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Single Shot MultiBox Detector (SSD) method shows outstanding performance by using multiscale feature maps in object detection task. However, the SSD method exhibits low accuracy in small object detection. In this paper, A Recursive Attention-Enhanced Bidirectional Feature Pyramid Network (RA-BiFPN) is proposed. Firstly, we designed the attention-enhanced bidirectional feature pyramid network (A-BiFPN) to improve the detection accuracy of the small object. The A-BiFPN is composed of bidirectional feature pyramid network (BiFPN) and the coordinate attention. Among them, the BiFPN employs top-down and bottom-up paths to aggregate features at different scales so that features at all scales contain rich semantic and detailed information. These features help coordinate attention that embeds positional information into channel attention so that the network can easily focus on the channels and locations related to the object in the feature map. Secondly, in order to enhance the ability of the A-BiFPN to characterize small targets, we adopted the recursive structure to feed back the output feature of the A-BiFPN into the backbone network. In this way, the recursive structure goes through the bottom-up backbone repeatedly to enrich the representation power of the A-BiFPN. The experimental results show that the detection accuracy of our method in PASCAL VOC, NWPU VHR-10 , KITTI and RSOD dataset is improved by 2.65%, 7.98% ,7.02% and 5.63% respectively compared to the original SSD.

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state with respect to the number of integration time-steps. This provides a significant improvement over previous approaches, while preserving the characteristic quantum speed-up in terms of the dimensionality of the underlying differential equations system, which similar time-marching quantum algorithms have previously demonstrated. Notably, by classically implementing the proposed algorithm, we showcase that it accurately captures the structural characteristics of the Lorenz system, reproducing both regular attractors–limit cycles–and the chaotic attractor within the chosen parameter regime.

This research introduces an improved control strategy for an active power filter (APF) system. It utilizes an adaptive super-twisting sliding mode control (STSMC) scheme. The proposed approach integrates an interval type-2 fuzzy neural network with a self-feedback recursive structure (IT2FNN-SFR) to enhance the overall performance of the APF system. The IT2FNN with STSMC proposed here consists of two components, with one being IT2FNN-SFR, which demonstrates robustness for uncertain systems and the ability to utilize historical information. The IT2FNN-SFR estimator is used to approximate the unknown nonlinear function within the APF. Simultaneously, the STSMC component is integrated to reduce system chattering, improving control precision and overall system performance. STSMC combines the robustness and simplicity of traditional sliding mode control, effectively addressing the chattering problem. To mitigate inaccuracies and complexities associated with manual parameter setting, an adaptive law of sliding mode gain is formulated to achieve optimal gain solutions. This adaptive law is designed within the STSMC framework, facilitating parameter optimization. Experimental validation is conducted to verify the harmonic suppression capability of the control strategy. The THD corresponding to the designed control algorithm is 4.16%, which is improved by 1.24% and 0.55% compared to ASMC and STSMC, respectively, which is below the international standard requirement of 5%. Similarly, the designed controller also demonstrates advantages in dynamic performance: when the load decreases, it is 4.72%, outperforming ASMC and STSMC by 1.15% and 0.38%, respectively; when the load increases, it is 3.87%, surpassing ASMC and STSMC by 1.07% and 0.36%, respectively.

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality.

Summary Recursive sequence generation (i.e., the ability to transfer recursive patterns to novel items) was recently reported in crows (Liao et al., 2022 , Science Advances , 8 [44], eabq3356). Here, we argue that although the reported data are certainly compatible with the recursion hypothesis, they can also be explained by other, much simpler mechanisms of associative learning.

Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with self-feedback recursive structure (IT2FNN-SFR STSMC) is proposed in this paper. IT2FNN has an uncertain membership function, which can enhance the nonlinear ability and robustness of the network. The historical information will be stored and utilized by the self-feedback recursive structure (SFR) at runtime. Therefore, the novel IT2FNN-SFR is designed to improve the dynamic approximation effect of the neural network and reduce the dependence of the controller on the actual mathematical model. The adaptive rate of each weight of the neural network is designed by the Lyapunov method and gradient descent (GD) algorithm to ensure the convergence and stability of the system. Super-twisting sliding mode control (STSMC) has strong robustness, which can effectively reduce system chattering, and improve control accuracy and system performance. The gain of the integral term in the STSMC is set as a constant, and the other gain is changed adaptively whose adaptive rate is deduced through the stability proof of the neural network, which greatly reduces the difficulty of parameter adjustment. The harmonic suppression ability of the designed control strategy is verified by simulation experiments.

Firstly, we present a more explicit formulation of the complete system D(N) of representatives of Manin’s symbols over Q, which was initially given by Shimura. Then, we establish a bijection between D(M)×D(N) and D(MN) for (M,N)=1, which reveals a recursive structure between Manin’s symbols of different levels. Based on Manin’s complete system Π(N) of representatives of cusps on X0(N) and Cremona’s characterization of the equivalence between cusps, we establish a bijection between a subset C(N) of D(N) and Π(N), and then establish a bijection between C(M)×C(N) and C(MN) for (M,N)=1. We also provide a recursive structure for elliptical points on X0(N). Based on these recursive structures, we obtain recursive algorithms for constructing Manin symbols over Q, cusps, and elliptical points on X0(N). This may give rise to more efficient algorithms for modular elliptic curves. As direct corollaries of these recursive structures, we present a recursive version of the genus formula and prove constructively formulas of the numbers of D(N), cusps, and elliptic points on X0(N).

Purpose – The purpose of this paper is to propose a holistic structural framework for a sustainable renewal that embraces all relevant contexts – individual, organizational, local-regional and worldwide. This should help humanity achieve a future in which society, economy and ecology are united in an evolutionary process based on multiple symbiosis. Design/methodology/approach – An integrative concept for sustainable renewal is presented, based on Beer’s Viable System Model (VSM). The core of that concept is a recursive structure, which organizes the tasks necessary for such renewal. The approach is both analytical and synthetic, proposing a design for the levels of recursion, making up a coherent whole. Findings – A structure is developed that enables agents at all recursive strata to generate variety in balance with the complexities they face. The organizational architecture based on the VSM, applied to each one of those levels, ensures the necessary and sufficient structural preconditions for the sustainability of the system under study. Practical implications – The concept proposed here is ready to be used as a blueprint for organizing the efforts for sustainability. It can help decision makers understand that the quest for sustainable renewal is a recursive issue involving all planes, from individual to global. Originality/value – The quest for the ecological sustainability of planet earth at this stage is not at all successful. The cybernetic model used here organizes the efforts for sustainability in a more effective way than conventional approaches. It also delivers powerful clues for sustainable renewal that are new, in particular a key to the sufficient structural preconditions for sustainability. This paper is an extended version of the Ross Ashby Memorial Lecture delivered by the author at the European Meeting of Cybernetics and Systems Research, Vienna, 24 April 2014, under the title “Organizing for Sustainability”.

Firstly, we present a more explicit formulation of the complete system D ( N ) of representatives of Manin’s symbols over Q , which was initially given by Shimura. Then, we establish a bijection between D ( M ) ×D ( N ) and D ( MN ) for ( M,N ) =1 , which reveals a recursive structure between Manin’s symbols of different levels. Based on Manin’s complete system Π ( N ) of representatives of cusps on X0 ( N ) and Cremona’s characterization of the equivalence between cusps, we establish a bijection between a subset C ( N ) of D ( N ) and Π ( N ) , and then establish a bijection between C ( M ) ×C ( N ) and C ( MN ) for ( M,N ) =1 . We also provide a recursive structure for elliptical points on X0 ( N ) . Based on these recursive structures, we obtain recursive algorithms for constructing Manin symbols over Q , cusps, and elliptical points on X0 ( N ) . This may give rise to more efficient algorithms for modular elliptic curves. As direct corollaries of these recursive structures, we present a recursive version of the genus formula and prove constructively formulas of the numbers of D ( N ) , cusps, and elliptic points on X0 ( N ) .

This study addresses the passive target tracking problem using the range difference (RD) information measured by sea-skimming anti-ship missiles. Apart from the conventional non-linear filtering approaches, the RD-based passive target tracking problem is newly formulated within the framework of the recently developed non-conservative robust Kalman filter (NCRKF). By applying NCRKF, the authors are able to cope with the performance degradation in the process of adopting linear measurement model as well as to determine the desirable missile formation in view of target tracking performance. Moreover, the proposed filter might be adequate for the real-time multiple-missile applications for its linear recursive structure. Through the simulations for a typical missile homing scenario, it is shown that the proposed scheme could provide faster convergence rate and better target tracking performance than the previous methods.