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The fabrication of robust and high-performance graphene-based electrodes on engineering plastics has garnered significant attention in recent years. In this study, we present a novel methodology to produce porous graphene structures derived from epoxy resin (EP) utilizing a straightforward laser direct-scribing process. Under the influence of a CO2 laser in an ambient atmosphere, EP undergoes a transformation to yield laser-induced graphene (LIG-APP/EP). Furthermore, this LIG-APP/EP was employed to construct an electrode for lithium-ion batteries, which exhibited outstanding electrochemical performance. Notably, the initial charge and discharge capacities of the LIG-APP/EP electrode material were recorded at 976 and 1,452 mAh g−1, respectively, with a coulombic efficiency of 67.2%. Such impressive performance can be ascribed to the hierarchical porous architecture of LIG-APP/EP and the concurrent doping with nitrogen (N) and phosphorus (P) atoms. Given these findings, LIG-APP/EP demonstrates significant potential for applications in advanced electrochemical systems. This innovative approach also offers profound implications for the sustainable recycling of discarded engineering plastics.

Objective: To study the relationship between syndrome elements and anterior communicating artery (ACoA) opening in patients with symptomatic severe carotid artery stenosis/occlusion. Methods: Thirty-six patients with symptomatic severe carotid stenosis/occlusion were collected, including 26 patients with cerebral infarction and 10 patients with transient ischemic attack (TIA). Syndrome elements at five time points were collected. Computer tomography angiography (CTA) combined with magenic resonance angiograp (MRA) was used to evaluate the primary collateral circulation, and the prognosis and syndrome elements were statistically analyzed according to whether the ACoA was open or not. Results: The ACoA was open more in the primary collateral circulation among patients with symptomatic severe carotid stenosis/occlusion. There was a statistically significant difference in national institute of health stroke scale (NIHSS) score improvement and good prognosis [the modified rankin scale (mRS) ≤ 2] between the ACoA open group and the ACoA non-open group on the 90th day (P < 0.05). The proportion of patients with internal wind syndrome, blood stasis syndrome, Qi deficiency syndrome, and Yin deficiency syndrome in the ACoA non-open group was higher than that in the open group. Conclusion: In the patients with severe carotid artery stenosis/ occlusion, the group with presence of anterior communicating artery had better prognosis. The syndrome elements are more complex in the group without the presence of anterior communicating artery. The proportion of Qi deficiency syndrome was positively correlated with the non-opening of anterior communicating artery. The imaging evaluation of collateral circulation can provide guidance for syndrome differentiation and treatment.

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]

Low‐cost, reliable, and efficient biosensors are crucial in detecting residual heavy metal ions (HMIs) in food products. At present, based on distance‐induced localized surface plasmon resonance of noble metal nanoparticles, enzyme‐mimetic reaction of nanozymes, and chelation reaction of metal chelators, the constructed optical sensors have attracted wide attention in HMIs detection. Besides, based on the enrichment and signal amplification strategy of nanomaterials on HMIs and the construction of electrochemical aptamer sensing platforms, the developed electrochemical biosensors have overcome the plague of low sensitivity, poor selectivity, and the inability of multiplexed detection in the optical strategy. Moreover, along with an in‐depth discussion of these different types of biosensors, a detailed overview of the design and application of innovative devices based on these sensing principles was provided, including microfluidic systems, hydrogel‐based platforms, and test strip technologies. Finally, the challenges that hinder commercial application have also been mentioned. Overall, this review aims to establish a theoretical foundation for developing accurate and reliable sensing technologies and devices for HMIs, thereby promoting the widespread application of biosensors in the detection of HMIs in food. [ABSTRACT FROM AUTHOR]

The transcription and replication of the Newcastle disease virus (NDV) strictly rely on the viral ribonucleoprotein (RNP) complex, which is composed of viral NP, P, L and RNA. However, it is not known whether other viral non-RNP proteins participate in this process for viral self-regulation. In this study, we used a minigenome (MG) system to identify the regulatory role of the viral non-RNP proteins V, M, W, F and HN. Among them, V significantly reduced MG-encoded reporter activity compared with the other proteins and inhibited the synthesis of viral mRNA and cRNA. Further, V interacted with NP. A mutation in residue W195 of V diminished V–NP interaction and inhibited inclusion body (IB) formation in NP-P-L-cotransfected cells. Furthermore, a reverse-genetics system for the highly virulent strain F48E9 was established. The mutant rF48E9-VW195R increased viral replication and apparently enhanced IB formation. In vivo experiments demonstrated that rF48E9-VW195R decreased virulence and retarded time of death. Overall, the results indicate that the V–NP interaction of the W195 mutant V decreased, which regulated viral RNA synthesis, IB formation, viral replication and pathogenicity. This study provides insight into the self-regulation of non-RNP proteins in paramyxoviruses. [ABSTRACT FROM AUTHOR]

This review aims at offering an up-to-date comprehensive summary of carbon fibers (CFs)-based composites, with the emphasis on smart assembly and purpose-driven specific functionalization for their critical applications associated with flexible sensors. We first give a brief introduction to CFs as a versatile building block for preparation of mutil-fountional materials and the current status of research studies on CFs. This is followed by addressing some crucial methods of preparation of CFs. We then summarize multiple possibilities of functionalising CFs, an evaluation of some key applications of CFs in the areas of flexible biosensors was also carried out.

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]