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Nanograined materials possess ultrahigh strength, while their processing and technological applications are constrained by inherent thermal and mechanical instability. Existing experiments show that the stability of Cu nanograins can be enhanced by performing a rapid heating pretreatment that reduces the grain boundary (GB) energy by changing the GB structure. The variation in the GB structure inevitably affects the migration mechanism of GBs. However, the effect of the pretreatment-induced variation in migration mechanisms on stability remains unclear. Here, the shear deformation of a series of ⟨ 100 ⟩ symmetrical tilt GBs after rapid heating pretreatment is systematically investigated by molecular dynamics simulations. The simulations of unheated GBs are also included for comparison. Our results show that the rapid heating pretreatment does not improve the mechanical stability of GBs with tilt angles larger than 36.87° but rather enhances the mechanical stability of those with tilt angles less than 36.87° by the transformation of migration behavior from the normal ⟨ 110 ⟩ mode to (i) a ⟨ 100 ⟩ mode; (ii) an inhomogeneous mixed one that reconciles the ⟨ 110 ⟩ and ⟨ 100 ⟩ modes; and (iii) an inhomogeneous ⟨ 110 ⟩ mode. The former leads to an increase in the critical shear stress that is required to initiate the migration, whereas the latter two result in a decrease in the migration distance. The variation in the GB migration mechanism is attributed to the change in the GB structure from an ordered kite structure to a disordered one. The research gives an atomic insight into the stabilizing mechanism of nanocrystalline materials with rapid heating pretreatment. [ABSTRACT FROM AUTHOR]

Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy using a dual-margin dynamic threshold matrix is designed to enhance learning opportunities for minority class instances and those that are challenging to classify, and combined with a random strategy, it can estimate the current class imbalance distribution by accessing only a limited number of instance labels. Finally, an instance-adaptive sampling strategy is proposed, which comprehensively considers the imbalance ratio and classification difficulty of instances, and combined with a weighted ensemble strategy, improves the classification performance of the ensemble classifier in imbalanced data streams. Extensive experiments and analyses demonstrate that AdaAL-MID can handle various complex concept drifts and adapt to changes in class imbalance ratios, and it outperforms several state-of-the-art active learning algorithms. [ABSTRACT FROM AUTHOR]

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream's characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations. [ABSTRACT FROM AUTHOR]

Irritable bowel syndrome (IBS) is a prevalent functional gastrointestinal disorder characterized by abdominal pain, bloating, diarrhea, and constipation. Recent studies have underscored the significant role of the gut microbiota in the pathogenesis of IBS. Physical exercise, as a non-pharmacological intervention, has been proposed to alleviate IBS symptoms by modulating the gut microbiota. Aerobic exercise, such as running, swimming, and cycling, has been shown to enhance the diversity and abundance of beneficial gut bacteria, including Lactobacillus and Bifidobacterium. These bacteria produce short-chain fatty acids that possess anti-inflammatory properties and support gut barrier integrity. Studies involving IBS patients participating in structured aerobic exercise programs have reported significant improvements in their gut microbiota's composition and diversity, alongside an alleviation of symptoms like abdominal pain and bloating. Additionally, exercise positively influences mental health by reducing stress and improving mood, which can further relieve IBS symptoms via the gut–brain axis. Long-term exercise interventions provide sustained benefits, maintaining the gut microbiota's diversity and stability, supporting immune functions, and reducing systemic inflammation. However, exercise programs must be tailored to individual needs to avoid exacerbating IBS symptoms. Personalized exercise plans starting with low-to-moderate intensity and gradually increasing in intensity can maximize the benefits and minimize risks. This review examines the impact of various types and intensities of physical exercise on the gut microbiota in IBS patients, highlighting the need for further studies to explore optimal exercise protocols. Future research should include larger sample sizes, longer follow-up periods, and examine the synergistic effects of exercise and other lifestyle modifications. Integrating physical exercise into comprehensive IBS management plans can enhance symptom control and improve patients' quality of life. [ABSTRACT FROM AUTHOR]

Protecting forest resources and preventing forest fires are vital for social development and public well-being. However, current research studies on forest fire warning systems often focus on extensive geographic areas like states, counties, and provinces. This approach lacks the precision and detail needed for predicting fires in smaller regions. To address this gap, we propose a Transformer-based time series forecasting model aimed at improving the accuracy of forest fire predictions in smaller areas. Our study focuses on Quanzhou County, Guilin City, Guangxi Province, China. We utilized time series data from 2021 to 2022, along with remote sensing images and ArcGIS technology, to identify various factors influencing forest fires in this region. We established a time series dataset containing twelve influencing factors, each labeled with forest fire occurrences. By integrating these data with the Transformer model, we generated forest fire danger level prediction maps for Quanzhou County. Our model's performance is compared with other deep learning methods using metrics such as RMSE, and the results reveal that the proposed Transformer model achieves higher accuracy (ACC = 0.903, MAPE = 0.259, MAE = 0.053, RMSE = 0.389). This study demonstrates that the Transformer model effectively takes advantage of spatial background information and the periodicity of forest fire factors, significantly enhancing predictive accuracy. [ABSTRACT FROM AUTHOR]

With the rapid growth of data information, the characteristics of data stream are diversified, which put forward new requirements for the study of concept drift processing methods. The previous detection ideas based on performance degradation cannot cope with the non-instant availability environment of labels, which promotes the research of un-labeled detection methods. The research of ensemble and incremental learning promotes the progress of drift adaptation method. Firstly, the drift detection methods are analyzed in detail in two cases, namely whether the label is available and immediate or not, and based on the model composition, the drift adaptation methods are analyzed and explained with emphasis on the ensemble method. Secondly, the drift processing methods of different ideas are comprehensively summarized from the core ideas, comparison models, advantages and limitations, and the drift processing methods of different ideas are compared as a whole. Finally, the future research directions in this field are given, including noise discrimination, threshold setting and criterion diversification. [ABSTRACT FROM AUTHOR]

[Objectives] In order to improve the blast resistance of lightweight aluminum foam sandwich panels, this paper studies the dynamic response of functionally graded aluminum foam sandwich panels under air blast loading using AUTODYN finite element software. [Methods] Under the conditions of the same height and weight of the core, the influence of the core layer arrangement on velocity response, plastic deformation and energy absorption are analyzed and discussed. [Results] The results show that the maximum velocity of the center point of the front face decreases with the increase of the core density of the first layer. Sandwich panels with high/low/middle core density arrangements provide the best blast resistance. The graded core absorbs most of the shock energy, and sandwich panels with a lower core density placed in the first layer have the best energy-absorbing performance. [Conclusions] The numerical analysis results in this paper can provide references for the optimum design of the core of aluminum foam sandwich panels.

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the "combinatorial explosion" problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given. [ABSTRACT FROM AUTHOR]

Challenges in expensive large-scale optimization problems, such as high coupling between variables, easy falling into local optimal solution, and computationally expensive objective function, resulted in the difficulty to achieve the global optimal solution. An inertial grouping and overlapping feature selection technique for cooperative revolutionary (IG-OFSA) algorithms was proposed to solve expensive large-scale optimization problems. In the proposed algorithm, firstly, a large-scale optimization problem was decomposed into several low-dimensional overlapping sub-problems by using overlapping feature selection technology, and each sub-problem was optimized in-dependently with the assistance of a surrogate model. Then, promising solutions found for each sub-problem would be merged into a context vector for expensive objective evaluation. In addition, an inertial grouping technology was used to control the frequency of regrouping during the optimization to extend the cycle of exploitation of the grouping scheme, and correspondingly improved the performance of optimization. The performance of IG-OFSA was tested on 15 CEC2013 benchmark problems and compared with three state-of-the-art algorithms. The experimental results showed that the performance of IG-OFSA was competitive to solve the expensive large-scale optimization problem, especially, good for solving problems with partially separable, overlapping or completely non-separable decision variables. [ABSTRACT FROM AUTHOR]

The microstructure evolution of AlN implanted with Eu during thermal treatment is characterized by transmission electron microscopy (TEM), high‐resolution X‐ray diffraction, and atomic force microscopy. Three regions with different contrast can be observed in the TEM for the implanted samples. The region A is 30 nm below the surface, the region B is about 30–100 nm below the surface and is the worst damaged area, and the region C is the area below the region B. There is a relatively large stress in the region B due to the large radius difference between Eu and Al ions. After annealing at 750 °C, the stress in region B is only partially released outward, resulting in the increase of lattice defects in regions A and C. Both A and C regions show the same contrast in the weak‐beam bright‐field image. After annealing at 1040 °C, there are almost no shadow regions, which indicates that the stress is completely released and the lattice damage is removed. Meanwhile, the results of cathodoluminescence (CL) show that the luminescence of Eu3+ is improved along with the stress releasing and damage removal. [ABSTRACT FROM AUTHOR]