Lanzhou University

Optimizing the electronic structure with increasing intrinsic stability is a usual method to enhance the catalysts’ performance. Herein, a series of cerium dioxide (CeO2−δ) based solid solution materials is synthesized via substituting Ce atoms with transition metal (Co, Cu, Ni, etc.), in which Co0.07Ce0.93O2−δ shows optimized band structure because of electron transition in the reaction, namely Co³⁺ (3d⁶4s⁰) + Ce³⁺ (4f¹5d ⁰6s⁰) → Co²⁺ (3d⁷4s⁰) + Ce⁴⁺ (4f⁰5d⁰6s⁰), with more stable electronic configuration. The in situ Raman spectra show a stable F2g peak at ≈452 cm⁻¹ of Co0.07Ce0.93O2−δ, while the F2g peak in CeO2−δ almost disappeared during HER progress, demonstrating the charge distribution of *H adsorbed on Co0.07Ce0.93O2−δ is more stable than *H adsorbed on CeO2−δ. Density functional theory calculations reveal that Co0.07Ce0.93O2−δ solid solution increases protonation capacity and favors for formation of *H in alkaline media. General guidelines are formulated for optimizing adsorption capacity and the volcano plot demonstrates the excellent catalytic performance of Co0.07Ce0.93O2−δ solid solution. The alkaline anion exchange membrane water electrolysis based on Co0.07Ce0.93O2−δ/NiFe LDH realizes a current density of 1000 mA cm⁻² at ≈1.86 V in alkaline seawater at 80 °C and exhibits long‐term stability for 450 h.

Purpose The relationship between ankyloglossia and speech is controversial. Our objective in the present study was to determine the most appropriate intervention and optimal timing for infants with speech articulation caused by ankyloglossia. Patients and Methods A total of 341 pediatric patients (aged 2 to 5 years) being referred for speech concerns due to ankyloglossia were enrolled in a randomized trial and assigned to either a surgical intervention (N = 166) or a no surgical intervention (N = 175) group. Subsequently, patients were further categorized into 3 groups according to age: 2 to < 3 years, 3 to < 4 years, and 4 to < 5 years. Measures of tongue appearance, tongue mobility, speech production, and parent and clinician intelligibility ratings were collected at preintervention (T0), 2-month postintervention (T1), 6-month postintervention (T2), and 12-month postintervention (T3). Results No statistically significant difference was found between surgical intervention and no surgical intervention groups for tongue appearance, tongue mobility, speech production, and intelligibility in the 2 to < 3 years age. However, there was significantly improved speech production and intelligibility in the surgical intervention group when compared to the no surgical intervention group in the 3 to < 4 and 4 to < 5 years old age. Conclusion Surgical intervention should not be performed too early for infants aged 2 to < 3 years with speech articulation caused by ankyloglossia, but rather watch and wait for the physiological growth of the lingual frenulum. The optimal timing range for surgical intervention is 4 to 5 years. This should provide certain significant guidance for infants with speech articulation caused by ankyloglossia.

Latewood width (LWW) indices of trees are considered a reliable proxy of summer precipitation in the Northern Hemisphere. However, the strong coupling and high correlation between earlywood width (EWW) and LWW indices often prevent registration of climate signals of the LWW index. In this study, 328-year-long earlywood width and latewood width chronologies were developed from Chinese pine at two sites in the Hasi Mountains, north central China. The climate responses of these chronologies were analyzed and the LWW index used to derive summer precipitation signals. Correlation analyses showed that LWW was particularly influenced by earlywood growth and recorded stronger climate signals of the previous year as EWW, rather than those of the current year with infrequent summer climate signals. However, after removing the effect of earlywood growth using a simple regression model, the adjusted LWW chronology (LWW adj) showed a strong relationship with July precipitation in dry years. This suggests that the LWW adj chronology has the potential to be used to investigate long-term variability in summer precipitation in drought-limited regions.

This article presents a Kalman–Koopman linear quadratic regulator (KKLQR) control approach to robotic systems. In the proposed approach, an optimal Koopman modeling method based on neural networks, in which continuous Koopman eigenfunctions are constructed without requiring any predefined dictionary, is proposed to obtain approximated linear models with high precision for robotic systems. Specifically, the linear model is constructed through a multistep prediction error minimization, which enables a long-term prediction capability. Furthermore, the Kalman filter is employed to alleviate the effects of disturbances in the KKLQR control approach. Experimental results show that the proposed KKLQR control approach achieves better prediction and control performance than other existing representative methods.

Enlightened by competitive and collaborative coordination behaviors widely observed in natural swarm systems, this work emphasizes these coordinating modes in multirobot systems and optimizes system stability along with resource utilization. Then, schemes are constructed to describe and model these two modes, where a k -winner-take-all concept is introduced as the driving principle of multirobot competition. In addition, a distributed coordination approach is established to effectively handle the above schemes aided with optimality theory, which is developed by a fusion of a recurrent neural dynamics solver and a distributed solver. The former is a single-layer neural dynamics model with a simple structure, and the latter transforms the involved global information to a distributed type via consensus. Both of them are carried out in the discrete-time domain to fit the actual application. Finally, the convergence and stability of the proposed coordination approach are proved via theoretical analysis and further demonstrated through simulations and experiments.

This article presents a deep bilinear Koopman model predictive control (DBKMPC) approach for modelling and control of unknown nonlinear systems. The bilinear model, which has the computational speed of a linear model and the predictive accuracy of a nonlinear model, can accurately characterize a large class of airborne and ground-based robotic systems. Specifically, a bilinear Koopman dynamic deep neural network (BKDDNN) is developed to learn the finite-dimensional bilinear Koopman operator in the lifting space without prior knowledge or system parameters. Moreover, the bilinear model is integrated into the standard model predictive control (MPC) optimization problem, facilitating the solution of the bilinear optimization problem. In such a way, the proposed DBKMPC avoids the problems of excessive inductive bias and selection difficulty of dictionary functions encountered by the existing methods, so that it enables a more effective solution to the problem of modeling and control of nonlinear robotic systems. The experimental results show that the proposed DBKMPC method surpasses the existing representative methods in terms of prediction and control performance.

Currently, the integration of artificial intelligence (AI) techniques with multimodal physiological signals represents a pivotal approach to detect affective disorders (ADs). With the increasing complexity and diversity of physiological signal modalities, researchers have introduced various AI methods using multimodal physiological signals to improve model classification performance and explainability to increase trust and facilitate clinical adoption. Among these methods, spiking neural networks (SNNs) stand out as a promising avenue due to their alignment with the operating principles of the human brain, robust biological explainability, and adeptness in processing spatial–temporal information in an efficient event-driven manner with low power consumption. Furthermore, the emergence of neuromorphic computing (NC) chips based on SNNs has greatly bolstered the field of NC, enabling effective support for objective, pervasive, and wearable AI-assisted medical diagnostic devices for ADs and other diseases. This article presents a review of recent achievements in multimodal AD detection and points out the associated challenges in utilizing multimodal physiological signals and NC based on SNNs for AD detection. Building upon this foundation, we give perspectives on future work. The intended readership for this review consists of researchers in the fields of cognitive computing, computational psychophysiology, affective computing, NC, and brain-inspired computing. We hope that this survey not only garners increased attention from the scientific community but also serves as a valuable guide for future studies in this field.

Underwater acoustic localization is a crucial technique for most underwater applications. However, in highly dynamic marine environments, underwater acoustic localization faces many challenges, such as the stratification effect, the clock asynchronization, the node drift, and environmental noises. Concerning above problems, we propose a new underwater localization algorithm for mobile underwater acoustic sensor networks (UASNs). At first, the measurement biases are modeled as the combination of constant biases and random biases according to the physical mechanism of their generation and distribution characteristics in measured data. Then, an error-summation-incorporated Newton iteration (ESINI) algorithm is designed to compute the localization result along the direction of constant biases decrease, and a Taylor expansion is used to approach the actual localization result along the direction of random biases decrease. Subsequently, a simplified Kalman filter (SKF) fuses the two localization results and enhances the localization accuracy. In this way, the proposed algorithm effectively increases the accuracy of localization results without adding extra measurement. Finally, theoretical analyses, simulations, and lake experiments are provided to verify the proposed algorithm's effectiveness and noise resistance performance.

The prevalence of cardiovascular disease, tumors, and other chronic illnesses has been steadily rising in recent years. Researchers have recently been employing cross-modal large-scale models and natural language generation models to address the significant visual and textual disparities in medical report generation tasks. However, these training processes presents challenges, such as difficulties matching cross-modal information and generating specialized medical terminology. To tackle these issues, we propose a Multifocal Region-Assisted Report Generation Network (MRARGN) to enhance cross-modal information matching. Specifically, we integrate a pre-trained ResNet-50 with multi-channel and attention mechanisms for trainable X-ray image representation. We then combine our proposed memory response matrix with OpenAI's contrastive pre-training results to construct a dynamic knowledge graph that stores lesion features and their corresponding texts. Finally, we incorporate attention mechanisms and forget gate units to generate comprehensive textual descriptions for different lesions, using an image and report alignment loss. We conduct ablation experiments on the IU-Xray and MIMIC-CXR datasets to evaluate our approach. The experimental results demonstrate that our proposed MRARGN outperforms most state-of-the-art approaches, including their own variants.

Dietary habits significantly influence the development of intestinal diverticular disease (IDD), a common gastrointestinal condition primarily affecting the colon. We performed a Mendelian randomization (MR) analysis on 20 diet‐related factors using data from the UK Biobank. IDD cases (n = 33,618) and controls (n = 329,381) were obtained from the FinnGen Biobank. Three key MR methods were applied: the inverse‐variance‐weighted (IVW) method as the primary approach to estimate causal relationships, along with the weighted median (WM) and MR‐Egger methods. Significant associations were found for pork intake (β = 1.06, p = 0.00244), nonoily fish intake (β = 0.709, p = 0.0449), oily fish intake (β = 0.246, p = 0.0222), and dried fruit intake (β = −0.953, p < 0.0001). After false discovery rate (FDR) adjustment, pork intake (q = 0.0244) and dried fruit intake (q < 0.0001) remained significant. Our results indicate that while pork and certain types of fish intake may elevate the risk of IDD, dried fruit intake may offer a protective effect. These findings highlight the potential of dietary changes in IDD prevention and management, though further research across diverse populations is needed.

Automatically detecting Ulva prolifera (U. prolifera) in rainy and cloudy weather using remote sensing imagery has been a long-standing problem. Here, we address this challenge by combining high-resolution Synthetic Aperture Radar (SAR) imagery with the machine learning, and detect the U. prolifera of the South Yellow Sea of China (SYS) in 2021. The findings indicate that the Random Forest model can accurately and robustly detect U. prolifera, even in the presence of complex ocean backgrounds and speckle noise. Visual inspection confirmed that the method successfully identified the majority of pixels containing U. prolifera without misidentifying noise pixels or seawater pixels as U. prolifera. Additionally, the method demonstrated consistent performance across different images, with an average Area Under Curve (AUC) of 0.930 (±0.028). The analysis yielded an overall accuracy of over 96%, with an average Kappa coefficient of 0.941 (±0.038). Compared to the traditional thresholding method, Random Forest model has a lower estimation error of 14.81%. Practical application indicates that this method can be used in the detection of unprecedented U. prolifera in 2021 to derive continuous spatiotemporal changes. This study provides a potential new method to detect U. prolifera and enhances our understanding of macroalgal outbreaks in the marine environment.

Robust and biocompatible hydrogels are recognized as promising biomimetic soft materials to improve human life quality. To ensure their stable, reliable, and safe service, the hydrogels are further required to have non‐contact and wireless stress sensing ability. Herein, a mechanoluminescence (ML) based micellar hydrogel is developed, in which the surface‐modified BaSi2O2N2: Eu²⁺ (M‐BSON) particles are chemically incorporated into the cross‐linked polyacrylamide/polymethyl acrylate (PAM/PMA) network structure. Because of the interactions between the M‐BSON particles and the PMA micelles, the as‐fabricated composite hydrogel exhibits enhanced mechanical properties with a mechanical strength of 2.73 MPa and a toughness of 3.40 MJ m⁻³, respectively. The chemical wrapping of the M‐BSON particles by the hydrophobic PMA micelles further protects the ML properties from water quenching, leading to remarkable stress‐induced luminescence under the water environment of hydrogel. Because of its desirable mechanical performance, attractive stress‐light responsiveness, and good biocompatibility, the M‐BSON incorporated hydrogel has the potential to be applied as an intelligent artificial ligament for stress self‐monitoring and failure warning. This work addresses the inhomogeneous dispersion and water quenching issues of the ML particles in hydrogel structure, which significantly promote ML applications in bionic engineering.

Background Major depressive disorder (MDD) is one of the most common mental disorders, and the number of individuals with MDD (MDDs) continues to increase. Therefore, there is an urgent need for an objective characterization and real‐time detection method for depression. Functional near‐infrared spectroscopy (fNIRS) is a non‐invasive tool, which is widely used in depression research. However, the process of how the brain activity of MDDs changes in response to external stimuli based on fNIRS signals is not yet clear. Method Energy landscape (EL) can describe the brain dynamics under task conditions by assigning energy values to each state. The higher the energy value, the lower the probability of the state occurring. This study compares the EL features of 60 MDDs with 60 healthy controls (HCs). Results Compared to HCs, MDDs have more local minima, smaller energy differences, smaller variations in basin sizes, and longer duration in the basin of global minimum (GM). The classification results indicate that using the four features above for depression detection yields an accuracy of 86.53%. Simultaneously, there are significant differences between the two groups in the duration of the major states. Conclusion The dynamic brain networks of MDDs exhibit more constraints and lower degrees of freedom, which might be associated with depressive symptoms such as negative emotional bias and rumination. In addition, we also demonstrate the strong depression detection capability of EL features, providing a possibility for their application in clinical diagnosis.

Delayed dynamical models demonstrate significant application value in depicting interactions and internal dynamics among different biological populations. Therefore, they have garnered significant interest from numerous scholars in both biology and mathematics. Based on previous studies, this article construct a novel delayed predator-prey model. By utilizing fixed point theory, inequality methods, and appropriate functions, this article examined the desirable properties of the solutions of the constructed delayed predator-prey system, including existence and uniqueness, boundedness, and non-negativity. This paper determines the parameter conditions for system stability and the occurrence of bifurcations by employing bifurcation theory and the stability theory of delayed differential equations. Using two control strategies, namely the mixed controller and the extended delay feedback controller, this paper effectively adjusts the stability domain of the delayed predator-prey systems and controls the time of bifurcation onset. The research explores how delays affect the stabilization of system and the adjustment of bifurcation. This paper provides computer simulation photos supporting the main obtained findings. The outcomes of this paper are groundbreaking and can provide critical guidance for the control and regulation of predator and prey population densities.

The development of efficient and durable electrocatalysts for the acidic oxygen evolution reaction (OER) is essential for advancing renewable hydrogen energy technology. However, the slow deprotonation kinetics of oxo‐intermediates, involving the four proton‐coupled electron steps, hinder the acidic OER progress. Herein, a RuTiOx solid solution electrocatalyst is investigated, which features bridged oxygen (Obri) sites that act as proton acceptors, accelerating the deprotonation of oxo‐intermediates. Electrochemical tests, infrared spectroscopy, and density functional theory results reveal that the moderate proton adsorption energy on Obri sites facilitates fast deprotonation kinetics through the adsorbate evolution mechanism. This process effectively prevents the over‐oxidation and deactivation of Ru sites caused by the lattice oxygen mechanism. Consequently, RuTiOx shows a low overpotential of 198 mV at 10 mA cm⁻²geo and performance exceeding 1400 h at 50 mA cm⁻²geo with negligible deactivation. These insights into the OER mechanism and the structure‐function relationship are crucial for the advancement of catalytic systems.

The YBa 2 Cu 3 [Formula: see text] (YBCO)-coated conductors (CCs) exhibit great potential in the manufacturing of ultrahigh field magnets, superconducting fault current limiter, etc., while the irreversible strain plays a crucial role in designing and optimizing the superconducting devices. However, it is a big challenge to obtain the strain dependence of critical current ([Formula: see text]) of the coated conductor YBCO CC (width = 10[Formula: see text]mm, critical current [Formula: see text]500[Formula: see text]A) because of easy destruction induced by strain concentration near the grips in the process of tension. In addition, factors such as non-uniform deformation, contact resistance, and even mechanoelectrical coupling effect, which can induce non-uniform performance degradation, will be huge barriers against the accurate measurement of irreversible strain. In this paper, the failure phenomena and inducement analysis are studied, and an optimized experimental program is proposed based on the test and finite element analysis, which reduces the measuring errors caused by some unfavorable factors. At the end, the complete electrical performance degradation curve of YBCO CC with high [Formula: see text] is given.