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This article investigates how the International Baccalaureate's 'global dream' for education interacts with the national priorities of governments. In mainland China, a thriving international school market has been met with tightened government control. We draw on the analytical lens of 'cosmopolitan nationalism' to demonstrate how a cosmopolitan openness to international schooling was subject to reversal when deemed to counter national interests. Through content analysis of government documents and state media, we discuss how representations of the International Baccalaureate were in tension with national agendas of the "China dream' and 'common prosperity". Finally, we reflect on the malleability of the International Baccalaureate across diverse national contexts.

Abstract Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images.

Abstract Dynamic Tensor Inversion (DTI) is an emerging issue in recent research, prevalent in artificial intelligence development frameworks such as TensorFlow and PyTorch. Traditional numerical methods suffer significant lagging error when addressing this issue. To address this, Zeroing-type Neural Dynamics (ZND) and Gradient-type Neural Dynamics (GND) are employed to tackle the DTI. However, these two methods exhibit inherent limitations in the resolution process, i.e. high computational complexity and low solution accuracy, respectively. Motivated by this technology gap, this paper proposes an Adaptive Coefficient Gradient Neural Dynamics (ACGND) for dynamically solving the DTI with an efficient and precise manner. Through a series of simulation experiments and validations in engineering applications, the ACGND demonstrates advantages in resolving DTI. The ACGND enhances computational efficiency by circumventing matrix inversion, thereby reducing computational complexity. Moreover, its incorporation of adaptive coefficients and activation functions enables real-time adjustments of the computational solution, facilitating rapid convergence to theoretical solutions and adaptation to non-statinary scenarios. Code is available at https://github.com/Maia2333/ACGND-Code-Implementation .

ABSTRACT: Meat qualities of free-range chicken (Xuan-Zhou) (XZ-FRC) are closely associated with slaughter age and directly influence the economic benefits of supplier and consumer's preference. Understanding of the relationship between meat qualities and ages will be of prime important to explore a better slaughter age of XZ-FRC. In this study, the quality traits of breast and thigh muscles from XZ-FRCs at 9 to 14 wk were analyzed to establish a relatively reliable method for selecting a better slaughter age. The results showed that the effects of slaughter ages on color (CIE L*, a* and b* values), shear force, centrifugal loss, and flavor of XZ-FRCs were significant (P < 0.05). There were greater differences in meat qualities, whatever breast or thigh muscles, between same or different ages. Eleven feature indexes used for colligation evaluation of slaughter age were selected by combining the quality characteristics and data analysis. The score of colligation evaluation for XZ-FRCs at 12 wk was higher than that at 9 and 14 wk, suggesting that the 12 wk was an optimal slaughter age. This work would provide a reference method that helps the producers of livestock and poultry to select a better slaughter age.

Purpose: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. Methods: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. Results: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p

Abstract Perovskite-type lead-free piezoelectric ceramics allow access to illustrious piezoelectric coefficients (d 33) through intricate composition design and experimental modulation. Developing a swift and accurate technology for identifying (K, Na)NbO3 (KNN)-based ceramic compositions with high d 33 in exceedingly large “compositional” space will establish an innovative research paradigm surpassing the traditional empirical trial-and-error method. Herein, we demonstrate an interpretable machine learning (ML) framework for quick evaluation of KNN-based ceramics with high d 33 based on data from published literature. Specifically, a thorough feature construction was carried out from the global and local dimensions to establish tree regression models with d 33 as the target property. Subsequently, the feature-property mapping rules of KNN-based piezoelectric ceramics are further optimized through feature screening. To intuitively understand the correlation mechanisms between ML regression targets and features, the sure independence screening and sparsifying operator (SISSO) method was employed to extract the essential descriptors to explain d 33. A straightforward descriptor, $${\text{e}}^{({{NM}}_{\text{B}}-{{MV}}_{\text{B}})}\cdot {ST}/{(I{D}_{\text{A}})}^{2}$$ e ( NM B − MV B ) ⋅ ST / ( I D A ) 2 , consisting of only four easily accessible parameters, can accelerate the evaluation of a series of novel KNN-based ceramics with high d 33 while exhibiting strong theoretical interpretability. This work not only provides a tool for the rapid discovery of high piezoelectric performance in KNN-based ceramics but also offers a data-driven route for the design of property descriptors in perovskites.

Accurate and timely information on urban vegetation (UV) can be used as an important indicator to estimate the health of cities. Due to the low cost of RGB cameras, true color imagery (TCI) has been widely used for high spatial resolution UV mapping. However, the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples, respectively. To address this issue, an index-guided semantic segmentation (IGSS) framework is proposed in this paper. Firstly, a novel cross-scale vegetation index (CSVI) is calculated by the combination of TCI and Sentinel-2 images, and the index value can be used to provide an initial UV map. Secondly, reliable UV and non-UV samples are automatically generated for training the semantic segmentation model, and then the refined UV map can be produced. The experimental results show that the proposed CSVI outperformed the existing five RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds, and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72% ∼ 88.16% and an F1 score of 87.73% ∼ 88.37%, which is comparable with the fully-supervised method.

Effective parent-teacher partnerships improve outcomes for autistic students. Yet, we know little about what effective partnerships look like for parents of autistic children from different backgrounds. We conducted interviews with 17 Chinese parents of autistic children attending Australian kindergartens/schools to understand their experiences. Parents appreciated the acceptance, opportunities and supports they received in Australia. They had high expectations of children; expectations not often shared by educators. Parents were respectful of teachers' expertise and polite and undemanding in interactions. Nevertheless, parents were frustrated by inconsistent teaching quality and inadequate communication. Navigating systems was also challenging and parents faced discrimination from teachers and their community. Recommendations include fostering open home-school communication, proactively seeking parents' expertise about children and explicitly scaffolding parents' self-advocacy.

In this work, the frequency dependence of ferroelectric and electrocaloric properties in barium titanate-based ceramics was studied based on Maxwell relations. It is found that the maximum and remnant polarization will decrease while the coercive field increases a lot with rising frequency from 0.1 to 10[Formula: see text]Hz, indicating that polarization rotation and domain switching become difficult at high frequencies. The electrocaloric properties show the different frequency dependence at different phase structures. Isothermal entropy change ([Formula: see text]) and adiabatic temperature change ([Formula: see text]) are similar around/above Curie temperature ([Formula: see text], showing tiny frequency dependence. However, [Formula: see text] and [Formula: see text] display the obvious frequency dependence below [Formula: see text], especially in the orthorhombic–tetragonal phase-transition region with a stable ferroelectric phase, and this frequency dependence becomes more obvious under a low-electric field. It is also found that increasing the frequency can weaken the electric field dependence of electrocaloric strength. This work gives a general profile of frequency dependence for electrocaloric properties in ferroelectric ceramics.

A phase-locked loop (PLL) of a phase-shifting power supply for the power system is designed in this paper. Taking the industrial frequency current as the reference signal, the input and output characteristics of the phase-frequency detector with charge pump are analyzed. In addition, the transfer function of the PLL system is analyzed by combining the passive second-order low-pass filter and the voltage-controlled oscillator. As a result, the influence of parameters such as charge pump current, phase margin, and loop bandwidth on the performance of the PLL is investigated, and the optimal parameters of each section are obtained. The results show that the optimized PLL has a locking time of 0.75 s, a pull-out range of 63.16 Hz, and a locking range of 43.82 Hz, with high stability and noise immunity.

BackgroundMechanical thrombectomy (MT) is one of the effective treatment methods for acute ischemic stroke (AIS), which requires a period of dual antiplatelet therapy (DAPT) after endovascular treatment. This study aimed to compare the efficacy and safety of 3-month DAPT and 1-month DAPT in AIS patients receiving MT through a retrospective study.MethodsAIS patients who received MT from May 2018 to March 2023 were grouped into a 1-month group (1-M group) and a 3-month group (3-M group) according to the duration of DAPT after MT. The primary outcome was the mRS score at 90 days. Secondary outcomes included a good prognosis (mRS score of 0–2) at 90 days post-surgery, 6-month mortality, recurrence of cerebral infarction, Barthel's index, Montreal Cognitive Assessment (MoCA) score, and incidence of symptomatic intracranial hemorrhage (sICH) during hospitalization.ResultA total of 147 patients with AIS were included in the final analysis, with 78 patients in the 1-M group and 69 patients in the 3-M group. The baseline and neurological characteristics were comparable between both groups. At 3-month follow-up, a total of 61 patients had an mRS of 0–2 at 90 days, with an average mRS of 3.3 ± 0.9 for all patients. There was no statistically significant difference in the mRS between the two groups of patients at 90 days (P > 0.05). There was no statistically significant difference in the mortality rate and incidence of sICH between the two groups of patients during the 6-month follow-up period (P > 0.05), but the recurrence rate of AIS in the 3-M group was lower than that in the 1-M group (P < 0.05). The improvement of Barthel index and MoCA in patients in the 3-M group was higher than those in the 1-M group at 6 months but not statistically different (P > 0.05).ConclusionFor AIS patients undergoing mechanical thrombectomy, compared to 1-month DAPT, 3-month DAPT can reduce the recurrence rate of IS during a 6-month follow-up period, without increasing the mortality and risk of cerebral hemorrhage.

As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.

Abstract Structural superlubricity, a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in micro-electro-mechanical systems devices, mechanical engineering, and energy resources. A critical step towards the practical application of structural superlubricity is the mass transfer and high throughput performance evaluation. Limited by the yield rate of material preparation, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and tribological performance measurement for structural superlubricity materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with structural superlubricity properties and complete the 100 graphite flakes assembly array to form various pre-designed patterns within 100 mins, which is 15 times faster than manual operation. Besides, the system is capable of automatically measuring the tribological performance of over 100 selected flakes on Si3N4, delivering statistical results for new interface which is beyond the reach of traditional methods. With its high accuracy, efficiency, and robustness, this machine learning-assisted system promotes the fundamental research and practical application of structural superlubricity.