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Earthquakes have the greatest destructive effect among all natural disasters. Posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and anxiety disorder (AD) are major psychiatric disorders (MPD) that can be triggered by exposure to earthquakes. Conventional treatments such as pharmacological treatments have several limitations. Acupuncture therapy as a complementary integrative medicine may be an effective alternative treatment for these limitations. This study aimed to identify the status of the clinical evidence regarding acupuncture therapy for earthquake survivors with MPD. We will follow the scoping review process as previously described. The study question is as follows: “Which types of clinical research designs, study types, study durations, adverse events, and clinical outcomes have been reported regarding acupuncture therapy for MPD in earthquake survivors?” Medline, Excerpta Medica dataBASE, Cochrane Central Register of Controlled Trials, Web of Science, Scopus, Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health Literature, PsycArticles databases, and Chinese, Korean, and Japanese databases will be comprehensively searched electronically from their inception to November 2022. Data from the included studies will be collected and descriptively analyzed in relation to our research question. We will collate, synthesize, and summarize the extracted data according to the analytical framework of a scoping review. The protocol will conform with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extensions of Scoping Reviews to ensure the clarity and completeness of our reporting in the whole phase of the scoping review (Protocol registration: https://osf.io/wfru7/ ). The findings of this scoping review will provide fundamental data that will help researchers identify appropriate research questions and design further studies on the use of acupuncture for MPD management in earthquake survivors. These results will be helpful for developing disaster site-specific research protocols for future clinical trials on this topic.

Facial emotion recognition (FER) systems are imperative in recent advanced artificial intelligence (AI) applications to realize better human–computer interactions. Most deep learning-based FER systems have issues with low accuracy and high resource requirements, especially when deployed on edge devices with limited computing resources and memory. To tackle these problems, a lightweight FER system, called Light-FER, is proposed in this paper, which is obtained from the Xception model through model compression. First, pruning is performed during the network training to remove the less important connections within the architecture of Xception. Second, the model is quantized to half-precision format, which could significantly reduce its memory consumption. Third, different deep learning compilers performing several advanced optimization techniques are benchmarked to further accelerate the inference speed of the FER system. Lastly, to experimentally demonstrate the objectives of the proposed system on edge devices, Light-FER is deployed on NVIDIA Jetson Nano.

This scoping review aimed to determine the current research status of acupuncture for major psychiatric disorder (MPD) in earthquake survivors. We followed the scoping review process described previously. A literature search on 14 electronic databases was conducted from inception to November 29, 2022. Data from the included studies were collected and descriptively analyzed to address our research question. Extracted data were collated, synthesized, and summarized the according to the analytical framework of a scoping review. This scoping review included nine clinical studies: four randomized controlled trials (RCTs) and five before-after studies. The most frequent MPD type among the included acupuncture studies was posttraumatic stress disorder (PTSD; 6/9, 66.67%). The most frequent acupuncture type was scalp electro-acupuncture (4/9, 44.44%), followed by manual acupuncture and ear acupressure/ear acupuncture (3/9, 33.33%). Studies using scalp electro-acupuncture all used common acupoints, including GB20, GV20, GV24, and EX-HN1. In general, the treatment period lasted between 4 and 12 weeks. Validated assessment tools for PTSD severity and accompanying symptoms were used for patients with PTSD, while the corresponding evaluation tools were used for patients with other diagnoses or clinical symptoms. Acupuncture-related adverse events were generally mild and temporary, such as mild bleeding and hematoma, and syncope was a rare but potentially serious adverse event (1/48 patients and 1/864 sessions over a treatment period of 4 weeks). Acupuncture studies for MPD after an earthquake mainly focused on PTSD. RCTs accounted for around half of the included studies. Scalp electro-acupuncture was the most common acupuncture type, and EX-HN1 and GV24 were the most important acupoints in the acupuncture procedures for MPD. The included studies mostly used validated symptom assessment tools, though some did not. Clinical studies in this field need to be further expanded regardless of the study type. https://osf.io/wfru7/.

The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge–cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge–cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge–cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.

Posttraumatic stress disorder is caused by traumatic events such as death, serious injury, and sexual violence. Military personnel and veterans are at high risk for posttraumatic stress disorder. Conventional posttraumatic stress disorder treatments have certain limitations. Complementary and integrative medicine treatments, especially acupuncture, are potential novel first-line treatments that may overcome these limitations. We aim to investigate the current status of the available clinical evidence related to acupuncture treatment for posttraumatic stress disorder in war veterans. We will follow the scoping review process as previously described. The study question is as follows: \"Which types of clinical research designs, study types, study durations, adverse events, and clinical outcomes have been reported regarding acupuncture therapy for posttraumatic stress disorder in military veterans?\" We will perform a comprehensive search of Medline, Excerpta Medica dataBASE, Cochrane Central Register of Controlled Trials, Web of Science, Scopus databases, Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health Literature, and PsycArticles databases, as well as Chinese, Korean, and Japanese databases, from inception to June 2022. Data from the included studies will be collected and descriptively analyzed in relation to our research question. The extracted data will be collated, synthesized, and summarized according to the analytical framework of a scoping review. The protocol of this study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews to ensure the clarity and completeness of our reporting in all phases of this scoping review (Protocol registration: https://osf.io/t723f/ ). The findings of this scoping review will provide fundamental data that will help researchers identify appropriate research questions and design further studies on the use of acupuncture for PTSD management in military veterans. These results will be helpful for developing disaster site-specific research protocols for future clinical trials on this topic.

Pairs trading is an investment strategy that exploits the short-term price difference (spread) between two co-moving stocks. Recently, pairs trading methods based on deep reinforcement learning have yielded promising results. These methods can be classified into two approaches: (1) indirectly determining trading actions based on trading and stop-loss boundaries and (2) directly determining trading actions based on the spread. In the former approach, the trading boundary is completely dependent on the stop-loss boundary, which is certainly not optimal. In the latter approach, there is a risk of significant loss because of the absence of a stop-loss boundary. To overcome the disadvantages of the two approaches, we propose a hybrid deep reinforcement learning method for pairs trading called HDRL-Trader, which employs two independent reinforcement learning networks; one for determining trading actions and the other for determining stop-loss boundaries. Furthermore, HDRL-Trader incorporates novel techniques, such as dimensionality reduction, clustering, regression, behavior cloning, prioritized experience replay, and dynamic delay, into its architecture. The performance of HDRL-Trader is compared with the state-of-the-art reinforcement learning methods for pairs trading (P-DDQN, PTDQN, and P-Trader). The experimental results for twenty stock pairs in the Standard & Poor’s 500 index show that HDRL-Trader achieves an average return rate of 82.4%, which is 25.7%P higher than that of the second-best method, and yields significantly positive return rates for all stock pairs.

Objective: The purpose of this study was to verify changes in a driver’s emotions through the physical characteristics of haptic signals. This is to improve the performance of drivers by designing haptic signals with emotional semantics. Background: Currently, drivers receive a variety of information through intelligent systems installed in their vehicles. Because this is mainly achieved through visual and auditory channels, an excessive amount of information is provided to drivers, which increases the amount of information and cognitive load that they must accept. This, in turn, can reduce driving safety. It is, therefore, necessary to develop a haptic signal, a sensory channel that has not been widely used in in-vehicle information systems. Methods: The experiment was performed to collect a driver’s emotions according to the haptic signal in a driving simulator. Haptic signals were designed by various frequencies and accelerations, and driver emotions were collected through Kansei engineering techniques and analyzed through factor analysis. To verify intelligibility, haptic signals were compared and evaluated based on response time, response rate, and amount of transmitted information. Results: The final determined emotional map consisted of dangerousness and urgency. Based on the emotional map, four emotional semantic haptic signals were designed. It was confirmed that these four signals displayed higher performance than the discriminability haptic signal in terms of response time, response rate, and amount of transmitted information. Conclusions: Using emotional maps, it is possible to design haptic signals that can be applied to various driving situations. These maps may also assist in securing design guidelines for haptic signals that apply to in-vehicle information systems.

Episodic ataxia (EA) is a rare neurological condition characterized by recurrent spells of truncal ataxia and incoordination. Five genes ( KCNA1 , CACNA1A , CACNB4 , SLC1A3 , and UBR4 ) have been linked to EA. Despite extensive efforts to genetically diagnose EA, many patients remain still undiagnosed. Whole-exome sequencing was carried out in 39 Korean patients with EA to identify pathogenic mutations of the five known EA genes. We also evaluated 40 candidate genes that cause EA as a secondary phenotype or cerebellar ataxia. Eighteen patients (46%) revealed genetic information useful for establishing a molecular diagnosis of EA. In 11 patients, 16 pathogenic mutations were detected in three EA genes. These included nine mutations in CACNA1A , three in SLC1A3 , and four in UBR4 . Three patients had mutations in two genes, either CACNA1A and SLC1A3 or CACNA1A and UBR4 , suggesting that SLC1A3 and UBR4 may act as genetic modifiers with synergic effects on the abnormal presynaptic activity caused by CACNA1A mutations. In seven patients with negative results for screening of EA genes, potential pathogenic mutations were identified in the candidate genes ATP1A2 , SCN1A , TTBK2 , TGM6 , FGF14 , and KCND3 . This study demonstrates the genetic heterogeneity of Korean EA, and indicates that whole-exome sequencing may be useful for molecular genetic diagnosis of EA.

This study aims to characterize the influence of short-term heat stress (HS; 4 day) in early lactating Holstein dairy cows, in terms of triggering blood metabolomics and parameters, milk yield and composition, and milk microRNA expression. Eight cows (milk yield = 30 ± 1.5 kg/day, parity = 1.09 ± 0.05) were homogeneously housed in environmentally controlled chambers, assigned into two groups with respect to the temperature humidity index (THI) at two distinct levels: approximately ~71 (low-temperature, low-humidity; LTLH) and ~86 (high-temperature, high-humidity; HTHH). Average feed intake (FI) dropped about 10 kg in the HTHH group, compared with the LTLH group (p = 0.001), whereas water intake was only numerically higher (p = 0.183) in the HTHH group than in the LTLH group. Physiological parameters, including rectal temperature (p = 0.001) and heart rate (p = 0.038), were significantly higher in the HTHH group than in the LTLH group. Plasma cortisol and haptoglobin were higher (p < 0.05) in the HTHH group, compared to the LTLH group. Milk yield, milk fat yield, 3.5% fat-corrected milk (FCM), and energy-corrected milk (ECM) were lower (p < 0.05) in the HTHH group than in the LTLH group. Higher relative expression of milk miRNA-216 was observed in the HTHH group (p < 0.05). Valine, isoleucine, methionine, phenylalanine, tyrosine, tryptophan, lactic acid, 3-phenylpropionic acid, 1,5-anhydro-D-sorbitol, myo-inositol, and urea were decreased (p < 0.05). These results suggest that early lactating cows are more vulnerable to short-term (4 day) high THI levels—that is, HTHH conditions—compared with LTLH, considering the enormous negative effects observed in measured blood metabolomics and parameters, milk yield and compositions, and milk miRNA-216 expression.

Post-stroke insomnia (PSI) is a highly prevalent complication after stroke. Current evidence of psychotropic drug use for PSI management is scarce and indicates harmful adverse events (AEs). Traditional East Asian herbal medicine is a widely used traditional remedy for insomnia. However, so far, no study has systematically reviewed the efficacy and safety of traditional east asian herbal medicine (HM) for PSI. Therefore, we perform meta-analysis to evaluate the effectiveness and safety of HM for PSI. After a comprehensive electronic search of 15 databases, we review the randomized controlled trials (RCTs) of HM use as monotherapy for PSI. Our outcomes were the Pittsburgh sleep quality index and total effective rate. In total, 24 RCTs were conducted with 1942 participants. HM showed statistically significant benefits in sleep quality. It also appeared to be safer than psychotropic drugs in terms of AEs, except when the treatment period was two weeks. The methods used for RCTs were poor, and the quality of evidence assessed was graded “low” or “moderate.” The findings of this review indicate that the use of HM as a monotherapy may have potential benefits in PSI treatment when administered as an alternative to conventional medications. However, considering the methodological quality of the included RCTs, we were uncertain of the clinical evidence. Further, well-designed RCTs are required to confirm these findings.