Search Results (264)

This study compares the elastic modal characteristics of reinforced-concrete cantilever beams containing four and six 12 mm longitudinal reinforcing bars using three-dimensional finite element analysis (FEA) and an exploratory machine learning (ML) exercise. Beam geometry, material properties, bond assumptions, and boundary conditions were held constant. Mesh refinement from a 50 mm to a 25 mm nominal element size changed the fundamental natural frequency by 0.18%, satisfying the adopted 1% convergence criterion. The Fine mesh FEA fundamental frequency of 36.048 Hz differed by 1.49% from the Euler–Bernoulli transformed-section estimate of 35.52 Hz. Block Lanczos extraction provided the first six natural frequencies and corresponding normalised mode shapes. Increasing the longitudinal steel area from 452.39 to 678.58 mm² changed the calculated frequencies by −0.02% to +0.19%, while the two layouts exhibited similar mode-shape topology under the assumed intact, linear-elastic, perfectly bonded conditions. Four regression algorithms were fitted to the 12 correlated mode–configuration records using only mode number and reinforcement count as inputs and natural frequency as the output. The reported R², MAE, and RMSE values describe complete dataset fitting or interpolation and do not establish generalisation to unseen configurations. No physical modal displacement, strain, or stress amplitude is reported because eigenvector scaling is arbitrary and no forced-response analysis with defined excitation and damping was performed. Full article

We study hierarchical spectral graph clustering under edge differential privacy (DP) through the lens of iterative eigenvector estimation on adjacency matrices. We propose a differentially private recursive spectral framework, where each binary partition is obtained via a rank-one noisy power method applied to induced adjacency sub-matrices. At each iteration, carefully calibrated Gaussian noise is injected into the matrix–vector multiplication, ensuring $(\epsilon ,\delta )$-edge DP under cumulative privacy accounting across both power iterations and recursive hierarchy levels while preserving the essential convergence properties of the classical power method. We provide a non-asymptotic analysis of the resulting noisy iterations, characterizing the trade-off between privacy and accuracy via explicit bounds on the eigenvector estimation error. In particular, we quantify how the noise variance, number of iterations, eigengap, and hierarchy depth jointly influence the accuracy of each recursive split and the overall clustering performance. Empirical evaluations on synthetic and real-world networks validate the theoretical predictions and demonstrate that the proposed method achieves strong multi-scale clustering performance under meaningful privacy budgets. Full article

Principal component analysis is a second-order method, it selects covariance dominant directions. In local data models, however, a structural component may be rare or intermittent and therefore have modest variance but large fourth-order response. This note demonstrates a straightforward fourth-order enhancement based on Z-eigenvectors of fourth-order tensors. We prove a separation result showing that, in a fourth-order-dominant regime, rank-k PCA selects nuisance directions, while successively selected fourth-order maximizing directions recover the structural subspace. In the noiseless case, the resulting structural projection has strictly smaller squared reconstruction error for every nonzero structural vector. For arbitrary deterministic errors, we give an explicit sufficient condition under which the same improvement holds. We also establish the stability of our residual recovery scheme under tensor perturbation. Numerical experiments include a controlled additive-noise study and an application-motivated synthetic baseball-swing example, illustrating the distinction between PCA and residual fourth-order recovery under perturbation and in mixed feature coordinates. Full article

Healthcare delivery fragmentation can be understood as a structural coordination problem within a socio-technical System of Systems, where autonomous but interdependent providers, insurers, suppliers, information systems, support services, and care recipients interact through uneven incentives, information flows, and dependencies. This paper develops a graph-theoretic structural assessment method for examining how an external governance influence may alter the modeled coordination conditions of such a system before empirical evaluation. A synthetic network is constructed to compare a status quo healthcare SoS with a proposed governance-augmented configuration in which an external governing entity introduces governance interfaces among constituent systems while preserving their operational autonomy. The two configurations are assessed using eigenvector centrality, PageRank, Katz centrality, clustering coefficient, HITS hubs and authorities, anchored structural-priority scenarios, and Monte Carlo sensitivity analysis. Results show that the governance-augmented configuration redistributes modeled structural priority rather than uniformly improving or suppressing any category. Providers and Care Recipients gain prominence and inbound recognition, Insurance and Information Communication Technology retain routing roles despite lower inbound prominence, and reduced clustering indicates lower modeled local closure. Scenario scoring and Monte Carlo analysis show that these interpretations depend on policy-weighting assumptions: Providers and Care Recipients have high probabilities of higher structural-priority scores, Insurance remains near neutral, and Information Communication Technology is slightly lower on average. These findings show that external governance can be modeled as an interface-setting mechanism that reshapes coordination structures while preserving constituent autonomy, without claiming that governance alone would resolve healthcare fragmentation or improve outcomes. Full article

Background/Objective: Cinnamaldehyde (CA) is a naturally occurring bioactive compound derived from the leaves, bark, roots, and flowers of the Chinese medicinal plant Cinnamomum cassia. It exhibits a broad spectrum of pharmacological properties, encompassing antioxidant, antibacterial, anti-diabetic, antifungal, and anticancer activities. Notably, it has shown potential therapeutic benefits in the management of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD). Ochratoxin A (OTA), a common contaminant found in foods such as cereals, coffee, and raisins, is also present in traditional Chinese medicinal materials, including Astragalus and liquorice. T2DM and NAFLD share intertwined pathophysiological pathways, including insulin resistance, dyslipidaemia, chronic low-grade inflammation and oxidative stress, with insulin resistance serving as the common pathological hub for both conditions. Consequently, they frequently co-occur and exacerbate each other. OTA exerts dual-targeted toxicity to the pancreas and liver, which may synergistically drive the development of the comorbidity of T2DM and NAFLD. These two processes are mutually causal and together constitute the pathological basis of metabolic comorbidity. Methods: Network toxicology employs toxicological data, gene expression, and protein–protein interaction (PPI) networks to predict the targets of toxins, while network pharmacology, based on systems biology principles, reveals how drugs exert regulatory effects through multiple targets and pathways. In this study, we employed an integrated network toxicology and network pharmacology approach to jointly decipher the potential mechanisms by which CA intervenes in OTA-induced comorbid T2DM-NAFLD. First, a network toxicology approach was employed to preliminarily screen for core toxicological targets responsible for OTA’s pathogenicity. Subsequently, network pharmacology was used to identify potential targets of CA-mediated intervention in the disease. Finally, the common overlap among the CA intervention targets, OTA toxicity targets, and disease targets was defined as the final set of potential targets for CA-mediated intervention in OTA-induced T2DM-NAFLD comorbidity. A PPI network was constructed using the STRING database, and topological analysis was performed with Cytoscape. Core targets were selected using the median values of six parameters—betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, LAC (local average connectivity) score, and network centrality—as cut-off thresholds, and the top 10 key genes were further identified using the cytoHubba plugin. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database, and the results were visualized on the CNSknowall platform. Lastly, molecular docking of the core targets was performed using the CB-DOCK2 platform to validate binding affinity. Results: Based on an integrated analysis of network toxicology, network pharmacology, and molecular docking, 10 key targets were systematically identified. These may serve as potential mediators of cinnamaldehyde in the treatment of OTA-induced T2DM-NAFLD comorbidity. Among these, six targets—albumin (ALB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), interleukin-6 (IL-6), tumor necrosis factor (TNF), actin beta (ACTB), and estrogen receptor 1 (ESR1)—possess crystal structures amenable to molecular docking. KEGG enrichment analysis revealed that CA and OTA jointly participate in key pathological processes such as the cancer pathway, the lipid and atherosclerosis pathway, the advanced glycation end-products–receptor for advanced glycation end-products (AGE-RAGE) signaling pathway, the phosphatidylinositol 3-kinase–protein kinase B (PI3K-Akt) signaling pathway, the TNF signaling pathway, and the interleukin-17 (IL-17) signaling pathway. OTA exacerbates inflammatory responses, impairs insulin signaling, promotes hepatic steatosis, and disrupts systemic metabolic homeostasis, ultimately contributing to T2DM-NAFLD comorbidity. Conversely, cinnamaldehyde counteracts these pathological processes through multiple mechanisms, including antioxidant and anti-inflammatory effects as well as regulation of glucose and lipid metabolism, thereby restoring metabolic homeostasis. Conclusions: This study has preliminarily identified the toxicological targets of OTA and the potential intervention targets of CA, offering new avenues for preventing and intervening in OTA-induced metabolic toxicity. Furthermore, it provides a theoretical basis for CA as a potential multi-target therapeutic agent and presents novel insights worthy of further investigation into the prevention of T2DM-NAFLD comorbidity. Full article

While multi-phase short circuits are reliably cleared by conventional overcurrent relays, single-phase-to-ground faults (SPGFs) in isolated, compensated, or resistance-grounded 6–10 kV distribution networks produce extremely low, highly distorted currents that frequently cause traditional zero-sequence protections to misoperate. Because SPGFs constitute the vast majority of network disturbances, resolving this specific low-current detection challenge remains a critical priority for grid resilience. This paper presents a preselective protection approach based on Vector Analysis of Reactive Asymmetry Current (VARAC), designed for implementation in digital relay protection and automation terminals. Instead of relying primarily on vulnerable zero-sequence quantities, the method derives diagnostic features from the reactive asymmetry structure of three-phase current phasors. A reactive asymmetry matrix is formed from pairwise imaginary cross-products, symmetrized to preserve real eigenvalues and stable modal interpretation. The dominant eigenvalue and eigenvector are then used to quantify fault intensity and directional skew through two decision features: a magnitude-based index and a normalized asymmetry ratio. This enables robust discrimination between normal and faulted operation, including low-current and compensated-fault conditions where conventional criteria lose sensitivity. Simulation and oscillographic evaluations show a clear separation between pre-fault and SPGF regimes, fast onset detection, and improved structural selectivity versus traditional zero-sequence indicators. The proposed algorithm is computationally lightweight and compatible with hierarchical distributed SCADA architectures, supporting coordinated monitoring, diagnostics, and adaptive protection functions in modern medium-voltage networks. Full article

After more than a century, factor analysis remains one of the most popular tools in applied statistics. Exploratory factor analysis tends to be driven by iterated principal axis factorization. Confirmatory factor analysis tends to rely on maximum likelihood estimation. This paper demonstrates the computational superiority of the Gauss–Newton method in minimizing the principal axis loss. On sample problems, Gauss–Newton is more than three orders of magnitude faster than popular implementations of maximum likelihood factor analysis. As part of this comparison, we derive an alternative Gauss–Newton method that leverages the MM principle of optimization. We also explore a simple perturbation correction that dramatically improves the accuracy of the factor loading matrices derived from approximate spectral decompositions. Finally, we suggest a robust version of iterated principal axis factorization that leverages the MM principle, block descent, and the Gauss–Newton method. These innovations are implemented in Julia code and applied to a sequence of representative problems. Full article

Microtubules contain ordered aromatic amino acid networks whose optical excitations have been proposed to support non-trivial energy-transfer dynamics. Here, we examined whether bound tryptamine ligands can perturb the excitonic structure of the tubulin tryptophan network. A virtual screen of 294 tryptamines was performed across seven known binding regions of the tubulin heterodimer using AutoDock Vina 1.2.6. From this screen, top-ranked tryptamine ligands were carried forward for excited-state analysis. Geometry optimization and time-dependent density functional theory (TD-DFT) calculations were used to obtain vertical excitation energies and transition dipole moments for the ligand-bound states in the ultraviolet range. These ligand properties were then incorporated into a tight-binding Hamiltonian describing the tubulin tryptophan excitation network in order to evaluate changes in exciton energies and eigenvector delocalization. The calculations indicate that tryptamine binding can modify the excitonic landscape of tubulin in a ligand-dependent manner, with the magnitude of the perturbation governed by excitation wavelength, transition dipole strength, and spatial orientation relative to the intrinsic tryptophan network. These results show that substituted tryptamines differ in how they perturb the modeled tubulin tryptophan excitonic manifold, but they do not by themselves establish experimentally resolvable modulation of tubulin or microtubule photophysics. The present work should therefore be interpreted as a first-pass computational screening framework for prioritizing ligands and defining future experimental tests. Full article

As renewable energy is increasingly integrated via high-voltage direct current (HVDC) transmission, hybrid multi-infeed receiving-end grids containing both line-commutated converters (LCC) and modular multilevel converters (MMC) have become common, and wideband resonance problems in power-electronized networks are growing more prominent. This paper proposes an active resonance analysis and suppression strategy for such systems. First, a wideband current source converter model and a wideband voltage source converter model are adopted to describe the LCC and MMC, respectively, and a positive-sequence s-domain model of the system is established. A two-stage s-domain nodal admittance matrix method is then applied to efficiently determine the wideband resonance modes and the corresponding mode shape eigenvectors. A dual criterion combining the matching degree between resonance frequencies and LCC characteristic harmonics with the modal damping ratio identifies high-risk resonance modes. On this basis, an active damping strategy that realizes a parallel virtual resistance on the AC side through MMC supplementary control is proposed, together with a quantitative design method for the virtual conductance. At the control implementation level, a modulation wave reconstruction bypass injection scheme superimposes the high-frequency damping command directly in the αβ stationary reference frame, thereby bypassing the PI controller and reducing the amplitude attenuation and phase distortion caused by the high-frequency limitation of the integral path. PSCAD/EMTDC simulation results on an IEEE 9-bus test system demonstrate that the proposed strategy effectively suppresses resonance amplification and wideband power oscillations excited by LCC characteristic harmonics without affecting the fundamental power transmission. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

Despite pressure to scale green building (GB) adoption in Indonesia, many government building projects underperform against their initial intended design, creating a compliance gap between the design and construction phases and reducing the GB rating and its potential benefits. This study investigated the barriers and drivers affecting the Indonesian government’s GB projects’ compliance using a systems thinking (ST) approach. A causal loop diagram (CLD) was constructed from stakeholder interviews and literature scoping, followed by semi-qualitative analysis, combining systems archetype identification, eigenvector centrality (EC), and influence mapping to propose potential leverage points as a basis for policy analysis of the current regulatory scenario. Key findings show that knowledge development, sustained stakeholder integration, project documentation readiness, and government support reinforce GB compliance, but are undermined by financial constraints. CLD analysis identified that the more sustainable factors, including regulation alignment, capacity building, and enhancing collaboration, should become a focus of interventions in the system, instead of focusing solely on the provision of funding. This study presents a novel exploration of the GB adoption problem in an Indonesian governmental context through a comprehensive and systems approach. Further research might require narrowing the system boundaries, broadening the literature and stakeholder validation, and performing quantitative modelling to test intervention scenarios to support rigorous decision-making processes. Full article

Assessing and optimizing regional ecological networks is critical for mitigating fragmentation-driven ecological risks and informing evidence-based territorial spatial planning in China. In this study, we developed a comprehensive evaluation framework integrating ecosystem services, ecological sensitivity, and landscape connectivity to identify ecological sources in Anyang City, China. We then extracted ecological corridors and nodes using circuit theory and constructed the city’s ecological network. Notably, we applied complex network theory combined with topological robustness analysis for optimization to enhance network stability. The analysis identified 43 ecological sources (820.72 km²; 11.16% of the region), predominantly distributed in western Anyang. A total of 82 corridors (460.35 km), 62 pinch points, and 120 barrier points were mapped—primarily in the west, revealing critical connectivity deficits. Network optimization through the addition of 10 strategic corridors significantly enhanced structural balance and functionality, with average degree, closeness centrality, clustering coefficient, eigenvector centrality, and graph density increasing by 5.55–12.19%, and their standard deviations decreasing by an average of 19.32%. Global efficiency (+8.74%), the largest connected component ratio (+0.73%), and node/edge recovery robustness (+17.44%/+18.08%) also improved markedly, confirming greater connectivity and resilience. Our methodology comprehensively integrates ecosystem functional services, disturbance resistance, and spatial structural stability, providing a practical reference for the construction and optimization of regional ecological networks in mountainous–plain transition zones of China. Full article

Thailand’s goal of becoming carbon-neutral by 2050 and producing no emissions by 2065 requires their reliable renewable energy means to be expanded upon quickly. Biomass is an important resource for this. Even though there are many biomass power plants in Thailand, the further expansion of biomass energy is being held back by several problems, such as unclear rules and feedstock instability, which is worsening because of climate change. This study formulates an investor-focused Analytic Hierarchy Process (AHP) framework to rank the policy instruments that bolstered investor confidence in 2024–2025. Expert opinions were gathered through a Delphi-validated process and examined via eigenvector-based weighting and consistency checks. The findings indicate that law and regulatory policy is the most successful intervention (0.31), followed by economic incentives (0.24) and R&D support (0.18). Sub-criteria analysis reveals that regulatory clarity and the stability of feedstock supply—aggravated by climate-induced yield risks—are the predominant factors influencing investment decisions. Sensitivity analysis substantiates this ranking, indicating that fundamental regulatory reform is necessary to realize the full efficacy of financial or technological incentives. These results provide policymakers with a clear method to make decisions about how to align biomass roadmaps with the needs of the private sector. This will help emerging economies make a smooth and long-lasting transition to clean energy. Full article

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and a mechanistic understanding. Time-resolved characterization of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 patients with MDD and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found patients with MDD exhibited significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges’ g = −0.51) and increased occipito–parieto–temporal state occupancy (p < 0.001; Hedges’ g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of DMN disruption in MDD, aligning with the emerging dynamical systems framework for mental health to advance the mechanistic understanding of MDD pathophysiology. Full article

The construction cost overrun is an important issue that should be addressed, as it directly impacts many construction companies. Our research objective is to study and rank the impacts of empirical attitudes on construction cost overruns using the matrix analysis method. The research began with a focus group of 78 respondents, including 40 project engineers from contractor construction companies, 18 heads of government sectors related to construction projects, and 20 construction academicians. Based on expert interviews and the focus group discussion, 13 relevant factors were identified, and a questionnaire survey was subsequently conducted for data collection. The results showed that labor shortages were the most important factor influencing construction cost overruns, with the highest eigenvector value of 0.1687. The ranking of factors based on the percentages of average construction cost overruns includes labor shortages, variation orders, financial capacity, material price fluctuations, and drawing conditions, accounting for 18.3%, 15.7%, 12.8%, 10.2%, and 9.0%, respectively. These findings demonstrate the applicability of matrix analysis as a systematic approach for prioritizing empirical managerial attitudes influencing construction cost overruns, thereby providing a structured basis for decision-making in construction cost risk management. Full article