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The magnetocaloric effect (MCE) provides a promising foundation for the development of solid-state refrigeration technologies that could replace conventional gas compression-based cooling systems. Current research efforts primarily focus on identifying cost-effective magnetic materials that exhibit large MCEs under low magnetic fields across broad temperature ranges, thereby enhancing cooling efficiency. However, practical implementation of magnetic refrigeration requires more than bulk materials; real-world devices demand efficient thermal management and compact, scalable architectures, often achieved through laminate designs or miniaturized geometries. Magnetocaloric materials with reduced dimensionality, such as ribbons, thin films, microwires, and nanostructures, offer distinct advantages, including improved heat exchange, mechanical flexibility, and integration potential. Despite these benefits, a comprehensive understanding of how size, geometry, interfacial effects, strain, and surface phenomena influence the MCE remains limited. This review aims to address these knowledge gaps and provide guidance for the rational design and engineering of magnetocaloric materials tailored for high-performance, energy-efficient magnetic refrigeration systems.

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input–Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations.

This paper presents a unified framework for high-precision dynamic target tracking that combines phase-map-based visual servoing with Model Predictive Control (MPC). Phase maps obtained from fringe projection provide dense, subpixel geometric feedback, enabling accurate end-effector velocity computation; however, their high dimensionality leads to substantial computational overhead that hinders real-time control. To overcome this limitation, we introduce a phase-map-specific dimensionality reduction strategy that constructs a low-dimensional control subspace through gradient-guided sparsification and PCA embedding while preserving the controllability of the original interaction model. An adaptive prediction horizon is further developed to regulate MPC complexity according to the rate of phase variation, enabling real-time deployment without compromising tracking accuracy. In addition, an Extended Kalman Filter (EKF) is integrated into the control loop to compensate for system delays and improve trajectory prediction in dynamic scenarios. Experimental results on multi-axis robotic manipulation demonstrate that the proposed approach achieves superior tracking accuracy and computational efficiency compared with conventional visual servoing methods, validating the feasibility of phase-map-driven predictive control for high-speed dynamic target tracking.

We study polar molecule scattering in quasi-one-dimensional geometries. Elastic and reactive collision rates are computed as a function of collision energy and electric dipole moment for different confinement strengths. The numerical results are interpreted in terms of first order scattering and of adiabatic models. Universal dipolar scattering is also discussed. Our results are relevant to experiments where control of the collision dynamics through one-dimensional confinement and an applied electric field is envisioned.

We consider the nonadiabatic dynamics of internal conversions (ICs) in systems rigid enough to allow a description of the coupled potential energy surfaces (PES) within the harmonic approximation. Through a hierarchical representation of the Hamiltonian, we define a set of sequentially coupled effective modes and obtain reduced-dimensionality models by truncating the sequence of modes. We systematically investigate the predictions on the electronic populations of these models and of a recently proposed mean-field mixed quantum-classical (MQC) approach, where the most important effective modes are treated at the quantum level and the motion of the remaining ones is approximated with a swarm of classical trajectories. As a test case, we consider a linear vibronic coupling (LVC) model for the ππ∗ / nπ∗ IC in thymine. LVC PES are computed both in gas phase and in water to explore the different performance of the investigated methods for different relative stabilities of the coupled PES. Reference full quantum dynamical (QD) results are obtained with the MultiLayer Multiconfigurational Time Dependent Hartree method. We show that reduced-dimensionality models work very well in the ultrafast time scale (< 100 fs). At longer times, they tend to predict smaller differences between ππ∗ and nπ∗ populations than those computed with full QD simulations because they cannot fully account for trapping mechanisms which are found to involve most of the molecular modes. The proposed MQC model always improves the agreement with reference full QD simulations, even when only few modes are included in the quantum partition. It correctly reproduces the quenching of oscillations in electronic populations and partially recovers the error of reduced-dimensionality models on the long-time populations.

Increasing the dimensionality of NMR experiments strongly enhances the spectral resolution and provides invaluable direct information about atomic interactions. However, the price tag is high: long measurement times and heavy requirements on the computation power and data storage. We introduce sparse fast Fourier transform as a new method of NMR signal collection and processing, which is capable of reconstructing high quality spectra of large size and dimensionality with short measurement times, faster computations than the fast Fourier transform, and minimal storage for processing and handling of sparse spectra. The new algorithm is described and demonstrated for a 4D BEST-HNCOCA spectrum.

Introduction. The problems of determining the profiles of electrophysical material properties are among the inverse problems of electrodynamics. In these studies, the focus is on the creation of a computer-economical method for reconstructing the profiles of electrical conductivity and magnetic permeability of metal planar objects under testing. These parameters can include the information about the results and quality of the production process or the effects of exposure to an aggressive environment. Registration of changes in electrophysical properties by means of eddy current testing allows for prompt adoption of effective management decisions regarding controlled processes. The simultaneous determination of these parameters because of non-contact indirect measurements of the electromotive force (EMF) by surface eddy current probes over the surface object with the subsequent restoration of the parameter distributions along its thickness by numerical methods is an urgent task. Objective. To create a computer-economical method for determining the electrophysical properties of objects by means of surrogate optimization with the accumulation of additional apriori knowledge about them in neural network metamodels with nonlinearly reduced dimensionality to improve the accuracy of simultaneous profile determination. Methodology. The method for determining the electrophysical properties of objects is based on homogeneous designs of experiments, surrogate optimization with the accumulation of apriori knowledge about them in metamodels with nonlinearly reduced dimensionality. Originality. Integration of multiple capabilities in the surrogate model that combine the advantages of high-performance computing and optimization algorithms in the factor space reduced by the Kernel PCA (Principal Component Analysis) method. The accumulated additional apriori knowledge about objects is incorporated into the neural network metamodel. This makes it possible to implicitly identify complex patterns hidden in the data that are characteristic of the eddy current measuring process and take them into account during reconstruction. Results. The reduction of the search space is a considerable result. It was possible due to the nonlinear Kernel-PCA transformations with the analysis of the eigenvalues of the kernel matrix and the restriction on the number of PCA principal components. The results confirmed the validity of a significant reduction in space without major loss of information. Another indicator of the effectiveness of the method is a high precision of the created surrogate models. The accuracy of the reduced dimensional metamodels was achieved by using a homogeneous computer design of experiment and deep learning networks. The adequacy and informativeness of the constructed surrogate models have been proved by numerical indicators. The efficiency of the method is demonstrated on model examples. References 36, table 5, figures 6.

针对陆地层间多次波压制效果不明显的问题,提出降维虚同相轴法,实现叠前陆地地震资料的层间多次波压制.对经过精确动校正的叠前道集,逐道应用虚同相轴法预测层间多次波,实现降维处理.与传统的虚同相轴法相比,运算量大大降低,且不再要求炮检点规则且密集地分布.同时,引入加权叠加的高信噪比参考道,参与叠前道集的互相关和褶积运算,提高了虚同相轴法的预测精度.将所提方法应用于中国西部地区实际陆地地震资料,取得明显的层间多次波压制效果.

The displacement of a sprint kayak can be described by a one-dimensional mathematical model, which, in its simplest case, is analogous to the free-fall problem with quadratic drag and constant propulsion. To describe realistic cases, it is necessary to introduce a propulsion capable of reproducing the characteristics of the kayak stroke, including periodicity, average force and effects of stroke frequency, among others. Addressing the problem in terms of a Fourier series allows us to separate the equation into two parts, one of which is equivalent to the constant propulsion case and results in an asymptotic expression, while the second accounts for the periodic contributions. This approach allows us to solve several cases of interest: to propose a quadrature rule for the asymptotic part that allows fast estimations; to compare results with the literature; and finally to propose a general mathematical method for this problem which could help to understand some key strategies in the kayak race.

Achieving efficient solid oxide fuel cell operation and simultaneous prevention of degradation effects calls for the development of precise on-line monitoring and control tools based on predictive, computationally fast models. The originality of the proposed modelling approach originates from the hypothesis that the innovative derivation procedure enables the development of a thermodynamically consistent multi-species electrochemical model that considers the electrochemical co-oxidation of carbon monoxide and hydrogen in a closed-form. The latter is achieved by coupling the equations for anodic reaction rates with the equation for anodic potential. Furthermore, the newly derived model is capable of accommodating the diffusive transport of gaseous species through the gas diffusion layer, yielding a computationally efficient quasi-one-dimensional model. This resolves a persistent knowledge gap, as the proposed modelling approach enables the modelling of multi-species fuels in a closed form, resulting in very high computational efficiency, and thus enable the model’s real-time capability. Multiple validation steps against polarisation curves with different fuel mixtures confirm the capability of the newly developed model to replicate experimental data. Furthermore, the presented results confirm the capability of the model to accurately simulate outside the calibrated variation space under different operating conditions and reformate mixtures. These functionalities position the proposed model as a beyond state-of-the-art tool for model supported development and control applications.