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Providing online instruction has become a normal part in primary and secondary education. The teachers' work engagement and intention are critical to the sustenance of high-quality online teaching. Therefore, this mixed-methods study proposed and validated a theoretical model from the perspective of the Job Demands-Resources Model to shed light on what drives teachers' online teaching work engagement and its consequent effect on continuance intention. The partial least squares structural equation was applied to 1066 primary and secondary school teachers' valid self-reported questionnaires to examine the proposed model. We found that it is the perceived usefulness of technology, rather than the ease of use, that affects teachers' online teaching engagement and intention; institutional support has the most influence on sustaining teachers' online teaching; urban teachers focus more on the perceived usefulness of technology, while rural teachers are concerned with their online teaching readiness. The follow-up qualitative research further yielded three major themes, including the availability of technical equipment and guidance constitutes teachers' online teaching basic needs, teachers emphasize the institutional support from parent, and teachers are not troubled by the use of technology but focus on its functions. These themes provided rich details and in-depth knowledge regarding the key aspects that influence teachers' online teaching work engagement and continuance intention in quantitative study. This study extends our understanding of the theory of work engagement in the context of online education and provides practical guidance for online teaching instruction in primary and secondary education.

Abstract Polar topologies, such as vortex and skyrmion, have attracted significant interest due to their unique physical properties and promising applications in high-density memory devices. To date, all known polar vortices are present in or induced by ferroelectric materials. In this study, we find polar vortex arrays in paraelectric SrTiO3. Using multislice electron ptychography, the evolution of vorticity along the vortex axis is revealed in twisted bilayers of SrTiO3 with deep-sub-angstrom resolution and one picometer accuracy. The surprising finding of polar vortices in a paraelectric crystal opens up opportunities for polarization physics and corresponding new devices.

Abstract Thermoelectrics converting heat and electricity directly attract broad attentions. To enhance the thermoelectric figure of merit, zT, one of the key points is to decouple the carrier-phonon transport. Here, we propose an entropy engineering strategy to realize the carrier-phonon decoupling in the typical SrTiO3-based perovskite thermoelectrics. By high-entropy design, the lattice thermal conductivity could be reduced nearly to the amorphous limit, 1.25 W m−1 K−1. Simultaneously, entropy engineering can tune the Ti displacement, improving the weighted mobility to 65 cm2 V−1 s−1. Such carrier-phonon decoupling behaviors enable the greatly enhanced μ W/κ L of ~5.2 × 103 cm3 K J−1 V−1. The measured maximum zT of 0.24 at 488 K and the estimated zT of ~0.8 at 1173 K in (Sr0.2Ba0.2Ca0.2Pb0.2La0.2)TiO3 film are among the best of n-type thermoelectric oxides. These results reveal that the entropy engineering may be a promising strategy to decouple the carrier-phonon transport and achieve higher zT in thermoelectrics.

BackgroundIn the past few decades, selective serotonin reuptake inhibitors (SSRIs) became widely used antidepressants worldwide. Therefore, the adverse reactions of patients after SSRI administration became a public and clinical concern. In this study, we conducted a pharmacovigilance study using the Adverse Event Reporting System (FAERS) database of the US Food and Drug Administration. Our main goal was to evaluate adverse events related to SSRIs, with a particular focus on abnormal weight gain and glucose/lipid metabolism disorders.MethodThe adverse event data for representative SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline) was extracted from the FAERS database from 2004Q1 to 2023Q4. The reporting odds ratio and proportional reporting ratio were employed to explore relevant adverse event reports (ADEs) signals. Univariate logistic regression analysis was utilized to explore factors associated with glucose/lipid metabolism abnormality following SSRIs treatment.ResultsWe identified 143,744 ADE reports associated with SSRIs and revealed significant abnormal signals related to weight gain and glucose/lipid metabolism in depressed patients. Variations were observed among different SSRIs medications. Specifically, citalopram was associated with abnormal weight gain (ROR: 4, 95% CI: 3.1-5.2) and hepatic steatosis (ROR: 2.8, 95% CI: 2.1-3.6); escitalopram was correlated with gestational diabetes (ROR: 9.1, 95% CI: 6.6-12.4) and cholestasis (ROR: 2.4, 95% CI: 1.75-3.38); fluoxetine was associated with obesity (ROR: 2.8, 95% CI: 2.08-3.78); fluvoxamine was linked to arteriospasm coronary (ROR: 13.87, 95% CI: 4.47-43.1); and sertraline was implicated in neonatal jaundice (ROR: 16.1, 95% CI: 12.6-20.6). Females and younger age are important risk factors for the development of associated adverse effects.ConclusionOur study screened for adverse effects associated with abnormal glucose/lipid metabolism, such as abnormal body weight and fatty liver, in depressed patients taking selective serotonin reuptake inhibitors by utilizing FAERS database. This provides valuable insights for healthcare professionals in accepting and managing patients treated with SSRIs.

Abstract Direct reduction of unactivated alkyl halides for C(sp3)-N couplings under mild conditions presents a significant challenge in organic synthesis due to their low reduction potential. Herein, we introduce an in situ formed pyridyl-carbene-ligated copper (I) catalyst that is capable of abstracting halide atom and generating alkyl radicals for general C(sp3)-N couplings under visible light. Control experiments confirmed that the mono-pyridyl-carbene-ligated copper complex is the active species responsible for catalysis. Mechanistic investigations using transient absorption spectroscopy across multiple decades of timescales revealed ultrafast intersystem crossing (260 ps) of the photoexcited copper (I) complexes into their long-lived triplet excited states (>2 μs). The non-Stern-Volmer quenching dynamics of the triplets by unactivated alkyl halides suggests an association between copper (I) complexes and alkyl halides, thereby facilitating the abstraction of halide atoms via inner-sphere single electron transfer (SET), rather than outer-sphere SET, for the formation of alkyl radicals for subsequent cross couplings.

The continuous expansion of wireless communication application scenarios demands the active tuning of electromagnetic (EM) metamaterials, which is essential for their flexible adaptation to complex EM environments. However, EM reconfigurable systems based on intricate designs and smart materials often exhibit limited flexibility and incur high manufacturing costs. Inspired by mechanical metastructures capable of switching between multistable configurations under repeated deformation, we propose a planar kirigami frequency selective surface (FSS) that enables mechanical control of its resonant frequency. This FSS is composed of periodically arranged copper square-ring resonators embedded in a kirigami-structured ecoflex substrate. Through simple tensile deformation, the shapes and positions of the square-ring resonators on the kirigami substrate are altered, resulting in changes to the coupling between capacitance and inductance, thereby achieving active tuning. Combining EM finite element simulations and transmittance measurements, we demonstrate that biaxial mechanical stretching allows for continuous adjustment of the FSS resonant frequency and −10 dB bandwidth. Additionally, the FSS exhibits excellent polarization and incident angle stability. Structural parameterization of the square-ring kirigami FSS was conducted to elucidate the deformation–electromagnetic coupling mechanism underlying the active tuning. These insights provide a foundation for guiding the application of square-ring kirigami FSS in various practical engineering domains.

The combustion optimization problem of the circulation fluidized bed boiler is regarded as a difficult multi-objective optimization problem that requires simultaneously improving the boiler thermal efficiency and reducing the NOx emissions concentration. In order to solve the above-mentioned problem, a new multi-objective optimization framework that incorporates an interpretable CatBoost model and modified slime mould algorithm is proposed. Firstly, the interpretable CatBoost model combined with TreeSHAP is applied to model the boiler thermal efficiency and NOx emissions concentration. Simultaneously, data correlation analysis is conducted based on the established models. Finally, a kind of modified slime mould algorithm is proposed and used to optimize the adjustable operation parameters of one 330 MW circulation fluidized bed boiler. The experimental results show that the proposed framework can effectively improve the boiler thermal efficiency and reduce the NOx emissions concentration, where the average optimization ratio for thermal efficiency reaches +0.68%, the average optimization ratio for NOx emission concentration reaches −37.55%, and the average optimization time is 6.40 s. In addition, the superiority of the proposed method is demonstrated by ten benchmark testing functions and two constrained optimization problems. Therefore, the proposed framework is an effective artificial intelligence approach for the modeling and optimization of complex systems.

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm’s iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon’s rank sum test shows that there are significant statistical differences between the RWOA and other algorithms

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students’ perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students’ perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students’ engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students’ emotions and achievement.

With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

In the current global context, digital finance (DF) and sustainable economic development (SED) are important topics. The synergies between DF and SED have already been proven. However, the measurement and quantitative analysis of the coupling coordination degree (CCD) of DF and SED have not received sufficient attention to date. Based on data from 55 cities in the Yellow River Basin (YRB) from 2011 to 2021, this study constructs an evaluation index system of DF and SED and measures their level, respectively. The proposed CCD model is then used to measure the CCD between the two systems. In addition, kernel density estimation, Markov chain, σ-convergence, β-convergence, and the quadratic assignment procedure (QAP) method are used to study the spatial pattern, distribution dynamic evolution trend, convergence, and influencing factors of the regional differences in the CCD. The results show that: (1) From 2011 to 2021, the CCD level showed a stable upward trend and regional heterogeneity, and the time stage characteristics were more obvious. (2) The center position and change interval of the overall distribution curve of the kernel density estimation gradually shifted to the right. The Markov transfer probability matrix shows that the CCD is more stable among different levels, indicating a phenomenon of "club convergence". (3) A convergence analysis shows that there are significant σ-convergence, absolute β-convergence, and conditional β-convergence. (4) The QAP regression shows that factors such as the regional differences in GDP per capita have a significant impact on the regional differences in the CCD. This study offers a comprehensive structure that can be used to examine the synergistic effects between DF and SED; the research findings can also provide perspectives for other areas.

Understanding the mechanisms and spatial correlations of crystallographic symmetry breaking in ferroelectric materials is essential to tuning their functional properties. While optical second harmonic generation (SHG) has long been utilized in ferroelectric studies, its capability for probing complex polar materials has yet to be fully realized. Here, we develop a SHG spectral imaging method implemented on a home-designed laser-scanning SHG microscope, and demonstrate its application for a model system of (K,Na)NbO3 single crystals. Supervised model fitting analysis produces comprehensive information about the polarization vector orientations and relative fractions of constituent domain variants as well as their thermal evolution across the polymorphic phase transitions. Multiple domains and phases are clearly delineated at different temperatures, suggesting the phase competitions in (K,Na)NbO3. Besides, we show that unsupervised matrix decomposition analysis can quickly and faithfully reveal domain configurations without a priori knowledge about specific material systems. The SHG spectral imaging method can be readily extended to other ferroelectric materials with potentials to be further enhanced.

Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.

Accurate measurement of defect structures is hindered by complex atomic configuration and/or crystal tilt. Here, the authors realize sub-nanometer mapping of crystal tilt and deep-subangstrom resolution and depth-dependent imaging of dislocations.

This article reviewed the urban vehicle access control policies derived from disparate spatiotemporal dimensions that aim to eliminate the negative externalities of traffic caused by urbanization. Urban access regulations are important tools often required to achieve the sustainable mobility vision of cities. Employing a systematic literature review methodology, this review summarized and analyzed various urban access control policies to enlighten policymakers and future scientific research. The results indicate that combinations of multiple-dimensional restriction policies (including inter-policy and intra-policy) have more significant effects than implementing a single policy. Classified according to their objectives, control policies were discussed in terms of their benefits and limitations. The authors are inspired to propose and describe five paradoxes of urban access control policies.

This paper is concerned with the robust synchronization analysis of delayed fractional order neural networks with uncertain parameters (DFNNUPs). Firstly, the DFNNUPs drive system model and response system model are established. Secondly, using multiple matrix quadratic Lyapunov function approach and inequality analysis technique, the robust synchronization conditions are derived in the form of the matrix inequalities. Finally, the correctness of the theoretical results is verified by an example.

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability.

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.