Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrospinning of Biomedical Nanofibers/Nanomembranes: Effects of Process Parameters

Nanotechnology has attracted great attention from researchers in modern science because nanomaterials have innovative and superior physical, chemical, and biological properties, and they can be altered and modified accordingly. As particles get smaller, their surface area increases compared to their volume. Electrospinning is one of the advanced techniques to produce ultrathin nanofibers and membranes, and it is one of the best ways to create continuous nanomaterials with variable biological, chemical, and physical properties. The produced fibers can be utilized in various domains such as wound dressing, drug release, enzyme immobilization, etc. This review examines the biomedical nanofibers/membranes produced by electrospinning techniques to investigate the effects of process parameters (e.g., solution characteristics, applied voltage, and ambient conditions) on nanofiber characteristics (physical, chemical, and mechanical properties). The solution parameters like (i) optimum concentration, (ii) higher molecular weight, and (iii) higher conductivity produce uniform nanofibers, smoother nanofibers, and a smaller and more uniform fiber diameter, respectively. In addition, process parameters such as (i) higher voltage and (ii) slower flow rate produce more polymer ejection from the nozzle and enhance the smoother fiber production, respectively. The optimum tip-to-collector distance is considered to be 13–15 cm. The ambient conditions such as (i) higher humidity and (ii) higher temperature produce thicker and thinner nanofibers, respectively. The controlled parameters through optimization process determine the size and quality of the fibers. The effects of each parameter are discussed in this review. The applications of nanofibers are also discussed.
On transport of charged particle in the solar wind

In impulsive SEP events, electrons and ions are accelerated at the Sun in a small region, during a short period and then “injected” into the solar wind, propagating to 1 AU and are observed by various spacecraft. Their propagation in the solar wind is a competing process between magnetic focusing due to large scale magnetic field geometry and pitch angle scattering due to small scale turbulence in the magnetic field. In a collisionless plasma like the solar wind, the effect of the pitch angle scattering is to alter particle pitch angles in a random but continuous manner during their propagation. Here we briefly discuss a direct Monte‐Carlo method that solves the transport equation by following individual particle trajectories. Mathematically, the approach can be understood under the framework of stochastic differential equation method. By casting the underlying Fokker‐Planck equation to a set of equations consisting of deterministic pieces and stochastic pieces, one can treat the Lorentz force of the larg.
PERCEIVED VALUE AND TRUST TOWARDS PATIENT LOYALTY: A LITERATURE REVIEW

Increasing patient loyalty through patient-provider relationships becomes the key factor in medical providers’ successes. Perceived value is the long-term value that customers provide to hospitals. The creation of added value for customers, and increasing the level of satisfaction will make a major contribution to the possibility of customers staying, and customers are increasingly valuable to service providers. This study explored the mediated relationship between patients’ perceived value, trust, and patient loyalty. This study aims to examine the perceived value and trust in patient loyalty both in hospitals and medical centers. This literature review study was searched from 3 databases, namely PubMed, Scopus, and Google Scholar published between January 1, 2018-May 31, 2022. Inclusion criteria were research on medical care loyalty, experience in the patient's perceived value of hospital services, and patient’s trust in the hospital. A total of 11 from 86 articles found were selected for review. Based on 11 articles that have been reviewed discussing customer value and trust in patient loyalty. Hospital and Medical Center as health provider has a relationship between patients’ perceived value of medical services, commitment to the patient-provider relationship, and patient loyalty. Furthermore, when the patient demonstrated higher levels of trust in a healthcare provider, the relationship of perceived value, commitment, and patient loyalty was also enhanced.
Direct writing three-dimensional Ba2TiSi2O8 crystalline pattern in glass with ultrashort pulse laser

The nonlinear optical crystal Ba2TiSi2O8 is induced three-dimensionally inside a glass by a femtosecond pulse laser with 800nm, 250kHz, and 150fs. Micro-Raman and photoluminescence spectra confirm that crystals can grow in the focal point of the laser beam. Continuous crystalline lines were written through moving the focal point of the laser beam inside the glass. The results demonstrate a convenient method to engrave three-dimensional patterns of crystals with nonlinear optical properties for integrated optical devices.The nonlinear optical crystal Ba2TiSi2O8 is induced three-dimensionally inside a glass by a femtosecond pulse laser with 800nm, 250kHz, and 150fs. Micro-Raman and photoluminescence spectra confirm that crystals can grow in the focal point of the laser beam. Continuous crystalline lines were written through moving the focal point of the laser beam inside the glass. The results demonstrate a convenient method to engrave three-dimensional patterns of crystals with nonlinear optical properties for integrated optical devices.
Trimetallic Nanoalloy of NiFeCo Embedded in Phosphidated Nitrogen Doped Carbon Catalyst for Efficient Electro-Oxidation of Kraft Lignin

Recently, electro-oxidation of kraft lignin has been reported as a prominent electrochemical reaction to generate hydrogen at lower overpotential in alkaline water electrolysis. However, this reaction is highly limited by the low performance of existing electrocatalysts. Herein, we report a novel yet effective catalyst that comprises nonprecious trimetallic (Ni, Fe, and Co) nanoalloy as a core in a phosphidated nitrogen-doped carbon shell (referred to as sample P-NiFeCo/NC) for efficient electro-oxidation of kraft lignin at different temperatures in alkaline medium. The as-synthesized catalyst electro-oxidizes lignin only at 0.2 V versus Hg/HgO, which is almost three times less positive potential than in the conventional oxygen evolution reaction (0.59 V versus Hg/HgO) at 6.4 mA/cm2 in 1 M KOH. The catalyst demonstrates a turnover frequency (TOF) three to five times greater in lignin containing 1 M KOH than that of pure 1 M KOH. More importantly, the catalyst P-NiFeCo/NC shows theoretical hydrogen production of about 0.37 μmoles/min in the presence of lignin, much higher than that in pure 1 M KOH (0.0078 μ moles/min). Thus, this work verifies the benefit of the NiFeCo nanoalloy incorporated in carbon matrix, providing the way to realize a highly active catalyst for the electro-oxidation of kraft lignin.
Metastable legged-robot locomotion

A variety of impressive approaches to legged locomotion exist; however, the science of legged robotics is still far from demonstrating a solution which performs with a level of flexibility, reliability and careful foot placement that would enable practical locomotion on the variety of rough and intermittent terrain humans negotiate with ease on a regular basis. In this thesis, we strive toward this particular goal by developing a methodology for designing control algorithms for moving a legged robot across such terrain in a qualitatively satisfying manner, without falling down very often. We feel the definition of a meaningful metric for legged locomotion is a useful goal in and of itself. Specifically, the mean first-passage time (MFPT), also called the mean time to failure (MTTF), is an intuitively practical cost function to optimize for a legged robot, and we present the reader with a systematic, mathematical process for obtaining estimates of this MFPT metric. Of particular significance, our models of walking on stochastically rough terrain generally result in dynamics with a fast mixing time, where initial conditions are largely ”forgotten” within 1 to 3 steps. Additionally, we can often find a near-optimal solution for motion planning using only a short time-horizon look-ahead. Although we openly recognize that there are important classes of optimization problems for which long-term planning is required to avoid ”running into a dead end” (or off of a cliff! ), we demonstrate that many classes of rough terrain can in fact be successfully negotiated with a surprisingly high level of long-term reliability by selecting the short-sighted motion with the greatest probability of success. The methods used throughout have direct relevance to machine learning, providing a physics-based approach to reduce state space dimensionality and mathematical tools to obtain a scalar metric quantifying performance of the resulting reduced-order system. Thesis Supervisor: Russell L. Tedrake Title: Assistant Professor, Department of Electrical Engineering and Computer Science Thesis Chair: Neville Hogan Title: Professor, Department of Mechanical Engineering and Department of Brain and Cognitive Sciences
Monofractal nature of air temperature signals reveals their climate variability

We use the discrete "wavelet transform microscope" to show that the surface air temperature signals of weather stations selected in Europe are monofractal. This study reveals that the information obtained in this way are richer than previous works studying long range correlations in meteorological stations. The approach presented here allows to bind the H\"older exponents with the climate variability. We also establish that such a link does not exist with methods previously carried out.
Automatic Segmentation and Classification System for Foliar Diseases in Sunflower

Obtaining a high accuracy in the classification of plant diseases using digital methods is limited by the diversity of conditions in nature. Previous studies have shown that classification of diseases made with images of lesions caused by diseases is more accurate than a classification made with unprocessed images. This article presents the results obtained when classifying foliar diseases in sunflower using a system composed of a model that automatically segments the leaf lesions, followed by a classification system. The segmentation of the lesions was performed using both Faster R-CNN and Mask R-CNN. For the classification of diseases based on lesions, the residual neural networks ResNet50 and ResNet152 were used. The results show that automatic segmentation of the lesions can be successfully achieved in the case of diseases such as Alternaria and rust, in which the lesions are well-outlined. In more than 90% of the images, at least one affected area has been segmented. Segmentation is more difficult to achieve in the cases of diseases such as powdery mildew, in which the entire leaf acquires a whitish color. Diseased areas could not be segmented in 30% of the images. This study concludes that the use of a system composed of a network that segments lesions, followed by a network that classifies diseases, allows us to both more accurately classify diseases and identify those images for which a precise classification cannot be made.
New Pyrochlore-like Compound Cu2OCl2 with S=1/2

We report here on the magnetic properties of Cu 2 OCl 2 , the structure of which can be regarded either as a pyrochlore-like corner-shared tetrahedral network with S =1/2 or cross-linking chains of edge-sharing CuO 2 Cl 2 squares. The magnetic susceptibility exhibits a broad maximum at approximately 140 K, indicating a low-dimensional feature, and drops at 70 K accompanied with a small specific-heat jump; however, it does not follow the typical S =1/2 linear chain model. The magnetic susceptibility can be explained by a mean field corrected chain model with a closed value of the intra- and interchain exchange constants; thus, magnetic frustration would be dominant over a low-dimensional feature in Cu 2 OCl 2 . From the band calculation results, we found that the Cu 3 d on-site Coulomb interaction is essential to describe the electronic structure of Cu 2 OCl 2 . We conclude that Cu 2 OCl 2 should be regarded as a 3D material that has the potential to combine the pyrochlore-like geometrical frustration and .
Contactless Fingerprint Recognition Using Deep Learning - A Systematic Review

Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications.
A construction of nested rough set approximations in information systems using dependency of attributes

This paper presents a new method for constructing nested rough set approximations in information systems. Our approach uses the notion of dependency of attributes in information systems. We begin with the notion of nested sequence of indiscernibility relations that can be defined from the dependency of attributes in information systems. With this notion, we can easily construct nested rough set approximations. In addition, we present the property of accuracy of approximation of sets in information systems.
Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.
Market Efficiency and Censoring Bias in College Football Gambling

The team totals and point spread betting lines jointly predict team scores in college football games. The gambling market’s score predictions are shown to be biased due to censoring at zero points. This presents arbitrage opportunities for gamblers who place wagers on the team totals line in cases for which expected bias is high. A naïve betting strategy that exploits this bias using only information contained within the two betting lines themselves yields a win percentage of over 55 percent over the past two decades. This significantly exceeds the typical transaction costs associated with wagers, indicating the market is semi-strong inefficient.
Mechanisms and effects of under-ice warming water in Ngoring Lake of Qinghai–Tibet Plateau

Abstract. The seasonal ice cover in lakes of the Qinghai–Tibet Plateau is a transient and vulnerable part of the cryosphere, whose characteristics depend on the regional climate: strong solar radiation in the context of the dry and cold environment because of the high altitude and relatively low latitude. We use the first under-ice temperature observations from the largest Tibetan freshwater lake, Ngoring Lake, and a one-dimensional lake model to quantify the mechanism of solar thermal accumulation under ice, which relies on the ice optical properties and weather conditions, as well as the effect of the accumulated heat on the land–atmosphere heat exchange after the ice breakup. The model was able to realistically simulate the feature of the Ngoring Lake thermal regime: the “summer-like” temperature stratification with temperatures exceeding the maximum density point of 3.98 ∘C across the bulk of the freshwater column. A series of sensitivity experiments revealed solar radiation was the major source of under-ice warming and demonstrated that the warming phenomenon was highly sensitive to the optical properties of ice. The heat accumulated under ice contributed to the heat release from the lake to the atmosphere for 1–2 months after ice-off, increasing the upward sensible and latent surface heat fluxes on average by ∼ 50 and ∼ 80 W m−2, respectively. Therefore, the delayed effect of heat release on the land–atmosphere interaction requires an adequate representation in regional climate modeling of the Qinghai–Tibet Plateau and other lake-rich alpine areas.
Search for the electron diffusion regions of the collionless magnetic reconnection on polar mission

The electron physics in the collisionless magnetic reconnection is studied using data from the Polar spacecraft. Among the types of discontinuities in space plasmas, the Electron Diffusion Region (EDR) at the center of the reconnection has the theoretically unique properties that its thickness is of order of the electron gyroradius, and in such a region electrons are demagnetized with a non-gyrotropic pressure tensor. These unusual properties of EDRs reflect the expected violation of guiding center theory for electrons and are exploited in this thesis. We use four dimensionless, diagnostic, single spacecraft observables derived from theoretical properties of EDRs to locate them. They are electron agyrotropy, out-of-interconnection-plane electron Mach number, and dimensionless thresholds for electric field strengths parallel and perpendicular to the magnetic field. These observables are constructible using the electron density, bulk velocity, pressure tensor, and the electromagnetic field data. With a 3-year survey using particle data from a slower version of Hydra’s moment producing system, M3, the vast preponderance of these dimensionless parameters are below unity, which is consistent with the theoretical expectations for most space plasmas being strongly magnetized. The unusual outliers with the demagnetization parameters over unity (< 1%) in the distribution are geophysically distributed near the magnetopause within 8-9Re shells and collected as potential reconnection sites, although a number of other possibilities are also considered in this thesis such as data processing anomalies, systematic effects of data acquisition and aliasing. It is shown that plasma particle data with the highest time resolution possible are needed to improve the time aliasing issues, and to sense the rapidly changing and short scale current structure like the EDR. We use a recently developed algorithm G3/T1, which reduces the aliasing time of the 3D analysis of products of the Polar
Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach

As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments.
Strain Response and Buckling Behavior of Composite Cylindrical Shells Subjected to External Pressure with One End Fixed and the Other under Free Boundary Conditions

This study aims to reveal the buckling behavior of filament-wound composite cylindrical shells subjected to external pressure. The boundary conditions of the cylindrical shells were one end fixed and the other free. The carbon fiber stacking sequences were [±90]2/([±20]/[±90]/[±40]/[±90]/[±60]/[±90])2/[±90]. Finite element software ANSYS 16.2 was used for the numerical simulation to predict the critical buckling pressure and buckling behavior of composite cylindrical shell. External hydrostatic pressure tests were conducted, where the buckling behavior and strain response were observed. Numerical simulation accurately predicted the critical buckling pressure of carbon fiber/epoxy filament composite cylindrical shells under external pressure with 3.5% deviation from the experimental results. The buckling modes simulated by the finite element method agreed well with the deformed shape observed in the experiment, which was characterized by the uniform distribution of the three hoop waves. Comparing the axial compressive strain and hoop compressive strain of the composite shell, it was found that the circumferential stiffness of the shell was weaker than the axial stiffness. In addition, a comparative study of the strains of the fixed-end and free-end metal control sleeves was carried out. The results show that the boundary conditions have a significant influence on the strain response of control sleeves.