CUP. We found that the bioconversion of the CUP induced the canonical nuclear factor-κB pathway via degradation of IκB in Caco-2 cells. To check the immune suppressive capacity of the aglycones of the CUP in vivo, we orally administered the bioconversion of the CUP (500 mg/kg) to mice for two weeks prior to the 3% dextran sulfate sodium treatment. The CUP-pretreated group showed improved body weight loss, colon length shortage, and intestinal inflammation than the control mice. We also found a significant decrease in the population of lamina propria Th17 cells in the CUP-pretreated group following dextran sodium sulfate (DSS) treatment and an increase in mRNA levels of occludin in CUP-treated Caco-2 cells. Pyrosequencing analysis revealed a decreased abundance of Alistipes putredinis and an increased abundance of Muribaculum intestinale in the feces of the CUP-pretreated mice compared to those of the control mice. Overall, these findings suggest that the pre-administration of CUP biometabolites may inhibit the development of murine colitis by modulating intestinal permeability and the gut microbiome. [ABSTRACT FROM AUTHOR]

As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS. [ABSTRACT FROM AUTHOR]

This study aims to compare directed transfer function (DTF), which is an effective connectivity analysis, derived from scalp EEGs between responder and nonresponder groups implanted with vagus-nerve stimulation (VNS). Twelve patients with drug-resistant epilepsy (six responders and six nonresponders) and ten controls were recruited. A good response to VNS was defined as a reduction of ≥50% in seizure frequency compared with the presurgical baseline. DTF was calculated in five frequency bands (delta, theta, alpha, beta, and broadband) and seven grouped electrode regions (left and right frontal, temporal, parieto-occipital, and midline) in three different states (presurgical, stimulation-on, and stimulation-off states). Responders showed presurgical nodal strength close to the control group in both inflow and outflow, whereas nonresponders exhibited increased inward and outward connectivity measures. Nonresponders also had increased inward and outward connectivity measures in the various brain regions and various frequency bands assessed compared with the control group when the stimulation was on or off. Our study demonstrated that the presurgical DTF profiles of responders were different from those of nonresponders. Moreover, a presurgical normal DTF profile may predict good responsiveness to VNS. [ABSTRACT FROM AUTHOR]

The accurate distribution of the replicated genome during cell division is essential for cell survival and healthy organismal development. Errors in this process have catastrophic consequences, such as birth defects and aneuploidy, a hallmark of cancer cells. PLK1 is one of the master kinases in mitosis and has multiple functions, including mitotic entry, chromosome segregation, spindle assembly checkpoint, and cytokinesis. To dissect the role of PLK1 in mitosis, it is important to understand how PLK1 localizes in the specific region in cells. PLK1 localizes at the kinetochore and is essential in spindle assembly checkpoint and chromosome segregation. However, how PLK1 localizes at the kinetochore remains elusive. Here, we review the recent literature on the kinetochore recruitment mechanisms of PLK1 and its roles in spindle assembly checkpoint and attachment between kinetochores and spindle microtubules. Together, this review provides an overview of how the local distribution of PLK1 could regulate major pathways in mitosis. [ABSTRACT FROM AUTHOR]

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group. [ABSTRACT FROM AUTHOR]

Micro/nano-precision glass molding (MNPGM) is an efficient approach for manufacturing micro/nanostructured glass components with intricate geometry and a high-quality optical finish. In MNPGM, the mold, which directly imprints the desired pattern on the glass substrate, is a key component. To date, a wide variety of mold inserts have been utilized in MNPGM. The aim of this article is to review the latest advances in molds for MNPGM and their fabrication methods. Surface finishing is specifically addressed because molded glass is usually intended for optical applications in which the surface roughness should be lower than the wavelength of incident light to avoid scattering loss. The use of molds for a wide range of molding temperatures is also discussed in detail. Finally, a series of tables summarizing the mold fabrication methods, mold patterns and their dimensions, anti-adhesion coatings, molding conditions, molding methods, surface roughness values, glass substrates and their glass transition temperatures, and associated applications are presented. This review is intended as a roadmap for those interested in the glass molding field. [ABSTRACT FROM AUTHOR]

We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To gain competitive advantages and sustainable service innovation, hotels are considering artificial intelligence technologies (AI), including robots, kiosks for service automation and chatbots. However, due to the change of the service process and unfamiliar communication interface, hotel customers may have difficulties in adopting the new change. In this paper, we tried to find out if the failure of AI-based services would affect customers' perception. For this, we designed the experiment by separating AI (i.e., chatbot) services and self-service technology (SST, i.e., pad) services and service failures and successful cases, respectively. As a result, SST showed more positive perceptions and revisit intention in the successful service situation. The service failure situation showed no differences between chatbot and SST. In addition, novelty and the need for interaction characteristics of customers showed significant differences between groups in terms of service success and failure, respectively. Additionally, we explored negative word-of-mouth (WOM) to learn further effects by service failures and successes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adversarial network, trained using paired raindrop and clean videos, is applied to remove image distortions by raindrops and to restore background information of coastal waves. Next, a hydrodynamic scene of propagated wave information is separated from surrounding environmental information in the enhanced coastal video imagery using a deep autoencoder network. Finally, propagating waves are tracked by registering consecutive images in the quality-enhanced and scene-separated coastal video imagery using a spatial transformer network. The instantaneous wave speed of each individual wave crest and breaker in the video domain is successfully estimated through learning the behavior of transformed and propagated waves in the surf zone using deep neural networks. Since it enables the acquisition of spatio-temporal information of the surf zone though the characterization of wave breakers inclusively wave run-up, we expect that the proposed framework with the deep neural networks leads to improve understanding of nearshore wave dynamics. [ABSTRACT FROM AUTHOR]

Zinc oxide (ZnO) has attractive photocatalytic properties. However, the high recombination rate of the photo-excited charge carriers on this material often restricts application. Here, we report that hybridization of one dimensional (1D) carbon nanotubes (CNT) on two dimensional (2D) porous ZnO nanosheets (NS) can be a promising strategy to overcome some of the challenges of ZnO. Specifically, a pulsed laser ablation technique was utilized to hybridize 1D CNT with 2D porous ZnO NS in environmentally friendly as well as super-economic (short time, less than 10 min) conditions. The synthesized ZnO NS-CNT hybrids show a significantly enhanced photocatalytic activity for water splitting relative to their counterpart ZnO NS. [ABSTRACT FROM AUTHOR]