Miracle-dz/UniFAR-FTUs huggingface dataset:
Student Psychological Prediction and Analysis System based on Improved Apriori Algorithm

Because college students are not fully mature psychologically, they are prone to all kinds of psychological problems, which not only bring great pressure to the teaching work, but also have certain management risks. This paper mainly studies the students' psychological prediction and analysis system based on the improved Apriori algorithm (AA). It adopts the improved AA under the matrix condition to collect and screen students' psychological assessment data, and carry out data mining (DM) and analysis on the relationship between psychological factors to get association rules. The prediction and analysis system is constructed by J2EE. It is better that the improved AA is better than other algorithms in students' psychological prediction through systematic testing.
Critical layer instability in baroclinic flows

It is shown that for all inviscid, adiabatic, quasi-geostrophic motions in a Boussinesq liquid or in a compressible atmosphere, subject to the usual boundary conditions, the integral over the fluid of the northward eddy flux of potential vorticity vanishes, provided a generalized interpretation of potential vorticity is adopted near rigid horizontal boundaries. For an infinitesimal disturbance in a normal mode to a general zonal flow, the presence of a critical layer within the fluid excludes the existence of stable modes, except in exceptional circumstances. A stable, or slowly growing mode, is associated with a down-gradient flux of potential vorticity near the critical layer, which does not vanish as the growth rate tends to zero, and can only be balanced by the general growth in the disturbance.
Simultaneous inference for multilevel linear mixed models—with an application to a large‐scale school meal study

In large multilevel studies effects of interest are often evaluated for a number of more or less related outcomes. For instance, the present work was motivated by the multiplicity issues that arose in the analysis of a cluster‐randomized, crossover intervention study evaluating the health benefits of a school meal programme. We propose a novel and versatile framework for simultaneous inference on parameters estimated from linear mixed models that were fitted separately for several outcomes from the same study, but did not necessarily contain the same fixed or random effects. By combining asymptotic representations of parameter estimates from separate model fits we could derive the joint asymptotic normal distribution for all parameter estimates of interest for all outcomes considered. This result enabled the construction of simultaneous confidence intervals and calculation of adjusted p‐values. For sample sizes of practical relevance we studied simultaneous coverage through simulation, which showed that the approach achieved acceptable coverage probabilities even for small sample sizes (10 clusters) and for 2–16 outcomes. The approach also compared favourably with a joint modelling approach. We also analysed data with 17 outcomes from the motivating study, resulting in adjusted p‐values that were appreciably less conservative than Bonferroni adjustment.
Digital holography: methods and applications

Fresnel or Fraunhofer holograms recorded on CCD arrays can be numerically reconstructed either by the discrete finite Fresnel transform or by solving the diffraction integral using the convolution theorem. Applications in holographic interferometric deformation measurement with effective digital filtering to enhance the interference phase are presented. The detection, location, and size determination in holographic particle analysis can use reconstructed images which are orthogonally oriented with respect to the hologram plane or employ different angular views reconstructed from a single hologram in a tomographic approach.
The Purpose and Limits of Electoral Accountability

The standard theory of electoral accountability treats the electorate as an appraiser of government performance on a range of complex issues, which re-elects or de-elects depending on its evaluation of that performance. This paper draws from studies on voter knowledge and behaviour to present a dilemma for the standard theory: either voters do not know how well their rulers have performed, or if they do, they do not base their votes on that knowledge. It is shown that, on either horn of the dilemma, the standard account of electoral accountability fails, and that attempts to deflate the dilemma through heuristic and aggregative approaches to voter knowledge, or proposals for alternative systems of government, are also inadequate. The paper then sets out and defends an alternative conception of electoral accountability, which holds that electoral accountability is not about micro-assessing a government’s actions. Rather, it is about preventing political rulers from committing or allowing substantial harms to come upon those they govern. This alternative conception is compatible with a realistic view of voter knowledge and behaviour, but nonetheless makes electoral accountability an essential component of a well-functioning democratic system.
Text Mining Classification and Prediction of Aviation Accidents Based on TF-IDF-SVR Method

Text data analysis of aviation accidents is an important decision-making basis for aviation safety prevention and management. Therefore, a classification and prediction method of aviation accident text data combining TF -IDF and support vector regression is proposed. Combining the unique heat coding and potential Dirichlet distribution (LDA) technology, the word vector matrix obtained by combining the subject term matrix and the word frequency inverse file frequency (TF -IDF) matrix is used as the input of support vector regression. Through the text information of aviation accident causes obtained from the NTSB text database, the text information of aviation accident is first preprocessed, including stop word processing, case conversion and other operations. Then the text information is converted into vector form for computer recognition and processing, and Dirichlet technology is used to extract and divide the subject words of accidents. After identifying the subject words of different levels of accidents, support vector machine (SVR) is used to classify the text. Through comparison with naive Bayes, random forest and logistic regression methods in the experiment, the results show that the proposed method has high accuracy, strong generalization ability, and can predict the subject words and grade content of aviation accidents with high probability.
Robust implicit self tuning regulator/MRAC convergence and stability

An implicit self tuning regulator (STR) is presented, based on Lyapunov analysis for the control of a class of multi-input and multi-output (MIMO) dynamical system. Linearity in the parameters is assumed to hold, but the estimation error is considered to be non zero; this allows control of a larger class of systems and also has the effect of producing a robust controller. Moreover, certainty equivalence is not used in the design overcoming a major problem in adaptive control. It is indicated that gradient-based parameter tuning of the STR, when employed for closed-loop control, can yield unbounded parameter estimates if: (1) linearity in the unknown parameters does not exactly hold, or (2) there are unknown disturbances acting on the system. Finally, this paper provides a comprehensive theory in the development of identification, prediction, and adaptive control schemes for discrete-time systems.
Secure Wireless Sensor Network Authentication by Utilized Modified SHA3 Hash Algorithm

Wireless Sensor Network is an integration between software and hardware network that makes use a number of sensors to identify physical phenomena. However, WSN is easily attacked by attackers because WSNs uses wireless communications together with their insufficient tamper resistance. Commonly, sensor nodes are more concerned regarding two major security problems which are node authentication and the privacy preserving. A well-structured authentication method is able to be sure that not any unauthorized node can fraudulently participate and obtain important information from WSNs. Hence, many schemes have been proposed to protect communications in WSNs by improving security of authentication. In this work we intended to improve password protection through authentication process by using Modified SHA3 that combined Chaotic maps and SHA-3 hash algorithm to create a hybrid method that have advantage of these techniques. The proposed model is also have been implemented on hardware to simulate real operation
Koudougou Station TEC’s Variability Seasonal Anomalies Analysis During Fluctuating Events Over Solar Cycle 24

This paper deals with the analysis of anomalies on variations of total electron content (TEC) during fluctuating geomagnetic events through solar cycle 24 at Koudougou station (lat 12°15'N; long:-2°20'E). Study of anomalies was done by comparing the variations of TEC during solstice and equinox seasons and variations of TEC at equinoxes to those at solstices. Comparison was made taking into account solar cycle phases and seasons influences. Equinoctial asymmetry is recorded at ascending and maximum phases. Winter anomaly is observed all night (0000 LT-04 00 LT and 2000 LT-2300 LT) at solar maximum and at all times during the ascending phase, except around 0600 LT and 1800 LT. Semi-annual asymmetry is observed during all phases of solar cycle 24 during fluctuating events.
On the Convergence of Bernstein-Stancu Polynomials in the Variation Seminorm

ABSTRACT This article studies the variation detracting property and rate of approximation of the Bernstein-Stancu polynomials in the space of functions of bounded variation with respect to the variation seminorm. Moreover, we will present Voronovskaya-type theorems for Bernstein-Stancu polynomials Bn, α, βf and for the first derivative of these polynomials. Finally we include some graphical examples.
Thermal stability of Fe, Co, Ni metal nanoparticles

The thermal stability of metal nanoparticles is discussed in both melting thermodynamics and interface thermodynamics. Emphases are put on the size and shape dependence of melting temperature, critical size and vacancy‐formation energy of both freestanding and embedded metal nanoparticles. The melting temperature depression and superheating phenomena for reported nanoparticle systems are explained. Critical sizes of Fe, Co, Ni metal nanocrystals that the crystals keep their crystallinity are calculated and the corresponding minimum melting temperatures predicted. The vacancy‐formation energies of Fe, Co, Ni small particles are also calculated as a reference. Theoretical predictions are consistent with experimental results. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Metagenomic Sequencing Identified Specific Bacteriophage Signature Discriminating between Healthy and Diarrheal Neonatal Piglets

Neonatal diarrhea is one of the most severe diseases in human beings and pigs, leading to high mortality and growth faltering. Gut microbiome-related studies mostly focus on the relationship between bacteria and neonatal diarrhea onset, and no research study has investigated the role of the gut virome in neonatal diarrhea. Here, using metagenomic sequencing, we characterized the fecal viral community of diarrheal and healthy neonatal piglets. We found that the viral community of diarrheal piglets showed higher individual heterogeneity and elevated abundance of Myoviridae. By predicting the bacterial host of the identified viral genomes, phages infecting Proteobacteria, especially E. coli, were the dominant taxa in neonatal diarrheal piglets. Consistent with this, the antibiotic resistance gene of E. coli origin was also enriched in neonatal diarrheal piglets. Finally, we established a random forest model to accurately discriminate between neonatal diarrheal piglets and healthy controls and identified genus E. coli- and genus listeria-infecting bacteriophages, including psa and C5 viruses, as key biomarkers. In conclusion, we provide the first glance of viral community and function characteristics in diarrheal and healthy neonatal piglets. These findings expand our understanding of the relationship among phages, bacteria and diarrhea, and may facilitate the development of therapeutics for the prevention and treatment of neonatal diarrhea.
Nanoparticles as a Delivery System of Antigens for the Development of an Effective Vaccine against Toxoplasma gondii

Nanoparticles include particles ranging in size from nanometers to micrometers, whose physicochemical characteristics are optimized to make them appropriate delivery vehicles for drugs or immunogens important in the fight and/or prevention of infectious diseases. There has been a rise in the use of nanoparticles in preventive vaccine formulations as immunostimulatory adjuvants, and as vehicles for immunogen delivery to target immune cells. Toxoplasma is important worldwide, and may cause human toxoplasmosis. In immunocompetent hosts, infection is usually asymptomatic, but in immunocompromised patients it can cause serious neurological and ocular consequences, such as encephalitis and retinochoroiditis. Primary infection during pregnancy may cause abortion or congenital toxoplasmosis. Currently, there is no effective human vaccine against this disease. Evidence has emerged from several experimental studies testing nanovaccines showing them to be promising tools in the prevention of experimental toxoplasmosis. For the present study, a literature review was carried out on articles published over the last 10 years through the PubMed database, pertaining to in vivo experimental models of T. gondii infection where nanovaccines were tested and protection and immune responses evaluated. This review aims to highlight the way forward in the search for an effective vaccine for toxoplasmosis.
Effect of diffusion on a two-species eco-epidemiological model

This paper deals with the stabilizing effect of diffusion on a prey – predator system where the prey population is infected by a microparasite. The predator functional response is a concave-type function. Conditions for the local as well as global stability of the model without diffusion are derived in terms of system parameters. It is also shown that an unstable equilibrium of the model without diffusion can be made stable by increasing the diffusion coefficients appropriately.
Recursive causal models

Abstract The notion of a recursive causal graph is introduced, hopefully capturing the essential aspects of the path diagrams usually associated with recursive causal models. We describe the conditional independence constraints which such graphs are meant to embody and prove a theorem relating the fulfilment of these constraints by a probability distribution to a particular sort of factorisation. The relation of our results to the usual linear structural equations on the one hand, and to log-linear models, on the other, is also explained
Crossing Minimization within Graph Embeddings

We propose a novel optimization-based approach to embedding heterogeneous high-dimensional data characterized by a graph. The goal is to create a two-dimensional visualization of the graph structure such that edge-crossings are minimized while preserving proximity relations between nodes. This paper provides a fundamentally new approach for addressing the crossing minimization criteria that exploits Farkas’ Lemma to re-express the condition for no edge-crossings as a system of nonlinear inequality constraints. The approach has an intuitive geometric interpretation closely related to support vector machine classication. While the crossing minimization formulation can be utilized in conjunction with any optimization-based embedding objective, here we demonstrate the approach on multidimensional scaling by modifying the stress majorization algorithm to include penalties for edge crossings. The proposed method is used to (1) solve a visualization problem in tuberculosis molecular epidemiology and (2) generate embeddings for a suite of randomly generated graphs designed to challenge the algorithm. Experimental results demonstrate the ecacy of the approach. The proposed edge-crossing constraints and penalty algorithm can be readily adapted to other supervised and unsupervised optimization-based embedding or dimensionality reduction methods. The constraints can be generalized to remove overlaps between any graph components represented as convex polyhedrons including node-edge and node-node intersections.
Directivity optimization for Yagi-Uda arrays of shaped dipoles

A method is presented for the maximization of the directivity of Yagi-Uda arrays whose elements are not straight conductors. The optimum shapes as well as the positions of the array elements are determined, both of which depend on the radius of the conductors. The moment method is used in the optimization process. An example shows that an optimized Yagi-Uda array of three 1.5 \lambda elements can achieve a maximum directivity of 11.8 dB.
Three-Axis Interferometric Fiber Optic Gyroscope With Silica Integrated Coupler Chip

Chip-scale interferometric fiber optic gyroscope (IFOG) has caught growing attention for its huge application potential in the low-cost consumer electronics market. In this article, a novel three-axis IFOG is proposed and assembled with an integrated coupler chip (ICC) fabricated on the silica platform. The ICC realizes bidirectionally multichannel optical interconnection without waveguide crossings. The test results indicate the weak wavelength dependence and comparatively low insertion loss at <inline-formula> <tex-math notation="LaTeX">$0.85 \mu \text{m}$ </tex-math></inline-formula> of the ICC. With <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>900-m polarization-maintaining fiber coils, the three-axis IFOG exhibits a long-term stability of <inline-formula> <tex-math notation="LaTeX">$< 0.16^{\circ }$ </tex-math></inline-formula>/h, which satisfies the requirements of the tactical grade systems. The novel IFOG scheme with ICC paves the way for the research on chip-scale multiaxis IFOGs.
Image Super-resolution Algorithm Based on Multi-scale Feature Expression and Double Attention Mechanism

In view of the existing image super-resolution algorithm cannot make full use of shallow characteristic information, it is difficult to learn the importance of the different characteristics and the problem of loss of high frequency information, this paper proposes a fusion dual attention mechanism of multi-scale feature extraction module is applied to image super-resolution reconstruction of the module can reuse the characteristic information of the different levels increase output characteristic figure of receptive field; The dual-attention mechanism is introduced in the module to focus on the reconstruction of high frequency information of images. In addition, subpixel convolution is used as upsampling method and joint loss function combining $L_{1}$ loss and perceived loss is used to supervise network training. Experimental results show that the proposed algorithm can achieve clearer texture detail reconstruction and better visual perception compared with relevant image super-resolution algorithms.
Principal component analysis for accelerating color bilateral filtering

In this paper, we propose to speed up bilateral filtering by principal component analysis (PCA)-based dimensionality compression method with constant-time bilateral filtering. Constant-time bilateral filtering speeds up the filtering by representing it as a summation of the multiple Gaussian filters. However, a simple implementation is of the order of O(K3) for color and suffers from the curse of dimensionality. A clustering-based approximation speedup solves this problem with an order of O(K) or O(K2). PCA can provide a more informative signal relative to the transformation matrix. We have accelerated the process by converting the color information of the input image from 3-channel to 1-channel by PCA, considering the constant-time bilateral filter as joint bilateral filtering, and using the transformed image as a guide image. This dimensional reduction allowed us to filter images with sufficient accuracy at a higher speed.
Experimental Study of a Soft Pneumatic Actuator for the Application of Robotic Gripper

Soft bending actuators are being used in many applications such as in soft robotics and medical devices due to their compliant behavior and safe interaction. These soft actuators are mainly actuated by pneumatic pressure because of cleaner and safer operations while dealing with delicate objects and human. A soft pneumatic actuator (SPA) with multi-chambered structure is more efficient than a single chamber SPA. In this paper, a multi-chambered SPA is designed and studied experimentally. The designed SPA is fabricated using two different soft hyperelastic materials namely, Ecoflex-0030 and Ecoflex-0050. The fabrication process utilized the 3D printing and casting techniques. From the experimental studies, it is found that the SPA fabricated using Ecoflex-0050 shows more uniform bending. Therefore, this SPA is further utilized for developing a soft pneumatic gripper for the robotic application. Three fingers of Ecoflex-0050 SPA are used for the soft robotic gripper, which is tested experimentally for handling delicate objects. From the experiments, it has been observed that the developed SPA is able to grasp and hold objects of different shapes and sizes with a load carrying capacity of approximately 650 gm.
Foundations of general relativistic high-pressure elasticity theory

A fully relativistic formulation of the concept of a perfectly elastic solid is developed with a view to application to such problems as (a) the interaction of gravitational radiation with planetary type bodies such as the Earth, and (b) the vibrations and deformations of neutron star crusts. For applications of the former kind a low-pressure Hookean idealization (i.e. a theory postulating a linear stress-strain relation) will often be sufficient. A prototype version of a relativistic Hookean theory suitable for this purpose has already been given by Rayner (1963), but it will be shown that Rayner’s theory needs minor corrections in order to be strictly self consistent. For applications to neutron stars a purely Hookean theory will in any case be inadequate and to replace it a high-pressure quasi-Hookean idealization is described. This idealization includes the important special case of the perfect (i.e. isotropic) solid in which the elastic properties may be described in term s of just two (nonlinear) functions of state, namely the pressure and the modulus of rigidity. The theory reduces to the familiar perfect fluid idealization in the limit when the modulus of rigidity is zero. The discussion is conceptually based on the use of two distinct manifolds: a fourdimensional manifold of space-time events, with a fundamental pseudo-Riemannian metric, and a three-dimensional manifold X of idealized particles, with a fundam ental measure representing a conserved particle number density. These manifolds are related by a canonical projection which sends world-lines in M onto points in X.