Structural optimization plays a crucial role in enhancing the performance of magnetic actuators. Traditional design approaches, such as parametric scanning, are limited by their reliance on empirical geometries. Meanwhile, widely used topology optimization techniques—including the Solid Isotropic Material with Penalization (SIMP) method and level-set methods—often encounter difficulties such as a large number of design variables, high computational cost, and unclear structural boundaries. To overcome these limitations, this paper proposes a novel gradient-free topology optimization method based on superellipses for designing magnetic actuator yokes. The proposed approach offers three key benefits: (1) It requires very few design variables, with each superellipse described by only seven parameters, thereby reducing the dimensionality of the design space and simplifying the optimization problem. (2) It yields clear and smooth structural boundaries without the need for post-processing. (3) It operates without gradient information, employing stochastic algorithms such as genetic algorithms that rely solely on objective function evaluations. A case study on yoke optimization demonstrates that our method achieves magnetic force output comparable to or better than the SIMP method, but with significantly fewer variables and a simpler implementation. This work provides an efficient and new tool for the conceptual design of magnetic actuators and related electromagnetic devices.

While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits, where the reward depends on the number of rounds elapsed since the last time an arm was pulled. We propose a new reinforcement learning (RL) algorithm tailored to this setting, named the State-Separated SARSA (SS-SARSA) algorithm, which treats the elapsed rounds as states. The SS-SARSA algorithm achieves efficient learning by reducing the number of state combinations required for Q-learning/SARSA, which often suffers from combinatorial explosion for large-scale RL problems. Additionally, it makes minimal assumptions about the reward structure and has lower computational complexity. Furthermore, we prove asymptotic convergence to an optimal policy under mild assumptions. Simulation studies demonstrate the superior performance of our algorithm across various settings.

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster.

While most current work on interpretable models has centered on post hoc explainability of individual predictive models, the structure of the hypothesis space from which such models are drawn has been largely neglected. This paper proposes a contrasting perspective in which explainability is treated not as an attribute of a single solution but as a structural property of the model space. By combining Rashomon set analysis with Pareto-based performance–model complexity trade-offs, we formulate a computational framework for identifying near-optimal and structurally simple models. A performance–model complexity trade-off landscape is constructed by systematically generating models under controlled complexity bounds and extracting Pareto-optimal solutions. The results show that explainability can emerge as a regional property of hypothesis spaces in which multiple interpretable models achieve competitive predictive performance. This perspective supports the identification of robust and auditable predictive solutions and complements traditional explainability approaches centered on isolated models. Cross-dataset replication on Wine (UCI) and Vehicle (UCI) confirms the generalizability of these findings.

We derive the eigenvalue bounds for symmetric block-tridiagonal multiple saddle-point systems preconditioned with the symmetric positive definite (SPD) preconditioner proposed by J. Pearson and A. Potschka in 2024 and further studied by L. Bergamaschi and coauthors, and for double saddle-point problems with inexact Schur complement matrices. The analysis applies to an arbitrary number of blocks. We validate the proposed estimates with both synthetic and realistic test problems, and show the good performance of the proposed preconditioner under the condition that the Schur complements are accurately approximated.

Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions. Most BO methods rely on Gaussian process (GP) surrogates, which perform well in continuous domains but encounter difficulties when decision variables include categorical or mixed discrete–continuous components. In particular, GP-based approaches typically require ad hoc numerical encodings of categorical variables that may fail to capture the structure of discrete decision spaces. In this work, we propose MNL-BO (Multinomial Logit Bayesian Optimization), a preference-based Bayesian optimization framework that replaces the GP surrogate with a multinomial logit (MNL) model trained from pairwise preference comparisons. The resulting surrogate provides a natural and interpretable representation of categorical alternatives while allowing continuous, discrete, and categorical variables to be handled within a unified optimization framework. The predictive utility estimates and uncertainty indicators generated by the MNL model are employed to formulate acquisition functions that reconcile exploration with exploitation. The proposed methodology is evaluated on three progressively complex optimization challenges: a purely categorical benchmark, a combinatorial Traveling Salesman problem, and a constrained mixed-variable engineering design problem concerning material selection in pressure vessel optimization. Multi-run tests provide consistent advantages over random search and exhibit stable convergence behavior across diverse random initializations. In addition to heuristic baselines such as local search and classical metaheuristics, we also compare against tree-based Bayesian optimization baselines inspired by the Sequential Model-based Algorithm Configuration (SMAC) framework. The results indicate that the proposed MNL-BO method achieves competitive performance under comparable evaluation budgets while providing an interpretable probabilistic surrogate for categorical decision spaces. These findings suggest that preference-based surrogate modeling provides a practical and flexible alternative for Bayesian optimization in categorical and mixed-variable optimization problems.

Multiple string matching is a fundamental operation in real-time analytics, cybersecurity, bioinformatics, and large-scale information retrieval. Nevertheless, existing approaches continue to face inherent trade-offs among preprocessing efficiency, verification overhead, and support for dynamic pattern updates, particularly in large and continuously evolving environments. This paper presents MMIVL, a high-performance algorithm founded on the multi-character inverted list (m-CIVL), a unified and inherently dynamic indexing framework for pattern management. By integrating positional information, termination semantics, and pattern associations within a single structure, m-CIVL enables direct matching without requiring a separate verification stage. MMIVL achieves a preprocessing complexity of O(|P|/s), a search complexity of O(|T| + nocc), and an update complexity of O(|p|/s), where s denotes the segment length. Extensive experiments on synthetic and real-world datasets demonstrate that MMIVL consistently outperforms representative baselines, with especially strong gains in large-scale scenarios, while maintaining stable performance and favorable memory efficiency. Overall, these results establish m-CIVL as an effective, scalable, and practically viable solution that unifies efficient preprocessing, high-throughput searching, and dynamic update capability for modern multiple string-matching applications.

Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a 100.0% query execution rate and an 87.3% reasoning accuracy, outperforming the SOTA baseline by 45.2% in terms of absolute accuracy.

Blockchain systems generate massive volumes of transactional data, yet most existing analytical approaches rely on query-based retrieval mechanisms that treat transactions as isolated records. In this paper, a trajectory-based framework for blockchain analysis is introduced where user activity is modeled as temporally ordered behavioral patterns. Four types of blockchain trajectories are formally defined: miner reward trajectories, sender value-and-fee trajectories, receiver value trajectories, and sender–receiver interaction trajectories. Unlike traditional query frameworks, trajectories are treated as first-class analytical objects, explicitly constructed and returned as outputs, thereby enabling structured temporal reasoning over blockchain behavior. To demonstrate the practicality of the approach, the proposed trajectory functions are implemented in Python 3.12 and experiments are conducted using real data from the Ethereum blockchain. Compared with conventional query-based approaches that return isolated transactions, the experimental results show that the proposed trajectory-based framework enables a more systematic identification of temporal behavioral patterns, including persistent miner dominance, recurrent zero-value interactions, sender–receiver role reversals and sender dominance by sending the highest values across several periods. The results show that trajectory-based modeling provides a systematic lens for uncovering temporal and structural regularities that are not readily observable through conventional query techniques. This work establishes a formal foundation for behavioral blockchain analytics and opens new research directions in centralization measurement, predictive modeling, and trajectory similarity analysis.

Most neural architectures model time as a one-dimensional real-valued variable, constraining temporal reasoning to sequential propagation along a single axis. We introduce Complex-Time Neural Networks (CTNN), a new class of architectures in which temporal coordinates are elements of the complex plane T = t + iτ ∈ ℂ, where Re(T) preserves chronological ordering and Im(T) encodes an orthogonal experiential dimension. Within this geometry, Im(T) < 0 defines a memory domain enabling retrospective retrieval, Im(T) = 0 corresponds to present-moment computation, and Im(T) > 0 defines an imagination domain for prospective projection. We prove the Expressive Separation Theorem (Theorem 1), establishing that, within the temporally coupled function class GTCP and under explicit Assumptions A1–A4 (in particular the bounded projection Assumption A3), CTNN accesses temporally coupled functions at O(1) cost with respect to temporal distance Δ1, Δ2, while real-time architectures incur Ω(Δ1 + Δ2) sequential steps. For layered compositions, this yields an exponential composition gap within GTCP under A1–A4. These advantages hold under the stated assumptions and may not directly generalize to broader function classes or large-scale settings where A3 cannot be maintained. Therefore, Theorem 1 provides a formal separation result for GTCP, while CTNN more broadly defines a geometric framework for temporal computation. As the first concrete instantiation of this framework, we develop Complex-Time Convolutional Neural Networks (CTCNN). CTCNN achieves state-of-the-art performance on Something-Something V2 (70.2 ± 0.4%, +1.1% over VideoMAE v2, p < 0.01), strong performance on Kinetics-400 (78.4 ± 0.3%), and substantial gains on Long Range Arena Path-X (87.3% vs. 79.6%, +7.7%), using 3.4× fewer parameters than VideoMAE v2. Learnable angular parameters α and β provide computationally interpretable parameters related to memory-access span and prospection breadth, with values varying systematically across task families.