Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of metaheuristic optimization algorithms for numerical solutions of optimal control problems

The objective in optimal control problems is to determine the control rule to be applied according to the objective function determined for a given system. Theoretically, different solution methods have been developed starting from calculus of variation such as Pontriagin, Hamilton, or Riccati. In this study, apart from analytical methods, it is proposed to apply algorithms inspired by nature to the same problem. Among these algorithms, genetic algorithm, firefly algorithm, Harris Hawk's optimization algorithm, and differential evolution optimization algorithm have been applied to given optimal control problems and their successes were compared in terms of statistical analysis such as minimum, maximum, average, Wilcoxon, and Friedman analysis. The originality of this study is to control the system with discrete control rule by partitioning the control signal, to be applied to the system which is wanted to be optimally controlled. This will allow the control of different techniques, such as MPC or NMPC, which will contribute to the determination of the global optimum control rule, especially in the solution of nonlinear systems. In addition, three different applications have been performed to demonstrate. As a result of these experiments, it has been shown that these algorithms can be applied successfully to such problems.
Evaluating the phase diagram at finite isospin and baryon chemical potentials in the Nambu-Jona-Lasinio model

We study the phase diagram of two-flavor dense QCD at finite isospin and baryon chemical potentials in the framework of the Nambu-Jona-Lasinio model. We focus on the case with arbitrary isospin chemical potential {mu}{sub I} and small baryon chemical potential {mu}{sub B{ 0.82m{sub {pi}}. At very large isospin chemical potential, {mu}{sub I}>6.36m{sub {pi}}, an inhomogeneous Larkin-Ovchinnikov-Fulde-Ferrell superfluid phase, appears in a window of {mu}{sub B}, which should in principle exist for arbitrary large {mu}{sub I}. Between the gapless and the Larkin-Ovchinnikov-Fulde-Ferrell phases, the pion superfluidmore » phase and the normal quark matter phase are connected by a first-order phase transition. In the normal phase above the superfluid domain, we find that charged pions are still bound states even though {mu}{sub I} becomes very large, which is quite different from that at finite temperature. Our phase diagram is in good agreement with that found in imbalanced cold atom systems. « less
Real-time data processing for in-line monitoring of a pharmaceutical coating process by optical coherence tomography

Coating of tablets is a widely applied unit operation in the pharmaceutical industry. Thickness and uniformity of the coating layer are crucial for efficacy as well as for compliance. Not only due to different initiatives it is thus essential to monitor and control the coating process in-line. Optical coherence tomography (OCT) was already shown in previous works to be a suitable candidate for in-line monitoring of coating processes. However, to utilize the full potential of the OCT technology an automatic evaluation of the OCT measurements is essential. The automatic evaluation is currently implemented in MATLAB and includes several steps: (1) extraction of features of each A-scan, (2) classification of Ascan measurements based on their features, (3) detection of interfaces (air/coating and coating/tablet core), (4) correction of distortions due to the curvature of the bi-convex tablets and the oblique orientation of the tablets, and (5) determining the coating thickness. The algorithm is tested on OCT data acquired by moving the sensor head of the OCT system across a static tablet bed. The coating thickness variations of single tablets (i.e., intra-tablet coating variability) can additionally be analyzed as OCT allows the measurement of the coating thickness on multiple displaced positions on one single tablet. Specifically, the information about those parameters emphasizes the high capability of the OCT technology to improve process understanding and to assure a high product quality.
The effects of organizational learning and entrepreneurial orientation on the corporate performance of Argentinean, Mexican, and Ecuadorian MSMEs

The aim of this study is to analyze the effects of Organizational Learning (OL) and Entrepreneurial Orientation (EO) on the profitability of MSMEs. To this end, field research was conducted on a sample of 1,620 companies located in Argentina (630), Mexico (550), and Ecuador (440). The data collection was carried out between May and September 2020, through a structured questionnaire addressed to the highest level of the company. The results were analyzed and validated using the partial least square (PLS) structural equation modeling (SEM) statistical technique. This work contributes to the development of the literature on dynamic capabilities, social capital theory, and stakeholder theory. Our results reveal that Entrepreneurial Orientation is a determinant variable for generating better Environmental Corporate Social Responsibility (ESCR) practices and is key to increasing the financial profitability of MSMEs. Furthermore, it is found that ESCR is a variable that partially mediates between EO and the Performance of MSMEs.
Theory of brain complexity and marital behaviors: The application of complexity science and neuroscience to explain the complexities of marital behaviors

The extant theories on the quality and stability of marital relationships have some difficulties in explaining some of the complexities of marital behaviors. The present article is an initial attempt to explain the complexities of marital behaviors based on the science of complexity and neuroscience. This article proposes a new theoretical framework relying on this simple argument that marital behaviors, as one of the most complex human behaviors, are the product of one’s brain’s complex adaptive system (CAS). Hence, to understand the complexities of marital behaviors, a movement toward familiarity with the brain’s CAS involved in marital behaviors needs to be started. The article presents the theory of brain complexity and marital behaviors (BCM) and outlines its assumptions, concepts, and propositions. Then, BCM is compared with the extant theories on happy and stable marriage, and finally, it was concluded by discussing the testability and the potential application of the theory. This article might inspire interdisciplinary studies of marital relationships, complex systems, and neuroscience and may have considerable practical implications.
Autonulling-Based Multichannel Impedance Measurement System for Capacitive Sensors

Capacitive sensors are widely used in a variety of contact and noncontact sensing applications. Low-power consumption and the small size of the capacitive sensors with high sensitivity make them an excellent choice for sensor arrays for the Internet of Things. The conventional capacitance measurement solutions, such as capacitance-to-digital converters FDC1004 from Texas Instruments and AD7746 from Analog Devices, provide excellent resolution but are limited by the number of channels and small measurement range. In this letter, we present a multichannel high-accuracy and wide-range impedance measurement system for capacitive sensors. The proposed system is based on lock-in amplification and autonulling techniques for accurately measuring the sensor capacitance. The proposed system utilized a time-division multiplexing-based approach with a frequency divider network for easy reconfigurable multichannel measurement. A prototype of the proposed system for 16-channel impedance measurement was built and tested. The proposed system was tested for different measurement ranges from 40 to 3000 pF, recording a resolution as low as 0.2 pF for capacitance measurement on all channels.
Parental Preconception and Pre-Hatch Exposure to a Developmental Insult Alters Offspring’s Gene Expression and Epigenetic Regulations: An Avian Model

Parental exposure to insults was initially considered safe if stopped before conception. In the present investigation, paternal or maternal preconception exposure to the neuroteratogen chlorpyrifos was investigated in a well-controlled avian model (Fayoumi) and compared to pre-hatch exposure focusing on molecular alterations. The investigation included the analysis of several neurogenesis, neurotransmission, epigenetic and microRNA genes. A significant decrease in the vesicular acetylcholine transporter (SLC18A3) expression was detected in the female offspring in the three investigated models: paternal (57.7%, p < 0.05), maternal (36%, p < 0.05) and pre-hatch (35.6%, p < 0.05). Paternal exposure to chlorpyrifos also led to a significant increase in brain-derived neurotrophic factor (BDNF) gene expression mainly in the female offspring (27.6%, p < 0.005), while its targeting microRNA, miR-10a, was similarly decreased in both female (50.5%, p < 0.05) and male (56%, p < 0.05) offspring. Doublecortin’s (DCX) targeting microRNA, miR-29a, was decreased in the offspring after maternal preconception exposure to chlorpyrifos (39.8%, p < 0.05). Finally, pre-hatch exposure to chlorpyrifos led to a significant increase in protein kinase C beta (PKCß; 44.1%, p < 0.05), methyl-CpG-binding domain protein 2 (MBD2; 44%, p < 0.01) and 3 (MBD3; 33%, p < 0.05) genes expression in the offspring. Although extensive studies are required to establish a mechanism–phenotype relationship, it should be noted that the current investigation does not include phenotype assessment in the offspring.
Ferguson Distributions Via Polya Urn Schemes

Let p be any finite positive measure on (the Borel sets of) a complete separable metric space X. We shall say that a random probability measure P* on X has a Ferguson distribution with parameter p if for every finite partition (B1, * . *, B) of X the vector p*(B,), * * *, p*(B,) has a Dirichlet distribution with parameter (Bj), *--, cp(B,) (when p(B,) = 0, this means p*(B,) = 0 with probability 1). Ferguson (3) has shown that, for any p, Ferguson p* exist and when used as prior distributions yield Bayesian counterparts to well-known classical nonpa- rametric tests. He also shows that p* is a.s. discrete. His approach involves a rather deep study of the gamma process. One of us (1) has given a different and perhaps simpler proof that Ferguson priors concentrate on discrete distributions. In this note we give still a third approach to Ferguson distributions, exploiting their connection with generalized Polya urn schemes. We shall say that a sequence (X,, n > 1} of random variables with values in X is a Poilya sequence with parameter 1a if for every B c X (1) P(X1 e B) = p(B)/p(X) and (2) P{X,+1 e B I1 **,, X = pn(B)/1p(X) where p. = p + 3 l(Xi) and 3(x) denotes the unit measure concentrating at x. Note that, for finite X, the sequence {XJ} represents the results of successive draws from an urn where initially the urn has p(x) balls of color x and, after each draw, the ball drawn is replaced and another ball of its same color is added to the urn. Note also that, without the restriction to finite X, for any (Borel measurable) function zS on X, the sequence {0(X")} is a P6lya sequence with parameter qSp, where q4(A) = p{l e Al. We now describe the connections between Polya sequences and Ferguson distributions.
Parameterization of the Output with Respect to the Relative Degree

: The partial feedback linearization is capable of expressing a complicated nonlinear system as a simple form for control. To get a proper linearized subsystem, it is most important to select a proper coordinate transformation. This paper plays a fundamental role in selecting the proper coordinate by revealing the system structure with respect to a relative degree. The relative degree is a key concept to treat the partial feedback linearization, especially, the input-output linearization of an input a ﬃ ne system. Using special functions of the state characterized in the relative degree, all functions are parameterized and classiﬁed according to their relative degrees. Applying the proposed parameterization to a coordinate transformation, the system structure with respect to relative degrees is revealed. Moreover using the input-output linearization with parameterized outputs, it is also shown that the internal dynamics of the partially linearized system is parameterized.
First‐order perturbation solution for rough surface scattering cross section including the effects of gradients

The transition matrix relating the incident pressure field to the scattered field in a fluid above a rough surface is derived to first order using Rayleigh–Rice perturbation theory, resulting in a general expression for scattering strength. A small region bordered above by the rough surface and below by a horizontal plane intersecting the surface at its lowest point contains a lossy homogeneous fluid. Below this point, the sediment is allowed to be vertically stratified, viscoelastic, or porous, supporting shear or Biot slow waves. Gradients strongly affect the scattering cross section if they cause a substantial portion of the incident energy to be redirected toward the interface.
Application of metal nanoparticles decorated carbon nanotubes in photovoltaics

Carbon nanotubes decorated with metal nanoparticles are introduced to photovoltaic application. The introduction of metal nanoparticles in the organic/organic-inorganic heterojunction solar cells is expected to improve the exciton dissociation (due to strong electric field at the metal-organics interface) and hence photovoltaic action. An improved photovoltaic action is indeed observed in n-Si/poly(3-octylthiophene) heterojunction solar cells incorporating multiwalled carbon nanotubes (MWCN) decorated with platinum metal nanoparticles (Pt: 20wt%, 10–15nm) as compared to pristine MWCN. The incorporation of metal nanoparticles should provide an alternative strategy to improve the photovoltaic performance of organic/organic-inorganic solar cells.
A Multi-Agent Computing Approach for Power System Production Simulation

: Along with the development of smart grid, demand response becomes the key character of it. Traditional method is unable to simulate the effect of the demand response to the power system operation. Intelligent engineering theory provides us a useful tool to address such difﬁculty. Based on the intelligent engineering, kinds of demand response resource are integrated and treated as efﬁciency power plant (EPP), an power system production simulation method with the multi agent computing approach is presented to simulate the EPP, among which generation agent (GA), efﬁciency power plant (EPPA),as well as the coordination agent(CA) is established. CA make the generation scheduling according to the bidding price of the power plant aiming to balance the power demand and supply, GA and EPPA adjust its bidding strategies on the basis of clear price and the proﬁts received. Comparison with equivalent energy function and sequence operation method, as well as analysis and calculation of the reliability indicator of EPP has been carried out based on the IEEE RTS 79 system demonstrate the validity of the proposed approach.
Penalized Empirical Likelihood via Bridge Estimator in Cox's Proportional Hazard Model

We propose the penalized empirical likelihood method via bridge estimator in Cox's proportional hazard model for parameter estimation and variable selection. Under reasonable conditions, we show that penalized empirical likelihood in Cox's proportional hazard model has oracle property. A penalized empirical likelihood ratio for the vector of regression coefficients is defined and its limiting distribution is a chi-square distributions. The advantage of penalized empirical likelihood as a nonparametric likelihood approach is illustrated in testing hypothesis and constructing confidence sets. The method is illustrated by extensive simulation studies and a real example.
A new model based on multi-aspect images and complex-valued neural network for synthetic aperture radar automatic target recognition

ABSTRACT During the last decades, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been vastly utilized in the military services and civil studies. Because of the sensibility of the SAR images of the imaging in the azimuth dimension, using the multi-aspect SAR image sequence is more practical than the single-aspect SAR image to achieve the superior classification accuracy in an actual SAR ATR task. At present time, multi-aspect SAR ATR models mostly utilize Recurrent Neural Networks (RNN) that depend on the sequence between samples and so suffer from the lack of information. In a practical work, a huge amount of training set is needed to train a deep learning network accurately, but it is so costly work to extract multi-aspect SAR images. So, in this article, a new model of multi-aspect SAR ATR is proposed by using a self-attention model. The proposed self-attention model is utilized to calculate the internal correlation between the original SAR images. At the same time, to develop the anti-noise capability of the proposed model and decrease the dependency on a huge amount of training data, a Convolutional Auto Encoder (CAE) is designed and utilized in the feature extraction section of the proposed model. On the other hand, unlike existing traditional methods, in this work we use both amplitude and phase information of SAR images to devolve the training process of the proposed model. It should be noted that all of the parameters of the proposed network are developed to a complex domain. In addition a complex backpropagation algorithm by using the gradient based model is used for training the network. In the end, experiments is obtained by using two MSTAR data set (the MSTAR-SOC and EOC). The experimental results prove that the proposed model not only can achieve a high recognitions rate on the case of sufficient training sample but also can obtain an acceptable rate in the case of small training samples in different complex situations. In addition, the simulation results demonstrate that by using the encoder of CAE in the proposed model, the whole configuration of the proposed model achieves the anti-noise capability that is a valuable benefit of any practical SAR ATR task.
Age effects of Moso bamboo on leaf isoprene emission characteristics

Isoprene is a highly reactive volatile organic compound that significantly affects atmospheric oxidant capacity, regional air quality, and climate change. Moso bamboo (Phyllostachys edulis), a species widely distributed in tropical and subtropical regions, particularly in China, is a strong isoprene emitter with great potential for carbon sequestration. Carbon sequestration is negatively correlated with culm age; however, the effect of this correlation on isoprene emissions remains unknown. In this study, we investigated the photosynthetic and isoprene emission characteristics of Moso bamboo at different culm ages. The results showed that the age effect on isoprene emission was different from that on photosynthesis; the net photosynthesis rate (Pn) was the highest in young, followed by mature, and then old bamboo, whereas the isoprene emission rate (Iso) was the highest in young, followed by old, and then mature bamboo. Moreover, the percentage of carbon loss as isoprene emission (C-loss) during photosynthesis of old bamboo was 35% higher than that of mature bamboo under standard conditions (leaf temperature: 30°C; light intensity: 1000 µmol m-2 s-1). Therefore, we strongly recommend considering the culm age when establishing an isoprene emission model of Moso bamboo. Additionally, because the Iso and C-loss of old bamboo were higher than those of mature bamboo, we suggest that attention should be paid to the management of bamboo age structure and timely felling of aged bamboo to reduce environmental risk.
a Semi-Active Control Policy to Reduce the Occurrence and Severity of End-Stop Impacts in a Suspension Seat with AN Electrorheological Fluid Damper

A semi-active on-off control policy has been developed to reduce the severity of suspension seat end-stop impacts caused by shocks or high magnitude vibration. An electrorheological fluid damper was used to realize the required two-state damping. The effects of the free travel (i.e., the relative displacement within which the suspension damper has low damping) and the on-state damping on end-stop impacts were investigated with a sinusoidal input motion. It was found that both a shorter free travel and higher on-state damping reduced both the occurrence of end-stop impacts and their severity. The control policy was also tested with a random signal at different input magnitudes. The on-off control policy improved the performance of the seat suspension when end-stop impacts would otherwise occur with high magnitude inputs, without causing poor vibration isolation with low magnitude inputs. It is concluded that a successful compromise can be achieved between steady vibration isolation and end-stop impact reduction.
Exploitation of feature correlations in target classification using differential geometry: a numerical approach

In classification problems where multiple features are extracted from the observations of one or more sensors, the features often exhibit some degree of correlation, or a functional relationship. Frequently, this is expected and arises because of the mapping between the parameters that define the object's equation of state and the sensor observables. Therefore, it is of interest to develop representations of the objects and classification algorithms that exploit the correlations between the features. An approach for developing these types of representations makes use of Differential Geometry. In this approach, the objects are represented as a mean surface in feature space. When the functional relationship between features can be expressed analytically, Differential Geometry is used to develop analytical expressions for class surfaces and classification algorithms. More complex problems require the use of numerical techniques. In this paper, some of the mathematical foundations of this approach are reviewed. In an example, tensor product non-uniform rational b-splines are employed to develop the description of class surfaces along with the associated metric tensor and geodesic equations, leading to classification algorithms. The resulting Surface Classifier performance is compared with that of a traditional Quadratic Classifier.
A superlinear convergent augmented Lagrangian procedure for contact problems

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation schemes. However their efficiency is strongly dependent on the value of the penalty parameter and usually results in a poor rate of convergence to the exact solution. In this paper we propose a new method to perform the augmentations. It is based on estimated values of the augmented Lagrangians. At each augmentation the converged state is used to extract some data. Such information updates a database used for the Lagrangian estimation. The prediction is primarily based on the evolution of the constraint violation with respect to the evolution of the contact forces. The proposed method is characterised by a noticeable efficiency in detecting nearly exact contact forces, and by superlinear convergence for the subsequent minimisation of the residual of constraints. Remarkably, the method is relatively insensitive to the penalty parameter. This allows a solution which fulfils the constraints very rapidly, even when using penalty values close to zero.