Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Multimode Beamforming Antenna for Space-Division Multiple Access Application

In this letter, a multimode antenna for adaptive space-division multiple access (SDMA) application is proposed. The antenna merges a monopole-like radiator and a multimode resonant cavity into a compact structure. The amplitudes of three orthogonal modes are separately controlled by three ports in the cavity. By tuning the excitation power ratio and phase (only 0°/180° is needed) at the three ports, the antenna pattern continuously scans across 360° in the H-plane. This controlling scheme makes both analog and digital beamforming easier and supports base station SDMA scenarios. The simulation and measurement results show the antenna has good scanning performance. The design is proved to be a good candidate for high throughput wireless communication systems, especially the miniaturized Internet of Things base station applications.
EVALUATION OF MIXED LOGIT AS A PRACTICAL MODELING ALTERNATIVE

This paper describes how, in the context of choice modeling, the approach mostly used s based on random utility theory. According to this theory, each individual has a utility function associated to each of the alternatives, choosing the one which maximizes his/her utility. This individual function can be divided into a systematic component, which considers the effect of the explanatory variables, and a random component that takes into account all of the effects not included in systematic component of the function. For example, the incapacity of the modeler to observe all the variables that have an influence in the decision, measurement errors, differences between individuals, incorrect perceptions of attributes and the randomness inherent to human nature. Depending on the assumptions made for the distribution of the random error term, different models can be derived. The paper presents the Multinominal Logit Model and the Probit Model and it evaluates the Mixed Logit model as a practical modeling alternative.
Can leader gratitude expression improve employee followership behavior? The role of emotional expression authenticity

Gratitude, as a traditional virtue of the Chinese nation, has been widely focused on by scholars, who in recent years have begun to shift their focus from trait gratitude and state gratitude to the interpersonal interaction outcomes of gratitude expression. We drew on the dual-strategy theory of social rank and the social functions of emotion and hypothesized that leader gratitude expression has a positive impact on positive followership behavior via perceived leader prestige and a negative impact on negative followership behavior via perceived leader dominance. Furthermore, we expected that the above relationship is stronger when employees perceive that the emotional expression authenticity of their leaders is high. We tested these hypotheses in an experimental study ( N = 184) and a field sample of leader−employee dyads ( N = 192). In Study 1, a between-participant scenario experimental design was used to manipulate the independent variable, namely, leader gratitude expression, with the scenario material developed by Ritzenhöfer et al. (2017) (leader gratitude expression condition vs. neutral condition), and 200 participants were invited to participate in the experimental study. When the test was administered, the participants were randomly assigned to a scenario to eliminate the effect of their own differences on the experimental results. A total of 184 participants who passed the attention test were retained. In Study 2, we collected 192 leader−employee dyadic data at two time points. At Time 1, employees needed to report leader gratitude expression and perceived authenticity as well as provide their demographic information. At Time 2, employees needed to report perceived leader prestige and dominance, while the leader needed to report the followership behavior of employees. We applied analysis of variance, confirmatory factor analysis, path analysis, and Monte Carlo method to analyze the data. The results were as follows: leader gratitude expression positively
Permeable diffractive optical elements for real-time sensing of running fluids

The real-time monitoring of physical and chemical parameters in running fluids is of importance for biomedical, biochemical, and environmental applications, such as the presence of biomarkers or chemomarkers, or the departure from some preset values of critical parameters. In this contribution we present a new generation of Permeable Diffractive Optical Elements (PDOE) based on photon sieves. In brief, the PDOE is made of passing holes properly placed on specific locations on a rigid surface. This arrangement makes PDOEs ideal to work with running fluids. Our PDOE is optimized maximizing the irradiance at is focal plane, maintaining an appropriate permeability ratio. The starting point is the classical Fresnel zone distribution. We have used two different optimization strategies to design a working PDOE: i) Particle Swarm Optimization has been applied to modify the distribution of holes on the PDOE simultaneously considering all of them; ii) an iterative minimization algorithm adding one hole at the time until filling the PDOE aperture. Both optimization algorithms generate focal spots that are compared to choose the design better suited for the proposed application. Once the PDOE is optimized and fabricated, the surface of the remaining rigid structure is nanostructured (for example using Laser Induced Periodic Surface Structures), or functionalized, to provide specific sensing capabilities. In addition, the PDOE is integrated within a pipe where the fluid under analysis circulates through. A proposal for the optoelectronic assembly of the device-including auxiliary optical elements, light sources, and detectors - is also presented in this contribution.
Ginkgo biloba Extract Preventively Intervenes in Citrobacter Rodentium-Induced Colitis in Mice

Inflammatory bowel disease (IBD) represents a highly recurrent gastrointestinal disorder and global public health issue. However, it lacks effective and safe strategies for its control. Although Ginkgo biloba extract (GBE) has been suggested to exhibit preventive and therapeutic activity for the control of IBD, whether its activity is associated with its ability to modulate intestinal microbiota remains to be addressed. To investigate the effect of GBE on controlling IBD, a Citrobacter Rodentium (CR)-induced mouse colitis model was used, and then histopathological examinations, biochemical assays, immunohistochemistry, and immunoblotting were performed to detect histological changes, cytokines, and tight junction (TJ) proteins in the intestine samples. We also studied 16s rRNA to detect changes in intestinal microbiota and used GC-MS to determine the microbiota-related metabolites short chain fatty acids (SCFAs). The results of our studies revealed that pre-treatment with GBE was sufficient for protecting the animals from CR-induced colitis. As a mechanism for GBE activity, GBE treatment was able to modulate the intestinal microbiota and increase the SCFAs capable of decreasing the pro-inflammatory factors and up-regulating the anti-inflammatory factors while elevating the intestinal-barrier-associated proteins to maintain the integrity of the intestines. Accordingly, our results led to a strong suggestion that GBE should be seriously considered in the preventive control of CR-induced colitis and in the development of effective and safe therapeutic strategies for controlling IBD.
Statistical Analysis Based on Bootstrap Method

The Bootstrap is an important method in nonparametric statistics.It has a resample with replacement from the original sample,computing the sample statistic for each bootstrap sample.Then a series of observations of statistic and empirical distribution of statistic are obtained further.The confidence intervals of mean and variance based on the bootstrap and hypothesis for mean testing based on the Bootstrap are mainly discussed.
Inflammatory Response and Exosome Biogenesis of Choroid Plexus Organoids Derived from Human Pluripotent Stem Cells

The choroid plexus (ChP) is a complex structure in the human brain that is responsible for the secretion of cerebrospinal fluid (CSF) and forming the blood–CSF barrier (B-CSF-B). Human-induced pluripotent stem cells (hiPSCs) have shown promising results in the formation of brain organoids in vitro; however, very few studies to date have generated ChP organoids. In particular, no study has assessed the inflammatory response and the extracellular vesicle (EV) biogenesis of hiPSC-derived ChP organoids. In this study, the impacts of Wnt signaling on the inflammatory response and EV biogenesis of ChP organoids derived from hiPSCs was investigated. During days 10–15, bone morphogenetic protein 4 was added along with (+/−) CHIR99021 (CHIR, a small molecule GSK-3β inhibitor that acts as a Wnt agonist). At day 30, the ChP organoids were characterized by immunocytochemistry and flow cytometry for TTR (~72%) and CLIC6 (~20%) expression. Compared to the −CHIR group, the +CHIR group showed an upregulation of 6 out of 10 tested ChP genes, including CLIC6 (2-fold), PLEC (4-fold), PLTP (2–4-fold), DCN (~7-fold), DLK1 (2–4-fold), and AQP1 (1.4-fold), and a downregulation of TTR (0.1-fold), IGFBP7 (0.8-fold), MSX1 (0.4-fold), and LUM (0.2–0.4-fold). When exposed to amyloid beta 42 oligomers, the +CHIR group had a more sensitive response as evidenced by the upregulation of inflammation-related genes such as TNFα, IL-6, and MMP2/9 when compared to the −CHIR group. Developmentally, the EV biogenesis markers of ChP organoids showed an increase over time from day 19 to day 38. This study is significant in that it provides a model of the human B-CSF-B and ChP tissue for the purpose of drug screening and designing drug delivery systems to treat neurological disorders such as Alzheimer’s disease and ischemic stroke.
Design and Development of a Traveling Wave Ferro-Microfluidic Device and System Rig for Potential Magnetophoretic Cell Separation and Sorting in a Water-Based Ferrofluid

The timely detection and diagnosis of diseases and accurate monitoring of specific genetic conditions require rapid and accurate separation, sorting, and direction of target cell types toward a sensor device surface. In that regard, cellular manipulation, separation, and sorting are progressively finding application potential within various bioassay applications such as medical disease diagnosis, pathogen detection, and medical testing. The aim of this paper is to present the design and development of a simple traveling wave ferro-microfluidic device and system rig purposed for the potential manipulation and magnetophoretic separation of cells in water-based ferrofluids. This paper details in full: (1) a method for tailoring cobalt ferrite nanoparticles for specific diameter size ranges (10–20 nm), (2) the development of a ferro-microfluidic device for potentially separating cells and magnetic nanoparticles, (3) the development of a water-based ferrofluid with magnetic nanoparticles and non-magnetic microparticles, and (4) the design and development of a system rig for producing the electric field within the ferro-microfluidic channel device for magnetizing and manipulating nonmagnetic particles in the ferro-microfluidic channel. The results reported in this work demonstrate a proof of concept for magnetophoretic manipulation and separation of magnetic and non-magnetic particles in a simple ferro-microfluidic device. This work is a design and proof-of-concept study. The design reported in this model is an improvement over existing magnetic excitation microfluidic system designs in that heat is efficiently removed from the circuit board to allow a range of input currents and frequencies to manipulate non-magnetic particles. Although this work did not analyze the separation of cells from magnetic particles, the results demonstrate that non-magnetic (surrogates for cellular materials) and magnetic entities can be separated and, in some cases, continuously pushed through the channel based on amperage, size, frequency, and electrode spacing. The results reported in this work establish that the developed ferro-microfluidic device may potentially be used as an effective platform for microparticle and cellular manipulation and sorting.
Radiation generation during laser and particle beam interactions in particle-in-cell codes

Radiation reaction can severely affect the motion of ultra-relativistic particles in intense electromagnetic fields. Here, we study particle-in-cell (PIC) methods of kinetic modeling of radiation reaction in classical and quantum regimes. We test the methods of photon emission from highly energetic particles moving in strong electromagnetic field in two open-source PIC codes Smilei and EPOCH. We benchmark the codes and discuss the differences.
Bolstering the Measurement of Racial Inequity of COVID-19 Vaccine Uptake

Inequities in COVID-19 vaccine uptake by racialized groups have been persistent throughout the vaccine rollout, leading to disparate burdens of COVID-19 outcomes. A cross-sectional study was conducted to determine COVID-19 vaccine uptake across racialized groups within the nine-county Finger Lakes region of New York State in December 2021. Cross-matching and validation were performed across multiple health information systems for the region to reduce the percentage of vaccine records with missing race information. Additionally, imputation techniques were applied to address the remaining missing values. Uptake of ≥1 dose of the COVID-19 vaccine by race was then examined. By December 2021, 828,551 individuals in our study region had received ≥1 dose of the COVID-19 vaccine, with ~25% having missing race values. Cross-matching and validation within existing records reduced this to ~7%. Uptake of ≥1 dose of a COVID-19 vaccine was greatest among individuals identifying as White, followed by those identifying as Black. The application of imputation techniques reduced the percent of missing race values to <1%; however, this reduction did not significantly change the distribution of vaccine uptake across race groups. Utilization of relevant health information systems, accompanied by imputation techniques, stands to greatly reduce the burden of missing race data within vaccine registries, facilitating accurate targeted interventions to mitigate inequities in COVID-19 vaccination.
Implementation of Cloud Point Extraction Using Surfactants in the Recovery of Polyphenols from Apricot Cannery Waste

The objective of this study was to investigate the feasibility of using Cloud Point Extraction (CPE) to isolate natural antioxidants (polyphenols) from apricot cannery waste (ACW). Four different food-grade surfactants (Genapol X-080, PEG 8000, Tween 80, and Lecithin) were tested at varying concentrations to evaluate the effectiveness of the technique. It was observed that low concentrations of surfactants in one-step CPE resulted in less than 65% polyphenol recovery, which necessitated further extraction steps. However, high concentrations of surfactants were found to significantly improve polyphenol extraction from ACW for all surfactants tested. Among the four surfactants, PEG 8000 was found to be the most effective in most circumstances; specifically, adding only 2% of the surfactant per step in a two-step CPE was enough to effectively extract polyphenols with recovery rates better than 99%. When 10% w/v of PEG 8000 was used, recoveries greater than 92% were obtained. Since PEG 8000 is a reagent with low toxicity and the CPE method is simple, rapid, cheap, sensitive, and selective, the extracted organic compounds from ACW can be used as natural antioxidants in food technology. This has important implications for the development of natural and sustainable food additives.
Research on short-term weather forecast model based on transformer combined with Seq2Seq model

Weather forecast is very important for people's production and life. In recent years, with the rapid development of deep learning, the powerful feature expression ability of deep neural networks makes the effect of prediction obviously improved. In this paper, the Transformer combined with the Seq2Seq model to extract the spatial and temporal characteristics of data, and multiple meteorological elements in the future can be predicted from the historical data of multiple meteorological elements. Through comparative experiments, the effectiveness of the Transformer combined with the Seq2Seq model in multi-factor short-term weather forecast has been verified, and the accuracy of meteorological factors prediction has been improved.
Second-order accurate finite-difference schemes on nonuniform grids

Finite-difference schemes of a locally high order of accuracy are constructed for the two-dimensional Poisson equation on an arbitrary nonuniform rectangular grid. Their monotonicity and conservativeness are analyzed. Estimates of stability with respect to boundary conditions and the right-hand side are obtained. Convergence of the schemes is estimated in the grid W 2 1 -, W 2 2 -, and C-norms. The possibility of extending the schemes to more general problems is indicated.
Hybrid Surrogate Model-Based Multi-Objective Lightweight Optimization of Spherical Fuel Element Canister

A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simultaneously. Accordingly, a hybrid surrogate model-based multi-objective optimization method with the numerical method for the lightweight and safe design of the SFE canister is proposed. To be specific, the drop analysis model of the SFE canister is firstly established where the finite element method—discrete element method (FEM–DEM) coupled method is integrated to simulate the interaction force between the SFE and canister. Through simulation, the design variables, optimization objectives, and constraints are identified. Then the hybrid radial basis function—response surface method (RBF–RSM) surrogate method is carried out to approximate and simplify the accurate numerical model. A non-dominated sorting genetic algorithm (NSGA-II) is used for resolving this multi-objective model. Optimal design is validated using comprehensive comparison, and the reduction of weight and maximum strain can be up to 2.46% and 44.65%, respectively. High-accuracy simulation with high-efficiency optimization is successfully demonstrated to perform the lightweight design on nuclear facilities.
Subdivision Shading for Catmull-Clark and Loop Subdivision Surfaces with Semi-Sharp Creases

Coarse meshes can be recursively subdivided into denser and denser meshes by dividing their faces into several smaller faces and repositioning the vertices according to carefully designed subdivision rules. This process leads to smooth surfaces, such as in the case of Catmull-Clark or Loop subdivision, but often suffers from shading artifacts near extraordinary points due to the lower quality of the normal field there, typically corresponding to only tangent-plane (and not higher) continuity at these points. The idea of subdivision shading is to apply the same subdivision rules that are used to subdivide geometry to also subdivide the normals associated with mesh vertices. This leads to smoother normal fields, which can be used for shading purposes, and this in turn removes the shading artifacts. However, the original subdivision shading method does not support sharp and semi-sharp creases, which are desired ingredients in subdivision surface modelling. We present two approaches to extending subdivision shading to work also on models with (semi-)sharp creases, and demonstrate this in the cases of Catmull-Clark as well as Loop subdivision.
Detecting Plant Disease in Corn Leaf Using EfficientNet Architecture—An Analytical Approach

The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the most prevalent corn diseases. The presence of a disease cannot be accurately detected by simply looking at the plant. This will lead to improper pesticide use, which harms people by bringing on chronic diseases. Therefore, maintaining food security depends on accurate and automatic disease detection. It might be possible to save time and stop crop degradation before it takes place by utilising digital technologies. Hence, applying modern digital technologies to identify the disease in the damaged corn fields automatically will be more advantageous to the farmers. Many academics have recently become interested in deep learning, which has aided in creating an exact and autonomous picture classification scheme. The use of deep learning techniques and their adjustments for detecting corn illnesses can greatly assist contemporary agriculture. To find plant leaf diseases, we employ image acquisition, preprocessing, and classification processes. Preprocessing includes procedures such as reading images, resizing images, and data augmentation. The suggested project is based on EfficientNet and improves the precision of the database of corn leaf diseases by tweaking the variables. Tests are run using DenseNet and Resnet on the test dataset to confirm the precision and robustness of this approach. The recognition accuracy of 98.85% that can be achieved using this method, according to experimental results, is significantly higher than those of other cutting-edge techniques.
Dynamic characterization of small fibers based on the flexural vibrations of a piezoelectric cantilever probe

In this paper, we present a cantilever-probe system excited by a piezoelectric actuator, and use it to measure the dynamic mechanical properties of a micro- and nanoscale fiber. Coupling the fiber to the free end of the cantilever probe, we found the dynamic stiffness and damping coefficient of the fiber from the resonance frequency and the quality factor of the fiber-cantilever-probe system. The properties of Bacillus subtilis fibers measured using our proposed system agreed with tensile measurements, validating our method. Our measurements show that the piezoelectric actuator coupled to cantilever probe can be made equivalent to a clamped cantilever with an effective length, and calculated results show that the errors of measured natural frequency of the system can be ignored if the coupled fiber has an inclination angle of alignment of less than 10°. A sensitivity analysis indicates that the first or second resonant mode is the sensitive mode to test the sample’s dynamic stiffness, while the damping property has different sensitivities for the first four modes. Our theoretical analysis demonstrates that the double-cantilever probe is also an effective sensitive structure that can be used to perform dynamic loading and characterize dynamic response. Our method has the advantage of using amplitude-frequency curves to obtain the dynamic mechanical properties without directly measuring displacements and forces as in tensile tests, and it also avoids the effects of the complex surface structure and deformation presenting in contact resonance method. Our method is effective for measuring the dynamic mechanical properties of fiber-like one-dimensional (1D) materials.