Miracle-dz/UniFAR-FTUs huggingface dataset:
Inversion models of aboveground grassland biomass in Xinjiang based on multisource data

Grassland biomass monitoring is essential for assessing grassland health and carbon cycling. However, monitoring grassland biomass in drylands based on satellite remote sensing is challenging.Statistical regression models and machine learning have been used for the construction of grassland biomass models, but the predictive power for different grassland types is unclear. Additionally, the selection of the most appropriate variables to construct a biomass inversion model for different grassland types must be explored. Therefore,1201 ground-truthed data points collected from 2014-2021,including 15 Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices,geographic location and topographic data,and meteorological factors and vegetation biophysical indicators were screened for key variables using principal component analysis (PCA). The accuracy of multiple linear regression models, exponential regression models, power function models, support vector machine (SVM) models, random forest (RF) models, and neural network models was evaluated for the inversion of three types of grassland biomass. The results were as follows: (1) The biomass inversion accuracy of single vegetation indices was low, and the optimal vegetation indices were the soil-adjusted vegetation index (SAVI) (R2 = 0.255), normalized difference vegetation index (NDVI) (R2 = 0.372) and optimized soil-adjusted vegetation index (OSAVI) (R2 = 0.285). (2)Grassland above-ground biomass (AGB) was affected by various factors such as geographic location,topography, and meteorological factors, and the inverse models using a single environmental variable had large errors. (3) The main variables used to model biomass in the three types of grasslands were different. SAVI, aspect, slope, and precipitation (Prec.) were selected for desert grasslands; NDVI,shortwave infrared 2 (SWI2), longitude, mean temperature, and annual precipitation were selected for steppe;and OSAVI, phytochrome ratio (PPR), longitude, precipitation, and temperature were selected for meadows. (4) The non-parametric meadow biomass model was superior to the statistical regression model. (5) The RF model was the best model for the inversion of grassland biomass in Xinjiang, and this model had the highest accuracy for grassland biomass inversion (R2 = 0.656, root mean square error (RMSE) = 815.6 kg/ha),followed by meadow (R2 = 0.610, RMSE = 547.9 kg/ha) and desert grassland (R2 = 0.441, RMSE = 353.6 kg/ha).
Closed-loop control of unsteadiness over a rounded backward-facing step

Abstract The two-dimensional, incompressible flow over a rounded backward-facing step at Reynolds number $\mathit{Re}= 600$ is characterized by a detachment of the flow close to the step followed by a recirculation zone. Even though the flow is globally stable, perturbations are amplified as they are convected along the shear layer, and the presence of upstream random noise renders the flow unsteady, leading to a broadband spectrum of excited frequencies. This paper is aimed at suppressing this unsteadiness using a controller that converts a shear-stress measurement taken from a wall-mounted sensor into a control law that is supplied to an actuator. A comprehensive study of various components of closed-loop control design – covering sensor placement, choice and influence of the cost functional, accuracy of the reduced-order model, compensator stability and performance – shows that successful control of this flow requires a judicious balance between estimation speed and estimation accuracy, and between stability limits and performance requirements. The inherent amplification behaviour of the flow can be reduced by an order of magnitude if the above-mentioned constraints are observed. In particular, to achieve superior controller performance, the estimation sensor should be placed upstream near the actuator to ensure sufficient estimation speed. Also, if high-performance compensators are sought, a very accurate reduced-order model is required, especially for the dynamics between the actuator and the estimation sensor; otherwise, very minute errors even at low energies and high frequencies may render the large-scale compensated linearized simulation unstable. Finally, coupling the linear compensator to nonlinear simulations shows a gradual deterioration in control performance as the amplitude of the noise increases.
The Influence of Exercise-Associated Small Extracellular Vesicles on Trophoblasts In Vitro

Exercise induces the release of small extracellular vesicles (sEVs) into circulation that are postulated to mediate tissue cross-talk during exercise. We previously reported that pregnant individuals released greater levels of sEVs into circulation after exercise compared to matched non-pregnant controls, but their biological functions remain unknown. In this study, sEVs isolated from the plasma of healthy pregnant and non-pregnant participants after a single bout of moderate-intensity exercise were evaluated for their impact on trophoblasts in vitro. Exercise-associated sEVs were found localized within the cytoplasm of BeWo choriocarcinoma cells, used to model trophoblasts in vitro. Exposure to exercise-associated sEVs did not significantly alter BeWo cell proliferation, gene expression of angiogenic growth factors VEGF and PLGF, or the release of the hormone human chorionic gonadotropin. The results from this pilot study support that exercise-associated sEVs could interact with trophoblasts in vitro, and warrant further investigation to reveal their potential role in communicating the effects of exercise to the maternal–fetal interface.
Comparative Analysis of Preference in Contemporary and Earlier Texts Using Entropy Measures

Research in computational textual aesthetics has shown that there are textual correlates of preference in prose texts. The present study investigates whether textual correlates of preference vary across different time periods (contemporary texts versus texts from the 19th and early 20th centuries). Preference is operationalized in different ways for the two periods, in terms of canonization for the earlier texts, and through sales figures for the contemporary texts. As potential textual correlates of preference, we measure degrees of (un)predictability in the distributions of two types of low-level observables, parts of speech and sentence length. Specifically, we calculate two entropy measures, Shannon Entropy as a global measure of unpredictability, and Approximate Entropy as a local measure of surprise (unpredictability in a specific context). Preferred texts from both periods (contemporary bestsellers and canonical earlier texts) are characterized by higher degrees of unpredictability. However, unlike canonicity in the earlier texts, sales figures in contemporary texts are reflected in global (text-level) distributions only (as measured with Shannon Entropy), while surprise in local distributions (as measured with Approximate Entropy) does not have an additional discriminating effect. Our findings thus suggest that there are both time-invariant correlates of preference, and period-specific correlates.
Interplay Between Priority Queues and Controlled Delay in Programmable Data Planes

The Tactile Internet needs to support multiple modalities, including haptic data simultaneously, to enable human and robot interaction in perceived real-time. Compared to video and audio, haptic data streams require an even lower delay and more shallow packet loss, which is a challenge for the underlying networks originally designed to support high bandwidths by having a large queue to buffer packets. This raises the need to study queuing management for different data streams. We examine the performance of CoDel++, which combines the well-known Controlled Delay (CoDel) protocol and Priority Queues. We leverage programmable data planes, which can be programmed with P4. Evaluation results show that CoDel++ produces superior results for high-priority data streams, i.e., reducing latency by at least 60% and packet loss by half compared to individual exploitation of Priority Queuing or CoDel individually.
Valorization of Food Waste to Produce Value-Added Products Based on Its Bioactive Compounds

The rapid growth of the global population and changes in lifestyle have led to a significant increase in food waste from various industrial, agricultural, and household sources. Nearly one-third of the food produced annually is wasted, resulting in severe resource depletion. Food waste contains rich organic matter, which, if not managed properly, can pose a serious threat to the environment and human health, making the proper disposal of food waste an urgent global issue. However, various types of food waste, such as waste from fruit, vegetables, grains, and other food production and processing, contain important bioactive compounds, such as polyphenols, dietary fiber, proteins, lipids, vitamins, organic acids, and minerals, some of which are found in greater quantities in the discarded parts than in the parts accepted by the market. These bioactive compounds offer the potential to convert food waste into value-added products, and fields including nutritional foods, bioplastics, bioenergy, biosurfactants, biofertilizers, and single cell proteins have welcomed food waste as a novel source. This review reveals the latest insights into the various sources of food waste and the potential of utilizing bioactive compounds to convert it into value-added products, thus enhancing people’s confidence in better utilizing and managing food waste.
Particle Swarm Optimization Based on Mutation Strategy

After studying the features of PSO (particle swarm optimization),the mutation factor was integrated into the algorithm to form its new version,i.e.,the mutational particle swarm optimization (MPSO) in which a mutation strategy was incorporated. With the mutation factor the algorithm can improve the developability of solution space so as to decrease the possibility that the PSO falls into local optima. The tested results of four well-known unconstrained benchmark optimization problems,a constrained problem and an actual application problem revealed that MPSO can successfully tackle both the high-dimensional unconstrained problems and the high-dimensional problems with linear constraints. Moreover,MPSO as a promising optimization algorithm has strong convergence and high stability.
Application of Bayesian machine learning for estimation of uncertainty in forecasted plume directions by atmospheric dispersion simulations

ABSTRACT When assisting emergency responses to a nuclear accident through atmospheric dispersion simulations, it is necessary to provide the prediction results and their uncertainties. This study develops an estimation method using machine learning for uncertainty in forecasted plume directions. The difference in plume directions derived from the meteorological forecast and analysis inputs was considered as the uncertainty in forecasted plume direction. Bayesian machine learning was used to predict the uncertainty based on the accumulated uncertainty estimation result in past cases. A three-day forecast simulation was conducted every day from 2015 to 2020, considering a hypothetical release of 137Cs from a nuclear facility to create training and test datasets for the machine learning. The findings reveal that the rate of good predictability was greater than 50% even in the forecast 36 h later when investigating the effectiveness of the Bayesian model on uncertainty prediction. The frequency of miss prediction of higher uncertainty was low (0.9%−7.9%) throughout the forecast period. However, the rate of over-prediction of uncertainty increased with forecast time up to 31.2%, which is acceptable as a conservative estimation. These results show that the Bayesian model in this study effectively estimates the uncertainty of plume directions predicted through atmospheric dispersion simulations. GRAPHICAL ABSTRACT
The Association between COVID-19 Related Anxiety, Stress, Depression, Temporomandibular Disorders, and Headaches from Childhood to Adulthood: A Systematic Review

Abstract Objective: The coronavirus belongs to the family of Coronaviridae, which are not branched single-stranded RNA viruses. COVID-19 creates respiratory problems and infections ranging from mild to severe. The virus features mechanisms that serve to delay the cellular immune response. The host’s response is responsible for the pathological process that leads to tissue destruction. Temporomandibular disorders are manifested by painful jaw musculature and jaw joint areas, clicks, or creaks when opening or closing the mouth. All these symptoms can be disabling and occur during chewing and when the patient yawns or even speaks. The pandemic situation has exacerbated anxieties and amplified the vulnerability of individuals. Therefore, from this mechanism, how the COVID-19 pandemic may have increased the incidence of temporomandibular disorders is perceived. The purpose of this review is to evaluate whether COVID-19-related anxiety has caused an increase in temporomandibular dysfunction symptoms in adults to children. Methods: PubMed, Web of Science, Lilacs, and Scopus were systematically searched, until 30 July 2022, to identify studies presenting: the connection between COVID-19 with temporomandibular disorders. Results: From 198 papers, 4 studies were included. Literature studies have shown that the state of uncertainty and anxiety has led to an increase in the incidence of this type of disorder, although not all studies agree. Seventy-three studies were identified after viewing all four search engines; at the end of the screening phase, only four were considered that met the PECO, the planned inclusion, and the exclusion criteria. All studies showed a statistically significant correlation between temporomandibular disorders and COVID-19 with a p < 0.05. Conclusions: All studies agreed that there is an association between COVID-19 and increased incidence of temporomandibular disorders.
Study of Chinese Text Categorization

In this paper,we compare the three models of k-nearest neighbor,support vector machines and maximum entropy in text categorization. By using two training data set that has been classified by term selection and remove irrelevant data seperately,we carry out some experiments using the three models.The result of the experiments shows that the maximum entropy is better than the other two classifiers on either Boolean value condition or adding the frequency of words.The maximum entropy performance is the best in the three models.We also find that when we add the information of frequency of words the classifiers' performance has some changes.Despite the influence of the particularity of documents,this result suggests that the different kind of term sets may cause different results to different classifier's performance.
Fractal analysis of Kozeny-Carman constant in the homogenous porous media

The semi-empirical Kozeny-Carman(KC) equation,the most well-known permeability-porosity relation,is widely used in the field of seepage flow in porous media.However,the physical mechanisms behind the empirical KC constant are not clear,and the KC constant has not been proved to be a constant.The fractal scaling laws of pores have been extensively found in porous media.Therefore,the effective permeability of homogenous porous media is presented and the analytical expression of KC constant is derived based on the fractal characteristics of porous media and the microcosmic geometrical model.The results indicate that the fractal analytical expression of KC constant depends on the micro-structure of porous media.As a function of porosity and fractal dimensions,KC constant increases with the increase of porosity.
IMPLEMENTATION OF THE SMARTPHONE CAMERA IN THE MEASURES OF NARROW STREET FACADES

This paper aims to perform metric measurements of narrow street façades using single image captured by smartphone’s camera. Since tight area accompanied by narrow street limits object to camera distance, object lines perpendicular to façade do not appear in image and consequently their vanishing point (VP) is hard to detect. Accordingly, semi-automated MATLAB® application was designed depending only on two orthogonal VPs. Novelty of work comes from using smartphone as a cost and time efficient tool for measurements, depending only on two VPs, and applying image line refinement approach exploiting detected VPs. Three single images were captured by three different smartphones. Then, undistorted single images were formed after calibrating cameras. Image lines for horizontal and vertical object lines were extracted semi-automatically. Two VPs were detected applying two models: Model-I solves for vanishing points’ Cartesian coordinates, whereas Model-II solves for angle coordinate peaks of histogram. Image line refinement approach was applied before applying cross-ratio using one horizontal and one vertical reference lines to calculate object lengths of 46 check lines (horizontal and vertical). Proposed models provided reliable and comparable results. Applying line refinement approach improved solution with best overall accuracy of 0.010 m and 0.011 m for Model-I and Model-II, respectively.
Melatonin Supplementation during the Late Gestational Stage Enhances Reproductive Performance of Sows by Regulating Fluid Shear Stress and Improving Placental Antioxidant Capacity

In this study, the effects of daily melatonin supplementation (2 mg/kg) at the late gestational stage on the reproductive performance of the sows have been investigated. This treatment potentially increased the litter size and birth survival rate and significantly increased the birth weight as well as the weaning weight and survival rate of piglets compared to the controls. The mechanistic studies have found that these beneficial effects of melatonin are not mediated by the alterations of reproductive hormones of estrogen and progesterone, nor did the glucose and lipid metabolisms, but they were the results of the reduced oxidative stress in placenta associated with melatonin supplementation. Indeed, the melatonergic system, including mRNAs and proteins of AANAT, MTNR1A and MTNR1B, has been identified in the placenta of the sows. The RNA sequencing of placental tissue and KEGG analysis showed that melatonin activated the placental tissue fluid shear stress pathway to stimulate the Nrf2 signaling pathway, which upregulated its several downstream antioxidant genes, including MGST1, GSTM3 and GSTA4, therefore, suppressing the placental oxidative stress. All these actions may be mediated by the melatonin receptor of MTNR1B.
Curvature-dependent surface energy for free-standing monolayer graphene

The purpose of the present work is to give a continuum model that can capture bending effects for free-standing graphene monolayers taking material’s symmetry properly into account. Starting from the discrete picture of graphene modelled as a hexagonal 2-lattice, we give the arithmetic symmetries. Confined to weak transformation neighbourhoods one is able to work with the geometric symmetry group. Use of the Cauchy–Born rule allows the transition from the discrete case to the continuum case. At the continuum level we use a surface energy that depends on an in-plane strain measure, the curvature tensor and the shift vector. Dependence of the energy on the curvature tensor allows for incorporating bending effects into the model. Dependence on the shift vector is motivated by the fact that discretely graphene is a 2-lattice. We lay down the complete and irreducible set of invariants for this surface energy amenable to available representation theory. This way we obtain the expression for the surface stress as well as the surface couple stress tensor, the first being responsible for the in-plane deformations and the second for the out-of-plane motions. Forms for the elasticities of the material are given accompanied by the field equations. The model, in its simplest form, predicts 13 independent scalar variables in the constitutive relations to be observed in experiments. The framework presented is valid for both materially and geometrically nonlinear theories. We also present the case where symmetry changes at the continuum level, without taking into account how energy behaves at the transition regime.
Using fuzzy numbers and OWA operators in the weighted average and its applications in decision making

We present a new approach for dealing with an uncertain environment when using the ordered weighted averaging � weighted averaging (OWAWA) operator. We call it the fuzzy OWAWA (FOWAWA) operator. The main advantage of this new aggregation operator is that it is able to represent the uncertain information with fuzzy numbers. Thus, we are able to give more complete information because we can consider the maximum and the minimum of the problem and the internal information between these two results. We study different properties and different particular cases of this approach. We also analyze the applicability of the new model and we develop a numerical example in a decision making problem about selection of fiscal policies.
Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model

In this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of postdata densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well-documented link between cross-entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models. Copyright 2001 by American Economic Association.
Parametric identification of linear systems operating under feedback control

This paper considers the indirect identification of closed-loop systems with colored noise when low-order controllers exist in the loop. A new form of bias-eliminated least-squares (BELS) method is developed which is based on a simple and efficient technique of estimating the colored-noise-induced bias through a combined use of the known information about the controller and a proper number of zero parameters introduced. The main feature of the presented BELS based method is that no prefiltering of sampled data is needed and an unbiased estimate for the closed-loop parameters is given in a direct manner. The presented method is verified via application to indirect identification of an open-loop unstable plant.