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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.

In view of the defects of the new domestic T800 carbon fiber reinforced plastic （CFRP）, such as lamellar tearing and fiber loss, which would be easily caused by large drilling force and torque during cutting, an experimental platform including a vertical ultrasonic machining center and force measurement system was constructed. Carbide twist drill, carbide candle stick drill and carbide dagger drill were used to compare the hole quality of T800 carbon fiber composite. Under the same technological parameters, the influences of three kinds of cutting tool structures on the cutting force, surface morphology of hole inlet and outlet, aperture deviation and surface roughness of T800 composite were compared with those of conventional drilling （CD）. The ultrasonic vibration assisted drilling （UVAD） method was adopted to carry out feasibility experiments on optimum tool structure. The results show that the carbide dagger drill can effectively suppress the surface defects of T800 composite materials, but the entrance aperture accuracy is poor. The ultrasonic vibration assisted dagger drilling can further improve the quality of hole making, especially significantly improve the aperture accuracy by improving the drilling stability.

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]

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.

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]

BackgroundThe radiation accident emergency drill is an important way to maintain and improve the response ability of emergency organizations, but at present, the evaluation of the drill is mainly based on the qualitative evaluation of the surface performance of the key links, there is not yet a systematic, full-flow, accurate and targeted evaluation method of exercises.PurposeThis study aims to improve the quality and real-time evaluation of radiation accident emergency drill.MethodsFirstly, the alternative evaluation indicators were summarized on the basis of literature survey. The method of expert investigation was used to check the alternative indexes and establish the evaluation index system. Then, the analytic hierarchy process (AHP) method was applied to the calculation of weight distribution, and the fuzzy comprehensive evaluation (FCE) was employed to build comprehensive evaluation model for radiation accident emergency exercises. Finally, based on the AHP-PCE, comprehensive scores of the evaluation indexes at all levels of three typical examples were calculated.ResultsThe FCE-based model synthesizes the stage of the drill preparation, the drill implementation and the drill summary, and forms the general goal of the radiation accident emergency drill effect. The evaluation system of radiation accident emergency maneuver is set up with 5 first-level indexes and 15 s-level indexes to realize the comprehensive evaluation of the radiation accident emergency drill.ConclusionsThe example verification shows that the AHP-FCE model has a good adaptability and rationality, and has a good application value in the evaluation of radiation accident emergency drill.

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]