Search Results (68)

This paper presents a new analytical framework for studying a class of three-dimensional symmetric systems within the theory of difference equations, constituting a natural extension of structurally reducible models in one and two dimensions. Despite the classical focus on linear equations or low-dimensional systems, the problem of solving nonlinear systems with intertwined structures and interdependent delays remains largely unexplored. Drawing on recent structural developments, we define a periodic symmetric system based on three sequences interacting through fractional hyperbolic tangent-type forms and show how this structure reveals embedded linear recurrences governing the internal evolution. By exploiting symmetry and periodicity properties, we derive exact representation formulas and highlight the structural mechanisms that enable a deeper understanding of the system’s dynamic behavior, despite its nonlinear nature and the complex interlacing of its indices. This study thus contributes to the expansion of the theory of solvable nonlinear systems and provides a unified approach to high-dimensional symmetric structures. To illustrate the practical relevance of the proposed framework, a cyclic SIR-type interaction interpretation is presented, supported by numerical simulations that demonstrate the impact of parameter symmetry on the system’s dynamical behavior. Full article

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer. Full article

To address the declining system inertia levels and the associated frequency security challenges arising from the increasing penetration of renewable generation, this study proposes a coordinated configuration of virtual inertia (VI) and fast frequency response (FFR) resources in low-inertia power systems. An improved system frequency response (SFR) model is established by incorporating synchronous inertia response (SIR), primary frequency response (PFR) and FFR. Through the improved model, analytical expressions for the rate of change in frequency (RoCoF) and the frequency nadir are derived as functions of each decision variable. These expressions reveal a decoupled mechanism in which each frequency security constraint drives the configuration of a specific resource type. A coordinated optimization model is then formulated to minimize total ancillary service cost subject to these frequency security constraints. Systematic case studies under multiple scenarios validate the proposed model and reveal that VI and FFR requirements increase monotonically with rising renewable penetration, with $H_{\mathrm{v}}=2.89$ s and $\alpha =0.19$ at 70% penetration. FFR is further shown to offer significantly greater cost effectiveness for nadir improvement than VI. These results provide quantitative guidance for the optimal configuration of both resource types under varying system conditions. Full article

Accurate shoreline positioning is critical for coastal monitoring and management, yet deep learning shoreline products are often evaluated using conventional waterbody segmentation metrics that do not explicitly measure boundary alignment. Using 20,689 NAIP aerial images covering the Great Lakes shoreline from the Coastal Aerial Imagery Dataset (CAID), we benchmark five semantic segmentation models and quantify the inconsistency between image-level segmentation accuracy (pixel accuracy, IoU) and shoreline positioning accuracy measured by the Shoreline Intersection Ratio (SIR) and Average Eulerian Distance (AED). Although segmentation performance is consistently high (pixel accuracy typically >98% and IoU often >90%), shoreline agreement is substantially lower and strongly landscape-dependent, with the poorest results in wetlands and urban scenes. Correlation analyses across coastal types and water-surface conditions show that the correspondence between segmentation metrics and SIR varies with shoreline morphology. Multivariate regressions confirm the shoreline-to-water ratio (SWR) as the dominant predictor of both SIR and AED, while shoreline complexity (SCI) and mean water hue (MWH) have weaker, context-dependent effects. These results demonstrate that high segmentation accuracy does not guarantee precise shoreline delineation and motivate shoreline-aware evaluation protocols. Full article

Several studies investigated risk and prognostic parameters for horses with colic; however, the consensus is still debated. The present work aimed to investigate colic outcomes and to identify risk factors in horses referred for colic. In this multicenter retrospective study, 236 clinical records of equids referred for colic at three different equine centers were reviewed. The following data were collected: history, signalment, physical examination at the time of admission, hematological and biochemical analysis, diagnosis, SIRS status and 0–6 point-scale SIRS score, colic type, treatment attempted, and outcome. Descriptive statistics were performed, and distribution of continuous variables was reported as median and percentile. A multivariable logistic regression model was applied to assess parameters associated with colic outcomes in horses (p < 0.05). A total of 138/236 horses were included in the study. The univariate analysis identified as potentially associated with the outcome: sex (p = 0.046), colic type (p < 0.001), treatment type (p < 0.001), SIRS score (p = 0.049), age (p-value = 0.057), heart rate (p = 0.013), and respiratory rate (p = 0.017). The logistic regression model indicated that colic type (p < 0.001) and age (p = 0.004) were significantly associated with a negative outcome. Equine colic risks are multifactorial; prognosis declines with age and strangulating obstructive non-strangulating colic. Poor outcomes link to cardiovascular signs like elevated heart rate, SIRS status and score, and blood lactate. Heterogeneity from diverse sites limits generalizability, but standardized protocols, binarized data, and a multicenter approach enhance robustness and representativeness while reducing local biases. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

As global emphasis on environmental, social, and governance practices intensifies, sustainability reporting emerges as a critical tool for corporate transparency and accountability. The study aims to assess the impact of sustainability reporting on the financial performance of listed companies in Jordan. Using a quantitative approach, a total of 588 individuals were surveyed from low-pollution and high-pollution industries using purposive sampling techniques. Partial Least Square Structural Equation Modeling (PLS-SEM) was used to conduct analysis of the data with the aid of SMART PLS4 software. The study finds that the impact of sustainability disclosures on firms’ financial performance in Jordan differs significantly by both the type of disclosure and the pollution intensity of the industry the firms belong to. Environmental impact reporting (EIR) and social impact reporting (SIR) both have positive and significant effects on financial performance, especially in low-pollution industries, probably because of a perceived proactive and authentic integration of sustainability practices. However, governance impact reporting (GIR) shows a negative relationship with financial performance, which implies that such disclosures may be perceived as compliance-driven or not authentic. These findings indicate that the context of the sustainability reporting strategy is an important element in determining its effect on financial performance. The multigroup analysis (MGA) results help us to gain a better understanding of how different sectors leverage financial value from disclosing their sustainability activities. The study confirms that sustainability disclosure is not just a compliance requirement, but an instrument that can help firms improve their financial performance. Finally, we recommend that future research should investigate deeper psychological and social mechanisms likely to influence stakeholder responses across different sectors and countries within the region. Full article

We investigate how homophily in adherence to anti-epidemic measures affects the final size of epidemics in social networks. Using a modified SIR model, we divide agents into two behavioral groups—compliant and non-compliant—and introduce transmission probabilities that depend asymmetrically on the behavior of both the infected and susceptible individuals. We simulate epidemic dynamics on two types of synthetic networks with tunable inter-group connection probability: stochastic block models (SBM) and networks with triadic closure (TC) that better capture local clustering. Our main result reveals a counterintuitive effect: under conditions where compliant infected agents significantly reduce transmission, increasing the separation between groups may lead to a higher fraction of infections in the compliant population. This paradoxical outcome emerges only in networks with clustering (TC), not in SBM, suggesting that local network structure plays a crucial role. These findings highlight that increasing group separation does not always confer protection, especially when behavioral traits amplify within-group transmission. Full article

Background: With a high human immunodeficiency virus (HIV) adult prevalence, people living with HIV (PLHIV) in Botswana continue to experience a high burden of comorbid HIV and cancer. We sought to investigate the trends of acquired immunodeficiency syndrome (AIDS) defining cancers (ADCs), non-AIDS defining cancers (NADCs), and associated risk factors in PLHIV compared with those without HIV. Methods: We analyzed data from adults aged ≥18 years reported in Botswana National Cancer Registry and National Data Warehouse. The crude, age-standardized incidence rate (ASIR), standardized incidence ratios (SIRs) of cancers and time trends were computed. Risk factors were determined using the Cox-regression model. Results: Over a 30-year period, 27,726 cases of cancer were documented. Of these, 13,737 (49.5%) were PLHIV and 3505 (12.6%) were people without HIV and 10,484 (37.8%) had an unknown HIV status. Compared to the HIV-uninfected, the PLHIV had higher and increasing trends in the cancer incidence overall during the study period (from 44.2 to 1047.6 per 100,000; p-trend < 0.001) versus (from 1.4 to 27.2 per 100,000; p-trend < 0.001). The ASIRs also increased in PLHIV for overall ADCs, NADCs and other sub-types like cervical, lung, breast, and conjunctiva cancers (p-trend < 0.001). Further, PLHIV had elevated SIRs for cervical cancer, Kaposi sarcoma in males and some NADCs. The most common risk factors were HIV infection and female sex for ADCs incidence and advanced age and being HIV-uninfected for NADCs incidence. Conclusions: Increasing trends of ADCs and NADCs during ART expansion were observed among PLHIV compared to those without HIV highlighting a greater need for targeted effective prevention and screening strategies including the provision of access to timely HIV and cancer treatment. Full article

Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version of our previous SARS-CoV-2 model formulated as input–output non-linear dynamical systems (IO-NLDS). Methods: This updated framework incorporates age-dependent contact patterns, immune waning, and new data sources, including seropositivity studies, hospital dynamics, variant trends, the effects of non-pharmaceutical interventions, and the dynamics of vaccination campaigns. Results: We analyze the dynamics of various datasets spanning the entire pandemic in Germany and its 16 federal states using this model. This analysis enables us to explore the regional heterogeneity of model parameters across Germany for the first time. We enhance our estimation methodology by introducing constraints on parameter variation among federal states to achieve this. This enables us to reliably estimate thousands of parameters based on hundreds of thousands of data points. Conclusions: Our approach is adaptable to other epidemic scenarios and even different domains, contributing to broader pandemic preparedness efforts. Full article

Background/Objectives: Prolonged use of immunosuppressive drugs (IMDs) is correlated with increased risk of cancer in transplant patients. However, detailed side-by-side analysis of cancer risk associated with individual IMDs in the same population is not available. The aim of this study was to identify drug-specific risks of cancer for commonly used transplant IMDs. Methods: We analyzed the risk of cancer for the IMDs commonly used in transplant patients (tacrolimus (TAC), cyclosporin (CY), sirolimus (SIR), mycophenolate (MMF), and their combinations) in Texas Medicare beneficiaries between 2007 and 2018. Results: Of 7721 transplant recipients receiving an IMD of interest, 2261 developed cancer. There was an increased risk of any cancer diagnosis with the use of TAC (HR: 1.49; 95% CI: 1.25–1.78) and CY (HR: 1.51; 95% CI: 1.19–1.92), and decreased risk with use of MMF (HR: 0.77; 95% CI: 0.67–0.89). Cancer-specific models revealed increased risk of liver cancer (HR: 5.25, 95% CI: 2.03–13.61) and decreased risk of ovarian/uterine cancer (HR: 0.25, 95% CI: 0.07–0.84) with TAC; increased risk of lung cancer with CY (HR: 5.06, 95% CI: 1.47–17.41); and increased risk of lymphoma associated with SIR (HR: 2.80, 95% CI: 1.00–7.81). Conclusions: TAC increases cancer risk, and MMF decreases cancer risk. Individual cancer types also vary in risk associated with individual IMDs. This study provides new information on IMD-specific cancer risk that can guide individualized screening/treatment decisions to reduce the risk associated with specific cancers after transplantation. Full article

Background: Colorectal cancer (CRC) is the most common cancer and the leading cause of cancer-related death globally. While survival improved, CRC patients face the risk of subsequent multiple primary cancers (MPCs). This study aimed to determine the incidence and identify risk factors associated with metachronous MPCs among CRC survivors. Methods: A retrospective analysis was performed on adults diagnosed with invasive colorectal adenocarcinoma at Flinders Medical Centre from 2011 to 2024, who had at least 6 months of post-CRC follow-up. Sociodemographic factors, clinical information, tumour characteristics, and treatment types were collected. Cumulative incidence function and sub-distribution hazard models were used to estimate the incidence and identify risk factors of developing MPCs. Results: Of the total 554 eligible study participants, 12% developed MPC, with a median follow-up time of 5 years (interquartile range: 2.8–7.6 years) until the diagnosis of MPC. Gastrointestinal, prostate, and haematological malignancies were the most common types of MPCs identified. The cumulative incidence and standardised incidence ratio (SIR) of an MPC were 20.9% (95% CI: 15.3–25.6) and 1.32 (95% CI: 1.03–1.68), respectively. Male sex, older age (>65 y), early-stage cancer, and loss of mismatch repair (MMR) protein expression were associated with an increased risk of developing MPCs. Conclusions: CRC survivors have a higher risk of developing an MPC compared to the general population. Sex, age, cancer stage, and MMR protein expression are factors associated with MPCs. Therefore, tailored surveillance based on the individual’s risk profile should be considered for timely diagnosis of subsequent cancers to improve long-term outcomes. Full article

Plant roots and soil properties play crucial roles in regulating soil hydrological processes, particularly in determining soil water infiltration capacity. However, the infiltration patterns and underlying mechanisms across different forest types in subtropical regions remain poorly understood. In this study, we measured the infiltration characteristics of three typical stands (pure Phyllostachys edulis forest, mixed Phyllostachys edulis-Cunninghamia lanceolata forest, and pure Cunninghamia lanceolata forest) using a double-ring infiltrometer. Stepwise multiple regression and structural equation modeling (SEM) were employed to analyze the effects of root traits and soil physicochemical properties on soil infiltration capacity. The results revealed the following: (1) The initial infiltration rate (IIR), stable infiltration rate (SIR), and average infiltration rate (AIR) followed the order pure Phyllostachys edulis stand > mixed stand > pure Cunninghamia lanceolata stand. (2) Compared to the pure Cunninghamia lanceolata stand, the IIR, SIR, and AIR in the pure Phyllostachys edulis stand increased by 6.66%, 35.63%, and 28.51%, respectively, while those in the mixed stand increased by 28.79%, 28.82%, and 33.51%. (3) Fine root biomass, root length density, non-capillary porosity, and soil bulk density were identified as key factors influencing soil infiltration capacity. (4) Root biomass and root length density affected infiltration capacity through both direct pathways and indirect pathways mediated by alterations in non-capillary porosity and soil bulk density. These findings provide theoretical insights into soil responses to forest types and inform sustainable water–soil management practices in Phyllostachys edulis plantations. Full article

This manuscript presents a Luenberger-type state observer for a class of nonlinear systems with multiple delays. Sufficient conditions are provided to ensure practical stability of the error dynamics. The exponential decay of the observation error dynamics is guaranteed through the use of Lyapunov–Krasovskii functionals and the feasibility of linear matrix inequalities (LMIs). Additionally, a time delay SIRS compartmental epidemiological model is introduced, where the time delays correspond to the transition rates between compartments. The model considers that a portion of the recovered population becomes susceptible again after a period that follows its recovery. Three time delays are considered, representing the exchange of individuals between the following compartments: ${\tau }_{1,2,3}$, the time it takes for an individual to recover from the disease, the time it takes for an individual to lose immunity to the disease, and the incubation period associated to the disease. It is shown that the effective reproduction number of the model depends on the rate at which the susceptible population becomes infected and, after a period of incubation, starts to be infectious, and the fraction of the infectious that recovers after a a certain period of time. An estimation problem is then addressed for the resulting delay model. The observer is capable of estimating the compartmental populations of Susceptible $S\left(t\right)$ and Recovered $R\left(t\right)$ based solely on the real data available, which correspond to the Infectious population $I_r\left(t\right)$. The $I_r\left(t\right)$ data used for the state estimation are from a 55-day period of the pandemic in Mexico, reported by the World Health Organization (WHO), before vaccination. Full article

We show how a rescaling of fractional operators with bounded kernels may help circumvent their documented deficiencies, for example, the inconsistency at zero or the lack of inverse integral operator. On the other hand, we build a novel class of linear operators with memory effects to extend the L-fractional and the ordinary derivatives, using probability tools. A Mittag–Leffler-type function is introduced to solve linear problems, and nonlinear equations are addressed with power series, illustrating the methods for the SIR epidemic model. The inverse operator is constructed, and a fundamental theorem of calculus and an existence-and-uniqueness result for differintegral equations are proven. A conjecture on deconvolution is raised, which would permit completing the proposed theory. Full article