Electron Drag Effect on Thermal Conductivity in Two-Dimensional Semiconductors.

Two-dimensional (2D) materials have shown great potential in applications as transistors, where thermal dissipation becomes crucial because of the increasing energy density. Although the thermal conductivity of 2D materials has been extensively studied, interactions between nonequilibrium electrons and phonons, which can be strong when high electric fields and heat current coexist, are not considered. In this work, we systematically study the electron drag effect, where nonequilibrium electrons impart momenta to phonons and influence the thermal conductivity, in 2D semiconductors using ab initio simulations. We find that, at room temperature, electron drag can significantly increase thermal conductivity by decreasing phonon-electron scattering in 2D semiconductors while its impact in three-dimensional semiconductors is negligible. We attribute this difference to the large electron-phonon scattering phase space and larger contribution to thermal conductivity by drag-active phonons. Our work elucidates the fundamental physics underlying coupled electron-phonon transport in materials of various dimensionalities.

Extraordinary Thermoelectric Properties of Topological Surface States in Quantum-Confined Cd3As2 Thin Films.

Topological insulators and semimetals have been shown to possess intriguing thermoelectric properties promising for energy harvesting and cooling applications. However, thermoelectric transport associated with the Fermi arc topological surface states on topological Dirac semimetals remains less explored. This work systematically examines thermoelectric transport in a series of topological Dirac semimetal Cd3As2 thin films grown by molecular beam epitaxy. Surprisingly, significantly enhanced Seebeck effect and anomalous Nernst effect are found at cryogenic temperatures when the Cd3As2 layer is thin. In particular, a peak Seebeck coefficient of nearly 500 µV K-1 and a corresponding thermoelectric power factor over 30 mW K-2 m-1 are observed at 5 K in a 25-nm-thick sample. Combining angle-dependent quantum oscillation analysis, magnetothermoelectric measurement, transport modeling, and first-principles simulation, the contributions from bulk and surface conducting channels are isolated and the unusual thermoelectric properties are attributed to the topological surface states. The analysis showcases the rich thermoelectric transport physics in quantum-confined topological Dirac semimetal thin films and suggests new routes to achieving high thermoelectric performance at cryogenic temperatures.

Voting based double-weighted deterministic extreme learning machine model and its application.

This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is affected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and the model performance is unstable. The V-DPELM model proposed in this paper can greatly alleviate the limitations of traditional models because of its direct determination of weight structure and voting mechanism strategy. Through extensive simulations on various real-world classification datasets, we observe a marked improvement in classification accuracy when comparing the V-DPELM algorithm to traditional V-ELM methods. Notably, when used for machine recognition classification of breast tumors, the V-DPELM method demonstrates superior classification accuracy, positioning it as a valuable tool in machine-assisted breast tumor diagnosis models.

Indirect Exchange Interaction Leads to Large Lattice Contribution to Magnetocaloric Entropy Change.

Materials with a large magnetocaloric response are highly desirable for magnetic cooling applications. It is suggested that a strong spin-lattice coupling tends to generate a large magnetocaloric effect, but no microscopic mechanism has been proposed. In this Letter, we use spin-lattice dynamics simulation to examine the lattice contribution to the magnetocaloric entropy change in bcc iron (Fe) and hcp gadolinium (Gd) with exchange interaction parameters determined from ab initio calculations. We find that indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange interaction in hcp Gd leads to longer-range spin-lattice coupling and more strongly influences the low-frequency long-wavelength phonons. This results in a higher lattice contribution toward the total magnetocaloric entropy change as compared to bcc Fe with short-range direct exchange interactions. Our analysis provides a framework for understanding the magnetocaloric effect in magnetic materials with strong spin-lattice couplings. Our finding suggests that long-range indirect RKKY-type exchange gives rise to a larger lattice contribution to the magnetocaloric entropy change and is, thus, beneficial for magnetocaloric materials.

Lattice Dynamics and Thermal Transport in Semiconductors with Anti-Bonding Valence Bands.

Achieving high thermoelectric performance requires efficient manipulation of thermal conductivity and a fundamental understanding of the microscopic mechanisms of phonon transport in crystalline solids. One of the major challenges in thermal transport is achieving ultralow lattice thermal conductivity. In this study, we use the anti-bonding character of the highest-occupied valence band as an efficient descriptor for discovering new materials with an ultralow thermal conductivity. We first examined the relationship between anti-bonding valence bands (ABVBs) and low lattice thermal conductivity in model systems PbTe and CsPbBr3. Then, we conducted a high-throughput search in the Materials Project database and identified over 600 experimentally stable binary semiconductors with an anti-bonding character in their valence bands. From our candidate list, we conducted a comprehensive analysis of the chemical bonds and the thermal transport in the XS family, where X = K, Rb, and Cs are alkaline metals. These materials all exhibit ultralow thermal conductivities less than 1 W/(m K) at room temperature despite simple structures. We attributed the ultralow thermal conductivity to the weakened bonds and increased phonon anharmonicity due to their ABVBs. Our results provide chemical intuitions to understand lattice dynamics in crystals and open up a convenient venue toward searching for materials with an intrinsically low lattice thermal conductivity.

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.

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.

Advances on intelligent algorithms for scientific computing: an overview.

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.

Mapping the pathways of photo-induced ion migration in organic-inorganic hybrid halide perovskites.

Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications.

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

Background: Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective: This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods: A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results: Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions: The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization.

A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem.

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.

Harmonic Noise-Tolerant ZNN for Dynamic Matrix Pseudoinversion and Its Application to Robot Manipulator.

As we know, harmonic noises widely exist in industrial fields and have a crucial impact on the computational accuracy of the zeroing neural network (ZNN) model. For tackling this issue, by combining the dynamics of harmonic signals, two harmonic noise-tolerant ZNN (HNTZNN) models are designed for the dynamic matrix pseudoinversion. In the design of HNTZNN models, an adaptive compensation term is adopted to eliminate the influence of harmonic noises, and a Li activation function is introduced to further improve the convergence rate. The convergence and robustness to harmonic noises of the proposed HNTZNN models are proved through theoretical analyses. Besides, compared with the ZNN model without adaptive compensation term, the HNTZNN models are more effective for tacking the problem of dynamic matrix pseudoinverse under harmonic noises environments. Moreover, HNTZNN models are further applied to the kinematic control of a four-link planar robot manipulator under harmonic noises. In general, the experimental results verify the effectiveness, superiority, and broad application prospect of the models.

GNN Model for Time-Varying Matrix Inversion With Robust Finite-Time Convergence.

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient neural network (GNN) is recognized as an effective method for static matrix inversion with exponential convergence. However, when it comes to time-varying matrix inversion, most of the traditional GNNs can only track the corresponding time-varying solution with a residual error, and the performance becomes worse when there are noises. Currently, zeroing neural networks (ZNNs) take a dominant role in time-varying matrix inversion, but ZNN models are more complex than GNN models, require knowing the explicit formula of the time-derivative of the matrix, and intrinsically cannot avoid the inversion operation in its realization in digital computers. In this article, we propose a unified GNN model for handling both static matrix inversion and time-varying matrix inversion with finite-time convergence and a simpler structure. Our theoretical analysis shows that, under mild conditions, the proposed model bears finite-time convergence for time-varying matrix inversion, regardless of the existence of bounded noises. Simulation comparisons with existing GNN models and ZNN models dedicated to time-varying matrix inversion demonstrate the advantages of the proposed GNN model in terms of convergence speed and robustness to noises.

A Variable-Parameter Noise-Tolerant Zeroing Neural Network for Time-Variant Matrix Inversion With Guaranteed Robustness.

Matrix inversion frequently occurs in the fields of science, engineering, and related fields. Numerous matrix inversion schemes are often based on the premise that the solution procedure is ideal and noise-free. However, external interference is generally ubiquitous and unavoidable in practice. Therefore, an integrated-enhanced zeroing neural network (IEZNN) model has been proposed to handle the time-variant matrix inversion issue interfered with by noise. However, the IEZNN model can only deal with small time-variant noise interference. With slightly larger noise interference, the IEZNN model may not converge to the theoretical solution exactly. Therefore, a variable-parameter noise-tolerant zeroing neural network (VPNTZNN) model is proposed to overcome shortcomings and improve the inadequacy. Moreover, the excellent convergence and robustness of the VPNTZNN model are rigorously analyzed and proven. Finally, compared with the original zeroing neural network (OZNN) model and the IEZNN model for matrix inversion, numerical simulations and a practical application reveal that the proposed VPNTZNN model has the best robust property under the same external noise interference.

Transient Strain-Induced Electronic Structure Modulation in a Semiconducting Polymer Imaged by Scanning Ultrafast Electron Microscopy.

Understanding the optoelectronic properties of semiconducting polymers under external strain is essential for their applications in flexible devices. Although prior studies have highlighted the impact of static and macroscopic strains, assessing the effect of a local transient deformation before structural relaxation occurs remains challenging. Here, we employ scanning ultrafast electron microscopy (SUEM) to image the dynamics of a photoinduced transient strain in the semiconducting polymer poly(3-hexylthiophene) (P3HT). We observe that the photoinduced SUEM contrast, corresponding to the local change of secondary electron emission, exhibits an unusual ring-shaped profile. We attribute the observation to the electronic structure modulation of P3HT caused by a photoinduced strain field owing to its low modulus and strong electron-lattice coupling, supported by a finite-element analysis. Our work provides insights into tailoring optoelectronic properties using transient mechanical deformation in semiconducting polymers and demonstrates the versatility of SUEM to study photophysical processes in diverse materials.

Spatiotemporal Imaging of Thickness-Induced Band-Bending Junctions.

van der Waals materials exhibit naturally passivated surfaces and an ability to form versatile heterostructures to enable an examination of carrier transport mechanisms not seen in traditional materials. Here, we report a new type of homojunction termed a "band-bending junction" whose potential landscape depends solely on the difference in thickness between the two sides of the junction. Using MoS2 on Au as a prototypical example, we find that surface potential differences can arise from the degree of vertical band bending in thin and thick regions. Furthermore, by using scanning ultrafast electron microscopy, we examine the spatiotemporal dynamics of charge carriers generated at this junction and find that lateral carrier separation is enabled by differences in the band bending in the vertical direction, which we verify with simulations. Band-bending junctions may therefore enable new optoelectronic devices that rely solely on band bending arising from thickness variations to separate charge carriers.

Probing Surface Photovoltage Effect Using Photoassisted Secondary Electron Emission.

While the properties of surfaces and interfaces are crucial to modern devices, they are commonly difficult to explore since the signal from the bulk often masks the surface contribution. Here we introduce a methodology based on scanning electron microscopy (SEM) coupled with a pulsed laser source, which offers the capability to sense the topmost layer of materials, to study the surface photovoltage (SPV) related effects. This method relies on a pulsed optical laser to transiently induce an SPV and a continuous primary electron beam to produce secondary electron (SE) emission and monitor the change of the SE yield under laser illumination. We observe contrasting behaviors of the SPV-induced SE yield change on n-type and p-type semiconductors. We further study the dependence of the SPV-induced SE yield on the primary electron beam energy, the optical fluence, and the modulation frequency of the optical excitation, which reveal the details of the dynamics of the photocarriers in the presence of the surface built-in potential. This fast, contactless, and bias-free technique offers a convenient and robust platform to probe surface electronic phenomena, with great promise to probe nanoscale effects with a high spatial resolution. Our result further provides a basis to understand the contrast mechanisms of emerging time-resolved electron microscopic techniques, such as the scanning ultrafast electron microscopy.

A Finite-Time Convergent and Noise-Rejection Recurrent Neural Network and Its Discretization for Dynamic Nonlinear Equations Solving.

The so-called zeroing neural network (ZNN) is an effective recurrent neural network for solving dynamic problems including the dynamic nonlinear equations. There exist numerous unperturbed ZNN models that can converge to the theoretical solution of solvable nonlinear equations in infinity long or finite time. However, when these ZNN models are perturbed by external disturbances, the convergence performance would be dramatically deteriorated. To overcome this issue, this paper for the first time proposes a finite-time convergent ZNN with the noise-rejection capability to endure disturbances and solve dynamic nonlinear equations in finite time. In theory, the finite-time convergence and noise-rejection properties of the finite-time convergent and noise-rejection ZNN (FTNRZNN) are rigorously proved. For potential digital hardware realization, the discrete form of the FTNRZNN model is established based on a recently developed five-step finite difference rule to guarantee a high computational accuracy. The numerical results demonstrate that the discrete-time FTNRZNN can reject constant external noises. When perturbed by dynamic bounded or unbounded linear noises, the discrete-time FTNRZNN achieves the smallest steady-state errors in comparison with those generated by other discrete-time ZNN models that have no or limited ability to handle these noises. Discrete models of the FTNRZNN and the other ZNNs are comparatively applied to redundancy resolution of a robotic arm with superior positioning accuracy of the FTNRZNN verified.