Search Results (2,717)

In this article, we consider a class of nonhomogeneous neutral linear systems with Caputo-type fractional derivatives, having incommensurate orders of differentiation and distributed delays. The main goal is to investigate the influence of the Perron condition on the stability properties of the corresponding homogeneous neutral linear system, when the nonhomogeneous system satisfies this condition. We first prove that, for any partially absolutely continuous initial functions, the investigated nonhomogeneous system has a unique global absolutely continuous solution. Furthermore, if the nonhomogeneous system satisfies the Perron condition, we establish that the fundamental and the extended fundamental matrices of the corresponding homogeneous system are uniformly bounded under certain boundedness conditions, which are also used in the classical case for systems with first-order derivatives. This uniform boundedness implies that the zero solution of the homogeneous system is uniformly stable. Finally, it is proved that the extended fundamental matrix Q(t,s) tends to zero as t → ∞, thereby demonstrating that the zero solution of the investigated homogeneous system is uniformly asymptotically stable. Full article

Building construction sites are dynamic systems in which safety decisions interact with time, cost, productivity, equipment movement, and spatial constraints. This systematic review examines how building-construction-stage safety is represented in optimization-based and optimization-linked decision studies published between 1 January 2016 and 30 June 2026. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-informed workflow used searches of the Web of Science Core Collection and IEEE Xplore, supplemented by Google Scholar and backward-citation checks. Seventy-nine studies met the core inclusion criterion, which required safety to appear as a quantified objective, constraint, evaluation metric, decision criterion, or prediction target. Studies were classified by problem type, safety role, method family, digital integration, and validation evidence. The synthesis identifies a problem-type-dependent formulation pattern: site-layout and scheduling studies mainly optimize safety or exposure objectives; crane/lifting studies distribute safety across constraints, objectives, and decision criteria; risk-decision studies use criteria or metrics; and prediction studies tune models whose targets are safety or risk outcomes. The core corpus is concentrated in site-layout and crane/lifting studies, whereas temporary works, monitoring-to-intervention, and construction-stage emergency response are less often formulated as optimization problems. Strict real-site/field evidence was identified in 7 of 79 studies, with an upper sensitivity bound of 11. Future research should prioritize transparent metrics, benchmarks, field validation, and closed-loop workflows. Full article

Hybrid marine energy systems that integrate wave and current technologies can improve resource complementarity and spatial utilization. However, the ranking stability of selected hybrid concepts under changes in criterion weights, score assumptions, and multi-criteria decision analysis (MCDA) methods requires further examination. This study extends an existing two-stage concept-selection procedure by evaluating four shortlisted wave energy converter–hydrokinetic turbine configurations using stochastic weight-space sampling, criterion-wise weight sensitivity, cross-method consistency, and bounded score-perturbation analyses. A fixed normalized decision matrix is first evaluated using the Simple Additive Weighting (SAW) method across three sets of 10,000 criterion-weight scenarios generated using normalized-uniform, Dirichlet α = 1, and Dirichlet α = 0.5 distributions. The same scenarios are then evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with ranking consistency quantified using Spearman’s rank correlation and complete-ranking agreement. Score sensitivity is subsequently examined through bounded one-point perturbations of the Stage 2 criterion scores, with SAW and TOPSIS recalculated under equal criterion weights to identify dominance-breaking and rank-reversal conditions. The oscillating water column–Savonius configuration, W1H3, remains first-ranked under all three sampled weight distributions because its normalized criterion scores are equal to or higher than those of every competing configuration across all five criteria. Criterion-wise sensitivity analysis shows that W1H3 is not outranked over the investigated weight range, although it ties with the point absorber–Savonius configuration, W2H3, when the full weight is assigned to mooring synergy or control compatibility. A crossover between W2H3 and the oscillating water column–hybrid Savonius–Darrieus configuration, W1H4, occurs at a co-location-feasibility weight of 0.384615. Across the three weight-sampling distributions, SAW and TOPSIS achieve complete-ranking agreement of 65.91–87.08%, with mean Spearman rank correlations of 0.9318–0.9742; the remaining differences are confined to the ordering of W2H3 and W1H4. Bounded score perturbations show that single one-point score change is sufficient to break the dominance of W1H3 over W2H3, whereas four changes are required for W2H3 to attain a unique first rank under both methods. The results demonstrate that W1H3 is rank-stable under the investigated weight and method variations for the adopted decision matrix, while the score-perturbation analysis identifies the bounded score changes under which the preferred ranking may change. Full article

Internet of Things(IoT)/edge intrusion-detection systems operate on distributed traffic and system-state data whose distributions vary across gateways, services, and attack conditions. We study a server-assisted federated setting in which a teacher reference is fitted from a permitted server-accessible training pool and explicitly distinguish this simulation assumption from fully decentralized deployment. The proposed framework evaluates progressive local training through boundary stabilization, confidence-weighted decision distillation, representation alignment, and validation-quality-aware aggregation. The evaluation uses a leakage-controlled protocol: server and client validation subsets are held out before federated training, update quality and early stopping use validation data only, and the final-test split is evaluated once. Results on NSL-KDD, CIC-IDS2017, Edge-IIoTset, and the ToN-IoT network dataset show competitive primary performance and stronger robustness in several severe label-skew settings. On the Telemetry of Things(ToN-IoT) with Dirichlet alpha = 0.1, the proposed method achieves 91.46 ± 5.54 F1, compared with 53.73 ± 49.00 for FedAvg and 53.77 ± 48.92 for FedProx. The results do not establish universal superiority or a universally optimal stage order: competing methods remain stronger in selected stable and attack-shift settings. The framework is therefore presented as a bounded, server-assisted robustness-oriented training strategy for heterogeneous IoT/edge intrusion detection. Full article

Income inequality remains a persistent structural challenge in advanced economies, with important consequences for economic stability, social cohesion, and the effectiveness of public policy. This study investigates the determinants of income inequality in the United Kingdom over the period 1980–2021 by combining ARDL, FMOLS, and NARDL approaches to assess long-run equilibrium relationships and potential nonlinear distributional effects. The ARDL bounds test confirms the presence of a stable long-run cointegrating relationship between the Gini index and its socioeconomic determinants, indicating that fiscal conditions, labour market dynamics, economic development, and income structure move together over time. The FMOLS estimates show that GDP per capita and unemployment are positively associated with inequality, whereas public social spending, government expenditure on education, tax revenue, and the income share of the fourth quintile are negatively related to the Gini index in the long run. These results underline the importance of redistribution, human capital investment, and labour market stability in moderating income disparities. The NARDL estimates suggest directionally different responses to positive and negative changes in the upper-middle-income share. Positive changes are associated with lower inequality (−0.931), while negative changes are associated with higher inequality (0.232); however, neither effect is statistically significant. The Wald test likewise fails to reject long-run symmetry (χ²(1) = 0.212, p = 0.646). Overall, the findings support a stable long-run inequality framework while indicating only limited evidence of asymmetric distributional adjustment in the United Kingdom. Full article

High Bandwidth Memory couples many DRAM dies to a host through independent channels, and a memory controller presents each channel to the workload as either available or isolated. This paper takes that binary service interface as the modeling primitive and builds a closed-form framework for evaluating stack and system behavior on top of it. Each die is treated as a binary component whose reliability is composed from DRAM, through-silicon via and micro-bump contributions, with the via bundle itself modeled as a threshold subsystem; the dies are then aggregated as a threshold structure over the stack, and stacks are aggregated over the system. The result is an evaluation whose cost grows linearly rather than exponentially with the number of dies, and which yields not only reliability but the full distribution of delivered bandwidth, its moments, and the sensitivity of system availability to each component. Three design questions are answered directly: where to direct reliability investment, how many stacks to provision for a given availability target, and which bandwidth threshold minimizes cost when bandwidth and reliability requirements are imposed together. The approximations the framework makes are bounded rather than assumed. A three-state baseline quantifies the error introduced by the binary representation and shows it is governed by a single measurable quantity, and distribution-free inequalities bound the effect of correlation among component failure mechanisms, which proves negligible in the regime where HBM parts are qualified. Application to a representative stack identifies DRAM cell reliability as the dominant bottleneck and shows that a single spare via per bundle is sufficient at typical defect rates. Full article

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

Trade-finance instruments may support export performance by alleviating financing constraints and reducing international payment risks. This study examines the short- and long-run relationships between Turkish Eximbank instruments and Turkey’s aggregate exports using annual data for 2001–2024. Three nested log-linear Autoregressive Distributed Lag (ARDL) models jointly incorporate short-term credits, medium- and long-term credits, insured shipment values and the real effective exchange rate. Intermediate-goods imports and external demand are added in the second model, while tariff and Russia–Ukraine war-period controls are introduced in the third. ADF, PP and KPSS tests examine the integration properties of the continuous variables and ARDL bounds tests evaluate the existence of long-run relationships. The bounds-test results support a relationship with levels in all three specifications, while the negative and significant error-correction coefficients indicate convergence toward equilibrium. Insured shipments have a positive and statistically significant short-run coefficient in every model and a positive long-run coefficient in the benchmark model. However, their long-run significance disappears after imported-input conditions and external demand are controlled for. Neither credit category exhibits a statistically significant relationship with exports. Intermediate-goods imports are positively associated with exports in both horizons, while external demand has a positive long-run relationship. The REER generally has the expected negative sign, but its significance is specification dependent. The findings identify insurance as the strongest short-run correlate of exports while showing that its long-run aggregate relationship is sensitive to macroeconomic conditions. Recipient-level data are required to establish causal program effects. Full article

To optimize expensive black-box functions over networks, one of the most dominant frameworks is distributed Bayesian optimization (DBO), where local information can be exchanged among agents. However, DBO suffers from low evaluation efficiency due to limited data in the initial stage and the high cost of evaluations among multiple objectives. To tackle these obstacles, we propose a prior-informed multi-task collaborative Bayesian optimization (PMCBO) algorithm over networks. Concretely, PMCBO integrates expert prior knowledge about the location of optimum into the distributed multi-task Bayesian optimization framework to reduce the cost of evaluations. Meanwhile, PMCBO combines multi-task Bayesian optimization with a collaborative mechanism to improve the evaluation efficiency. Furthermore, we rigorously prove that the cumulative regret bound of PMCBO can achieve sub-linearly with high probability, where the acquisition functions employ expected improvement (EI) and upper-confidence bound (UCB) based on a Gaussian process surrogate. Finally, we implement various experiments to evaluate the effectiveness of PMCBO. The experimental results demonstrate that PMCBO can achieve state-of-the-art performance and benefit all clients based on diverse benchmarks and prior characteristics. Full article

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional variational autoencoder (cVAE), a class-conditional latent denoising diffusion probabilistic model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits, implemented and evaluated using a classical quantum-circuit simulator, to augment minority arrhythmia classes, and present results based on the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over ten independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification. To our knowledge, the proposed QLR module is the first use of a parameterized quantum circuit as a distributional refiner within a generative augmentation pipeline. While its performance is comparable to that of the classical latent diffusion framework under the present experimental conditions, the proposed approach demonstrates the feasibility of integrating quantum latent operators into generative medical AI pipelines and provides a foundation for future investigations on quantum-enhanced representation learning and data augmentation. Full article

During software testing, memory errors in C/C++ can silently corrupt the program state. Address-based memory sanitizers, while offering practical performance and compatibility, are fundamentally unable to distinguish spatial errors that skip redzones or temporal errors that occur after memory reuse. Moreover, their reuse-delay quarantine mechanisms impose significant space and time overhead. We propose a taxonomy of memory sanitizers based on validity encoding and violation detection. Guided by this taxonomy, we introduce fused metadata, a single 8-byte word that encodes an object’s end address and a 6-bit identity tag. MixSan, a prototype built on RangeSanitizer (RSan), stores the same identity tag in pointer high bits through Intel Linear Address Masking (LAM) U57 and validates both the tag and the spatial bound with a unified 3-ALU-op check. On SPEC CPU2006, MixSan incurs a 1.58× geomean runtime overhead, comparable to RSan’s 1.61× overhead. On the Larson allocator benchmark, MixSan and the uninstrumented tcmalloc both scale with thread count, whereas RSan throughput falls; MixSan’s multi-thread plateau is more than 30× that of RSan. In a custom 97-test suite targeting post-reuse temporal errors and redzone-skipping spatial errors, MixSan’s mean single-run detection rate is 98.41% over ${10}^4$ independent executions per program, matching the theoretical $63/64$ rate given a uniform 6-bit tag distribution, whereas ASan and RSan do not detect these constructed cases. Full article

This paper investigates distributed optimization-assisted output consensus for nonlinear multi-agent systems with mutually incommensurate Caputo orders, unavailable velocity-like states, bounded disturbances, measurement noise, and actuator saturation. A mixed-power exact penalty flow generates practical optimal references from local costs and intermittent neighbor broadcasts. The penalty gain and a smoothing bias bound are determined from a public interval, topology information, and certified local gradient data without prior knowledge of the aggregate optimizer. An autonomous decaying threshold provides event-triggered communication, an initial condition-independent fixed-time practical certificate for the integer-order optimizer, and exclusion of finite-time event accumulation. The physical layer is analyzed with established Caputo quadratic inequalities and agentwise Mittag–Leffler comparison. Fractional reference and command filters, a composite observer, and two-gain anti-saturation compensation form the output feedback controller, while the physical result is formulated as a finite-horizon regional verification certificate. Numerical studies include same-model and communication budget comparisons, a recent method-inspired optimizer benchmark, certificate tightening, and robustness tests for initialization, the fractional order, measurement noise, and the integration step size. Full article

Accurate detection of tomato flowering stages is important for greenhouse crop management and automated pollination, but it remains challenging because tomato flowers are small, densely distributed, frequently occluded, and visually similar across adjacent developmental stages. To address these problems, this study proposes YOLO11-FAL, an improved object detection model based on YOLO11 for tomato flowering stage detection in greenhouse environments. The original C3k2 modules are replaced with C3k2_Faster to reduce redundant spatial computation and enhance local structural feature representation. An Attentional Scale Sequence Fusion (ASF) structure is introduced into the neck network to strengthen multi-scale feature interaction, and a Localization Quality Estimation Head (LQEHead) is incorporated to recalibrate classification confidence using bounding-box distribution information. Experiments were conducted on a self-constructed tomato flower dataset containing Bud, Anthesis, and Post-anthesis stages under varying illumination conditions. YOLO11-FAL achieved 92.00% Precision, 88.88% mAP@0.5, 62.21% mAP@0.75, and 56.72% mAP@0.5:0.95. The model contained 2.384 M parameters and achieved 49.58 FPS on an NVIDIA Jetson AGX Orin (NVIDIA Corporation, Santa Clara, CA, USA) using TensorRT FP16, with a measured onboard power consumption of 11.07 W. These results indicate that YOLO11-FAL provides a practical and efficient visual perception approach for greenhouse tomato flowering stage detection. Full article

The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions. Full article

Multi-server private information retrieval (PIR) based on function secret sharing (FSS) has emerged as a prominent paradigm for achieving sublinear communication. However, standard FSS constructions require full server participation, making them highly vulnerable to single-node fail-stop faults. Existing fault-tolerant schemes mitigate this but inevitably inflate the response overhead to scale with the database size N (e.g., $O\left(\sqrt{N}\right)$). To overcome this limitation, we propose a fault-tolerant PIR (FT-PIR) protocol based on a newly designed $(t,p)$-threshold distributed point function (FT-DPF). By introducing a hierarchical recursive patching mechanism, our scheme transforms rigid all-party evaluations into flexible t-out-of-p reconstructions. This architecture completely decouples the response communication from N and ensures efficient client-side reconstruction via lightweight XOR aggregations. Formal analysis proves that our stateless protocol guarantees $(t-1)$-computational privacy under the semi-honest model. Theoretical analysis demonstrates that the proposed FT-PIR achieves a response complexity bounded by $\mathcal{O}\left({\mathcal{F}}_{\mathrm{maxlevel}}(t,p)\right)$. Comprehensive experimental evaluations confirm that our implementation significantly reduces practical communication and computation overheads, outperforming the state-of-the-art scheme. Full article