Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches.

Objectives: To develop and compare machine learning models to classify individuals vulnerable to Hwa-byung (HB) using an existing HB personality scale and to evaluate the efficacy of these models in predicting HB vulnerability.

Methods: We analyzed data from 500 Korean adults (aged 19-44) using HB personality and symptom scales. We used various machine learning techniques, including the random forest classifier (RFC), XGBoost classifier, logistic regression, and their ensemble method (RFC-XGC-LR).

Risk preferences in decision-making: A construal level perspective.

This study examines the impact of construal levels on preferences for risky choices in decision-making scenarios, focusing on how situational framing moderates these effects. We explored the role of construal level as a key moderator of the influence of risky-choice framing. Our findings show significant differences in preferences for risky options between gain- and loss-framing, especially in high-level construal contexts.

Toward an Asian-based bodily movement database for emotional communication.

Most current databases for bodily emotion expression are created in Western countries, resulting in culturally skewed representations. To address the obvious risk this bias poses to academic comprehension, we attempted to expand the current repertoire of human bodily emotions by recruiting Asian professional performers to wear whole-body suits with 57 retroreflective markers attached to major joints and body segments, and express seven basic emotions with whole-body movements in a motion-capture lab. For each emotion, actors performed three self-created scenarios that covered a broad range of real-life events to elicit the target emotion within 2-5 seconds.

Enhancing Clinical History Taking Through the Implementation of a Streamlined Electronic Questionnaire System at a Pediatric Headache Clinic: Development and Evaluation Study.

Background: Accurate history taking is essential for diagnosis, treatment, and patient care, yet miscommunications and time constraints often lead to incomplete information. Consequently, there has been a pressing need to establish a system whereby the questionnaire is duly completed before the medical appointment, entered into the electronic health record (EHR), and stored in a structured format within a database.

Objective: This study aimed to develop and evaluate a streamlined electronic questionnaire system, BEST-Survey (Bundang Hospital Electronic System for Total Care-Survey), integrated with the EHR, to enhance history taking and data management for patients with pediatric headaches.

Development of a Short-Form Hwa-Byung Symptom Scale Using Machine Learning Approaches.

: Hwa-byung (HB), also known as "anger syndrome" or "fire illness", is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) and various machine learning techniques (i.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

Background: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP.

Information technology-supported integrated health service for older adults in long-term care settings.

Background: To examine the effectiveness and safety of a data sharing and comprehensive management platform for institutionalized older patients.

Methods: We applied information technology-supported integrated health service platform to patients who live at long-term care hospitals (LTCHs) and nursing homes (NHs) with cluster randomized controlled study. We enrolled 555 patients aged 65 or older (461 from 7 LTCHs, 94 from 5 NHs).

Symptoms and Conditions in Children and Adults up to 90 Days after SARS-CoV-2 Infection: A Retrospective Observational Study Utilizing the Common Data Model.

There have been widespread reports of persistent symptoms in both children and adults after SARS-CoV-2 infection, giving rise to debates on whether it should be regarded as a separate clinical entity from other postviral syndromes. This study aimed to characterize the clinical presentation of post-acute symptoms and conditions in the Korean pediatric and adult populations. : A retrospective analysis was performed using a national, population-based database, which was encoded using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

Maze-solving in a plasma system based on functional analogies to reinforcement-learning model.

Maze-solving is a classical mathematical task, and is recently analogously achieved using various eccentric media and devices, such as living tissues, chemotaxis, and memristors. Plasma generated in a labyrinth of narrow channels can also play a role as a route finder to the exit. In this study, we experimentally observe the function of maze-route findings in a plasma system based on a mixed discharge scheme of direct-current (DC) volume mode and alternative-current (AC) surface dielectric-barrier discharge, and computationally generalize this function in a reinforcement-learning model.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product.

Impact of metformin on cardiovascular and kidney outcome based on kidney function status in type 2 diabetic patients: a multicentric, retrospective cohort study.

Metformin is the primary treatment for type 2 diabetes mellitus (T2DM) due to its effectiveness in improving clinical outcomes in patients with preserved renal function, however, the evidence on the effectiveness of metformin in various renal functions is lacking. We performed a retrospective, multicenter, observational study used data of patients with T2DM obtained from three tertiary hospitals' databases. Patients given metformin within run-in periods and with at least one additional prescription formed the metformin cohort.

Gestalt neurons and emergent properties in visual perception: A novel concept for the transformation from local to global processing.

Gestalten in visual perception are defined by emergent properties of the whole, which cannot be predicted from the sum of its parts; rather, they arise by virtue of inherent principles, the Laws of Seeing. This review attempts to assign neurophysiological correlates to select emergent properties in motion and contour perception and proposes parallels to the processing of local versus global attributes by classical versus contextual receptive fields. The aim is to identify Gestalt neurons in the visual system to account for the Laws of Seeing in causal terms and to explain "Why do things look as they do" (Koffka, 1935, p.

Development and Verification of a 480 nm Blue Light Enhanced/Reduced Human-Centric LED for Light-Induced Melatonin Concentration Control.

With the inherent sleep and wake cycle regulated by natural sunlight, the human body has evolved over millennia to be active during the day and to rest at night. However, maintaining an optimal 24 h cycle has become increasingly problematic in modern society as more people spend the majority of the day indoors. Many research groups have reported that inadequate artificial lighting interferes with melatonin production and disrupts the circadian rhythm.

Patient-Centered Economic Burden of Exudative Age-Related Macular Degeneration: Retrospective Cohort Study.

Background: Exudative age-related macular degeneration (AMD), one of the leading causes of blindness, requires expensive drugs such as anti-vascular endothelial growth factor (VEGF) agents. The long-term regular use of effective but expensive drugs causes an economic burden for patients with exudative AMD. However, there are no studies on the long-term patient-centered economic burden of exudative AMD after reimbursement of anti-VEGFs.

Snoring-related polygenic risk and its relationship with lifestyle factors in a Korean population: KoGES study.

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors.

Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning.

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA).

Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias.

Synthesis of Nano-Structured Ge as Transmissive or Reflective Saturable Absorber for Mode-Locked Fiber Laser.

Amorphous-Ge (α-Ge) or free-standing nanoparticles (NPs) synthesized via hydrogen-free plasma-enhanced chemical vapor deposition (PECVD) were applied as transmissive or reflective saturable absorbers, respectively, for starting up passively mode-locked erbium-doped fiber lasers (EDFLs). Under a threshold pumping power of 41 mW for mode-locking the EDFL, the transmissive α-Ge film could serve as a saturable absorber with a modulation depth of 52-58%, self-starting EDFL pulsation with a pulsewidth of approximately 700 fs. Under a high power of 155 mW, the pulsewidth of the EDFL mode-locked by the 15 s-grown α-Ge was suppressed to 290 fs, with a corresponding spectral linewidth of 8.

FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment.

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities.

Exploring the potential of OMOP common data model for process mining in healthcare.

Background And Objective: Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g.

Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning.

In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the intra prediction for depth video. The proposed neural network consists of both a spatial feature prediction network and a clustering network.