Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous-stage Runge-Kutta methods based on weighted orthogonal polynomials

We develop continuous-stage Runge-Kutta methods based on weighted orthogonal polynomials in this paper. There are two main highlighted merits for developing such methods: Firstly, we do not need to study the tedious solution of multi-variable nonlinear algebraic equations associated with order conditions; Secondly, the well-known weighted interpolatory quadrature theory appeared in every numerical analysis textbook can be directly and conveniently used. By introducing weight function, various orthogonal polynomials can be used in the construction of Runge-Kutta-type methods. It turns out that new families of Runge-Kutta-type methods with special properties (e.g., symplectic, symmetric etc.) can be constructed in batches, and hopefully it may produce new applications in numerical ordinary differential equations.
Directed preferential attachment models

The directed preferential attachment model is revisited. A new exact characterization of the limiting in- and out-degree distribution is given by two \emph{independent} pure birth processes that are observed at a common exponentially distributed time $T$ (thus creating dependence between in- and out-degree). The characterization gives an explicit form for the joint degree distribution, and this confirms previously derived tail probabilities for the two marginal degree distributions. The new characterization is also used to obtain an explicit expression for tail probabilities in which both degrees are large. A new generalised directed prefererantial attachment model is then defined and analysed using similar methods. The two extensions, motivated by empirical evidence, are to allow double-directed (i.e.\ undirected) edges in the network, and to allow the probability to connect an ingoing (outgoing) edge to a specified node to also depend on the out-degree (in-degree) of that node.
Static Plasma Confinement by Harmonic Magnetic Fields

A new class of magnetic fields, deduced from the extension of force-free fields to the complex domain, is presented. The compatibility of such kind of fields with the magnetic diffusion equation for harmonic fields, in absence of plasma flow velocity, is shown. This allows to produce a static Lorentz force density in a resistive plasma and possibly to balance pressure forces in the momentum equation. An explicit example of equilibrium solution for a cylindrical plasma column with scalar pressure is presented and its connections with previous experimental results are discussed. Some discussion is also devoted to the question of the existence of this kind of closed equilibria.
Twistor and 'weak' gauge structures in the framework of quaternionic analysis

The earlier proposed conditions of (bi)quaternionic differentiability are nonlinear, give rize to the 2-spinor and the self-dual gauge structures and may be considered as the it generating system of equations (GSE) with respect to the source-free Maxwell, Yang-Mills and eikonal equations. We present the general solution of the GSE in terms of twistor variables, analize its rather specific gauge symmetry and demonstrate the relation of GSE to the equations of shear-free null congruences and, consequently, - to effective metrics of Kerr- Shild type. The concept of particles as singularities of physical fields associated with the solutions of GSE is developed
Short Text Semantic Similarity Measurement Approach Based on Semantic Network

: Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE.
Deforming Representations of SL(2,R)

The spherical principal series representations $\pi(\nu)$ of SL(2,$\mathbb R$) is a family of infinite dimensional representations parametrized by $\nu\in\mathbb C$. The representation $\pi(\nu)$ is irreducible unless $\nu$ is an odd integer, in which case it is indecomposable. We find a new continuous family of representations $\Pi(\nu)$ such that $\pi(\nu)$ and $\Pi(\nu)$ have the same composition factors, and $\Pi(\nu)$ is completely reducible, for all $\nu$. We also describe a connection between this construction and families of invariant Hermitian forms on the representations.
Human Orphan Cytochrome P450 2U1 Catalyzes the ω-Hydroxylation of Leukotriene B4

Cytochrome P450 2U1 (CYP2U1) identified from the human genome remains poorly known since few data are presently available on its physiological function(s) and substrate(s) specificity. CYP2U1 mutations are associated with complicated forms of hereditary spastic paraplegia, alterations of mitochondrial architecture and bioenergetics. In order to better know the biological roles of CYP2U1, we used a bioinformatics approach. The analysis of the data invited us to focus on leukotriene B4 (LTB4), an important inflammatory mediator. Here, we show that CYP2U1 efficiently catalyzes the hydroxylation of LTB4 predominantly on its ω-position. We also report docking experiments of LTB4 in a 3D model of truncated CYP2U1 that are in agreement with this hydroxylation regioselectivity. The involvement of CYP2U1 in the metabolism of LTB4 could have strong physiological consequences in cerebral pathologies including ischemic stroke because CYP2U1 is predominantly expressed in the brain.
Design of muscle state sensing system based on flexible micro airbag structure

Sensing muscle state is an important method for obtaining human movement intentions. In this paper, air pressure chips were used as the sensitive elements to complete the design of a new type of flexible force sensing system based on the flexible micro airbag structure. The system was used to conduct experiments on 4 subjects to collect the force myography (FMG) signals of five different hand gestures. The gesture classification accuracies of 85.3%, 94.2%, and 99.3% were obtained by using different pattern recognition algorithms of LDA, SVM, and KNN. Preliminary experimental results show that the sensing system designed in this paper can effectively sense muscle states and has great potential for application in human-computer interaction control.
The Effect of Verbal Encouragement on Performance and Muscle Fatigue in Swimming

Background and Objectives: Verbal encouragement (VE) can be used to enhance performance in several sports, even though no studies have been conducted among swimmers and only a few effects have been reported in elite athletes. Besides influencing motor performance, VE is also known to enhance the physical load, thus potentially increasing the probability of developing fatigue. With this in mind, this study aimed to explore the effects of VE in swimmers in order to fill in the knowledge gap concerning the aquatic environment. Materials and Methods: Each athlete swam a maximal 200 m freestyle trial under two different conditions: one trial with VE and the other without VE. The two main outcome measures were: (1) performance velocity (m/s); and (2) muscle fatigue, investigated by means of surface electromyography. Sixty swimmers were recruited, aged 18.63 ± 3.46 years (median 18 years), 28 men (47%), and 32 women (53%), with 7.03 ± 3.9 years of experience. Results: With VE, performance significantly improved in the swim trial (p < 0.001, effect size (ES) −0.95, large). When breaking the results down into the first half (first (0–100 m) vs. the second half (100–200 m)), the ES was large in the first part (−1.11), indicating an improvement in performance. This worsened, however, in the second part of the trial (ES 0.63). In the multivariate analysis, years of experience were found to be a significant predictor of the change in overall performance (p = 0.011). There was a significant increase in muscle fatigue induced by VE, overall, and during the second half, but not during the first half of the trial. Conclusions: The present study indicates that VE during a middle-distance event (200 m) increases performance most in swimmers with little experience. However, it has a negative impact on fatigue.
A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

Currently, a significant portion of sustained economic growth still depends on a high input of resources. We must fully understand the significance, difficulty, and long-term nature of resource conservation and environmental protection. We must also intensify our efforts to protect the ecological environment. We must gradually form a production mode, lifestyle, and consumption mode conducive to environmental protection. We must also establish a benign interactive relationship with the environment. This study offers a big data analysis and neural network integration optimization design strategy for a new kind of environmental and economic coordination prediction model. The data of the new type of environmental and economic impact with numerous parameters are preprocessed using big data analysis and principal component analysis. A neural network integration system is used to create the prediction model, and prediction research and error analysis are conducted to enhance the new kind of environmental and economic model. The simulation test analysis is completed lastly. According to the simulation findings, the proposed arithmetic has an accuracy that is 8.56% higher than that of the conventional arithmetic. A sustainable improvement management system with environmental objectives, environmental management planning plans, and environmental monitoring systems can be established with help from the new environmental and economic coordination prediction model, which can also assist in predicting the potential environmental impact caused by economic development activities. This will ensure sustainable development as a result of the mechanism.
Improvement of light penetration based silkworm gender identification with confined regions of interest

Based on our previous work on light penetration-based silkworm gender identification, we find that unwanted optical noises scattering from the surrounding area near the silkworm pupa and the transparent support are sometimes analyzed and misinterpreted leading to incorrect silkworm gender identification. To alleviate this issue, we place a small rectangular hole on a transparent support so that it not only helps the user precisely place the silkworm pupa but also functions as a region of interest (ROI) for blocking unwanted optical noises and for roughly locating the abdomen region in the image for ease of image processing. Apart from the external ROI, we also assign a smaller ROI inside the image in order to remove strong scattering light from all edges of the external ROI and at the same time speed up our image processing operations. With only the external ROI in function, our experiment shows a measured 86% total accuracy in identifying gender of 120 silkworm pupae with a measured average processing time of 38 ms. Combining the external ROI and the image ROI together revamps the total accuracy in identifying the silkworm gender to 95% with a measured faster 18 ms processing time.
Butterfly diversity, richness, and density patterns in Sierra Nevada (SE Spain): Conservation implications under a global change scenario

Understanding factors that regulate the patterns of diversity, richness, and density of organisms is of enormous importance to manage biodiversity in a global change context. We investigate the importance of environmental factors in regulating the patterns of diversity, richness, and density of diurnal butterflies in the Sierra Nevada mountain range (SE Spain). Diversity, richness, and density seem to be strongly correlated, being mainly defined by land‐use and topographic‐related variables. The sites with the highest values correspond to areas of dense scrubland, near patches of forest, oriented to the west and with a high rate of solar radiation but not excessively exposed from the topographical point of view. Topographic wetness index seems to be also important but with a positive relation in the case of density and negative in the case of diversity and richness. Our results show a pattern of mid‐elevation maxima, with highest values between 1600 and 2000 masl and lower in the summits and the piedmont (<1400 masl). This brings us the possibility to identify a series of areas of high value for the conservation of butterflies in this protected area, with important applications towards the conservation and management of this group of insects. Finally, a series of management recommendations to improve the conservation status of the butterfly communities in the Sierra Nevada protected area are inferred. One of the main recommendations is based on the maintenance of spatial heterogeneity, taking into account traditional land uses that enhance biodiversity.
Reservoir Dynamic Interpretability for Time Series Prediction: A Permutation Entropy View

An echo state network (ESN) is an efficient recurrent neural network (RNN) that is widely used in time series prediction tasks due to its simplicity and low training cost. However, the “black-box” nature of reservoirs hinders the development of ESN. Although a large number of studies have concentrated on reservoir interpretability, the perspective of reservoir modeling is relatively single, and the relationship between reservoir richness and reservoir projection capacity has not been effectively established. To tackle this problem, a novel reservoir interpretability framework based on permutation entropy (PE) theory is proposed in this paper. In structure, this framework consists of reservoir state extraction, PE modeling, and PE analysis. Based on these, the instantaneous reservoir states and neuronal time-varying states are extracted, which are followed by phase space reconstruction, sorting, and entropy calculation. Firstly, the obtained instantaneous state entropy (ISE) and global state entropy (GSE) can measure reservoir richness for interpreting good reservoir projection capacity. On the other hand, the multiscale complexity–entropy analysis of global and neuron-level reservoir states is performed to reveal more detailed dynamics. Finally, the relationships between ESN performance and reservoir dynamic are investigated via Pearson correlation, considering different prediction steps and time scales. Experimental evaluations on several benchmarks and real-world datasets demonstrate the effectiveness and superiority of the proposed reservoir interpretability framework.
Symmetric primal dual simplex pivoting decision strategy (spdspds) for linear programming

The Symmetric Primal-Dual Simplex Pivoting Decision Strategy (spdspds) is a novel iterative algorithm to solve linear programming (LP) problems. Each iteration is based on a systematic selection and application of one among the newly identified set of four (or possibly six) distinct types of simplex pivots defined over a symmetric primal-dual pair of LP. The two (or possibly four) types of classical (standard) simplex pivots are the Primal Standard Pivot with positive (or zero) indicator, and the Dual Standard Pivot with negative (or zero) indicator. The two newly identified pivot types are: the Primal Tricky Pivot with positive indicator and the Dual Tricky Pivot with negative indicator. If more than one candidate pivot element/cell is of the same type, then a selection among them can be made based on a measure of goodness that is defined as the decrease in the infeasibility index of such cells. If further pivoting is not possible, then the tableau is checked for the terminal type to facilitate the problem classification. An analysis of the evolution of the Tableau data entries pattern observed as the iterations proceed is also presented. A classification of the LP problem into one of the six types is identified based on the data entries pattern observed in the final Tableau.
Karhunen-Loeve expansions of alpha-Wiener bridges

We study Karhunen-Loeve expansions of the process $(X_t^{(\alpha)})_{t\in[0,T)}$ given by the stochastic differential equation $dX_t^{(\alpha)} = -\frac\alpha{T-t} X_t^{(\alpha)} dt+ dB_t,$ $t\in[0,T),$ with an initial condition $X_0^{(\alpha)}=0,$ where $\alpha>0,$ $T\in(0,\infty)$ and $(B_t)_{t\geq 0}$ is a standard Wiener process. This process is called an $\alpha$-Wiener bridge or a scaled Brownian bridge, and in the special case of $\alpha=1$ the usual Wiener bridge. We present weighted and unweighted Karhunen-Loeve expansions of $X^{(\alpha)}$. As applications, we calculate the Laplace transform and the distribution function of the $L^2[0,T]$-norm square of $X^{(\alpha)}$ studying also its asymptotic behavior (large and small deviation).
3D Printing in Regenerative Medicine: Technologies and Resources Utilized

Over the past ten years, the use of additive manufacturing techniques, also known as “3D printing”, has steadily increased in a variety of scientific fields. There are a number of inherent advantages to these fabrication methods over conventional manufacturing due to the way that they work, which is based on the layer-by-layer material-deposition principle. These benefits include the accurate attribution of complex, pre-designed shapes, as well as the use of a variety of innovative raw materials. Its main advantage is the ability to fabricate custom shapes with an interior lattice network connecting them and a porous surface that traditional manufacturing techniques cannot adequately attribute. Such structures are being used for direct implantation into the human body in the biomedical field in areas such as bio-printing, where this potential is being heavily utilized. The fabricated items must be made of biomaterials with the proper mechanical properties, as well as biomaterials that exhibit characteristics such as biocompatibility, bioresorbability, and biodegradability, in order to meet the strict requirements that such procedures impose. The most significant biomaterials used in these techniques are listed in this work, but their advantages and disadvantages are also discussed in relation to the aforementioned properties that are crucial to their use.
Parental Educational Anxiety during Children’s Transition to Primary School in China

Children’s transition from preschool to primary school has long been recognized as a particularly challenging period that can induce parental educational anxiety. As a true portraiture of Chinese parenting, educational anxiety during this transition has attracted considerable attention, given its detrimental effects on children’s development and education. This study was aimed at identifying the characteristics of parental educational anxiety and its influencing factors during children’s transition to primary school in China. We examined parental educational anxiety and parental educational experience through a questionnaire survey. The sample comprised 26,295 families with children in grade one distributed across nine cities in five provinces. The results indicate that (1) Chinese parents experience some degree of educational anxiety during their children’s transition to primary school and are more worried about their children’s learning and social adaptation processes than about how the curriculum is taught; (2) scores for parental educational anxiety varied significantly across different regions and urban/rural areas; (3) family socioeconomic status, parents’ educational concepts, preschools’ entrance preparation work, and primary school enrollment adaptation work are all important factors influencing parental educational anxiety during this transition. Our findings highlight the prevalence of parental educational anxiety in China and the need for effective measures to facilitate a smooth transition.
CYTOGENETICAL STUDIES ON THE IMPACT OF GAMMA RAYS TO INDUCE VARIATION IN VITRO IN PAULOWNIA (PAULOWNIA TOMENTOSA)

aulownia ( Paulownia tomentosa L) is the one useful trees in China. All parts of paulownia tree (leaves, flowers, wood, bark, roots and seeds) have been used for many medicinal and industrial purposes. Buds were cultured on ¾ Murashige and Skoog (MS) solid medium supplemented with 0.2 mg/l benzyl adenine and 0.1 mg/l kinetin. The survival percentage and mean shoot length of irradiated plantlets with gamma radiation doses of 10, 20, 30, 40 and 50 Gy were calculated after eight weeks. The results showed a decrease in the survival percentage and mean shoot length by increasing gamma radiation doses. The lethal dose was 50 Gy and the optimal dose for explant survival was 10 Gy. Anatomical and ultrastructure of un-irradiated and irradiated paulownia were examined based on photonic and electron microscopy for plantlets leaves after 16 days from culturing. Anatomical studied of leaf surface showed variations in epidermal cells, trichomes, stomata and mesophyll cells. The ultrastructure sections showed formation of plastoglobule and starch granules which indicates a reduced carbon metabolism at sublethal dose of 40 Gy.
Repeated Episodes of Ischemia/Reperfusion Induce Heme-Oxygenase-1 (HO-1) and Anti-Inflammatory Responses and Protects against Chronic Kidney Disease

Preconditioning episodes of ischemia/reperfusion (IR) induce protection against acute kidney injury (AKI), however their long-term effect still unknown. We evaluated AKI to chronic kidney disease (CKD) transition, after three-mild or three-severe episodes of IR. AKI was induced by single bilateral IR (1IR), or three episodes of IR separated by 10-day intervals (3IR) of mild (20 min) or severe (45 min) ischemia. Sham-operated rats served as controls. During 9-months, the 1IR group (20 or 45 min) developed CKD evidenced by progressive proteinuria and renal fibrosis. In contrast, the long-term adverse effects of AKI were markedly ameliorated in the 3IR group. The acute response in 3IR, contrasted with the 1IR group, that was characterized by an increment in heme oxygenase-1 (HO-1) and an anti-inflammatory response mediated by a NFkB-p65 phosphorylation and IL-6 decrease, together with an increase in TGF-β, and IL-10 expression, as well as in M2-macrophages. In addition, three episodes of IR downregulated endoplasmic reticulum (ER) stress markers expression, CHOP and BiP. Thus, repeated episodes of IR with 10-day intervals induced long-term renal protection accompanied with HO-1 overexpression and M2-macrophages increase.