Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II

Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications’ needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications’ needs. To sum up, the optimization process aims to convert industrial applications’ requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements.
Short Circuiting Transfer, Formation, and Microstructure of Ti-6Al-4V Alloy by External Longitudinal Magnetic Field Hybrid Metal Inert Gas Welding Additive Manufacturing

In this work, the external longitudinal magnetic field hybrid metal inert gas welding (M-MIG) additive manufacturing method is employed to produce the Ti-6Al-4V alloy part. The effect of process parameters on the droplet transfer formation and microstructure of the part was studied by a high-speed camera, optical microscope, and electron backscattered diffraction. The results showed that a typical short-circuiting transfer was obtained with the wire feeding speed of 2 m/min–4 m/min. An external longitudinal magnetic field had an obvious effect on the arc shape. The uniform formation of the deposition layer was obtained with the wire feeding speed of 4 m/min. The width of M-MIG deposition layer was greater than that of the MIG, and the width of M-MIG deposition layer was increased with the increase of the magnetic excitation current. The microstructure of the deposition layer was mainly comprised of acicular martensite α’ and massive martensite αm. In addition, the β grain size in the M-MIG was less than that of the MIG. The average microhardness of the MIG deposition layer was 281.6 HV, which was less than that of M-MIG.
INTERNAL STRUCTURE OF HYPERON CONFIGURATIONS OF STELLAR MASSES

The internal structure of equilibrium configurations of stellar masses possessing a density of the order of that of the atomic nucleus and higher is studied. It is shown that the space metric inslde the configurations deviates markedly from the Euclidean metric. In addition, the total number of baryons in severai configurations is calculated. For large values of the total number of baryons, there are two solutions with different total energies. Of these, the configurations considered to be wholly stable are those associated with a larger mass defect. (auth)
Are the X(4160) and X(3915) charmonium states

Inspired by the newly observed states X(4160) and X(3915), we analyze the mass spectra of these states in different quark models and calculate their strong decay widths by the {sup 3}P{sub 0} model. According to the mass spectra of charmonium states predicted by the potential model, the states {chi}{sub 0}(3{sup 3}P{sub 0}), {chi}{sub 1}(3{sup 3}P{sub 1}), {eta}{sub c2}(2{sup 1}D{sub 2}), {eta}{sub c}(4{sup 1}S{sub 0}) all can be candidates for the X(4160). However, only the decay width of the state {eta}{sub c2}(2{sup 1}D{sub 2}) in our calculation is in good agreement with the data reported by Belle, and the decay of {eta}{sub c2}(2{sup 1}D{sub 2}){yields}DD, which is not seen in experiment, is also forbidden. Therefore, it is reasonable to interpret the charmonium state {eta}{sub c2}(2{sup 1}D{sub 2}) as the state X(4160). For the state X(3915), although the mass of {chi}{sub 0}(2{sup 3}P{sub 0}) is compatible with the experimental value, the calculated strong decay width is much larger than experimental data. Hence, the assignment of X(3915) to the charmonium state {chi}{sub 0}(2{sup 3}P{sub 0}) is disfavored in our calculation.
Testing generalized 
CP
 symmetries with precision studies at DUNE

We examine the capabilities of the DUNE experiment in probing leptonic CP violation within the framework of theories with generalized CP symmetries characterized by the texture zeros of the corresponding CP transformation matrices. We investigate DUNE's potential to probe the two least known oscillation parameters, the atmospheric mixing angle $\theta_{23}$ and the Dirac CP-phase $\delta_{\rm CP}$. We fix theory-motivated benchmarks for ($ \sin^2\theta_{23}, \delta_{\rm CP} $) and take them as true values in our simulations. Assuming 3.5 years of neutrino running plus 3.5 years in the antineutrino mode, we show that in all cases DUNE can significantly constrain and in certain cases rule out the generalized CP texture zero patterns.
Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.
The Ocean's Biological Pump: In Situ Oxygen Measurements in the Subtropical Oceans

The magnitude and distribution of the ocean's biological pump (the downward flux of organic carbon (OC) from the ocean surface) influences the pCO2 of the atmosphere and the O2 content of the deep sea, but has not been well quantified. We determine this flux in the ocean's five subtropical gyres using upper‐ocean oxygen mass balance and measurements of T, S, and pO2 by autonomous profiling floats. Our results suggest that the biological OC pump is not globally uniform among the subtropical gyres: values in the North Pacific and Atlantic indicate distinct autotrophy (1–2 mol C m−2 yr−1) while near zero values in the S. Indian Ocean suggest the possibility of net heterotrophy. There is a correlation between the surface water iron/nitrate ratio and the magnitude of the biological pump suggesting an important role for nitrogen fixation in controlling the global distribution.
A Revised Meiyu-Season Onset Index for Taiwan Based on ERA5

Revisiting the defined Meiyu onset of Central Weather Bureau (CWB), this study applied a newer reanalysis dataset and added multiple timing and duration criteria to improve the Meiyu onset index. The previous Meiyu onset index was based on horizontal and vertical wind shears using older-generation reanalysis data. The horizontal shear captures the cyclonic vorticity while the vertical shear depicts overturning. However, this older index tends to predict the onset date too early from the actual maximum precipitation. After applying the modification that is described in this paper, the newer Meiyu onset index consistently leads the maximum precipitation in Taiwan only by a few days, except for two years over the 30-year analysis period. The implication of this modified and improved Meiyu onset index is that it can substitute model precipitation that tends to be problematic, as well as studying climate change impacts.
The evolution of environmental management: from stage models to performance evaluation

In the past two decades, academics and practitioners have attempted to improve understanding of environmental management by classifying companies' environmental behaviour, and evaluating their performance. Driven by both research and societal interest, this has resulted in a wave of stage or phase models, and a range of typologies. This article gives an overview of the development of such environmental management models, analysing their characteristics, strengths and weaknesses. An evolution can be noted in the direction of typologies and non-linear models to deal with organisational and strategic complexities. Models are starting to pay more attention to the management side. To overcome problems of operationalisation and limited company- and sector-specificity, environmental performance evaluation systems have emerged more recently. Although comprehensive performance assessments are still unavailable, the tenets of such a system can already be delineated. The paper presents these components, and draws conclusions on the contribution of environmental management models and performance evaluation systems.
Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 µm to 1.5 µm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs.
Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity - A Review

Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.
Possible Evidence for Cosmic-Ray Acceleration in the Type Ia SNR RCW 86: Spatial Correlation between TeV Gamma-Rays and Interstellar Atomic Protons

We present a detailed morphological study of TeV gamma-rays, synchrotron radiation, and interstellar gas in the young Type Ia supernova remnant (SNR) RCW 86. We find that the interstellar atomic gas shows good spatial correlation with the gamma-rays, indicating that the TeV gamma-rays from RCW 86 are likely predominantly of hadronic origin. In contrast, the spatial correlation between the interstellar molecular cloud and the TeV gamma-rays is poor in the southeastern shell of the SNR. We argue that this poor correlation can be attributed to the low-energy cosmic rays (∼1 TeV) not penetrating into the dense molecular cloud due to an enhancement of the turbulent magnetic field around the dense cloud of ∼10–100 μG. We also find that the southwestern shell, which is bright in both synchrotron X-ray and radio continuum radiation, shows a significant gamma-ray excess compared with the interstellar proton column density, suggesting that leptonic gamma-rays via inverse Compton scattering possibly contribute alongside the hadronic gamma-rays. The total cosmic-ray energies of the young TeV gamma-ray SNRs—RX J1713.7−3946, Vela Jr, HESS J1731−347, and RCW 86—are roughly similar, which indicates that cosmic rays can be accelerated in both the core-collapse and Type Ia supernovae. The total energy of cosmic rays derived using the gas density, ∼1048–1049 erg, gives a safe lower limit due mainly to the low filling factor of interstellar gas within the shell.
Best Response Adaptation for Role Games

Consider a large population of individuals which can be in one of two distinct roles. The role of the individual is switched every now and then, and interactions occur between randomly paired individuals in different roles. These interactions are represented by a bimatrix game and individuals are modeled as boundedly rational expected utility maximizers who choose their actions according to a myopic best response rule. The resulting dynamics of the population state is given by a system of differential equations and differential inclusions. If the bimatrix game is zero-sum, the population state converges to a fixed point set corresponding to the set of Nash equilibria of this game. Moreover, if the zero-sum game has a unique Nash equilibrium, the global attractor of the population state is a unique and explicitly computable fixed point, even if the set of fixed points is a continuum (which is the case, if the unique Nash equilibrium is completely mixed). This global attractor does neither depend on the rates of role switching and strategy reviewing, nor on the relative size of the two subpopulations of individuals in different roles.
Heritability and Genetic Parameters for Semen Traits in Australian Sheep

Simple Summary Semen traits associated with ejaculate volume, gross motility, concentration, and percent post-thaw motility are crucial determinants of successful reproduction, which, in turn, can influence the profitability of seedstock and commercial sheep enterprises. Previous studies in cattle, where artificial insemination is also widely used, have reported declining trends in ejaculate traits. Such a trend, if observed in sheep, could negatively impact conception outcomes, and contribute to reduced reproductive efficiency. Selective breeding to improve ejaculate traits could provide a means to ensure continued efficiency in ovine reproduction. Therefore, the objective of this study was to estimate heritability and genetic correlations between four routinely assessed ejaculate quality and quantity traits in sheep breeds commonly farmed in Australia. An analysis of ejaculate traits collected over 20 years indicated that all ejaculate quality and quantity traits were lowly heritable (0.081–0.17), with gross motility and volume having relatively higher heritability estimates compared to concentration and percent post-thaw motility. Overall, the results indicate that ejaculate quality and quantity could potentially be improved via selective breeding. Abstract Semen characteristics including volume, gross motility, spermatozoal concentration, and percent post-thaw motility are routinely assessed to determine the quality and quantity of an ejaculate prior to use in artificial breeding programs. Currently, artificial breeding programs in sheep place relatively little emphasis on ram-side factors, such as the fertilising potential of an ejaculate, which may contribute to variability in conception outcomes. Estimating genetic parameters for ejaculate quality and quantity traits could provide insights into whether selective breeding can be used to improve such ram-side traits, improving ovine reproductive performance and farm profitability. Therefore, in this study, a total of 11,470 ejaculate records, including data for ejaculate volume, gross motility, spermatozoal concentration, and percent post-thaw motility, collected over a 20-year period was used to estimate genetic parameters in sheep. Univariate and bivariate mixed model analysis was performed including a variety of fixed effects such as breed, age at collection, centre of collection, collection number, season of collection, and method of collection; and the permanent environmental effects associated with each ram, stud and year of collection, and the breeding value of rams included as random effects. The heritability for ejaculate volume, gross motility, concentration, and percent post-thaw motility was estimated to be 0.161, 0.170, 0.089, and 0.081. Repeatability estimates were moderate, ranging between 0.4126 and 0.5265. Overall, results indicate that semen traits are lowly heritable and moderately repeatable, indicating that these traits are significantly influenced by environmental variables.
Relaxation Subgradient Algorithms with Machine Learning Procedures

In the modern digital economy, optimal decision support systems, as well as machine learning systems, are becoming an integral part of production processes. Artificial neural network training as well as other engineering problems generate such problems of high dimension that are difficult to solve with traditional gradient or conjugate gradient methods. Relaxation subgradient minimization methods (RSMMs) construct a descent direction that forms an obtuse angle with all subgradients of the current minimum neighborhood, which reduces to the problem of solving systems of inequalities. Having formalized the model and taking into account the specific features of subgradient sets, we reduced the problem of solving a system of inequalities to an approximation problem and obtained an efficient rapidly converging iterative learning algorithm for finding the direction of descent, conceptually similar to the iterative least squares method. The new algorithm is theoretically substantiated, and an estimate of its convergence rate is obtained depending on the parameters of the subgradient set. On this basis, we have developed and substantiated a new RSMM, which has the properties of the conjugate gradient method on quadratic functions. We have developed a practically realizable version of the minimization algorithm that uses a rough one-dimensional search. A computational experiment on complex functions in a space of high dimension confirms the effectiveness of the proposed algorithm. In the problems of training neural network models, where it is required to remove insignificant variables or neurons using methods such as the Tibshirani LASSO, our new algorithm outperforms known methods.
The hyperbolization theorem for fibered 3-manifolds

Teichmuller spaces and Kleinian groups Real trees and degenerations of hyperbolic structures Geodesic laminations and real trees Geodesic laminations and the Gromov topology The double limit theorem The hyperbolization theorem for fibered manifolds Sullivan's theorem Actions of surface groups on real trees Two examples of hyperbolic manifolds that fiber over the circle Geodesic laminations Bibliography Index.
The Universe as a Nonuniform Lattice in the Finite-Dimensional Hypercube II.Simple Cases of Symmetry Breakdown and Restoration

This paper continues a study of ﬁeld theories speciﬁed for the nonuniform lattice in the ﬁnite-dimensional hypercube with the use of the earlier described deformation parameters. The paper is devoted to spontaneous breakdown and restoration of symmetry in simple quantum-ﬁeld theories with scalar ﬁelds. It is demonstrated that an appropriate deformation opens up new possibilities for symmetry breakdown and restoration. To illustrate, at low energies (far from the Plancks) it oﬀers high-accuracy reproducibility of the same results as with a nondeformed theory. In case of transition from low to higher energies and vice versa it gives description for new types of symmetry breakdown and restoration depending on the rate of the deformation parameter variation in time, and indicates the critical points of the previously described lattice associated with a symmetry restoration. Besides, such a deformation enables one to ﬁnd important constraints on the initial model parameters having an explicit physical meaning.