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Adaptive filtering algorithms are currently successfully employed in a number of fields.But, a disadvantage of traditional real-valued fixed step-size adaptive filtering algorithm is that it is unable to meet both requirement of convergence rate and the steady-state error.In this article, we presented cosine function and geometric algebra(GA) based variable step-size technique for adaptive filtering.Initially, a multi-dimensional signal is represented as a GA multi-vector for the vectorization process in the proposed approach of adaptive filtering with variable step-size based on GA.Then, by establishing a non-linear function relationship between error signal e(n) and the step-size factor μ , given method of adaptive filtering resolves the contradiction among steady-state error and the convergence rate.Finally, simulation results illustrate that in comparison with other existing adaptive filtering algorithms, the defined approach performs better in terms of convergence rate, steady-state error, robustness against impulsive noise and the computational complexity. [ABSTRACT FROM AUTHOR]

The introduction of dual carbon targets will significantly impact power system development. Despite this, there is currently limited research on achieving system evolution and transition while ensuring safety, low-carbon output, and efficiency, as well as quantitatively analyzing the resulting changes dual carbon targets will have on the power system. Co-evolution of the power system offers a solution to balance the impact of dual carbon goals and enhance interaction among system entities, thereby facilitating the achievement of these goals. Our study focuses on constructing an evolutionary topological network by analyzing the dynamic evolution rule of power systems. We investigate the co-evolution pattern of power systems by analyzing the relationship between the role of power system agents and their dynamic structures. Furthermore, we analyze the future structural changes of power systems, which can provide theoretical support for achieving dual carbon goals in the power system. Our findings highlight key measures to promote synergistic evolution, including increasing energy storage capabilities, stabilizing renewable energy supply, breaking inter-provincial barriers in electricity transmission, and developing a multi-level intelligent power system. Through link analysis, we discover that future power systems will maintain a mild coordination among agents rather than implementing large-scale realignment and reconfiguration. We posit that overcoming obstacles can be achieved by fostering cohesion between the network and agents through technological innovation and widespread market diffusion to drive co-evolution. [ABSTRACT FROM AUTHOR]

With regards to the problem of multidimensional signal processing in the field of adaptive filtering, geometric algebra based higher-order statistics algorithms were proposed. For instance, to express a multidimensional signal as a multi-vector, the adaptive filtering algorithms described in this work leverage all of the benefits of GA theory in multidimensional signal processing. GA space is employed to extend the traditional least-mean absolute third (LMAT) and newly deduced least-mean mixed third-fourth (LMMTF) adaptive filtering methods for multi-dimensional signal processing. The objective of the presented GA-based least-mean absolute third (GA-LMAT) and GA-based least-mean mixed third-fourth (GA-LMMTF) algorithms is to minimize the cost functions by using higher-order statistics of the error signal e(n) in GA space. The simulation's results revealed that at significantly smaller step size, the given GA-LMAT algorithm is better than the others in terms of steady-state error and convergence rate. Besides, the defined GA-LMMTF algorithm mitigates for the instability of GA-LMAT as the step size increases and illustrates an improved performance relative to mean absolute error and convergence rate. [ABSTRACT FROM AUTHOR]

In the present contribution, we demonstrate that the sluggish kinetics of oxygen evolution reaction (OER) over the bismuth sulfide (Bi2S3) photoanode, which severely restricts its photoelectrochemical activity, is markedly accelerated by employing a sulfate-containing electrolyte. First-principle calculation points to the spontaneous adsorption of sulfate ( SO 4 2 − ) on Bi2S3 and its capacity of stabilizing the OER intermediates through hydrogen bonding, which is further reinforced by increasing the local density of states near the Fermi level of Bi2S3. Meanwhile, the electron transfer is also promoted to synergistically render the rate-determining step (from O* to OOH*) of OER over Bi2S3 kinetically facile. Last but not least, benefitting from such enhanced OER activity and efficient charge separation resulted from depositing Bi2S3 on the zinc oxide nanorods (ZnO NRs), forming a core–shell heterojunction, its photocurrent density achieves 8.61 mA·cm−2 at 1.23 VRHE, far surpassing those reported for additional Bi2S3-based and several state-of-the-art photoanodes in the literature and further exceeding their theoretical limit. The great promise of the Bi2S3/ZnO NRs is in view of such outperformance, the superior Faradaic yield of oxygen of more than ∼ 80% and the outstanding half-cell applied bias photon-to-current efficiency of ∼ 1% well corroborated. [ABSTRACT FROM AUTHOR]

Emotion analysis, an integral aspect of human–machine interactions, has witnessed significant advancements in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a profound need for a comprehensive review of pivotal elements within this domain. Our paper delves deep into the realm of emotion analysis, examining multimodal data sources encompassing speech, text, images, and physiological signals. We provide a curated overview of relevant literature, academic forums, and competitions. Emphasis is laid on dissecting unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. We further discuss the nuances of multimodal data fusion techniques, spotlighting early, late, model, and hybrid fusion strategies. Key findings indicate the essentiality of analyzing emotions across multiple modalities. Detailed discussions on emotion elicitation, expression, and representation models are presented. Moreover, we uncover challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. Our comprehensive review serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights garnered from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability. [ABSTRACT FROM AUTHOR]