Search Results (7,657)

This paper develops a numerical algorithm for treating the modified anomalous time-fractional sub-diffusion problems (MAFSDPs). The proposed numerical algorithm relies on the tau method. The basis functions, namely, shifted Dickson polynomials of the second kind, are employed to obtain the proposed numerical solutions. Many theoretical formulas of the Dickson polynomials of the second kind and their shifted polynomials, such as the linearization formula, derivative relations, and some specific definite integrals, are developed. These formulas will serve as a fundamental basis for designing our proposed numerical algorithm. The approximate solution is expressed as a truncated double expansion in shifted Dickson basis functions. The utilization of the tau method transforms the equation, along with its underlying conditions, into a system of algebraic equations that can be numerically treated. Rigorous convergence of the double-shifted expansion is studied. Numerical examples are included to verify the accuracy and applicability of the proposed algorithm. In addition, comparisons with some existing numerical methods are presented to confirm the superior performance of our algorithm. Full article

Systems thinking is a core competence in logistics management, as decisions across transportation, warehousing, and delivery functions are highly interconnected and often generate delayed, trade-off, or system-wide consequences. Despite the increasing integration of generative artificial intelligence (GenAI) tools into logistics education, limited research has examined how to enhance systems thinking in such contexts. Drawing on triadic reciprocal determinism, this study conceptualizes systems thinking enhancement as an emergent outcome of interactions among behavioral regulation, cognitive conditions, and environmental scaffolding. Using survey data from 236 logistics management students in Chinese universities, we integrate Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine both net effects and configurational mechanisms. Results show that self-regulated learning exhibits the strongest positive association with systems thinking, while germane cognitive load is positively associated and extraneous cognitive load is negatively associated with systems thinking. Teacher GenAI scaffolding is linked to more favorable cognitive load allocation. fsQCA findings further reveal that high-level systems thinking emerges from specific combinations where self-regulated learning and germane cognitive load are fundamental conditions, whereas the absence of self-regulated learning consistently leads to low-level systems thinking. These findings provide guidance for the design of GenAI-supported curricula and scaffolding strategies. Full article

Introduction/Objectives: Body composition changes and diet quality may contribute to metabolic complications and graft outcomes after kidney transplantation. We evaluated the relationships between diet quality and CT-derived body composition components (skeletal muscle mass, muscle quality/myosteatosis, and visceral adiposity) and explored their associations with metabolic markers and graft function. Materials and Methods: In this single-center retrospective cross-sectional study, we included 161 adult first kidney transplant recipients (KTRs) with a functioning graft and ≥12 months of follow-up. Body composition was quantified on routine abdominal CT at the L3 level using skeletal muscle index (SMI), mean muscle attenuation (Hounsfield units) for myosteatosis, and visceral adipose tissue area (VAT). Diet quality was scored using the Revised Diet Quality Index (DQI-R). Graft function was followed with creatinine-based estimated glomerular filtration rate (eGFR) calculated by the CKD-EPI equation. Results: Mean age was 45.7 ± 13.2 years and 58% were men. The prevalence of low muscle mass was 26.0%, myosteatosis 73.5%, and visceral obesity (VAT ≥ 100 cm²) 45.6%. No participant had “good” diet quality; 48.4% had poor diet quality. DQI-R showed a weak positive correlation with SMI (r = 0.157; p = 0.047) but was not significantly related to VAT, subcutaneous adipose tissue (SAT), Kidney transplant recipient (VSR) or myosteatosis. In multivariable models, age and VAT were associated with HbA1c, whereas body composition and diet quality variables were not independent predictors of eGFR. Myosteatosis was independently associated with older age. Conclusions: Visceral adiposity and impaired muscle quality frequently clustered and were linked to metabolic status. These findings support post-transplant follow-up strategies that go beyond BMI and integrate body composition and nutritional assessment within a multidisciplinary care model. Full article

Lyapunov-exponent-based diagnostics are widely used to quantify deterministic chaos in optically injected semiconductor lasers (OISLs). In most numerical implementations, the optical field is represented either in phasor coordinates $(A,\psi ,N)$ or in Cartesian quadrature coordinates $(X,Y,N)$. Although these representations are mathematically related through a smooth coordinate transformation away from vanishing field amplitude, their numerical realizations can exhibit markedly different robustness in variational calculations, directly impacting the reliability of Lyapunov exponent estimation and chaoticity maps. In this work, we present a systematic assessment of the numerical reliability of Lyapunov-based chaos analysis in master-slave optically injected semiconductor lasers using both phasor and quadrature formulations. The full Lyapunov spectrum was computed via a noise-free variational method that integrates the nonlinear dynamics together with the corresponding Jacobian equations using a fourth-order Runge-Kutta scheme combined with periodic QR orthonormalization. High-resolution Lyapunov maps were constructed in the injection strength-frequency detuning parameter space, and the consistency between both formulations was quantitatively evaluated. While both approaches reproduce the overall structure of chaotic and non-chaotic regions, the phasor formulation may generate spurious positive Lyapunov exponents in regimes where the optical field amplitude approaches low values. These discrepancies originate from singular terms proportional to $1/A$ and $1/A^2$ in the variational Jacobian of the phasor model, which can lead to numerical amplification and artificial chaotic signatures. The quadrature formulation avoids these singularities and provides numerically stable and physically consistent Lyapunov spectra across the explored parameter space. The results establish practical guidelines for robust chaos quantification in optically injected semiconductor lasers and highlight the importance of representation choice in variational Lyapunov analysis of nonlinear photonic systems. Full article

In this paper, we investigate the properties of the boundedness of fractional integral operators $K_{\alpha }$ defined on general measure metric spaces. We study their action in Lebesgue spaces $L^p\left(Y\right)$, Morrey spaces $L_{\phi }^p\left(Y\right)$, and extend our analysis to fractional Sobolev spaces $W^{\alpha ,p}\left(Y\right)$. Using classical dyadic decomposition and the Hardy–Littlewood maximal operator, we establish sharp bounds for $K_{\alpha }$ in terms of kernel parameters and the geometric structure of the space. A significant contribution of this work is the proof that $K_{\alpha }$ is bounded from $W^{\alpha ,p}\left(Y\right)$ to $L^q\left(Y\right)$, where thus linking our operator-theoretic framework with the theory of nonlocal and fractional partial differential equations. These results provide valuable tools for studying regularity, a priori estimates, and solution mappings in nonlocal problems involving the fractional Laplacian and related operators on irregular or non- Euclidean domains. Full article

To address pressure-drop-induced safety risks in high-drop gravity-fed irrigation pipelines, this study investigates coordinated prevention and control strategies that integrate air release and vacuum valve groups with flow-adaptive valve closure rules. A large-scale self-pressurized irrigation network (1.33 × 10⁸ m²) in Karamay, Xinjiang, China, is selected as a representative case study. Based on one-dimensional transient flow modeling, pressure drop and negative-pressure characteristics induced by inlet valve closure in the main pipeline are analyzed using wave speed theory, governing differential equations, and the finite difference method. A coordinated protection framework is proposed that explicitly links valve operating patterns with the spatial configuration of protective devices. Unlike conventional schemes that rely on empirical layouts and fixed closure rules, this study introduces a critical-flow-velocity-based valve grouping method combined with flow-dependent valve closure strategies. Simulation results demonstrate that a strategically optimized configuration of air release and vacuum valves along the main pipeline is sufficient to eliminate negative pressure under all operating conditions. For flow rates below 6 m³/s, linear valve closure ensures safe operation, whereas a two-stage closure is required for higher flow rates (6–10 m³/s). As flow increases, reducing the fast-closure ratio and extending the total closure time effectively suppress pressure-drop-dominated transient effects at vulnerable inlet sections. By effectively mitigating transient pressure surges, the proposed coordinated “valve closure-protection device” strategy improves system adaptability to flow variability and provides practical engineering guidance for the safe operation of gravity irrigation systems, particularly high-gradient self-pressurized networks. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

Accurately quantifying the inequality of plant organ size distributions, such as leaf area, is essential for understanding plant resource allocation strategies, and this is commonly achieved using Lorenz curves. Previous studies have shown that the performance equation (PE) and its generalized form (GPE) effectively describe Lorenz curves that are rotated 135° counterclockwise around the origin and shifted rightward by $\sqrt{2}$ units. However, few studies have compared the fitting performance of PE (and GPE) with other traditional equations generating Lorenz curves in modeling empirical leaf area distributions, and even fewer have considered the validity of linear approximation assumptions in these nonlinear models. To address this gap, we quantified the inequality of leaf area distributions in Semiarundinaria densiflora, a bamboo species for which the abundant and measurable leaves per culm provide an ideal system for examining the ecological strategies underlying leaf allocation patterns. Five nonlinear models were employed to fit the leaf area distribution: PE, GPE, the Sarabia equation (SarabiaE), the Sarabia–Castillo–Slottje equation (SCSE), and the Sitthiyot–Holasut equation (SHE). Model performance was assessed using root-mean-square error (RMSE) and Akaike information criterion (AIC), while nonlinearity curvature measures were applied to evaluate the close-to-linear behavior of parameter estimates. In addition, the Lorenz asymmetry coefficient (LAC) was used to quantify the asymmetry of the Lorenz curves. Our results showed a clear trade-off between predictive accuracy and linear approximation behavior. Among the five models, GPE achieved the best fit, with the lowest RMSE and AIC values, yet did not show good close-to-linear behavior. In contrast, SHE provided the poorest fit but demonstrated the strongest close-to-linear properties. LAC values indicated that relatively abundant, larger leaves disproportionately contributed to the inequality in leaf area distribution. These findings highlight an inherent trade-off in using Lorenz-based models to describe leaf area frequency distributions: predictive accuracy does not necessarily align with statistical validity. By integrating model fit, nonlinearity diagnostics, and asymmetry assessment, this study provides new perspectives and methodological tools for future investigations into inequality in plant organ size distributions and their ecological significance. Full article

Modeling ship maneuvering dynamics presents numerous challenges, including long-term multi-step recursive error accumulation, insufficient generalization under distributed control rates, and high-frequency disturbance amplification effects. Traditional analytical models heavily rely on vessel-specific trials to characterize strongly nonlinear coupling terms and perform parameter identification, making it difficult to balance efficiency and accuracy under complex operating conditions. This paper presents a ship maneuvering-oriented integration of an invertible Koopman representation and a NeuralODE-based continuous-time predictor. The IKN reconstructs strongly coupled state spaces while enhancing representational invertibility, whereas NeuralODE directly fits the control differential equations governing ship maneuvering dynamics and supports continuous-time prediction. Experiments validate multi-rate control performance under ideal and disturbed data conditions, assessing error accumulation and extrapolation stability through long-term multi-step propagation. Evaluations utilize the KVLCC2-type L7 ship model with a 0.25 s sampling interval and a 200 s prediction horizon, validated against a multi-rate control test set. The results indicate that, compared to the baseline neural ODEs model without IKN, the normalized root mean square error (NRMSE) of state quantities decreased by 12.68% on average. In typical operational scenarios such as constant-speed emergency turns and variable-speed sine sweep maneuvers, the average state NRMSE was 7.96% lower than the LSTM model and 53.85% lower than the IKN–Koopman operator network. Noise experiments demonstrated that when introducing simulated sensor noise at 5%, 10%, and 20% into the dataset, the average state NRMSE remained at 5.98%, 8.24%, and 10.06%, respectively. This confirms the method’s stable prediction performance under varying noise intensities. Full article

The fatigue behaviour of a 90° long radius elbow, which is adjacent to the feedwater nozzle in a BWR, was analyzed. The start-up and shutdown transients were considered. A thermo-mechanical finite element analysis was carried out to determine the stresses induced by thermal transients, considering the environmental conditions in the reactor feedwater pipe. In addition, the Palmgren–Miner methodology and the ASME B&PVC code fatigue curve were applied to evaluate the accumulated damage and service life of the component. Environmental correction factors were considered to estimate environmentally assisted fatigue. Reductions in fatigue life were observed. In the second part of this paper, a part-through thickness semielliptical crack was also postulated in the internal surface of the elbow. It was aligned along the axial direction at the crown zone. Its growth was modelled using the Paris equation, evaluating the risk of failure using fracture parameters. It was found that the vulnerable area is located on the inner surface of the elbow, due to the concentration of stress caused by the curved geometry. Failure assessment diagrams (FADs) were plotted. It was found that the crack depth is the main factor governing crack behaviour under the conditions studied. The results provide a methodology for assessing the integrity of pipes subjected to specific environmental and operating conditions. Full article

Accurate modeling of dam reservoir sedimentation is crucial for effective reservoir management. Traditional approaches for estimating sedimentation include the Hydraulic Approach (HA) and the Empirical Approach (EA). HA involves complex computations and requires substantial data, while the EA relies on equations like the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE), which use subjective parameters and lead to inaccurate estimations. This study introduces a novel approach called the hydrological approach, which integrates the sediment rating curve (SRC) and the dam reservoir elevation-capacity curve (ECC) to estimate reservoir sedimentation and evolution of the ECC. This HA leads to a newly developed equation for the estimation of the sediment rise and the corresponding sediment volume. The approach is applied to the Wadi Fatimah Dam in Saudi Arabia. By combining rainfall data from 1985 to 2022 and performing rainfall–runoff hydrological modeling combined with the proposed HA, sediment accumulation trends and reservoir capacity reductions are estimated from past to present. Validation through ground survey and geophysical investigations in 2008 confirms model accuracy. Findings reveal significant sediment buildup, with an estimated average of 7.5 m rise from 1985 to 2008. The study’s main findings highlighted the urgent need for effective sediment management strategies in arid regions, where sedimentation rates are notably higher than in other regions. Full article

Energy-based cities often develop resource-dependent spatial structures that reinforce carbon-intensive daily routines, yet the mechanisms linking neighborhood form to low-carbon consumption remain unclear. This study investigates the core urban area of Ordos, China, by integrating geographic information system (GIS)-derived 5D built-environment indicators with questionnaire data from 825 residents and estimating a structural equation model (SEM) with bootstrap mediation tests. The results show clear dimension-specific effects. Density, land-use mix, and street connectivity have significant positive total effects on low-carbon consumption behavior and retain significant direct effects after the mediators are introduced, indicating partial mediation. By contrast, distance to transit and shopping accessibility operate mainly through the perceived built environment and psychological factors, with non-significant residual direct effects, indicating full mediation. Psychological factors show the strongest direct association with behavior (β = 0.545, p < 0.001), and the perceived built environment also exerts an indirect effect through psychological factors. Overall, the findings indicate that low-carbon transition in energy-based cities depends not only on spatial upgrading, but also on neighborhood environments that enhance perceived convenience and behavioral readiness. Full article

The accelerated integration of intelligent agents in user-centered digital environments has intensified research in the field of Human–Robot Interaction, especially regarding mechanisms for adaptive, intuitive, and cognitively aligned communication. The present study develops and empirically examines a structural model of BCI-inspired adaptive agents designed to support coordinated interaction in HRI contexts. The study analyzes users’ perceptions of standardized hypothetical interaction scenarios involving BCI-inspired adaptive digital agents, where BCI inspiration is conceptual and refers to adaptive architectures interpreting behavioral cues rather than direct neural signal acquisition. The proposed model integrates four main constructs—perceived technological innovation, user involvement, agent adaptivity, and digital synergy—and examines their associations with user satisfaction in digital collaborative environments. Data were collected through an anonymous questionnaire (N = 268) and analyzed using structural equation modeling with the PLS-SEM method. The structural model demonstrates substantial explanatory power, accounting for 66.8% of the variance in user satisfaction (R² = 0.668). The study contributes by empirically supporting a scenario-based structural evaluation framework suitable for early-stage adaptive HRI system design. The results highlight the role of digital synergy in aligning innovation, engagement, and adaptive behavior in BCI-inspired adaptive HRI systems, providing directions for the design of adaptive robotic agents oriented toward coordinated interaction, user-centered integration, and responsible use in collaborative digital ecosystems. Full article

Soil erosion is a growing environmental problem in arid regions, where land-use changes and landscape fragmentation directly influence land degradation. This study estimated soil loss in the Tarabillas sub-basin, located in the Chihuahuan Desert, Mexico. To this end, the Universal Soil Loss Equation (USLE) was applied and integrated with Geographic Information System (GIS) tools. Landsat TM and OLI satellite imagery were classified through supervised techniques, achieving overall accuracies above 89%. The analysis was supported by comparing erosion patterns associated with land-use changes occurring during the 1990–2021 period, assessed through cross-tabulation matrices and landscape metrics. The results show that although the average erosion potential of the sub-basin remained constant at approximately 12.45 t ha⁻¹ yr⁻¹, erosion redistributed spatially, concentrating in areas where agriculture has replaced natural vegetation. Shrublands and grasslands continue to dominate the high erosion categories due to their wide spatial extent and high erodibility. These findings highlight that fragmented agricultural expansion constitutes the main driver of landscape transformation and soil vulnerability, emphasizing the importance of integrating remote sensing, GIS, and empirical models to support sustainable land management in arid regions. Full article

Coastal wetlands represent significant sources of greenhouse gases (GHGs) and serve as crucial ecological interfaces between terrestrial and marine environments, substantially contributing to global biogeochemical cycles. However, GHG emission fluxes are strongly influenced by complex anthropogenic activities, yet their underlying microbial mechanisms remain poorly understood. This study investigated seven representative human-impacted sites within the Yellow River Delta. Employing a combined approach of in vitro microcosm cultivation, molecular biology, and multivariate statistical analysis, we investigated the integrated mechanisms controlling nitrous oxide (N₂O) and methane (CH₄) fluxes, with consideration of soil depth, environmental factors, microbial communities, and functional microbes. The results indicated that significant differences in GHG fluxes among different anthropogenic activities and soil depths (p < 0.05). Surface soil N₂O fluxes were positive within sewage irrigation areas (20.98–35.08 mg N₂O-N m⁻² h⁻¹) and tourism development areas (12.52–23.87 mg N₂O-N m⁻² h⁻¹), while mariculture areas displayed negative fluxes. CH₄ fluxes were positive exclusively in natural areas (surface soil: 25.02–55.54 mg CH₄-C m⁻² h⁻¹; deep soil: 8.38–356.68 mg CH₄-C m⁻² h⁻¹), while other areas predominantly showed negative values (surface soil: −130.98–44.32 mg CH₄-C m⁻² h⁻¹; deep soil: −106.16–65.24 mg CH₄-C m⁻² h⁻¹). Furthermore, a structural equations model highlighted the pivotal role of key functional microbes in soil carbon–nitrogen cycling (e.g., nirK, nosZII, and SRB) involved in soil carbon–nitrogen cycling in negatively regulating N₂O and CH₄ fluxes. The study also revealed distinct microbial responses across diverse habitats, underscoring the significant role of Proteobacteria in wetland soil. This research enhances our understanding of GHG dynamics in coastal wetlands and provides scientific evidence and potential regulatory pathways for enhancing soil biological mitigation functions and achieving carbon neutrality and sustainability within wetland ecosystems. Full article