Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals. Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories. Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR). Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (>20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean. Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.
Mediating Effect of Product Innovation on Market Orientation and Marketing Performance of SME’S During the COVID-19 Pandemic in Indonesia

The role of MSMEs is very large for Indonesia's economic growth, where the contribution of MSMEs to the gross domestic product (GDP) reaches 60.5% and to employment of 96.9% of the total national employment absorption. However, during the Covid-19 pandemic, the government imposed a semi-lockdown by limiting business operating hours, by limiting 50% of the capacity of arriving consumers and reducing operating hours to only 20.00 at night. This policy resulted in a decrease in product demand by 84.8%. However, the MSMEs coffee shop only had an impact of 16%. Based on this condition, researchers are interested in conducting research on MSMEs, especially coffee shops during a pandemic. The purpose of this study to analyze the Market Orientation effect of Product Innovation on Marketing Performance and to analyze the role of Product Innovation in mediating the influence of Market Orientation on Coffee Shop Marketing Performance in Medan City. The survey was conducted on 143 coffee shop entrepreneurs who still survived from the COVID-19 pandemic in the city of Medan. Data collection was carried out using a questionnaire with direct interview with the coffee shop owner. Data Analysis using the Partial Least Squares (PLS) analysis technique. The results indicate that market orientation has a positive and significant effect on product innovation at Coffee Shop in Medan. Market orientation has not significant effect on marketing performance at Coffee Shop in Medan City. Product innovation has a positive and significant effect on the marketing performance of the Coffee Shop in Medan City, and there is a role between product innovation in mediating the effect of market orientation on marketing performance at the Coffee Shop in Medan City. Coffee shop should establish mechanisms to obtain information about customer needs and expectations and to disseminate and effectively use this information among business functions. © 2023 WITPress. All rights reserved.
Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease. Methods Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software. Results A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great. Conclusions Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.
Theoretical prediction of mechanical stability of ferromagnetic fcc Fe-Cu alloys from first principles

The mechanical and magnetic properties of fcc Fe-Cu alloys were investigated theoretically by first-principles method based on the special quasi-random structures. The calculated results showed that the mechanical stability of ferromagnetic fcc Fe-Cu alloys is a highly nonlinear concentration dependence, and the theoretical phase boundary of metastable fcc Fe-Cu alloys is about 30 at. % Cu concentration. The ferromagnetic fcc phase stability of Fe-Cu alloys increases with decreasing the magnetic moment, and the maximum magnetic moment was predicted to be about 1.73 μB/atom for metastable fcc Fe-Cu alloys.
Continuous wave pump–probe experiment on a planar microcavity

The effect of two CW laser beams resonant with the polariton dispersion of a GaAs planar microcavity is discussed. A new coherent polariton emission is observed when the pump and the probe beams are both resonant with the dispersion at finite k‐vectors. This feature is the idler of an optical parametric amplifier (OPA) and is generated by probe‐stimulated scattering of pairs of polaritons from the pump. OPA behaviour is also observed in the optical parametric oscillator (OPO) regime. A study is made of the effect of varying the probe power on the interaction between the pump OPO and the probe OPA states; the OPA idler is found to be a much stronger emission than the signal of the OPO on its own. A simple theoretical model is used to simulate the pump‐probe interactions. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.
Serum brain-derived neurotrophic factor as diagnosis clue for Alzheimer's disease: A cross-sectional observational study in the elderly

Objective Brain-derived neurotrophic factor (BDNF) has not been validated as a diagnostic marker for Alzheimer's disease (AD). To provide a different perspective, this study aimed to evaluate the relationship between serum levels of mature BDNF (mBDNF) and precursor BDNF (proBDNF) in AD and to investigate whether serum BDNF levels or the ratio of mBDNF levels to proBDNF levels (M/P) could be a valuable biomarker for determining the risk of AD in elderly individuals. Method A total of 126 subjects who met the inclusion criteria were assigned to either the AD group (n = 62) or the healthy control group (HC, n = 64) in this cross-sectional observationl study. Serum levels of mBDNF and proBDNF were measured using enzyme immunoassay kits. We analyzed the Mini-Mental State Examination (MMSE) scores from the two groups and examined the associations between AD and BDNF metabolism. Results The serum concentration of proBDNF was significantly higher in ADs (4140.937 pg/ml) than in HCs (2606.943 pg/ml; p < 0.01). The MMSE significantly correlated with proBDNF (p < 0.01, r = −0.686) and M/P (p < 0.01, r = 0.595) in all subjects. To determine the risk for AD, the area under the receiver operating characteristic curve was calculated, which was 0.896 (95% confidence interval 0.844–0.949) for proBDNF and 0.901 (95% 0.850–0.953) for proBDNF and M/P combined. Conclusion We observed a correlation between low serum proBDNF levels and higher MMSE scores in AD. The most effective diagnostic strategy was the combination of proBDNF and M/P, whereas mBDNF levels performed poorly when we evaluated the predictive model.
The Sounds of Disease‐Carrying Mosquitoes

In the summer of 1947, the authors made recordings of a number of species of mosquitoes in West Africa. The equipment used to make the recordings is described, and some of the records obtained are listed. In the summer of 1948, the authors went to Cuba where they recorded the rounds of the female Anopheles albimanus mosquito. The recordings were played back in the Husillo Swamp there for the purpose of calling male mosquitoes of the same species. Relatively large numbers of mosquitoes were killed in the sound‐baited trap. It is believed that this was the first time that a sound‐baited trap has been used successfully for catching mosquitoes. Mosquito sounds are quite distinctive; gross differences occur and are readily detected both by listening and by wave analysis. In general, male sounds seem higher pitched than female; examination of sound spectrograms suggests that the difference is due in considerable degree to the differences in harmonic emphasis. All fundamental sounds seem to occur in the center o.
Transcriptome analysis reveals defense-related genes and pathways during dodder (Cuscuta australis) parasitism on white clover (Trifolium repens)

Dodders (Cuscuta australis R. Br.) are holo-parasitic stem angiosperms with an extensive host range that have significant ecological and economic potential impact on the ecosystem and the agricultural system. However, how the host plant responds to this biotic stress remains mostly unexplored. To identify the defense-related genes and the pathways in white clover (Trifolium repens L.) induced by dodder parasitism, we performed a comparative transcriptome analysis of the leaf and root tissues from white clover with and without dodder infection by high throughput sequencing. We identified 1,329 and 3,271 differentially expressed genes (DEGs) in the leaf and root tissues, respectively. Functional enrichment analysis revealed that plant-pathogen interaction, plant hormone signal transduction, and phenylpropanoid biosynthesis pathways were significantly enriched. Eight WRKY, six AP2/ERF, four bHLH, three bZIP, three MYB, and three NAC transcription factors showed a close relationship with lignin synthesis-related genes, which defended white clover against dodder parasitism. Real-time quantitative PCR (RT-qPCR) for nine DEGs, further validated the data obtained from transcriptome sequencing. Our results provide new insights into understanding the complex regulatory network behind these parasite-host plant interactions.
Horizontal seismic wave at ground surface from transfer function based on ambient noise

Earthquake detection can be improved by ensuring that seismometer sites experience little artificial noise in the surrounding environment. To minimize noise, seismological stations should be positioned in rocky mountainous areas without nearby valleys, away from significant human activity. However, such surface sites may be scarce when constructing dense monitoring networks, necessitating the use of underground sites to ensure low noise levels. The Korean Meteorological Administration is currently installing new underground seismometers to increase seismic monitoring capacity. However, seismic data on the ground surface are also required for engineering technological developments (to reduce damage to structural components). Therefore, borehole seismic stations without surface seismometers need to estimate ground surface motion from borehole record data. We propose a transfer function that converts motion within boreholes to surface seismic waves using ambient noise, thereby facilitating estimation of ground surface motions using borehole seismometers. As a result, predicting ground surface motion from borehole record data becomes possible.
Investigation of the Effect of PbO Doping on Telluride Glass Ceramics as a Potential Material for Gamma Radiation Shielding

The purpose of this paper is to study the effect of PbO doping of multicomponent composite glass-like ceramics based on TeO2, WO3, Bi2O3, MoO3, and SiO2, which are one of the promising materials for gamma radiation shielding. According to X-ray diffraction data, it was found that the PbO dopant concentration increase from 0.10 to 0.20–0.25 mol results in the initialization of the phase transformation and structural ordering processes, which are expressed in the formation of SiO2 and PbWO4 phases, and the crystallinity degree growth. An analysis of the optical properties showed that a change in the ratio of the contributions of the amorphous and ordered fractions leads to the optical density increase and the band gap alteration, as well as a variation in the optical characteristics. During the study of the strength and mechanical properties of the synthesized ceramics, depending on the dopant concentration, it was found that when inclusions in the form of PbWO4 are formed in the structure, the strength characteristics increase by 70–80% compared to the initial data, which indicates the doping efficiency and a rise in the mechanical strength of ceramics to external influences. During evaluation of the shielding protective characteristics of the synthesized ceramics, it was revealed that the formation of PbWO4 in the structure results in a rise in the high-energy gamma ray absorption efficiency.
A Real-Time Face Detection Method Based on Blink Detection

Face anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face. In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system using a legitimate user’s picture. Aiming at this problem and the influence of illumination in real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. The method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.
Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection

ABSTRACT Diabetic retinopathy (DR) is a micro vascular problem caused by diabetes that can lead to loss of sight. The early detection of diabetic retinopathy is important to avoid the severity of sightlessness. In this manuscript, a comparative analysis of several deep learning methods for DR identification is proposed. The input fundus images are taken from a standard dataset pre-processed by the Mathematical Morphology process. Moreover, the images are segregated using a Multilevel segmentation of the Region of interest (ROI) based on the split and merge algorithm. After that, an original deep learning architecture is utilized to categorize the segregated fundus images. Deep learning methods, such as Convolution neural network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Fuzzy K-means cluster (FKM) and Discriminant Analysis (DA) are proposed to classify the DR. The proposed DR identification and detection with CNN provides 65.54% SP, 100% SE, 78.54% SV and 96.95% ACC. Finally, CNN shows better performance than other classifiers.
Fastsound: Probing the Origin of Cosmic Acceleration by Galaxy Clustering at z ~ 1.3 with Subaru/fmos

, and detected the redshift space distortionat this redshift range for the rst time. The redshift space distortion (RSD) signal will be used to derivea measurement of the growth rate of large scale structure, which will provide a test for modi ed gravityas a possible origin of accelerated cosmic explansion. Here we present an overview and the current statusof the project.Key words: large-scale structure of Universe | dark energy1. INTRODUCTIONThe most signi cant mystery concerning our current pic-ture of the Universe is the accelerating nature of the cos-mic expansion. This is well-described by the ‘CDM’model which supplements dark matter with a repulsivecosmological constant. However this has severe concep-tual problems, in that while it is consistent with all cur-rent observations (see Frieman et al. 2008, for a review)it is an entirely empirical model without a fundamentalphysical basis. The natural interpretation of as rep-resenting the zero-point energy of the vacuum fails by122 orders of magnitude and an extreme ne-tuning isrequired for it to appear now in the long history of theuniverse. This has motivated numerous ideas for newphysics of ‘dark energy’ or ‘modi ed gravity’ to explainthe large scale cosmic dynamics of spacetime (e.g., Cald-well & Kamionkowski 2009, for a theoretical review). Large scale redshift surveys of galaxies are now widelyrecognized as one of the most powerful approaches totackle this important and dicult problem. This is be-cause they are able to simultaneously measure the e ectsof dark energy elds (which modify the expansion) andmodi cations of gravity (which also a ect the growth ofstructures in the Universe). Low redshift surveys suchas 2dFGRS and SDSS have delivered a wealth of cosmo-logical results this way but the results remain consistentwith CDM and have, frustratingly, not provided newclues to the underlying physics.The redshift space distortion (RSD) observed ingalaxy redshift survey data is one of powerful tools totest the theory of gravity on cosmological scales. Thecore idea is to measure the two dimensional clustering ofhttp://pkas.kas. orggalaxies both across and along the line-of-sight. Becausethis is measured in ‘redshift space’ an anisotropic sta-tistical distortion arises in galaxy clustering due to thepeculiar velocity of galaxies falling in to larger struc-tures. This distortion allows us to measure the infallrate, and comparison with the mass uctuations thenprovides a constraining test of the theory of gravity (e.g.the review by Hamilton 1998). The quantity that willbe measured is the growth rate f
A method of photometric data extraction for asteroids from time-domain surveys

The lightcurves of asteroids are essential for determining their physical characteristics, including shape, spin, size, and surface composition. However, most asteroids are missing some of these basic physical parameters due to lack of photometric data. Although a few telescopes or surveys are specially designed for photometric lightcurve observations of asteroids, many ground-based and space-based sky surveys for hunting new exoplanets, transient events, etc., should capture numerous small Solar System objects. This will benefit the physical studies of these objects. In order to extract data of these moving objects from time-domain photometric surveys, we have developed a new method using the model tree algorithm in the field of machine learning. A dedicated module is built to automatically identify moving objects in dataset, and extract their photometric and astrometric data. As the first application of this novel method, we have analyzed data in five fields of the Yunnan-Hong Kong wide field photometric (YNHK) survey, from which 538 lightcurves of 211 asteroids are successfully extracted. Meanwhile, we also tested the method based on the data from NASA’s Transiting Exoplanet Survey Satellite, and the result proves the reliability of our method. With derived lightcurves of 13 asteroids from the YNHK survey, we have determined their synodic spin periods, among which the periods of 4 asteroids are estimated for the first time. In future, we are going to apply this method to search for small objects in the outer part of the Solar System from the Chinese Space Station Telescope survey.
A Unified Approach to Linear Differential Algebraic Equations and their Adjoints

Instead of a single matrix occurring in the standard setting, the leading term of the linear differential algebraic equation is composed of a pair of well matched matrices. An index notion is proposed for the equations. The coefficients are assumed to be continuous and only certain subspaces have to be continuously differentiable. The solvability of lower index problems is proved. The solution representations are based on the solutions of certain inherent regular ordinary differential equations that are uniquely determined by the problem data. The assumptions allow for a unified treatment of the original equation and its adjoint. Both equations have the same index and are solvable simultaneously. Their fundamental solution matrices satisfy a relation that generalizes the classical Lagrange identity.