Miracle-dz/UniFAR-FTUs huggingface dataset:
CdTe-QDs Affect Reproductive Development of Plants through Oxidative Stress

With the continuous development of industry, an increasing number of nanomaterials are widely used. CdTe-QDs is a nanomaterial with good optical properties, but its release into the natural environment may pose a potential threat. The toxicity of nanoparticles in plants is beginning to be questioned, and the effect on phytotoxicity is unclear. In this study, we simulated air pollution and soil pollution (CdTe-QDs concentrations of 0, 0.2, 0.4, 0.8 mmol/L) by spraying and watering the seedlings, respectively. We determined the transport pathways of CdTe-QDs in Arabidopsis thaliana and their effects on plant reproductive growth. Spraying CdTe-QDs concentration >0.4 mmol/L significantly inhibited the formation of fruit and decreased the number of seeds. Observation with a laser confocal scanning microscope revealed that CdTe-QDs were mainly transported in plants through the vascular bundle, and spraying increased their accumulation in the anthers and ovaries. The expression level of genes associated with Cd stress was analyzed through RT-qPCR. CdTe-QDs significantly increased the expression levels of 10 oxidative stress-related genes and significantly decreased the expression levels of four cell-proliferation-related genes. Our results reveal for the first time the transport of CdTe-QDs in Arabidopsis flowers and demonstrate that QDs can cause abnormal pollen morphology, form defects of pollen vitality, and inhibit pollen tube growth in Arabidopsis through oxidative damage. These phenomena ultimately lead to the inability of Arabidopsis to complete the normal fertilization process and affect the reproductive growth of the plant.
Beam Image Stacking-Based Interactive Beam Tracking for Cell-Free mmWave Massive MIMO Systems

We investigate the problem of beam tracking in a cell-free millimeter wave (mmWave) massive MIMO scenario with multiple access points (APs) and multiple mobile users (UEs). The difficulty in handling multiple APs, UEs and the unknown of UEs’ movement, such as their directions and speeds which is crucial for the beam prediction in the next time slot, poses overhead and performance challenges for beam tracking. To address these challenges, we propose a novel adaptive distributed training and interaction framework and develop a distributed deep Q-network (DDQN) beam tracking algorithm based on the beam image stacking (BIS) method. To enhance beam tracking performance, we make significant improvements and optimizations to the action design of DDQN. Simulation results demonstrate significant performance improvements compared to existing methods. Notably, our algorithm is able to dynamically adapt to changes in the environment, leading to more efficient and accurate beam tracking.
An exploration of institutional and personal barriers to online academic engagement at a Brazilian university

The recent global pandemic has raised institutional awareness around the world concerning the importance of having high quality online learning options for students. Learner engagement is often correlated with quality outcomes such as student academic success and student satisfaction. Learner engagement is commonly thought of as having three important dimensions: affective engagement, behavioral engagement, and cognitive engagement (ACE framework). Engagement is also enabled or limited by facilitators/barriers. Three important categories of facilitators/barriers are learner characteristics, personal environment, and course environment. Elements in each of these three areas enable or are barriers to students fully engaging in a course. This research explored what the barriers are to students fully engaging in their online courses at a Brazilian university to determine which areas will be most productive for the university program administrators and designers to focus on increase student academic engagement. A survey was applied to students from the Brazilian university under graduation online programs. It included items related to engagement facilitators barriers in the three areas described in the ACE framework and received 429 valid responses. The affective and behavioral dimensions were perceived by students as the lower engagement indicators in the ACE framework. Among facilitators or barriers for engagement, the ones under the course environment category were predominantly perceived as barriers, while learner characteristics and student environment were perceived as facilitators. However, all three categories were more barrier than facilitator for over 40% of the students. Although course environment is the barrier most under control of the institutions, understanding students´ personal environment and characteristics of learning can help them to provide support and facilitate full engagement in online courses.
Gadjieva's conjecture, $K$-functionals, and someapplications in weighted Lebesgue spaces

We prove that Gadjieva’s conjecture holds true as stated in her PhD thesis. The positive solution of this conjecture allows us to obtain improved versions of the Jackson–Stechkin type inequalities obtained in her thesis and some others. As an application, an equivalence of the modulus of smoothness with the realization functional is established. We obtain a characterization class for the modulus of smoothness.
Frequency selection for indoor ranging using compressive sensing

Nowadays, indoor ranging and localization have become necessary in daily life. Due to the multi-path propagation and noise in the indoor environment, phase domain ranging method using multi-frequency has been proposed which achieves accurate estimation of indoor target. However, as the indoor communication is usually carried on Bluetooth Low Energy (BLE) or Zigbee, high efficiency is indispensable in the face of limited bandwidth and measuring time. Thus, in this thesis, we aim to reduce the number of frequencies used in the ranging while keeping an acceptable estimation accuracy. We at first build the signal model and study the ambiguity range of the problem. Then based on the estimation theory and the concept of compressive sensing (CS) theory, we take the Cramer Rao Lower Bound (CRLB) and the matrix coherence as the criteria and select the optimal subset on given tone set. To test the performance of selected subset, we utilize gridless reconstruction algorithms, noiseless global matched filter (NL-GMF) and atomic norm minimization (ANM), to estimate target distance with the subset in both simulated data and real data and provide the mean square error (MSE), the estimation probability and the successful estimation probability in various estimation conditions.
Distinguish Abnormal Tensile and Torsional Stresses of Steel Wire Rope Using Magnetoelastic Effect

Nondestructive testing and structural health monitoring are highly important for the safe operation of industrial systems. Steel wire ropes are key load-bearing components in these systems. Under the condition where multiple wire ropes are used simultaneously, the safe operation of the system will then be affected if the stress in these wire ropes is inconsistent. To date, research has focused mainly on changes in the tensile stress in steel wire ropes. Steel wire ropes are also affected by torsional stress during actual operation. The spiral structure of the wire rope means that when the torsional stress in the wire rope changes, then the tensile stress will also change. Abnormal tensile or torsional stresses in a wire rope will cause abnormal tensile stress in the wire rope. However, the treatment methods for these two stresses are different. It is thus necessary to distinguish the tensile and torsional stresses in steel wire ropes. This study uses the magnetoelastic effect to measure abnormal tensile and torsional stresses in steel wire rope through the time-domain and frequency-domain characteristics of a white noise signal. The method is to detect and distinguish the relative change of stresses in the multiple steel wire ropes. The frequency spectrum of the detection signal is used to detect abnormal stresses. The root mean square and center of gravity frequency of the detection signal are extracted to distinguish abnormal tensile and torsional stresses in multiple steel wire ropes and monitor its health.
Simulating realistic correlation matrices for financial applications: correlation matrices with the Perron–Frobenius property

ABSTRACT This article is concerned with the simulation of correlation matrices with realistic properties for financial applications. Previous studies found that the major part of observed correlation matrices in financial applications exhibits the Perron-Frobenius property, namely a dominant eigenvector with only positive entries. We present a simulation algorithm for random correlation matrices satisfying this property, which can be augmented to take into account a realistic eigenvalue structure. From the construction principle applied in our algorithm, and the fact that it is able to generate all such correlation matrices, we are further able to prove that the proportion of Perron-Frobenius correlation matrices in the set of all correlation matrices is in dimension d.
A review of Structure, Properties, and Chemical Synthesis of Magnetite Nanoparticles

Extensive studies were devoted to iron oxide nanoparticles (IONPs), in recent years. Iron oxides are chemical compounds that have various polymorphic forms, including maghemite (γ-Fe 2 O 3 ), magnetite (Fe 3 O 4 ), and Hematite (α-Fe 2 O 3 ). Among them, the most important studied is magnetite (Fe 3 O 4 ) due to its low cost and low toxicity, besides its unique magnetic and physicochemical characteristics which qualified it for use in various applications such as biomedical and technological applications. Magnetic particles should be small and have a narrow size distribution for these applications. The smaller the size of the iron oxide particles, the greater their reactivity and biodegradability. In this review, we display summary information of magnetite (Fe 3 O 4 ) nanoparticles in terms of structure, characteristics, and preparation methods. Because the prepared strategy has been proven to be critical for preferable control of the particle size and shape, in addition, to producing monodispersed magnetite (Fe 3 O 4 ) nanoparticles with a direct effect on their characteristics and applications, special attention will be placed on chemical preparation techniques including Hydrothermal synthesis, Co-precipitation technique, Sol-Gel process, and thermal decomposition method. This review offers specific information for selecting appropriate synthetic methods for obtaining appropriate sizes, shapes, and magnetic properties of magnetite (Fe 3 O 4 ) nanoparticles (NPs) for target applications.
Front Microrheology of Biological Fluids

We present a study of front microrheology through the development of a microfluidic device and method that describes accurately the non-linear rheology of blood, by means of a simple optical detection method based on tracking the fluid-air interface moving inside a microchannel. We study the behavior of Newtonian fluids of different viscosities and densities, as well, we performed measures for blood at different red blood cells concentration and at different days from its extraction. We have developed a scaling method which allows us to determine a relation between the red blood cell properties at different days from its extraction, according to the agreggation properties of red blood cell. Our results have been compared with theoretical and bibliographical results, which shows realiable results with an error around 6%. In general, our device and method is usefull as a viscometer and rheometer, as well as, it enables to establish a relation between blood viscosity and its red blood cells characteristics.
Study of the Effect of Active Regions on the Scattering Polarization in the Solar Corona

The solar photospheric/chromospheric light exciting atoms/ions is not homogeneous because of the presence of active regions (ARs). The effect of ARs on the scattering polarization at the coronal level is an important ingredient for a realistic determination of the magnetic field. This effect is usually disregarded or mixed with other effects in the sense that the degree of its importance is not well known. The aim of this paper is to study the effect of atmospheric inhomogeneities on the coronal scattering polarization. We determined quantitatively the importance of the atmospheric inhomogeneities by using given geometries of solar ARs (plages and sunspots).
4D Remeshing Using a Space-Time Finite Element Method for Elastodynamics Problems

In this article, a Space-Time Finite Element Method (STFEM) is proposed for the resolution of mechanical problems involving three dimensions in space and one in time. Special attention will be paid to the non-separation of the space and time variables because this kind of interpolation is well suited to mesh adaptation. For that purpose, we have developed a technique of 4D mesh generation adapted to space-time remeshing. A difficulty arose in the representation of 4D finite elements and meshes. This original technique does not require coarse-to-fine and fine-to-coarse mesh-to-mesh transfer operators and does not increase the size of the linear systems to be solved, compared to traditional finite element methods. Space-time meshes are composed of simplex finite elements. Computations are carried out in the context of the continuous Galerkin method. We have tested the method on a linearized elastodynamics problem. Our technique of mesh adaptation was validated on elementary examples and applied to a problem of mobile loading. The convergence and stability of the method are studied and compared with existing methods. This work is a first implementation of 4D space-time remeshing. A stability criterion for the method is established, as well as a convergence rate of about two. Using simplex elements, it is possible to develop a technique of mesh adaptation able to follow a mobile loading zone.
The Effect of Online Food Delivery Service Quality on Customer Satisfaction and Customer Loyalty: The Role of Personal Innovativeness

The online food delivery mobile service industry has emerged as a new channel of food delivery and marketing to capture a bigger share and sale in the food industry. This modern sort of business conveyance has gotten to be exceptionally prevalent, particularly among youthful, active, and working individuals. In addition, this business has become more competitive due to the increasingly new business entries and users’ expectation resulted from the anxiety of online food ordering and time convenience, but not at the expense of providing quality products and services. Subsequently, it is exceptionally vital that online benefit suppliers get it the nature and necessities as well as the related angles of the online benefit industry that are considered critical to the clients in this developing market. This study tries to explore and gauge information to guide the online service providers regarding the requirements and attributes of this new service delivery system. Using adopted questionnaire from Mobile Service Quality (M-S-QUAL), this study aims to investigate the relationships between customers perceived service quality of online food delivery (OFD) and its influence on customer satisfaction and customer loyalty, moderated by personal innovativeness. The data are collected from the customers or users of any online food delivery service and will be analyzed using correlations and Hierarchical Moderated Regression Analysis.
Programming an n-Back task in Qualtrics using HTML and JavaScript

The n-back task is an extensively used cognitive test that assesses working memory. The task is well-suited to virtual administration as it reliably produces similar results to in-person administration and is easily adapted to asynchronous operation. However, the procedure to program this task into various computer programs and software is not widely known. Therefore, this tutorial aims to provide researchers with simple yet detailed step-by-step instructions on how to program an n-back task in Qualtrics using HTML and JavaScript. This tutorial is meant to be easily followed by the layperson without extensive knowledge of computer programming.
Rotation Speed Recovery Strategy Based On Particle Swarm Optimization for Variable Power Curve Optimization

With the increase of wind power capacity incorporated into the grid, the permeability of wind power increases continuously, which leads to the reduction of the equivalent inertia of the system. Rotor speed recovery will cause the secondary frequency dips when DFIG uses rotor inertial control to participate in system frequency modulation. In order to optimize the secondary frequency dips, this paper uses PSO (Particle Swarm Optimization) to optimize the Parameters of Variable Power Functions on the basis of the previously proposed variable power curve strategy. This article simulates this strategy through setting up a system by Simulink. Simulate the results of optimization, compared with the traditional recovery strategy, validity of the proposed strategy is demonstrated through simulation and analysis.
Co-Evolution of Dual Opinions Under Asynchronous Updating

Inspired by the dual attitudes theory that implicit opinions are individuals’ inner evaluations affected by experience while explicit opinions are external expressions of these evaluations, we propose an asynchronous co-evolution model of dual opinions, where individuals update explicit opinion at each time step but change their implicit opinion based on their own clock. Furthermore, we introduce the after-effect of observed opinion information in this model, which enables individuals to update implicit opinions not only based on the opinion information observed at the current time but also on the information received from the past period of time. We analyze the dynamics of dual opinions in two discussion scenarios: a group of individuals with similar and opposite initial opinions. In the former scenario, rigorous analysis suggests that dual opinions are polarized to extreme opinions, mathematically verifying the empirical finding that group discussion intensifies individuals’ preferences, resulting in group polarization. In the latter scenario, our investigation shows that individuals with low bias show acceptance (inward conformity) while those with high bias exhibit compliance (outward conformity). We further analyze the influence of parameters on the co-evolution of dual opinions.
Predictive Roles of Baseline Stromal Tumor-Infiltrating Lymphocytes and Ki-67 in Pathologic Complete Response in an Early-Stage Triple-Negative Breast Cancer Prospective Trial

Simple Summary High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). In this study of 408 patients enrolled in a prospective early-stage TNBC neoadjuvant chemotherapy trial, we aimed to identify clinicopathologic features that could be combined with sTILs to better predict pCR. Applying a training set and a testing set, we found that integrating high Ki-67 (cutoff > 35%) and high sTIL (cutoff ≥ 20%) in a model of computed response scores could predict a pCR rate of 65%. This model may refine the selection of early-stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies. Abstract High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associated with pCR could enhance our ability to predict the group of patients on whom treatment de-escalation strategies could be tested. In this prospective early-stage TNBC neoadjuvant chemotherapy study, pretreatment biopsies from 408 patients were evaluated for their clinical and demographic features, as well as biomarkers including sTILs, Ki-67, PD-L1 and androgen receptor. Multivariate logistic regression models were developed to generate a computed response score to predict pCR. The pCR rate for the entire cohort was 41%. Recursive partitioning analysis identified ≥20% as the optimal cutoff for sTILs to denote 35% (143/408) of patients as having high sTILs, with a pCR rate of 59%, and 65% (265/408) of patients as having low sTILs, with a pCR rate of 31%. High Ki-67 (cutoff > 35%) was identified as the only predictor of pCR in addition to sTILs in the training set. This finding was verified in the testing set, where the highest computed response score encompassing both high sTILa and high Ki-67 predicted a pCR rate of 65%. Integrating Ki67 and sTIL may refine the selection of early stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies.
OmniScent: An Omnidirectional Olfaction-Enhanced Virtual Reality 360° Video Delivery Solution for Increasing Viewer Quality of Experience

The introduction of multiple sensorial effects to improve viewer multimedia experiences has resulted in the novel field coined mulsemedia. Virtual Reality (VR) is one of the recent enabling technologies for mulsemedia distribution and solutions can be enhanced to include scent dispensers, haptic devices and other equipment, for creation of immersive user experiences. In the past, a number of research works and real-life implementations have integrated smells and visual media. However, the scents are generated from a single location around a user, which can be inaccurate in the case of 360° VR experiences. This paper presents the architecture, principle, implementation and a feasibility study for OmniScent, an omnidirectional olfaction-enhanced VR 360° video delivery solution. The approach integrates four olfaction devices placed around a user, integrated with 360° videos. Scents are released during the video playout in the same direction the related objects appear in the video (e.g., front, back, left or right). An omnidirectional olfaction-enhanced video player was implemented using the WebXR technology, allowing the videos to be seen on both Web browsers and VR headsets. User testing demonstrated that over 70% of experiment participants noticed that the diverse scents, including apple and spices, were presented from multiple directions. Performance tests in terms of Round Trip Time (RTT) and resource consumption indicate that the solution performs well in commercial PC setups.
A computational model of bio-inspired tunable lenses

Abstract Bio-inspired tunable lenses are composed of soft dielectric elastomer membranes, clear liquids and rigid frames. Being a new type of optical lens, its focus can be adjusted by the applied voltage. For this type of micro-lens to be practically used in real life, the functional relationship between the voltage and the focus needs to be determined, together with the impact of lens parameters such as the pre-stretching of soft dielectric elastomer membranes, the scale and the volume of liquids on this relationship, as well as on the maximum change of lens focus. In this paper, a compact computational model is proposed based on the assumption of homogeneous deformation and application of energy methods. The model results fit well with the experimental data. By using this model, the relationship curve between the applied voltage and the focus has been given, and it has been verified that such lens will not fail in operation. Finally, by analyzing the three parameters of lens, we can also demonstrate that the pre-stretching of dielectric membranes and the scale affect the relationship between the applied voltage and the focus the most, with the volume of liquids having the least impact.