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Magnetocaloric materials, which exploit reversible temperature changes induced by magnetic field variations, are promising for advancing energy-efficient cooling technologies. The potential integration of two-dimensional materials into magnetocaloric systems represents an emerging opportunity to enhance the magnetocaloric cooling efficiency. In this study, we use atomistic spin dynamics simulations based on first-principles parameters to systematically evaluate how magnetocaloric properties transition from three-dimensional (3D) to two-dimensional (2D) ferromagnetic materials. We find that 2D features such as reduced Curie temperature, sharper magnetic transition, and higher magnetic susceptibility are beneficial for magnetocaloric applications, while the relatively higher lattice heat capacity in 2D can compromise achievable adiabatic temperature changes. We further propose GdSi 2 as a promising 2D magnetocaloric material. Our calculation predicts that GdSi 2 exhibits an isothermal entropy change Δ S M of 22.5 J kg − 1 K − 1 and an adiabatic temperature change Δ T a d of 6.2 K, near the hydrogen liquefaction temperature (T C ≈ 25 K). Our analysis offers valuable theoretical insights into the magnetocaloric effect in 2D ferromagnets and demonstrates that 2D ferromagnets hold promise for cooling and thermal management applications in compact and miniaturized nanodevices. [ABSTRACT FROM AUTHOR]

This research aims to address the limitations inherent in the traditional Extreme Learning Machine (ELM) algorithm, particularly the stochastic determination of input-layer weights and hidden-layer biases, which frequently leads to an excessive number of hidden-layer neurons and inconsistent performance. To augment the neural network's efficacy in pattern classification, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the input matrix and alleviate multicollinearity issues during the computation of the input weight matrix. This paper introduces an enhanced ELM methodology, designated the PCA-DP-ELM algorithm, which integrates PCA with Double Pseudo-Inverse Weight Determination (DP). The PCA-DP-ELM algorithm proposed in this study consistently achieves superior average classification accuracy across various datasets, irrespective of whether assessed through longitudinal or cross-sectional experiments. The results from both experimental paradigms indicate that the optimized algorithm not only enhances accuracy but also improves stability. These findings substantiate that the proposed methodology exerts a positive influence on pattern classification. [ABSTRACT FROM AUTHOR]

The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. [ABSTRACT FROM AUTHOR]

Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (< 10 K) ranges, while important applications such as hydrogen liquefaction call for efficient magnetic refrigerants for the intermediate temperature range of 10–100 K. For efficient use in this range, new magnetocaloric materials with matching Curie temperatures need to be discovered, while conventional experimental approaches are typically time-consuming and expensive. Here, we report a computational material discovery pipeline based on a materials database containing more than 6000 entries auto-generated by extracting reported material properties from the literature using a large language model. We then use this database to train a machine learning model that can efficiently predict the magnetocaloric properties of materials based on their chemical composition. We further verify the magnetocaloric properties of the predicted compounds using ab initio atomistic spin dynamics simulations to complete the computational material discovery. Using this approach, we identify 11 new promising magnetocaloric materials for the target temperature range. Our work demonstrates the potential of combining large language models, machine learning, and ab initio simulations to efficiently discover new functional materials. [ABSTRACT FROM AUTHOR]

Understanding the transport of photogenerated charge carriers in semiconductors is crucial for applications in photovoltaics, optoelectronics, and photo-detectors. While recent experimental studies using scanning ultrafast electron microscopy (SUEM) have demonstrated that the local change in the secondary electron emission induced by photoexcitation enables direct visualization of the photocarrier dynamics in space and time, the origin of the corresponding image contrast still remains unclear. Here, we investigate the impact of photoexcitation on secondary electron emissions from semiconductors using a Monte Carlo simulation aided by time-dependent density functional theory. Particularly, we examine two photoinduced effects: the generation of photocarriers in the sample bulk and the surface photovoltage (SPV) effect. Using doped silicon as a model system and focusing on primary electron energies below 1 keV, we found that both the hot photocarrier effect immediately after photoexcitation and the SPV effect play dominant roles in changing the secondary electron yield (SEY), while the distribution of photocarriers in the bulk leads to a negligible change in SEY. Our work provides insights into electron–matter interaction under photo-illumination and paves the way toward a quantitative interpretation of the SUEM contrasts. [ABSTRACT FROM AUTHOR]

Microscale energy transport processes are crucial in microelectronics, energy-harvesting devices, and emerging quantum materials. To study these processes, methods that can probe transport with conveniently tunable length scales are highly desirable. Transient grating spectroscopy (TGS) is such a tool that can monitor microscale energy transport processes associated with various fundamental energy carriers including electrons, phonons, and spins. Having been developed and applied for a long time in the chemistry community, TGS has regained popularity recently in studying different transport regimes in solid-state materials. In this Tutorial, we provide an in-depth discussion of the operational principle and instrumentation details of a modern heterodyne TGS configuration from a practitioner's point of view. We further review recent applications of TGS in characterizing microscale transport of heat, charge, spin, and acoustic waves, with an emphasis on thermal transport. [ABSTRACT FROM AUTHOR]

In engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered. Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem. Nevertheless, the original ZNN (OZNN) and the integral‐enhanced ZNN (IEZNN) usually fail to deal with the TVMI problem under unbounded noises, such as linear noises. Therefore, a neural network model that can handle the TVMI under linear noise interference is urgently needed. This paper develops a double integral‐enhanced ZNN (DIEZNN) model based on a novel integral‐type design formula with inherent linear‐noise tolerance. Moreover, its convergence and robustness are verified by derivation strictly. For comparison and verification, the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments. The experiments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises. In general, the DIEZNN model is an innovative work and is proposed for the first time. Satisfyingly, the errors of DIEZNN are always less than 1 × 10−3 under linear noises, whereas the error norms of OZNN and IEZNN models are not convergent to zero. In addition, these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN. [ABSTRACT FROM AUTHOR]

Mechanisms of lithium electrodeposition, which overwhelmingly affect lithium metal battery performance and safety, remain insufficiently understood due to its electrochemical complexity. Novel, nondestructive, and in situ techniques to probe electrochemical interfaces during lithium electrodeposition are highly desirable. In this work, we demonstrate the capability of transient grating spectroscopy to monitor lithium electrodeposition at the micrometer scale by generating and detecting surface acoustic waves that sensitively interact with the deposited lithium. Specifically, we show that the evolution of the frequency, velocity, and damping rate of the surface acoustic waves strongly correlates with the lithium nucleation and growth process. Our work illustrates the sensitivity of high-frequency surface acoustic waves to micrometer scale changes in electrochemical cells and establishes transient grating spectroscopy as a versatile platform for future in situ investigation of electrochemical interfaces. [ABSTRACT FROM AUTHOR]

We discuss the state-of-the-art and remaining challenges in the fundamental understanding and technology development for controlling light-matter interactions in nanophotonic environments in and away from thermal equilibrium. The topics covered range from the basics of the thermodynamics of light emission and absorption to applications in solar thermal energy generation, thermophotovoltaics, optical refrigeration, personalized cooling technologies, development of coherent incandescent light sources, and spinoptics.

Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time series data. First, the maximum information coefficient (MIC) method is applied to handle data features, and the wavelet transform technique is employed to remove noise from the data. Subsequently, WaveNet utilizes its scalable convolutional network to extract representations of wind power data and effectively capture long-range temporal information. These representations are then fed into the MMoE architecture, which treats multistep time series prediction as a set of independent yet interrelated tasks, allowing for information sharing among different tasks to prevent error accumulation and improve prediction accuracy. We conducted predictions for various forecasting horizons and compared the performance of the proposed model against several benchmark models. The experimental results confirm the strong predictive capability of the WaveNet–MMoE framework. [ABSTRACT FROM AUTHOR]

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients. [ABSTRACT FROM AUTHOR]

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas. [ABSTRACT FROM AUTHOR]

The dynamic Sylvester equation (DSE) is frequently encountered in engineering and mathematics fields. The original zeroing neural network (OZNN) can work well to handle DSE under a noise-free environment, but may not work in noise. Though an integral-enhanced zeroing neural network (IEZNN) can be employed to solve the DSE under multiple-noise, it may fall flat under linear noise, and its convergence speed is unsatisfactory. Therefore, an accelerated double-integral zeroing neural network (ADIZNN) is proposed based on an innovative design formula to resist linear noise and accelerate convergence. Besides, theoretical proofs verify the convergence and robustness of the ADIZNN model. Moreover, simulation experiments indicate that the convergence rate and anti-noise ability of the ADIZNN are far superior to the OZNN and IEZNN under linear noise. Finally, chaos control of the sine function memristor (SFM) chaotic system is provided to suggest that the controller based on the ADIZNN has a smaller amount of error and higher accuracy than other ZNNs. [ABSTRACT FROM AUTHOR]