This work presents a new graphical method for plotting the residue loci of linear time-invariant control systems as the gain varies. While the classical root locus shows the trajectory of the closed-loop poles, it does not reveal the relative weight of each pole in the system response. The residues of the partial-fraction expansion provide precisely this information, since they determine the magnitude and phase of each modal component. The proposed residue-locus method establishes a set of construction rules analogous to those of Evans’ root locus, allowing the loci to be sketched and interpreted directly in the s-plane. The proposed construction rules are developed analytically and illustrated using representative low-order systems with different pole-zero configurations. For the second-order case with two open-loop poles and no zeros, the residues asymptotically satisfy A1≃−jK/2 and A2≃jK/2, with residue-locus asymptotes at 90∘ and 270∘. The residue-locus method, used together with the root-locus method, provides a unified analytical technique for control-system analysis and design, since it simultaneously describes how the location and the contribution of each closed-loop pole evolve with the gain. The method is intended as a complementary tool for studying transient response, modal dominance, and the relative activation of system modes.
Against the background of global carbon neutrality goals and urban ecological transformation, traditional two-dimensional green-space indicators cannot fully reflect spatial–ecological benefits, and conventional 3D green-space calculation and landscape maintenance methods suffer from low accuracy and low efficiency. This paper proposes a theoretical technical framework combining multimodal remote sensing data, LiDAR point clouds and domain large language models for urban green-space scenarios. Firstly, a multimodal feature fusion strategy is constructed to adapt to complex urban environments, and a hierarchical calculation workflow for individual trees and regional 3D green-space is designed. Secondly, a full-life-cycle carbon accounting system for landscape engineering is established, which clarifies the accounting boundaries and quantitative methods of carbon sources and carbon sinks. Thirdly, a human–computer collaborative decision framework is proposed to realise intelligent recommendations for low-carbon maintenance schemes. This study focuses on theoretical framework construction and method exploration, and verifies the technical feasibility through simulation analysis. Different from existing independent research on green-space calculation or carbon accounting, this work integrates the two tasks and innovates a multimodal model application mode for landscape industry. This study focuses on theoretical framework construction and methodological exploration; feasibility assessment is only carried out via simulation deduction, rather than real-world field validation.
This paper examines the robustness of circular neighbor-balanced designs (CNBDs), a significant issue in experimental design when observations are missing and errors are correlated. Treatments may affect not only the designated unit but also its adjacent units, hence complicating the estimation of treatment effects in assessments utilizing adjoining plots. This paper proposes to measure the robustness of CNBDs by analyzing two models, one with and one without block effects, under four correlation structures: MA(1), NN, AR(1), and ARMA(1,1). It considers the robustness of these designs in terms of A- and D-efficiencies for direct and neighboring effects. We observed that designs with lower correlations between observations also yield smaller efficiency losses when data is missing, resulting in more robust designs. Some neighbor designs offer strong performance with missing observations and correlated errors, minimizing efficiency losses. This study contributes to experimental design for CNBDs under challenging conditions for missing observations and correlated data—that is significant.
Internet of Things (IoT) traffic shifts as device communication patterns evolve, potentially reducing the reliability of static intrusion detectors. Existing drift monitors detect distributional changes but do not inherently distinguish shifts requiring model adaptation from those warranting security alerts. This paper proposes an unsupervised framework for encrypted IoT traffic using a unidirectional gated recurrent unit (GRU) autoencoder trained on benign flows. Two Page–Hinkley (PH) statistics monitor reconstruction error: PH-UP triggers low-rank adaptation (LoRA), while PH-DOWN raises an alert without adaptation. Following adaptation, the detection threshold is recalibrated using an unlabelled window. On the CIC IoT-DIAD 2024 dataset, the static detector achieved an F1 score of 0.9518 and a false-positive rate (FPR) of 1.11%. Under synthetic covariate shift, its F1 score fell to 0.7241 (FPR: 55.7%), while Rank-8 LoRA achieved 0.9550 (FPR: 1.45%). Across five random seeds, the adapted system achieved an F1 score of 0.9438 ± 0.0207, but performance on the original traffic fell to 0.9239 ± 0.0415. PH-UP detected the simulated shift, while PH-DOWN raised an alert on a simulated brute-force error window. Evaluation was limited to one dataset, constructed error streams, and cloud central processing unit (CPU) experiments; adaptive-evasion attacks and edge hardware remain unevaluated.
This study compares different methane chemical combustion mechanisms with varying levels of complexity. The mechanisms studied include the widely used and accepted global mechanism WD2 with 2 reactions, and skeletal and detailed mechanisms SG35, Z42, DRM22, and GRI-3.0 with 35, 42, 104, and 325 reactions, respectively. The computational cost of the mechanisms was compared, with the results showing that the global mechanism WD2 was slower and converged more slowly compared to the skeletal mechanisms of similar size. The skeletal and detailed mechanisms showed increased cost with a power relationship to the number of reactions. The mechanisms were compared to experimental data to determine accuracy, with the Z42 mechanism found to be the best compromise between computational cost and accuracy, while the global mechanism WD2 had reduced accuracy and increased computational cost. The SG35 mechanism performed well in terms of speed, but with reduced accuracy. The study concludes that for more detailed information, postprocessing with GRI-3.0 is required.
