Miracle-dz/UniFAR-FTUs huggingface dataset:
Composition and Antioxidative and Antibacterial Activities of the Essential Oil from Farfugium japonicum

The composition of volatile oils of the leaf and stem of Farfugium japonicum (L.) Kitamura were prepared by supercritical fluid extraction (SFE)-CO2. A total 47 and 40 compounds were identified by GC/MS analysis, respectively, and only 13 compounds coexisted. The main constituent types in the leaf oil included alcohols (34.1%), hydrocarbons (24.1%), terpenoids (16.2%), benzenes (7.5%), and fatty acids (4.9%). In the stem oil, the constituent types chiefly included benzenes (18.8%), ketones (13.9%), terpenoids (17.0%), fatty acids (8.8%), phenolics (8.7%), steroids (8.6%), hydrocarbons (8.0%), and esters (5.7%). The predominant volatile compounds in the stem were 2-(1-cyclopent-1-enyl-1-methylethyl) cyclopentanone (11.7%), 1,2,3,4,5,6,7,8-octahydro- 9,10-dimethyl-anthracene (8.4%), 5-heptylresorcinol (6.5%), and α-sitosterol (5.2%). Those in the leaf mainly included (E)-3-hexen-1-ol (13.7%) and (Z)-3-hexen-1-ol (14.0%). This demonstrated a significant difference in the composition of both oils. Further study showed that stem oils demonstrated the highest DPPH (1,1-diphenyl-2-pinylhydrazyl) and ·OH free radical scavenging capacities at IC50 values of 9.22 and 0.90 mg/mL, respectively. In addition, they demonstrated the strongest antibacterial capacity against the Gram-positive bacteria methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) at a minimum inhibitory concentration (MIC) of 0.16 mg/mL. This could be due to the SFE-CO2 extraction and the high accumulation of benzenes, terpenoids, and phenolics in the stem. In particular, the monoterpenes presented in terpenoids could play a special role in these findings.
Research on Performance Model of Radial Foil Air Bearings for Aeronautical Cooling Turbine Unit

In this paper, it is accurately constructed the performance/lubrication characteristics model of air radial bearing for cooling turbine unit, which breakdowns frequently, and it is formed the lubrication characteristics modeling and analysis method of the performance of air bearing for cooling turbine unit, guides the product forward design. It can be applied to the quality improvement and design improvement of aeronautical cooling turbine unit. The research object of this paper is the most typical cantilever elastic foil radial bearing in aviation applications, which mainly consists of a bearing seat and a curved foil. One end of the foil is fixed and the other section is freely lap to the adjacent foil to provide elastic support for the rotating shaft. In the Matlab language environment, the lubrication characteristics of turbine cooler air radial bearing at microscopic level were modeled, and the Reynolds equation of air radial bearing lubricating oil film was established. The weighted margin method and finite element method were used to reduce the order and discretize the equation. At the same time, based on the radial elastic deformation of foil air, establish a beam element model is used to calculate the elastic deformation of foil air bearing and initial oil film thickness equation, elastic deformation will foil with foil gas film thickness equation of gas dynamic pressure bearing phase coupling, to carry out the friction pair of fluid-solid coupling iteration, the bearing capacity of the air bearing, friction torque and the static characteristics of the numerical calculation method. Finally, compared with the simulation results of lubrication gas film pressure distribution, gas film thickness distribution and other static characteristics of elastic foil dynamic pressure gas bearings, the accuracy of the conclusions of the failure performance model presented in this paper is verified under the micro-lubrication characteristics of air radial bearings. The influence of basic parameters of air bearings on static characteristics of foil dynamic pressure gas bearings such as bearing capacity, friction torque and foil deformation was analyzed, and the influencing factors of key factors of air bearings were analyzed to support the design to improve characteristics of aeronautical cooling turbine unit.
Repeated Photobiomodulation Induced Reduction of Bilateral Cortical Hemodynamic Activation During a Working Memory Task in Healthy Older Adults

Transcranial photobiomodulation (tPBM) is an emerging non-invasive light-based neuromodulation technique that shows promising potential for improving working memory (WM) performance in older adults. However, the neurophysiological mechanisms associated with tPBM that underlie the improvement of WM and the persistence of such improvement have not been investigated. Sixty-one healthy older adults were recruited to receive a baseline sham stimulation, followed by one-week active tPBM (12 min daily, 1064-nm laser, 250 mW/cm2) and three-week follow-ups. N-back WM task was conducted on post-stimulation of the baseline, the first (Day 1) and seventh (Day 7) days of the active treatment, and at the follow-ups. During the task, functional near-infrared spectroscopy (fNIRS) imaging was employed to record the cortical hemodynamic changes. Brain activations during the active and follow-up sessions were compared with the baseline to determine how tPBM had changed cortical hemodynamic activity and how long these changes persisted. We found that tPBM stimulation on Day 1 induced significantly decreased activation in the right hemisphere during the 3-back. The decreased activation expanded from only the right hemisphere on Day 1 to both hemispheres on Day 7. The decreased activation persisted for one week in the right supramarginal gyrus and the left angular gyrus and two weeks in the left somatosensory association cortex. These activation changes were accompanied by significantly improved task accuracy during the N-back. These findings provide important evidence for understanding neural mechanisms underlying cognitive enhancement after tPBM.
Numerical Simulation of Integrated Generation and Shaping of Airy and Bessel Vortex Beams Based on All-Dielectric Metasurface

Integrating multiple independent functions into a single optical component is one of the most important topics in research on photoelectric systems. In this paper, we propose a multifunctional all-dielectric metasurface that can achieve a variety of non-diffractive beams depending on the polarization state of the incident light. Using the anisotropic TiO2 rectangular column as the unit structure, the three functions of generating polygonal Bessel vortex beams under left-handed circularly polarized incidence, Airy vortex beams under right-handed circularly polarized incidence and polygonal Airy vortex-like beams under linearly polarized incidence are realized. In addition, the number of polygonal beam sides and the position of focal plane can be adjusted. The device could facilitate further developments in scaling complex integrated optical systems and fabricating efficient multifunctional components.
High Sensitivity Microwave Sensor for Material Characterization Using Square Split Ring Resonator

Microwave sensors are the most extensively utilized sensors in the food industry, quality control, biomedical, and industrial applications. The perturbation method, in which the dielectric characteristics of the resonator affect Q-factor and resonance frequency, is used in this study to address the weakness of this technique. For material characterization of solid and liquid samples, this proposed sensor worked at 2.5GHz in the 1GHz to 4GHz range. These sensors were built on a substrate of RT/Duroid Roger 5880 with a dielectric constant of 2.2, a loss tangent of 0.0009, and a copper thickness of 0.00175mm. As a result, at 2.5GHz, this square split ring resonator (SSRR) sensor produces narrow resonance, low insertion loss, and a high Q-factor value of 430. As a result, the SSRR sensor's sensitivity is 98.59%, which is higher than earlier investigations. This evidence supports the proposed sensor's application as a material characterization tool, particularly for identifying material properties.
Stock trend prediction using sentiment analysis

These days, the vast amount of data generated on the Internet is a new treasure trove for investors. They can utilize text mining and sentiment analysis techniques to reflect investors’ confidence in specific stocks in order to make the most accurate decision. Most previous research just sums up the text sentiment score on each natural day and uses such aggregated score to predict various stock trends. However, the natural day aggregated score may not be useful in predicting different stock trends. Therefore, in this research, we designed two different time divisions: 0:00t∼0:00t+1 and 9:30t∼9:30t+1 to study how tweets and news from the different periods can predict the next-day stock trend. 260,000 tweets and 6,000 news from Service stocks (Amazon, Netflix) and Technology stocks (Apple, Microsoft) were selected to conduct the research. The experimental result shows that opening hours division (9:30t∼9:30t+1) outperformed natural hours division (0:00t∼0:00t+1).
Improved Color Balance and Fusion for Enhancement of Underwater Image

In underwater photography, the absorption and scattering of light may probably cause low contrast, blurred images, and color cast. We present an effective method for improving the quality of underwater images that have been degraded by medium scattering and absorption. It combines color compensation with multi-scale image fusion. Color compensation consists of independently correcting the value of $\mathbf{R}$ channel and G-B channel of the input image and adjusting the white balance of the corrected image. After color compensation, the color of degraded image is restored effectively, but the blurring of image edges and details receiving scattering effects cannot be remedied. This problem can be solved by multi-scale image fusion. In our method, we use Gaussian pyramid and Laplace pyramid images for fusion, and these two feature pyramids are constructed by using the image after the illumination adjustment. The experimental results illustrate that dark-area sensitivity, global contrast, and edge sharpness of images have been improved by our methods. Moreover, in terms of visual effects and assessment metrics of underwater images, our method excels over other methods.
Study on phase change of Gaussian beam induced by thermal blooming effect

When a high-power laser beam propagates in an absorbing medium, the thermal blooming effect cause the phase distortion of the beam, which will seriously affect the beam quality. In this paper, with the help of the laser propagation theory and thermo-optic coefficient experiment, the phase change of Gaussian beam propagating in various concentrations of ink-ethanol is investigated. The result shows that the phase change is proportional to the input power, which may provide the theoretical and experimental basis for phase compensation, and have potential applications in the adaptive optics, underwater optical communications, biological tissue imaging and other related field.
Numerical Analysis of Eigenvalue Solution of Disk Rescmator

A formulation is proposed to calculate the frequencies of the eigemnodes for a resonator with a thin conductor disk placed in the median plane betwe-en two infinite parallel conductor plates. The numerical tialysis"is c~ried out for the E- and Ek? -rnodes, and these eigenvalues are calculated as the function of the ratio of the disk radius to the distance between me d! sk and one of the @finite conductor 'plates. It is shown that at a ratio gre"ater than a certain vah! e"the exact eigenvalue is smaller than the one predicted by ap- plying the conventional method for two-dimensional bifurcation of rectangular waveguide, but the latter becomes closel to me exact one with 'increasing ratio. The availability of our exact eigenvalues is demonstrated in "determining experimentally the dielectric constant of Teflon plate specimen by applying those vtiues. Then the constancy of the measured dielectric const~t is confirmed irrespective of the modes and the ratios,
A Heterogeneous Resource Deployment Strategy of SDR Based on Meta-Heuristic Algorithm

Software-defined radio (SDR) introduces flexibility and reconfigurability to wireless communication system, facilitating dynamic switching from one radio functions to another, in other words, the offloading and reallocation of platform resources from one SDR application to another. It's challenging to minimize the computing and storage resources of heterogeneous hardware platform, and to seek local optimal waveform application deployment in software-definable communication terminals. this paper focuses on waveform component deployment and optical scheduling of heterogeneous resource for SDR applications. Based on the scenario of SDR waveform application (SWA) mapping to heterogeneous processors, we propose an optimization deployment strategy for adding integer-coded particle swarm optimization (IEPSO) to the SDR operating environment.
Planar circularly polarization filter antenna design for the 2.4GHz Bluetooth band

In this paper, a planar microstrip circular polarized monopole antenna with filtering function is proposed, and circular polarization is achieved by cutting a triangular structure and a semicircular structure on a square monopole patch and loading two open stub lines with right angle bending. The choice of tapered gradient line between feeder and patch is more conducive to adjusting impedance matching; The filtering function is achieved by integrating a fifth-order microstrip hairpin filter loaded with multiple serrated edges directly into the microstrip feeder of the CP antenna. The center frequency of the designed filter antenna is 2.45GHz, and the simulation results show that the 3dB axial ratio bandwidth is 200MHz, the gain is 2.8 dB, and the omnidirectionality is better. It has good frequency selectivity and out-of-band rejection, and has good application value in the field of Bluetooth communication.
Scrambled Semi-tensor Product Compressed Sensing

In compressed sensing (CS) framework, reducing the storage space for the measurement matrix is an important issue. Semi-tensor product CS (STP-CS) scheme is a new CS sampling model which can address this concern since the final measurement matrix for STP-CS scheme can be generated by using a primary measurement matrix with the small size. However, the recovery performance of STP-CS scheme suffers from severe degradation if the size of the primary measurement matrix is too small. In this paper, a scrambled STP-CS scheme is proposed to address this concern. Firstly, a random permutation operation is utilized to scramble the original signal before CS sampling. Then, the scrambled signal is sampled by using STP-CS. Since the scrambling operation reduces the mutual coherence coefficient (MCC) of the final measurement matrix, the proposed method can improve the recovery performance significantly. Finally, the effectiveness of the proposed method is demonstrated by simulation.
Microbial response to warming and cellulose addition in a maritime Antarctic soil

Maritime Antarctic King George Island (South Shetland Islands) has experienced rapid warming in recent decades, but the impacts on soil organic matter (SOM) decomposition remain ambiguous. Most vegetation cover is dominated by bryophytes (mosses), whereas a few vascular plants, such as Deschampsia antarctica and Colobanthus quitensis grow interspersed. Therefore, SOM is mainly enriched with carbohydrates and C‐alkyl, provided by mosses, which lack lignin as a precursor for aromatic compounds and humus formation. However, there is no clear answer to how substrate and temperature increase changes in Antarctic microbial respiration. We determined in what way SOM mineralization changes with temperature and substrate addition by characterizing the temperature sensitivity (Q10) of soil respiration in an open‐top chamber warming experiment. We hypothesized that: (a) cold‐tolerant microorganisms are well adapted to growing in maritime Antarctic soils (~ 0°C), so would not respond to low and moderate temperature increases because they undergo various metabolic mechanism adjustments until they experience increasing temperatures toward optimum growth (e.g., by enzyme production); and (b) cellulose, as a complex carbonaceous substrate of vegetated areas in Maritime Antarctic soils, activates microorganisms, increasing the Q10 of soil organic carbon (SOC) mineralization. Soils (5–10 cm) were sampled after four consecutive years of experimental warming for SOC composition, microbial community structure, and C mineralization at 4, 12, and 20°C with and without cellulose addition. Functional group chemoheterotrophs, represented mainly by Proteobacteria, decomposed more refractory SOC (aromatic compounds), as indicated by nuclear magnetic resonance (NMR) spectroscopy, in ambient plots than in warming plots where plants were growing. The C‐CO2 efflux from the incubation experiment remained stable below 12°C but sharply increased at 20°C. Q10 varied between 0.4 and 4 and was reduced at 20°C, whereas cellulose addition increased Q10. In conclusion, as confirmed during field studies in a climate scenario, cold‐tolerant microorganisms in maritime Antarctic soils were slightly affected by increasing temperature (e.g., 4–12°C), with reduced temperature sensitivity, as summarized in a conceptual model.
Establishment of Production Standards for Web-based Metaverse Content: Focusing on Accessibility and HCI

Metaverse technology is expanding to industries in various fields, such as medical, national defense, and education, and training simulation programs have been mainstream so far. However, there have been increasing attempts to apply metaverse content to web-based platforms linked to social media services and, as a result, we face the problem of access to web-based metaverse content. Unlike traditional content, metaverse content interacts with many users, so content accessibility is the first important part to consider. In other words, to maximize the quality of metaverse content, it is essential to pull out the optimal UX through a detailed HCI (human computer interaction) design. Metaverse content development methodologies have effective methods proposed by many researchers. However, they are limited to web-based metaverse content that limits the use of high-end hardware. They are ineffective for platforms such as PCs and VR devices, as most studies focus on improving the visual performance of PCs or high-performance VR devices. Therefore, unlike existing research, the key theme of our research is to study optimized development standards that can be applied to web-based metaverse content and find out their effects through experiments. We created a development standard to be applied to a Web-based platform based on the existing metaverse content development methodology. Then, we redeveloped the VR content into the metaverse content and named them the VR build and the metaverse build. We had 25 people play virtual reality builds and metaverse builds simultaneously. Then, we measured the overall experience with an evaluation tool called the Game Experience Questionnaire (GEQ); the GEQ is a proven tool for evaluating content experiences by dividing them into positive/negative scales. When comparing the results measured from the two builds, the metaverse build showed consistent results with a higher positive scale, and a lower negative scale, than the VR build. The results showed that users indeed rated metaverse content positively. The bottom line is that the web-based metaverse content development standards that we have produced are practical. However, since generalization is limited, continuous research will be needed in more experimental groups in the future.
Can Eye Gaze Improve Emotional State Detection on Off the Shelf Smart Devices Jiwan Kim Doyoung Lee Jaeho Kim

Smartphones and wearable technology have revolutionized digital healthcare through the use of rich sensor data. Digital phenotyping, a field that uses smartphone data to detect or recognize cognitive, behavioral, or affective states and traits, is often based on data from sensors (such as inertial or touch sensors), activity or user logs, and user-generated content. In this paper, we propose the use of eye gaze as a new digital biomarker for affect detection, leveraging the advanced capabilities of off the-shelf smart devices. We designed two studies in the Instagram use scenario to detect emotional state change using gaze data. The first study took place in a controlled setting and help us understand the value of gaze features for affect detection. In this study, we achieved a peak accuracy of 76.4% for binary valence detection. The second study will be conducted over a longer period in real-world settings, with a larger population, to assess the effectiveness of our approach. By including this new sensing modality in affective digital phenotyping, we plan to improve the reliability and robustness of emotional state detection.
Effective Traffic Prediction with Self-Supervised Contrastive Learning

Taxi demand prediction has recently attracted increasing research interest due to the growing availability of large-scale traffic data, which could empower various real-world applications. Accurate taxi demand prediction can improve vehicle utilization, reduce the time for passengers to wait for taxis, and mitigate traffic congestion. Although both spatial dependencies and temporal dynamics have been considered, most of the previous methods with over-complicated models might easily achieve suboptimal performance due to the overfitting issue. Contrastive unsupervised learning has recently shown encouraging progress, which has great potential to learn effective data representations without extensive manual labeling. In this paper, we utilize contrastive learning to construct an effective auxiliary task to learn feature representations of data in a self-supervised manner. The model learned via contrastive learning can be subsequently applied for downstream tasks, which is proven to be more robust against overfitting. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our proposed model over other compared methods for taxi demand prediction.
Analysis of convergence in nonlinear magnetostatic finite elements problems

Deals with convergence difficulties using Newton-Raphson method in nonlinear magnetostatic problems. It is shown use of magnetostatic scalar potential can lead to a very unstable iterative process because of the shape of the residual function which is to be cancelled. Such effects do not exist when the vector potential is used and Newton-Raphson method is much more efficient. A simple example points out the behavior of Newton-Raphson method for both formulations. A method for reducing the CPU time required for determining the relaxation factor used to insure convergence in the case of scalar potential is also presented. >