Hierarchical PtCuMnP Nanoalloy for Efficient Hydrogen Evolution and Methanol Oxidation.

The higher amount of Pt usage and its poisoning in methanol oxidation reaction in acidic media is a major setback for methanol fuel cells. Herein, a promising dual application high-performance electrocatalyst has been developed for hydrogen evolution and methanol oxidation. A low Pt-content nanoalloy co-doped with Cu, Mn, and P is synthesized using a modified solvothermal process. Initially, ultrasmall ≈2.9 nm PtCuMnP nanoalloy is prepared on N-doped graphene-oxide support and subsequently, it is characterized using several analytical techniques and examined through electrochemical tests. Electrochemical results show that PtCuMnP/N-rGO has a low overpotential of 6.5 mV at 10 mA cm-2 in 0.3 m H2 SO4 and high mass activity for the hydrogen evolution reaction. For the methanol oxidation reaction, the PtCuMnP/N-rGO electrocatalyst exhibits robust performance. The mass activity of PtCuMnP/N-rGO is 6.790 mA mg-1 Pt , which is 7.43 times higher than that of commercial Pt/C (20% Pt). Moreover, in the chronoamperometry test, PtCuMnP/N-rGO shows exceptionally good stability and retains 72% of the initial current density even after 20,000 cycles. Furthermore, the PtCuMnP/N-rGO electrocatalyst exhibits outstanding performance for hydrogen evolution and methanol oxidation along with excellent anti-poisoning ability. Hence, the developed bifunctional electrocatalyst can be used efficiently for hydrogen evolution and methanol oxidation.

Van der Waals Colloidal Crystals.

A general guiding principle for colloidal crystallization is to tame the attractive enthalpy such that it slightly overwhelms the repulsive interaction. As-synthesized colloids are generally designed to retain a strong repulsive potential for the high stability of suspensions, encoding appropriate attractive potentials into colloids has been key to their crystallization. Despite the myriad of interparticle attractions for colloidal crystallization, the van der Waals (vdW) force remains unexplored. Here, it is shown that the implementation of gold cores into silica colloids and the resulting vdW force can reconfigure the pair potential well depth to the optimal range between -1 and -4 kBT at tens of nanometer-scale colloidal distances. As such, colloidal crystals with a distinct liquid gap can be formed, which is evidenced by photonic bandgap-based diffractive colorization.

Differentiating changes in movement-related EEG response induced by transcranial direct current stimulation using convolutional neural network.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can modulate neuronal excitability and induce brain plasticity. Although tDCS has been studied with various methods, more research is needed on the movement-related electroencephalography (EEG) changes induced by tDCS. Moreover, it is necessary to investigate whether these changes can be distinguished through a convolutional neural network (CNN)-based classifier. In this study, we measured the EEG during the voluntary foot-tapping task of participants who received tDCS or sham stimulation and evaluated the classification performance. As a result, significantly higher classification accuracy was shown using the ß band (88.7±9.4%), which is more related to motor function, than in the other bands (71.4±10.6% for δ band, 64.1±13.4% for θ band, and 65.7±10.9% for α band). Consequently, EEG changes during the voluntary foot-tapping task induced by tDCS appeared large in the ß band, implying that it is effective in classifying whether tDCS was given or not, and plays an important role in identifying the effect of tDCS.

A Hybrid Deep Learning Emotion Classification System Using Multimodal Data.

This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, including tailored corporate services, AI advancement, and more. Additionally, most sentiment classification techniques in speaking situations are based on a single modality: voice, conversational text, vital signs, etc. However, analyzing these data presents challenges because of the variations in vocal intonation, text structures, and the impact of external stimuli on physiological signals. Korean poses challenges in natural language processing, including subject omission and spacing issues. To overcome these challenges and enhance emotion classification performance, this paper presents a case study using Korean multimodal data. The case study model involves retraining two pretrained models, LSTM and CNN, until their predictions on the entire dataset reach an agreement rate exceeding 0.75. Predictions are used to generate emotional sentences appended to script data, which are further processed using BERT for final emotion prediction. The research result is evaluated by using categorical cross-entropy (CCE) to measure the difference between the model's predictions and actual labels, F1 score, and accuracy. According to the evaluation, the case model outperforms the existing KLUE/roBERTa model with improvements of 0.5 in CCE, 0.09 in accuracy, and 0.11 in F1 score. As a result, the HDECS is expected to perform well not only on Korean multimodal datasets but also on sentiment classification considering the speech characteristics of various languages and regions.

Enhanced Thermal Stability and Conductivity of FeF3 Using Ni-Coated Carbon Composites: Application as High-Temperature Cathodes in Thermal Batteries.

Cathode active materials and conductive additives for thermal batteries operating at high temperatures have attracted research interest, with a particular focus on compounds offering high thermal stability. Recently, FeF3 has been proposed as a candidate for high-voltage cathode materials; however, its commercialization is hindered by its low conductivity. In this study, conductive additives, such as Ni-coated carbon composites (multi-walled carbon nanotubes (MWCNTs) and carbon black (CB)), were utilized to enhance the thermal stability and conductivity of FeF3. The incorporation of metal-carbon conductive additives in the FeF3 composite increased the thermal stability by more than 10 wt.% and ensured high capacity upon conductivity enhancement. The FeF3@Ni/MWCB 15 wt.% composite containing 30 wt.% Ni exhibited a discharge capacity of ∼86% of the theoretical capacity of 712 mAh/g. The use of Ni-coated carbon-based conductive additives will allow the application of FeF3 as an effective high-temperature cathode material for thermal batteries.

FeF3/(Acetylene Black and Multi-Walled Carbon Nanotube) Composite for Cathode Active Material of Thermal Battery through Formation of Conductive Network Channels.

Considerable research is being conducted on the use of FeF3 as a cathode replacement for FeS2 in thermal batteries. However, FeF3 alone is inefficient as a cathode active material because of its low electrical conductivity due to its wide bandgap (5.96 eV). Herein, acetylene black and multi-walled carbon nanotubes (MWCNTs) were combined with FeF3, and the ratio was optimized. When acetylene black and MWCNTs were added separately to FeF3, the electrical conductivity increased, but the mechanical strength decreased. When acetylene black and MWCNTs were both added to FeF3, the FeF3/M1AB4 sample (with 1 wt.% MWCNTs and 4% AB) afforded a discharge capacity of approximately 74% of the theoretical capacity (712 mAh/g) of FeF3. Considering the electrical conductivity and mechanical strength, this composition was confirmed to be the most suitable.

Recovering quantum entanglement after its certification.

Entanglement is a crucial quantum resource with broad applications in quantum information science. For harnessing entanglement in practice, it is a prerequisite to certify the entanglement of a given quantum state. However, the certification process itself destroys the entanglement, thereby precluding further exploitation of the entanglement. Resolving this conflict, here, we present a protocol that certifies the entanglement of a quantum state without complete destruction and then probabilistically recovers the original entanglement to provide useful entanglement for further quantum applications. We experimentally demonstrate this protocol in a photonic quantum system and highlight its usefulness for selecting high-quality entanglement from a realistic entanglement source. Moreover, our study reveals various trade-off relations among the physical quantities involved in the protocol. Our results show how entanglement certification can be made compatible with subsequent quantum applications and be beneficial to sort entanglement for better performance in quantum technologies.

A Customized Deep Sleep Recommender System Using Hybrid Deep Learning.

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.

Strain and crystallographic identification of the helically concaved gap surfaces of chiral nanoparticles.

Identifying the three-dimensional (3D) crystal plane and strain-field distributions of nanocrystals is essential for optical, catalytic, and electronic applications. However, it remains a challenge to image concave surfaces of nanoparticles. Here, we develop a methodology for visualizing the 3D information of chiral gold nanoparticles ≈ 200 nm in size with concave gap structures by Bragg coherent X-ray diffraction imaging. The distribution of the high-Miller-index planes constituting the concave chiral gap is precisely determined. The highly strained region adjacent to the chiral gaps is resolved, which was correlated to the 432-symmetric morphology of the nanoparticles and its corresponding plasmonic properties are numerically predicted from the atomically defined structures. This approach can serve as a comprehensive characterization platform for visualizing the 3D crystallographic and strain distributions of nanoparticles with a few hundred nanometers, especially for applications where structural complexity and local heterogeneity are major determinants, as exemplified in plasmonics.

Structural Characterization and Anti-Inflammatory Effects of 24-Methylcholesta-5(6), 22-Diene-3ß-ol from the Cultured Marine Diatom Phaeodactylum tricornutum; Attenuate Inflammatory Signaling Pathways.

In the present investigation, 24-methylcholesta-5(6), 22-diene-3ß-ol (MCDO), a major phytosterol was isolated from the cultured marine diatom, Phaeodactylum tricornutum Bohlin, and in vitro and in vivo anti-inflammatory effects were determined. MCDO demonstrated very potent dose-dependent inhibitory effects on the production of nitric oxide (NO) and prostaglandin E2 (PGE2) against lipopolysaccharide (LPS)-induced RAW 264.7 cells with minimal cytotoxic effects. MCDO also demonstrated a strong and significant suppression of pro-inflammatory cytokines of interleukin-1ß (IL-1ß) production, but no substantial inhibitory effects were observed on the production of cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) at the tested concentrations against LPS treatment on RAW macrophages. Western blot assay confirmed the suppression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein expressions against LPS-stimulated RAW 264.7 cells. In addition, MCDO was assessed for in vivo anti-inflammatory effects using the zebrafish model. MCDO acted as a potent inhibitor for reactive oxygen species (ROS) and NO levels with a protective effect against the oxidative stress induced by LPS in inflammatory zebrafish embryos. Collectively, MCDO isolated from the cultured marine diatom P. tricornutum exhibited profound anti-inflammatory effects both in vitro and in vivo, suggesting that this major sterol might be a potential treatment for inflammatory diseases.

Anti-Inflammatory, Neurotrophic, and Cytotoxic Oxylipins Isolated from Chaenomeles sinensis Twigs.

Oxylipins are important biological molecules with diverse roles in human and plants such as pro-/anti-inflammatory, antimicrobial, and regulatory activity. Although there is an increasing number of plant-derived oxylipins, most of their physiological roles in humans remain unclear. Here, we describe the isolation, identification, and biological activities of four new oxylipins, chaenomesters A-D (1-4), along with a known compound (5), obtained from Chaenomeles sinensis twigs. Their chemical structures were determined by spectroscopic (i.e., NMR) and spectrometric (i.e., HRMS) data analysis including 1H NMR-based empirical rules and homonuclear-decoupled 1H NMR experiments. Chaenomester D (4), an omega-3 oxylipin, showed a potent inhibitory effect on nitric oxide (NO) production in lipopolysaccharide (LPS)-activated BV-2 cells (NO production, 8.46 ± 0.68 µM), neurotrophic activity in C6 cells through the induction of the secretion of nerve growth factor (NGF, 157.7 ± 2.4%), and cytotoxicity in A549 human cancer cell lines (IC50 = 27.4 µM).

Ultralow-Loss Substrate for Nanophotonic Dark-Field Microscopy.

For the colloidal nanophotonic structures, a transmission electron microscope (TEM) grid has been widely used as a substrate of dark-field microscopy because a nanometer-scale feature can be effectively determined by TEM imaging following dark-field microscopic studies. However, an optically lossy carbon layer has been implemented in conventional TEM grids. A broadband scattering from the edges of the TEM grid further restricted an accessible signal-to-noise ratio. Herein, we demonstrate that the freely suspended, ultrathin, and wide-scale transparent nanomembrane can address such challenges. We developed a 1 mm by 600 µm scale and 20 nm thick poly(vinyl formal) nanomembrane, whose area is around 180 times wider than a conventional TEM grid, so that the possible broadband scattering at the edges of the grid was effectively excluded. Also, such nanomembranes can be formed without the assistance of carbon support; allowing us to achieve the highest signal-to-background ratio of scattering among other substrates.

Enantioselective sensing by collective circular dichroism.

Quantitative determination and in situ monitoring of molecular chirality at extremely low concentrations is still challenging with simple optics because of the molecular-scale mismatch with the incident light wavelength. Advances in spectroscopy1-4 and nanophotonics have successfully lowered the detection limit in enantioselective sensing, as it can bring the microscopic chiral characteristics of molecules into the macroscopic scale5-7 or squeeze the chiral light into the subwavelength scale8-17. Conventional nanophotonic approaches depend mainly on the optical helicity density8,9 by localized resonances within an individual structure, such as localized surface plasmon resonances (LSPRs)10-16 or dielectric Mie resonances17. These approaches use the local chiral hotspots in the immediate vicinity of the structure, whereas the handedness of these hotspots varies spatially. As such, these localized resonance modes tend to be error-prone to the stochasticity of the target molecular orientations, vibrations and local concentrations18,19. Here we identified enantioselective characteristics of collective resonances (CRs)20 arising from assembled 2D crystals of isotropic, 432-symmetric chiral gold nanoparticles (helicoids)21,22. The CRs exhibit a strong and uniform chiral near field over a large volume above the 2D crystal plane, resulting from the collectively spinning, optically induced dipoles at each helicoid. Thus, energy redistribution by molecular back action on the chiral near field shifts the CRs in opposite directions, depending on the handedness of the analyte, maximizing the modulation of the collective circular dichroism (CD).

Clinical Characteristics of Patients with Dental Malocclusion: An Otolaryngologic Perspective.

Purpose: Allergic rhinitis (AR), which is a major cause of upper airway obstruction, may affect the development of the dental malocclusion. This retrospective study was aimed to investigate association between AR and dental malocclusion in otolaryngologic perspectives. Methods: Patients (n = 217) referred to the otolaryngology department before initiating orthodontic treatment were recruited. The frequency and severity of AR symptoms, sinonasal outcome test (SNOT-22) scores, physical examination findings, acoustic rhinometry results, and treatment modalities were retrospectively assessed. Patients with positive skin prick test findings (SPT) (n = 173; orthodontic group) were compared with age- and sex-matched patients being treated for AR (AR group). Results: We found that 76.5% of the enrolled patients had subjective nasal symptoms, and 93.1% patients showed abnormal physical examination findings such as inferior turbinate hypertrophy (82.0%), adenotonsillar hypertrophy (31.8%), or deviated nasal septum (7.4%). The 173 (79.7%) patients with positive SPT results exhibited a significantly higher incidence of rhinorrhoea, sneezing, and inferior turbinate hypertrophy compared to those with negative SPT results. The proportion of patients who underwent pharmacological or surgical treatments was significantly higher among patients with nasal obstruction (92.0%) than among patients without nasal obstruction (36.9%). The frequency and mean visual analogue symptom scores for nasal obstruction, rhinorrhoea, and sneezing, as well as all SNOT-22 domain scores, were significantly higher in the AR group than in the orthodontic group. The minimal cross-sectional area measured with acoustic rhinometry showed no significant difference between groups. Conclusion: Patients with dental malocclusion had a high SPT (+) rate and a high prevalence of structural abnormalities of the upper airway. The early detection and treatment of subclinical AR, other rhinological problems, and structural abnormalities of the upper airway in patients with malocclusion may help us manage malocclusion from an otolaryngologic perspective.

Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

Application of subject-specific helmets for the study of human visuomotor behavior using transcranial focused ultrasound: a pilot study.

BACKGROUND AND OBJECTIVE: As a novel non-invasive human brain stimulation method, transcranial focused ultrasound (tFUS) is receiving growing attention due to its superior spatial specificity and depth penetrability. Since the focal point of tFUS needs to be fixated precisely to the target brain region during stimulation, a critical issue is to identify and maintain the accurate position and orientation of the tFUS transducer relative to the subject's head. This study aims to propose the entire framework of tFUS stimulation integrating the methods previously proposed by the authors for tFUS transducer configuration optimization and a subject-specific 3D-printed helmet, and to validate this complete setup in a human behavioral neuromodulation study. METHODS: To find the optimal configuration of the tFUS transducer, a numerical method based on subject-specific tFUS beamlines simulation was used. Then, the subject-specific 3D-printed helmet has been applied to effectively secure the transducer at the estimated optimal configuration. To validate this tFUS framework, a common behavioral neuromodulation paradigm was chosen; the effect of the dorsolateral prefrontal cortex (DLPFC) stimulation on anti-saccade (AS) behavior. While human participants (n=2) were performing AS tasks, tFUS stimulations were randomly applied to the left DLPFC right after the fixation target disappeared. RESULTS: The neuromodulation result strongly suggests that the cortical stimulation using the proposed tFUS setup is effective in significantly reducing the error rates of anti-saccades (about -10 %p for S1 and -16 %p for S2), whereas no significant effect was observed on their latencies. These observed behavioral effects are consistent with the previous results based on conventional brain stimulation or lesion studies. CONCLUSIONS: The proposed subject-specific tFUS framework has been effectively used in human neuromodulation study. The result suggests that the tFUS stimulation targeted to the DLPFC can generate a neuromodulatory effect on AS behavior.

High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI.

Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.

Long-term efficacy of turbinoplasty compared with medical treatment in patients with allergic rhinitis.

BACKGROUND: The effectiveness of turbinate surgery has been proven in patients with allergic rhinitis (AR). OBJECTIVES: This study evaluated the long-term efficacy of turbinoplasty in AR and to compare the results with those of medical treatment. METHODS: This study included 192 patients diagnosed with AR who underwent surgical or medical treatment. Medical records were reviewed to assess pre-treatment and 2-year post-treatment frequency and severity of AR symptoms, prescription frequency for AR medication, and satisfaction score for treatment. Parameters for 5-year post-treatment efficacy were acquired via a telephone survey. A total of 128 patients who had undergone turbinoplasty were defined as a 'turbinoplasty group,' and 64 patients who were treated only with medications were defined as a 'medication group'. Allergic symptom, medication, and satisfaction scores were compared. RESULTS: Patients in the turbinoplasty group demonstrated significant improvements in all allergic symptom scores, while those in the medication group showed significant symptom improvement only in nasal obstruction on long-term follow-up. Patients in the turbinoplasty group also showed a lower prescription frequency after treatment and higher subjective satisfaction scores than those in the medication group. CONCLUSIONS: This long-term follow-up study demonstrated that turbinoplasty for AR appears to be an effective treatment option compared with medical therapy alone.

Anion Exchange Membranes for Fuel Cell Application: A Review.

The fuel cell industry is the most promising industry in terms of the advancement of clean and safe technologies for sustainable energy generation. The polymer electrolyte membrane fuel cell is divided into two parts: anion exchange membrane fuel cells (AEMFCs) and proton exchange membrane fuel cells (PEMFCs). In the case of PEMFCs, high-power density was secured and research and development for commercialization have made significant progress. However, there are technical limitations and high-cost issues for the use of precious metal catalysts including Pt, the durability of catalysts, bipolar plates, and membranes, and the use of hydrogen to ensure system stability. On the contrary, AEMFCs have been used as low-platinum or non-platinum catalysts and have a low activation energy of oxygen reduction reaction, so many studies have been conducted to find alternatives to overcome the problems of PEMFCs in the last decade. At the core of ensuring the power density of AEMFCs is the anion exchange membrane (AEM) which is less durable and less conductive than the cation exchange membrane. AEMFCs are a promising technology that can solve the high-cost problem of PEMFCs that have reached technological saturation and overcome technical limitations. This review focuses on the various aspects of AEMs for AEMFCs application.

Age-Related Differences of Vestibulo-Ocular Reflex Gains Based on Presbyvestibulopathy Objective Criteria / 대한이비인후과학회지

Background and Objectives@#With the growing acknowledgment of age-related vestibular impairments, consensus diagnostic criteria for presbyvestibulopathy (PVP) have been recently published. The PVP criteria retains its objectivity with video head impulse test (vHIT), rotatory chair test (RCT) and caloric test. These objective tests share testing principles, but targeted frequencies vary across the tests. The PVP criteria are applicable for subjects of age over 60. However, there are different age cut-offs for older male in different areas. The purpose of the present study was to explore whether the PVP diagnostic criteria were appropriate for early elderly (Eel) patients and to identify the most sensitive test for age-related loss of vestibular function.Subjects and Method Eligible patients, aged 60 years or older, complained of dizziness for at least 3 weeks, and have undergone at least one of the followings: vHIT, RCT, or caloric test. We selected two groups based on age: the Eel group (aged >60 years but <65 years) and the late elderly (Lel) group (aged ≥75 years). @*Results@#The vestibulo-ocular reflex (VOR) gains differed significantly between the Eel and Lel groups for vHIT and caloric gain test. No Eel patient met the PVP criteria but 26.1% of Lel patients met the criteria for vHIT, and the proportions of such patients in the two groups differed significantly. @*Conclusion@#Eel subjects could be considered to differ from Lel subjects. Given the significance of both averages, VOR difference and the proportional difference in terms of meeting the PVP criteria and vHIT may be optimal when evaluating age-related changes.