Search Results (125)

Modeling under uncertainty remains a fundamental challenge in engineering and nonlinear system identification, particularly when both interpretability and computational efficiency are required. Interval type-2 adaptive neuro-fuzzy inference systems (IT2ANFIS) have demonstrated strong capabilities in handling uncertainty; however, the considered approaches typically introduce uncertainty either in the center or in the width of membership functions, limiting their representational flexibility. To address this problem, this paper proposes a novel IT2ANFIS model that simultaneously incorporates uncertainty in both the centers and widths of Gaussian membership functions, enabling a more expressive yet compact representation of uncertainty. First, a formulation of the proposed membership function is developed. This allows the application of gradient-based learning with explicitly derived update rules for both premise and consequent parameters. Second, the proposed approach avoids explicit type-reduction procedures by directly aggregating lower and upper firing strengths. This eliminates the need for computationally intensive iterative algorithms and improves inference efficiency. Third, a local first-order gradient analysis is used to define a practical reference for learning-rate scaling. In addition, multiple learning-rate strategies, including fixed, switching, and moment-based strategies, are investigated to evaluate their influence on convergence and model performance. Furthermore, the proposed approach is validated through benchmark systems and experimental evaluation on a DC motor system. The results demonstrate improved modeling accuracy and robustness compared to conventional ANFIS-based approaches. The proposed framework provides a practical and computationally efficient approach to uncertainty-aware nonlinear system modeling and related engineering applications. Full article

Road safety authorities each hold accident records that, when combined, could train stronger severity prediction models, yet these records can be neither centralized, for privacy and governance reasons, nor naively merged, because jurisdictions encode severity under incompatible ontologies. This paper recasts that impasse as an information fusion problem and fuses model updates from multiple road safety data silos into a single severity model while every raw record stays at its source. Three components act together: model-level fusion under differential privacy, a reliability-weighted aggregation rule that trusts each source based on its measured quality rather than its size, and a per-source centered logit adjustment layer that reconciles mismatched label priors without double-correcting the shared class imbalance. The primary evaluation is a clean cross-silo setting: five United States state datasets (US Accidents) that share one severity ontology but are held by distinct custodians. Here, over five seeds with 95% confidence intervals, effect sizes, and Holm–Bonferroni correction, private fusion recovers most of a centralized upper bound while keeping data local ($0.599$ balanced accuracy versus $0.624$ when centralized and $0.544$ when local-only), and reliability-weighted fusion attains the highest macro F1 of all methods ($0.567$). Reliability weighting yields a small but consistent robustness advantage under privacy noise; in leave-one-state-out transfer, its improvement over uniform averaging is large on every held-out state but, after Holm correction, survives in two out of five cases. Crucially, we also report a boundary honestly; a United Kingdom source that encodes injury severity—an ontologically different target from the US traffic impact scale—is used as a deliberate out-of-ontology transfer stress test, and a transfer to it collapses to chance ($0.50$, $p=0.62$). Two further honest results are reported: alignment raises accuracy everywhere but does not close the across-source gap, and a membership inference attack reveals no measurable leakage for differential privacy to remove. Full article

As a meta-heuristic inspired by human student learning behaviors, the original Student Psychology-Based Optimization (SPBO) suffers from insufficient exploitation of historical population records, simplistic individual interaction patterns and high risk of falling into local optima. This work develops an enhanced SPBO (ESPBO) embedding three dedicated learning mechanisms. The adaptive knowledge-accumulation learning component imports personal historical best, global elite and population-mean information into position update formulas to sustain coherent search trajectories and enhance convergence precision. The multi-level peer collaborative learning module categorizes agents into excellent, intermediate and under-performing groups based on fitness values. Customized learning rules are configured for each group to enable diverse information sharing: elite individuals expand promising search regions, medium-level agents learn from counterparts, and inferior individuals move toward high-quality candidates. The progressive examination feedback component dynamically modulates search intensity by measuring the fitness improvement of each individual, so as to better balance global exploration and local exploitation. Comparative numerical experiments are carried out on CEC2017 and CEC2022 benchmark test suites against multiple advanced meta-heuristic algorithms. Results indicate that ESPBO exhibits outstanding accuracy and robustness on unimodal, multimodal, hybrid and composite test functions. To explore its real-world applicability, ESPBO is adopted for mobile-robot path-planning simulations under multi-scale grid maps. Simulation results from 20 × 20, 40 × 40 and 60 × 60 environments illustrate that ESPBO stably produces collision-free trajectories, outperforming comparative algorithms in path length, smoothness and safety performance. It is demonstrated that the three embedded learning mechanisms substantially strengthen the optimization capacity of vanilla SPBO, and ESPBO possesses considerable application potential for numerical optimization as well as mobile robot path-planning scenarios. Full article

Background: Associative learning, the process of binding stimuli, responses, and outcomes, is critical for behavioral adaptation. While event-related potentials (ERPs) like the P300 effectively index the cognitive effort and context updating required during trial-and-error learning, the precise cortical generators of these signals and their shift across development remain difficult to isolate due to the electroencephalography (EEG) inverse problem. The aim of the study was to examine the differences in P300 localization depending on the associative learning task stage and participants’ age. Methods: A total of 148 participants (aged 7–21) completed a visual-motor associative learning task while undergoing 64-channel EEG recording. Source localization was performed over the 300–500 ms (P300) time window as a conjunction of results of four inversion algorithms at FWE-corrected p < 0.05: multiple sparse priors with greedy search (GS), independent and identically distributed (IID), low-resolution electromagnetic tomography (LOR), and empirical Bayes beamformer (EBB). Exploratory three-algorithm (IID, LOR, and EBB) conjunction analysis results were also reported. Results: A spatial convergence analysis of Early > Late-stage differential maps revealed a consensus across all four models for both cue Onset-locked (right inferior parietal lobule/angular gyrus) and Feedback-locked (occipital, temporal (including bilateral middle temporal gyrus), and orbitofrontal regions) activity. The exploratory three-algorithm analysis additionally implicated left middle temporal gyrus and angular gyrus for cue Onset-locked rule acquisition. No results were produced for age-related effect surviving either the four-algorithm or three-algorithm consensus criterion. Conclusions: This multi-algorithm consensus links active rule search to distributed cortical pattern, involving angular (for cue stimulus processing) and middle temporal gyri (for feedback processing), while no age-related changes in these areas have been demonstrated. Full article

Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonly exchange larger trainable states. This paper presents FedTIP, a prompt-level federated method for client-local few-shot adaptation of temporal interaction graphs. A centralized historical stage constructs a temporal knowledge bank whose encoder is frozen during downstream federation. Clients optimize interaction-conditioned prompts on local supervised events and transmit a compact prompt-side message. Prototype anchoring and a reliability rule based on support coverage and update magnitude combine heterogeneous client updates, while prompt-side proximal regularization and stage-scoped negative sampling preserve the temporal protocol. Experiments on Wikipedia, Reddit, and MOOC cover temporal node classification and transductive and inductive link prediction. FedTIP records the highest mean AUC-ROC in the nine reported task–dataset cells, with gains of 1.83–14.69 points over the strongest federated baseline in each cell. Its measured cumulative float32 uplink is 0.40 MiB per downstream task, 64.6–99.6% below the evaluated baselines. Full article

When a lithium-ion cell’s series resistance is underestimated, the pre-update terminal-voltage innovation contains the first-order term $e_k\approx -I_k{R}_{0,k}\delta b$. This term breaks conditional sign symmetry and creates an odd response under current reversal. We test that mechanism before using it as an activation rule. The operational null is a near-zero conditional innovation centre with weak innovation–current coupling; declared falsifiers are comparable coupling under the nominal model, the wrong correlation sign under positive resistance error, failure of charge/discharge polarity reversal, or negative-control activation approaching ohmic-mismatch activation. A persistence-confirmed gate combines normalised-innovation-squared exceedances, innovation–current compatibility, a positive local resistance correction, and five consecutive qualifying windows. Sixty settings were ranked on 10 calibration seeds and frozen before disjoint holdouts. From 1.0× to 2.0× R₀, the sign-imbalance index increased from 0.0040 to 0.0786, and |$corr\left(e,I\right)$| increased from 0.0658 to 0.7777. The 30-seed static holdout produced 0/30 nominal activations, 27/30 detections at 1.5×, and 30/30 detections at 2.0–3.0×. A disjoint linear-drift audit yielded 0/30 pre-ramp activations and 30/30 detections at a median 1.71× multiplier, reducing late-drift SOC RMSE from 3.129% to 0.895%. A signed-current audit confirmed the predicted polarity reversal. An estimator-unseen audit gave 30/30 ohmic detections but retained 2/30 current-linked non-ohmic and 1/30 current-bias triggers, so the rule is not a unique fault classifier. NASA and LG records remain diagnostics of fixed versus always-on adaptation; they do not validate the gate or independent absolute SOC. The contribution is a falsifiable residual-symmetry mechanism with an explicit evidence boundary, rather than a post hoc symmetry label or hardware claim. Full article

Log parsing converts raw system logs into structured templates and is a key preprocessing step for Artificial Intelligence for IT Operations (AIOps). Existing parsers face a practical trade-off: rule-based methods offer high throughput but limited adaptability across heterogeneous log sources, whereas Large Language Model (LLM)-based parsers achieve broader semantic coverage at the cost of inference latency, privacy exposure, and cloud dependency. This paper presents FlanBC, a log parsing framework that formulates template extraction as a BIO (Beginning, Inside, Outside) sequence-labeling task and integrates a Flan-T5 semantic encoder, Bidirectional Long Short-Term Memory (BiLSTM) layers for local sequential modeling, and a Conditional Random Field (CRF) decoder for structured label prediction. Log-specific preprocessing and a subword-to-token alignment mechanism adapt the general-purpose encoder to semi-structured log data. A layer-freezing strategy reduces the number of parameters updated during training. The framework supports local inference without external API dependency. Experiments on three benchmark datasets from LogHub (HDFS, BGL, OpenStack) under a supervised random-split setup evaluate parsing accuracy, training efficiency, statistical stability across random seeds, and component contributions. FlanBC achieves a Group Accuracy of 99.32% on HDFS and 98.47% on BGL, with an inference throughput of 700+ logs/s on a consumer-grade GPU. On OpenStack, performance is lower (GA = 92.54%), reflecting the challenge that diverse natural-language-like logs pose for compact encoder-based models. Under a stricter template-disjoint split that prevents template overlap between training and test sets, FlanBC achieves an average Group Accuracy of 91.14%, indicating that the model generalizes to unseen templates beyond in-distribution recognition. Ablation results indicate that the semantic encoder, BiLSTM module, and CRF decoder each contribute to prediction accuracy. These findings suggest that domain-adapted semantic encoders combined with structured decoding offer a practical accuracy–efficiency balance for log parsing in settings where local, cloud-free inference is preferred. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

Physics-informed neural networks (PINNs) approximate partial differential equations (PDEs) by enforcing governing equations and boundary conditions during training, but their accuracy depends on how collocation points are distributed and updated. We propose spatiotemporal compositional active sampling (STCAS), a reference-assisted offline configuration procedure that uses an analytic or high-accuracy numerical solution to rank complete three-stage sampling plans. It screens eight fixed rules, forms a task-specific shortlist, and evaluates bounded fixed, switched, and locally blended plans with independent selection sets and a composition guard. A safety-anchor decision retains the standard PINN unless the selected candidate is at least 5% better. Across five evaluations on 18 analytically specified two-dimensional Poisson tasks, this protocol improves 16 task means and ties two, reducing aggregate relative-$L_2$ error by 12.8% (hierarchical-bootstrap 95% interval $[6.78\%,19.32\%]$; one-sided paired Wilcoxon $p=2.19\times {10}^{-4}$). Against the confirmed fixed plan, aggregate error decreases by 8.2%. In comparison experiments designed for two transfer tasks and matched for main PINN training budgets, STCAS achieves the lowest aggregate mean reported error among the compared methods for both a steady convection–diffusion equation and a nonlinear time-dependent Burgers equation; its offline search cost is additional. Full article

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

Graph-based nonnegative matrix factorization (GNMF) has been widely used for dimensionality reduction and data clustering because it can preserve the intrinsic geometric structure of data. However, many existing GNMF-based methods still rely on full sample similarity graphs, resulting in high computational costs; moreover, their sparsity constraints usually treat all features uniformly, making it difficult to distinguish structurally important features from redundant or noisy ones. To address these issues, this paper proposes a feature-graph-guided adaptive Log-L_2,1 sparse NMF with anchor dual graphs under a logarithmic framework. Specifically, anchor-based representations are simultaneously constructed in the sample and feature spaces to approximate the corresponding full-scale graphs. The sample anchor graph preserves the local manifold structure among samples, whereas the feature anchor graph plays a dual role: it preserves structural relationships among features and provides degree information for generating the adaptive weights g_i of the row-wise Log-L_2,1 penalty imposed on the basis matrix U. Consequently, structurally well-connected features receive weaker sparsity penalties, while weakly connected and potentially redundant features are more strongly suppressed. In addition, a logarithmic reconstruction framework is introduced to reduce the influence of large residuals caused by noise and outliers. These mechanisms jointly integrate sample structure preservation, feature structure preservation, and feature-aware sparse learning within a unified graph-NMF model. To optimize the model, multiplicative update rules are derived, while the nonnegativity of the factor matrices is inherently preserved throughout the iterations. Extensive evaluations on several benchmark datasets demonstrate the effectiveness and robustness of the proposed method. Full article

High-resolution and regularly updatable land cover maps are essential for local-scale environmental monitoring, water resource management, and territorial governance, yet existing global and regional products fail to provide the spatial detail and thematic richness required for operational applications in complex Mediterranean landscapes. This study presents an operational workflow for high-resolution LULC mapping and its application to Sardinia for the reference year 2020, developed within the Sardinia Land Cover Mapping Project in collaboration with the Agenzia del Distretto Idrografico della Sardegna (ADIS). The workflow integrates multi-temporal SAR and multispectral satellite imagery with high-resolution ancillary geospatial vector datasets through a semi-automatic pipeline combining hierarchical cascade pixel-based classification, multi-resolution image segmentation, geometric overlay of infrastructure vector layers, and an iterative accuracy-driven reclassification cycle. The classification combines automated rule-based procedures, semi-automatic threshold-based methods, and expert photo-interpretation to address the high thematic and spatial complexity of the Sardinian landscape. The resulting map comprises 35 land cover classes at the third and selected fourth CORINE levels, with a minimum mapping unit of 400 m² and an overall weighted accuracy of 82.4%. Designed as a dynamic product updatable on an annual basis, it represents an operational tool for local environmental governance, spatial planning, and resource management. Full article

The recently proposed Animated Oat Optimization (AOO) algorithm exhibits competitive search behavior, but its fixed branching rules and limited use of inter-individual information may cause diversity loss and premature stagnation. This study proposes an Improved Animated Oat Optimization algorithm (IAOO) that integrates the original AOO operator, a hybrid DE/rand/1–DE/best/1 operator with a decreasing scale factor, and an elite neighborhood-directed local search within a feedback-driven framework. Strategy probabilities are updated according to normalized successful fitness gains, enabling search effort to adapt to the current optimization state. IAOO was evaluated through 30 independent runs on the CEC2017 (dim = 30/100), CEC2020, and CEC2022 suites and achieved Friedman mean ranks of 1.50, 1.37, 2.70, and 1.83, respectively, achieving competitive Friedman mean ranks among the compared algorithms and demonstrating statistically supported advantages on most benchmark suites. In three-dimensional UAV reference-path planning, IAOO reduced the mean path cost from 406.26 for AOO to 298.94, corresponding to a 26.4% reduction, while the standard deviation decreased from 67.01 to 40.73. Its runtime increased only from 26.99 s to 27.13 s. These results indicate that feedback-based operator cooperation improves solution quality and robustness with limited computational overhead. The current UAV model produces geometrically feasible and kinematically constrained reference paths; full six-degree-of-freedom tracking validation remains future work. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

Conventional end-to-end multipath transmission selects complete paths at the source or ingress, limiting the ability of intermediate nodes to adapt forwarding decisions to locally observed network conditions. Hop-by-hop multipath transmission provides greater flexibility by allowing each node to select a next-hop option among multiple next hops, but it must jointly address forwarding-loop prevention, path efficiency, and adaptive traffic scheduling. This paper proposes the Adaptive Hop-by-Hop Multipath Transmission Framework (AHM-TF), which integrates multipath structure construction with hop-by-hop traffic scheduling. Its Weighted-Priority Loop Pruning (WPLP) procedure constructs destination-oriented, loop-safe next-hop sets while controlling structural detours. Over the resulting forwarding structure, Exponentially Weighted Hop-by-Hop Scheduling (EWHS) combines structural path cost with locally observed congestion pressure to update node-local traffic-splitting probabilities. For a fixed self-loop-free directed topology with positive structural link costs, WPLP prevents persistent forwarding loops and preserves destination reachability under the previous-hop exclusion rule. Experiments on five network topologies show that WPLP achieves higher path availability than several existing structure-construction methods while maintaining low mean relative path stretch. Its path availability is broadly comparable to LFID, whereas its mean relative path stretch is lower on all evaluated topologies for $k\ge 3$. Transmission experiments on the GEANT topology show that AHM-TF performs comparably to the strongest multipath baselines under low load and provides clearer benefits as contention increases. Under the medium load, AHM-TF improves aggregate goodput by 14.87%, reduces the block loss ratio by 12.43 percentage points, and reduces mean one-way block delay by 2.37% relative to ECN-WRR. Under the high load, it retains the best results on these three metrics. These improvements are accompanied by higher block delay variation and block reordering, indicating reduced temporal and ordering stability in block delivery. Full article