Space Mapping Research Articles

Mapping of Sensitive Coastal Spaces and Environments to Preserve in the west of the Coastal Zone of Côte d’Ivoire

Mapping of Sensitive Coastal Spaces and Environments to Preserve in the west of the Coastal Zone of Côte d’Ivoire

An Application of φ-Metric and Related Best Proximity Point Results Generalizing Wardowski’s Fixed Point Theorem

Abstract In this article, an application of φ-metric is given via a geometrical example to show how it can help to measure distance for non-planar surfaces where the classical metric becomes incapable. We also introduce the concept of best proximity point and proximal contraction for a class of mappings in a φ-metric space and prove a best proximity point theorem for such class of contraction mappings from which the famous “Wardowski’s fixed point theorem” can be deduced as a particular case. To support our theorem, we provide an example in which Wardowski’s metric fixed point theorem cannot be applied.

Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods.

Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

On generalizations of some fixed point theorems in semimetric spaces with triangle functions

In the present study, we prove generalizations of Banach, Kannan, Chatterjea, Ćirić-Reich-Rus fixed point theorems, as well as of the fixed point theorem for mapping contracting perimeters of triangles. We consider corresponding mappings in semimetric spaces with triangle functions introduced by Bessenyei and Páles. Such an approach allows us to derive corollaries for various types of semimetric spaces such as metric spaces, ultrametric spaces, and b-metric spaces. The significance of these generalized theorems extends across multiple disciplines, such as optimization, mathematical modeling, and computer science. They may serve to establish stability conditions, demonstrate the existence of optimal solutions, and improve algorithm design.

Relevance of Platinum Underlayer Crystal Quality for the Microstructure and Magnetic Properties of the Heterostructures YbFeO3/Pt/YSZ(111).

The hexagonal ferrite h-YbFeO3 grown on YSZ(111) by pulsed laser deposition is foreseen as a promising single multiferroic candidate where ferroelectricity and antiferromagnetism coexist for future applications at low temperatures. We studied in detail the microstructure as well as the temperature dependence of the magnetic properties of the devices by comparing the heterostructures grown directly on YSZ(111) (i.e., YbPt_Th0nm) with h-YbFeO3 films deposited on substrates buffered with platinum Pt/YSZ(111) and in dependence on the Pt underlayer film thickness (i.e., YbPt_Th10nm, YbPt_Th40nm, YbPt_Th55nm, and YbPt_Th70nm). The goal was to deeply understand the importance of the crystal quality and morphology of the Pt underlayer for the h-YbFeO3 layer crystal quality, surface morphology, and the resulting physical properties. We demonstrate the relevance of homogeneity, continuity, and hillock formation of the Pt layer for the h-YbFeO3 microstructure in terms of crystal structure, mosaicity, grain boundaries, and defect distribution. The findings of transmission electron microscopy and X-ray diffraction reciprocal space mapping characterization enable us to conclude that an optimum film thickness for the Pt bottom electrode is ThPt = 70 nm, which improves the crystal quality of h-YbFeO3 films grown on Pt-buffered YSZ(111) in comparison with h-YbFeO3 films grown on YSZ(111) (i.e., YbPt_Th0nm). The latter shows a disturbance in the crystal structure, in the up-and-down atomic arrangement of the ferroelectric domains, as well as in the Yb-Fe exchange interactions. Therefore, an enhancement in the remanent and in the total magnetization was obtained at low temperatures below 50 K for h-YbFeO3 films deposited on Pt-buffered substrates Pt/YSZ(111) when the Pt underlayer reached ThPt = 70 nm.

Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping

The zero-shot image classification technique aims to explore the semantic information shared between seen and unseen classes through visual features and auxiliary information and, based on this semantic information, to complete the knowledge migration from seen to unseen classes in order to complete the classification of unseen class images. Previous zero-shot work has either not extracted enough features to express the relationship between the sample classes or has only used a single feature mapping method, which cannot fully explore the information contained in the features and the connection between the visual–semantic features. To address the above problems, this paper proposes an embedded zero-shot image classification model based on bidirectional feature mapping (BFM). It mainly contains a feature space mapping module, which is dominated by a bidirectional feature mapping network and supplemented with a mapping network from visual to category label semantic feature space. Attention mechanisms based on attribute guidance and visual guidance are further introduced to weight the features to reduce the difference between visual and semantic features to alleviate the modal difference problem, and then the category calibration loss is utilized to assign a larger weight to the unseen class to alleviate the seen class bias problem. The BFM model proposed in this paper has been experimented on three public datasets CUB, SUN, and AWA2, and has achieved 71.9%, 62.8%, and 69.3% and 61.6%, 33.2%, and 66.6% accuracies under traditional and generalized zero-sample image classification settings, respectively. The experimental results verify the superiority of the BFM model in the field of zero-shot image classification.

Viscosity extragradient algorithms with double inertial extrapolation steps for quasimonotone variational inequalities and fixed point problems

We study two viscosity extragradient algorithms with double inertial extrapolation steps for finding a common solution of a variational inequality governed by a quasimonotone operator and of a fixed point problem with a demicontractive mapping in a real Hilbert space. The strong convergence theorems are proved under certain appropriate conditions. The step sizes of our algorithms are updated by simple calculations without knowing the prior information of the operator norms. Numerical examples in finite- and infinite-dimensional spaces are given to show the performance of the proposed algorithms.

On the coefficient inequalities for some classes of holomorphic mappings in complex Banach spaces

On the coefficient inequalities for some classes of holomorphic mappings in complex Banach spaces

Nonexpansive maps in nonlinear smooth spaces

We introduce the notion of a nonlinear smooth space generalizing both CAT ⁡ ( 0 ) \operatorname {CAT}(0) spaces as well as smooth Banach spaces. We show that this notion allows for a unified treatment of several results in functional analysis. Namely, we substantiate the usefulness of this setting by establishing a nonlinear generalization of an important result due to Reich in Banach spaces. On par with the linear context, this nonlinear version entails a convergence proof of several other methods. Here, we establish the convergence of a general form of the Halpern-type schema for resolvent-like families of functions. We furthermore prove the convergence of the viscosity generalization of Halpern’s iteration (even for families of maps) generalizing a result due to Chang. This work is set in the context of the ‘proof mining’ program, and the results are complemented with quantitative information like rates of convergence and of metastability (in the sense of T. Tao).

Compositional dependence of direct transition energies in SixGe1−x−ySny alloys lattice-matched to Ge/GaAs

SixGe1−x−ySny ternary alloys are a candidate material system for use in solar cells and other optoelectronic devices. We report on the direct transition energies and structural properties of Ge-rich SixGe1−x−ySny alloys with six different compositions (up to 10% Si and 3% Sn), lattice-matched to Ge or GaAs substrates. The direct interband transitions occurring at energies between 0.9 and 5.0 eV were investigated using spectroscopic ellipsometry, and the resulting data were used to obtain the dielectric functions of the SixGe1−x−ySny layer by fitting a multilayer model. Values for the E0, E1, Δ1, E0′, and E2 transition energies were then found by identifying critical points in the dielectric functions. Structurally, the composition of the samples was measured using energy-dispersive x-ray measurements. The lattice constants predicted from these compositions are in good agreement with reciprocal space maps obtained through x-ray diffraction. The results confirm that a 1 eV absorption edge due to direct interband transitions can be achieved using relatively low Si and Sn fractions (<10% and <3%, respectively), although the bandgap remains indirect and at lower energies. The higher-energy critical points show smaller shifts relative to Ge and match results previously observed or predicted in the literature.

Common Fixed-Point Theorem for Expansive Mappings in Dualistic Partial Metric Spaces

O'Neill [1] introduces the concept of dualistic partial metric space. In this study, we prove some common fixed-point theorems for dualistic expanding mappings defined on a dualistic partial metric space. Some famous conclusions of [2] and [3] are extended and generalized by our result. Additionally, we offer an example that demonstrates the value of these dualistic expanding mappings.

Moduli Spaces of Quadratic Maps: Arithmetic and Geometry

Abstract We establish an implication between two long-standing open problems in complex dynamics. The roots of the $n$th Gleason polynomial $G_{n}\in{\mathbb{Q}}[c]$ comprise the $0$-dimensional moduli space of quadratic polynomials with an $n$-periodic critical point. $\operatorname{Per}_{n}(0)$ is the $1$-dimensional moduli space of quadratic rational maps on ${\mathbb{P}}^{1}$ with an $n$-periodic critical point. We show that if $G_{n}$ is irreducible over ${\mathbb{Q}}$, then $\operatorname{Per}_{n}(0)$ is irreducible over ${\mathbb{C}}$. To do this, we exhibit a ${\mathbb{Q}}$-rational smooth point on a projective completion of $\operatorname{Per}_{n}(0)$, using the admissible covers completion of a Hurwitz space. In contrast, the Uniform Boundedness Conjecture in arithmetic dynamics would imply that for sufficiently large $n$, $\operatorname{Per}_{n}(0)$ itself has no ${\mathbb{Q}}$-rational points.

Developing a Framework for Assessing Transformative Competencies of Junior High School Students Through Construct Modelling Approach

This research is designed to develop a comprehensive assessment framework to study junior high school students’ transformative competencies in three dimensions: creating new value, reconciling tensions and dilemmas, and taking responsibility. Researchers developed and verified the quality of the assessment framework among the 120 junior high school students purposively selected from four schools in the northeastern and central regions of Thailand as the test-takers. The project is segmented into four phases: creation of the construct map, item design, outcome space, and Wright map. The framework’s efficacy is analyzed using the Rasch model and the Maximum Likelihood Estimation method. Validity is rigorously evaluated through empirical evidence derived from the Wright map and cognitive interviews. Reliability is assessed via the standard error of measurement, test information function, expected-a-posteriori reliability, and Infit Mean Square value. Key findings included the development of a five-level construct map and the formulation of scaled open-ended questions based on the test takers’ responses. A cut-off point is determined by dividing the threshold level by the number of tests at corresponding levels, adhering to Wright map criteria for each dimension. Subsequent item analysis and modeling confirmed the internal structure’s validity and reliability. This framework equips them with skills to navigate and effectively address complex real-world challenges, enhancing targeted educational strategies, promoting superior learning outcomes, and preparing students for advanced academic and professional environments.

Analysis of local strain fields around individual threading dislocations in GaN substrates by nanobeam x-ray diffraction

Position-dependent three-dimensional reciprocal space mapping (RSM) by nanobeam x-ray diffraction (nanoXRD) was performed to reveal the strain fields produced around individual threading dislocations (TDs) in GaN substrates. The distribution and Burgers vector of TDs for the nanoXRD measurements were confirmed by prerequisite analysis of multi-photon excited photoluminescence and etch pit methods. The present results demonstrated that the nanoXRD can identify change in the lattice plane structure for all types of TDs, i.e., edge-, screw-, and mixed TDs with the Burgers vector of b = 1a, 1c and 1m + 1c. Strain tensor components related to edge and/or screw components of the TDs analyzed from the three-dimensional RSM data showed a nearly symmetrical strained region centered on the TD positions, which were in good agreements with simulation results based on the isotropic elastic theory using a particular Burgers vector. The present method is beneficial in that it allows non-destructive analysis of screw components of TDs that tend to contribute to leakage characteristics and are not routinely accessible by conventional structural analysis. These results indicate that nanoXRD could be a powerful way to reveal three-dimensional strain fields associated with arbitrary types of TDs in semiconductor materials, such as GaN and SiC.

Temperature dependence of the rain-gas and snow-gas partition coefficients for nearly a thousand chemicals

Compared to the particle-gas partition coefficients (KPG), the rain-gas (KRG) and snow-gas (KSG) partition coefficients are also essential in studying the environmental behavior and fate of chemicals in the atmosphere. While the temperature dependence for the KPG have been extensively studied, the study for KRG and KSG are still lacking. Adsorption coefficients between water surface-air (KIA) and snow surface-air (KJA), as well as partition coefficients between water-air (KWA) and octanol-air (KOA) are vital in calculating KRG and KSG. These four basic adsorption and partition coefficients are also temperature-dependent, given by the well-known two-parameters Antoine equation logKXY = AXY + BXY/T, where KXY is the adsorption or partition coefficients, AXY and BXY are Antoine parameters (XY stand for IA, JA, WA, and OA), and T is the temperature in Kelvin. In this study, the parameters AXY and BXY are calculated for 943 chemicals, and logKXY can be estimated at any ambient temperature for these chemicals using these Antoine parameters. The results are evaluated by comparing these data with published experimental and modeled data, and the results show reasonable accuracy. Based on these coefficients, temperature-dependence of logKRG and logKSG is studied. It is found that both logKRG and logKSG are linearly related to 1/T, and Antoine parameters for logKRG and logKSG are also estimated. Distributions of the 943 chemicals in the atmospheric phases (gas, particle, and rain/snow), are illustrated in a Chemical Space Map. The findings reveal that, at environmental temperatures and precipitation days, the dominant state for the majority of chemicals is the gaseous phase. All the AXY and BXY values for logKSG, logKRG, and basic adsorption and partition coefficients, both modeled by this study and collected from published work, are systematically organized into an accessible dataset for public utilization.

Fixed points of generalized integral type α−F contraction mappings in metric-like spaces

This article focuses on generalized integral type α − F contraction mappings in metric-like spaces and certain fixed point results in this setting. We also present some examples to support the validity of the results.

Parametric topological entropy on orbits of arbitrary multivalued maps in compact Hausdorff spaces

The Adler-Konheim-McAndrew type definitions and the Bowen-Dinaburg-Hood type definitions of parametric topological entropy will be considered on orbits and coincidence orbits of nonautonomous multivalued maps in compact Hausdorff spaces. Their mutual relationship and their link to various further types of definitions like those of (parametric) preimage entropy, will be investigated. In this way, several recent results of the other authors will be generalized and extended into a new setting.

On Fixed Point for Nonexpansive Mappings in Partial Metric Spaces

On Fixed Point for Nonexpansive Mappings in Partial Metric Spaces

Influence of GaN substrate miscut on the XRD quantification of plastic relaxation in InGaN

In the epitaxy of semiconducting materials, substrate miscut is introduced to improve the morphology of deposited layers. However, in the mismatched epitaxial system, the substrate miscut also changes the stress geometry in the deposited layer, thus influencing the relaxation processes. In this paper, we show that InGaN layers grown on misoriented (0001)-GaN substrates relax by preferential activation of certain glide planes for misfit dislocation formation. Substrate misorientation changes resolved shear stresses, affecting the distribution of misfit dislocations within each dislocation set. We demonstrate that this mechanism leads to an anisotropic strain as well as a tilt of the InGaN layer with respect to the GaN substrate. It appears that these phenomena are more pronounced in structures grown on substrates misoriented toward 〈112¯0〉 direction than corresponding structures with 〈1¯100〉 misorientation. We reveal that the lattice of partially relaxed InGaN has a triclinic deformation, thus requiring advanced XRD analysis. The presentation of just a single asymmetric reciprocal space map commonly practiced in the literature can lead to misleading information regarding the relaxation state of partially relaxed wurtzite structures.

Pre-stack seismic inversion based on model-constrained generative adversarial network

Pre-stack seismic inversion usually uses various traditional algorithms to estimate elastic parameters such as P-wave velocity, S-wave velocity, and density. It is hard to derive accurate elastic parameters due to their non-uniqueness and high dimensionality between elastic parameters and seismic data, the calculation of elastic parameters is inaccurate. Convolutional Neural Networks (CNNs) have high-dimensional feature space mapping capabilities, which are utilized to establish mapping relationships between seismic data and elasticity parameters. However, their effectiveness is greatly affected by label data, and at the same time, due to the lack of enough label data, resulting in a low degree of fitting between prediction results and real data. In addition, conventional seismic inversion methods based on CNNs lack physical model constraints, resulting in low accuracy and poor interpretability of prediction results. We propose a Cycle-consistent Generative Adversarial Network based on a geophysical mechanism (SeisInv-CycleGAN). Deterministic inversion results and labeled data are combined into hybrid geophysical data as a training set of SeisInv-CycleGAN with geophysical constraints. At the same time, the residual (seismic loss) between the seismic data synthesized by forward modeling and the actual data is used as part of the loss function. The SeisInv-CycleGAN does not require building an initial model, and it can achieve higher accuracy in prediction results with a small amount of labeled data.