Search Results (8,097)

We investigate the Lagrange formulation for the one-dimensional Saint Venant–Exner system. The system describes shallow-water equations with a bed evolution, for which the bedload sediment flux depends on the velocity, $Q\left(t,x\right)=A_g{u}^m,m\ge 1$. In terms of the Lagrange variables, the nonlinear hyperbolic system is reduced to one master third-order nonlinear partial differential equation. We employ Lie’s theory and find the Lie symmetry algebra of this equation. It was found that for an arbitrary parameter m, the master equation possesses four Lie symmetries. However, for $m=3$, there exists an additional symmetry vector. We calculate a one-dimensional optimal system for the Lie algebra of the equation. We apply the latter for the derivation of invariant functions. The invariants are used to reduce the number of the independent variables and write the master equation into an ordinary differential equation. The latter provides similarity solutions. Finally, we show that the traveling-wave reductions lead to nonlinear maximally symmetric equations which can be linearized. The analytic solution in this case is expressed in closed-form algebraic form. Full article

To address the issues of insulation layer damage and conductor exposure in offshore floating photovoltaic systems occurring in shallow marine regions characterized by significant tidal ranges under multi-field coupling effects, an overhead cable laying scheme based on the hybrid pile–floater structure is proposed, while its mechanical response is investigated in this paper. The motion response model of the floating platform, considering wind load, wave load, current load, and mooring load, as well as the equivalent density and mathematical model of the overhead cable are established. The mechanical response characteristics of the overhead cable are analyzed through finite element analysis software. The results indicate that the overhead cable’s mechanical response is influenced by the span length and coupled wind–ice loads. Specifically, the tension exhibits a nonlinear increasing trend, while the deflection shows differential variations driven by the antagonistic interaction between wind and ice loads. The influence of ice loads on the configuration of overhead cables is significantly weaker than that of wind loads. This study provides crucial theoretical support for enhancing the lifespan of the overhead cable. Full article

Thermo-hydrodynamic (THD) lubrication is a key mechanism in injection pumps, where frictional heating and heat transfer strongly influence lubrication performance. Accurate numerical modeling remains challenging due to the nonlinear coupling of temperature- and pressure-dependent fluid properties and the high computational cost of iterative solvers. The rising relevance of bio-hybrid fuels, however, demands the investigation of a great number of fuel mixtures and conditions, which is currently infeasible with traditional solvers. Physics-informed neural networks (PINNs) have recently been applied to lubrication problems; existing approaches are typically restricted to stationary cases or rely on data to improve training. This work presents a novel, purely physics-based PINN framework for solving coupled, transient THD lubrication problems in injection pumps. By embedding the Reynolds equation, energy conservation laws, and temperature- and pressure-dependent fluid models directly into the loss function, the proposed approach eliminates the need for any simulation or experimental data. The PINN is trained solely on physical laws and validated against an iterative solver for 16 transient test cases across two fuels and eight operating scenarios. The good agreement of PINN and iterative solver demonstrates the strong potential of PINNs as efficient, scalable surrogate models for transient THD lubrication and iterative design applications. Full article

We investigate the existence of positive normalized (mass-constrained) solutions for the fractional nonlinear Schrödinger equation ${(-\Delta )}^{s}v+V\left(x\right)v=\lambda v+{\mu \left|v\right|}^{p-2}v+{\left|v\right|}^{2_s^{*}-2}v\mathrm{in}{\mathbb{R}}^N,{\parallel v\parallel }_2^2=b^2,$ where $N>2s$, $s\in (0,1)$, $\mu >0$, $p\in (2,2_s^{*})$, and $2_s^{*}={\displaystyle \frac{2N}{N-2s}}$. Here, $\lambda \in \mathbb{R}$ denotes the Lagrange multiplier associated with the prescribed mass $b>0$. The potential $V\in C^1\left({\mathbb{R}}^N\right)$ is allowed to be nonconstant and satisfies $V\left(x\right)\to V_{\infty }$ as $\left|x\right|\to \infty$; moreover, the perturbations induced by $V-V_{\infty }$ and $x·\nabla V$ are assumed to be small in the quadratic-form sense compared with the fractional Dirichlet form ${\parallel (-\Delta )}^{s/2}{v\parallel }_2^2$. Using the Caffarelli–Silvestre extension, we establish a Pohozaev identity adapted to the presence of $V\left(x\right)$ and introduce a Pohozaev manifold on the $L^2$-sphere. Combining Jeanjean’s augmented functional approach with a splitting analysis at the Sobolev-critical level, we construct compact Palais–Smale sequences below a suitable critical energy level. As a consequence, we prove the existence of positive normalized solutions for small masses $b\in (0,b_0)$ in the $L^2$-critical and $L^2$-supercritical regimes (with respect to the lower-order power p). Full article

This work investigates the synthesis and characterization of Barium Titanate (BT) nanoparticles, which exhibit non-linear optical properties, with a focus on their potential application in biomedical imaging. BT nanoparticles are active in second harmonic generation (SHG), enabling deep tissue imaging with a high signal-to-noise ratio. A major objective of this study is to advance in the understanding of the interactions between these nanoparticles and model biological systems. To this end, monolayers of 1,2-dipalmitoyl-sn-glycerol-3-phosphocholine (DPPC) spread on aqueous sub-phase are employed as model bio-interfaces, and the effects of BT nanoparticles on their properties are investigated using physicochemical experimental techniques such as Langmuir trough and Dynamic Light Scattering, also in the presence of albumin, a representative serum protein. The results evidence nanoparticle incorporation into the lipid layer, affecting its phase behavior, as well as the spontaneous formation of protein coronas around NPs, which is further confirmed by super-resolution optical microscopy Full article

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design. Full article

Seismic performance evaluation of existing buildings is essential for defining effective mitigation strategies in earthquake-prone regions. This study investigates the seismic performance of low-rise unreinforced masonry (URM) residential buildings located in several cities in the Albanian territory. Material properties were obtained from experimental tests conducted on representative samples and subsequently adopted in the development of analytical models. Three-dimensional finite element models were generated based on the collected geometric data and experimentally determined material characteristics. Nonlinear static (pushover) analyses were carried out to assess the seismic capacity and identify the potential failure mechanisms of the buildings. The numerical results showed significant variation in performance depending on the building typology, with some cases reaching the near-collapse limit state under design-level earthquakes. The capacity curves and performance points obtained from the models demonstrate the pronounced influence of construction techniques, boundary conditions, and material properties on the seismic response. The results indicated that URM residential buildings exhibit distinctive seismic performance characteristics influenced by their construction techniques and material properties. Based on the findings, recommendations for retrofit strategies are proposed to enhance the seismic resilience of such structures. Full article

Cemented paste backfill (CPB) is widely used in mining operations to enhance underground stope stability, production, and safety. Accurately predicting paste flow behaviours in backfill reticulation circuits is crucial for efficient delivery control and asset longevity. However, the predictions remain challenging due to complex rheology and flow-induced particle heterogeneities of CPB. This study develops a computational fluid dynamics (CFD)-based analysis framework to investigate flow dynamics of the CPB and the wear conditions of the pipes, considering slip layer and shear-induced particle migration. Experimental loop tests are conducted to measure pressure drops of CPB at different velocities, providing data for validating the developed CFD model. Simulation results are in good agreement with the measured pressure drops and wear rates of the internal pipeline wall. Furthermore, comparisons with existing models indicate that the developed model provides more accurate predictions. Microscopical analyses reveal that shear-induced particle migration leads to the formation of a distinct plug flow region, with particles accumulating near the unyielded boundary. Meanwhile, a low particle concentration near the pipe wall reduces local viscosity and pressure drop. Parametric studies reveal that increased flow velocity and reduced pipe diameter significantly elevate both pressure drop and wear rate, while higher solid concentrations induce nonlinear rheological effects. Full article

Computer-based engineering design tools can quicken the cadence for machine design, which enables companies to compete better in the global marketplace. The application of nonlinear optimization and tradespace analysis methods allows the exploration of design variables within dynamic mechanisms. In this paper, the design of a classical machine, the Seth Thomas pendulum street clock, which offered precision timekeeping and time display at the turn of the 20th century, will be investigated from a modern perspective. A mathematical model serves as the basis for the genetic algorithm optimization method to assess the system design in terms of accuracy, mass, quality factor, and bending stress. To validate the model, experimental data was collected on a 1906 Seth Thomas Model 04 movement. The engineering study findings indicate that the target accuracy, quality factor, and bending stress can be achieved with pendulum mass and gear thickness reductions of 1.4% and 50.3%, respectively. The tradespace exploration offers a visualization of the machine’s performance per design variable adjustments for greater insight into the original solution and subsequent recommended changes. Overall, this mechanical machine review enables an assessment of original design choices made over a century ago and provides an awareness of engineering’s progress during this period. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article

Construction of precast reinforced concrete (PRC) industrial halls in seismically active areas has been increasing in recent decades. As connections are one of the most sensitive and vulnerable zones of PRC structures, there is a need to pay special attention to their investigation and modeling in seismic analysis. Knowing that each PRC system is specific and unique, this study aims to evaluate the actual seismic performances of PRC industrial halls built in the AMONT system, which represent a significant portion of the existing industrial building stock in Italy, the Balkans, and Turkey. As there is a lack of published research data on its specific joints, the results of the quasi-static full-scale experiments carried out up to failure on the models of four characteristic connections are presented. Since the implementation of nonlinear dynamic analysis in everyday engineering practice can be demanding, a simplified model of the structure considering the effects of the connections’ stiffness is proposed in this paper. The differences in the roof top displacements between the proposed model and the model with the rigid joints of the analyzed frames are in the range from 16.53% to 66.93%. The values of inter-story drift ratios are larger by 10–100% when the real stiffness of connections is considered, which is above the limit value provided by standard EN 1998-1. These results confirm the necessity of considering the nonlinear behavior and stiffness of connections in precast frame structures when determining displacements, which is particularly important for the verification of the serviceability limit state of structures in seismic regions. Full article

In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility. Full article

Improving the thermal performance of building envelopes is an effective approach for reducing energy consumption and carbon emissions in cold and heating-dominated regions. This study presents an experimentally calibrated thermal–economic optimization of external wall insulation systems for residential buildings in Northwest China, using Xi’an as a representative cold–dry continental climate. A guarded hot-box apparatus was employed to measure the steady-state thermal transmittance (U-value) of multilayer wall assemblies incorporating expanded polystyrene (EPS), extruded polystyrene (XPS), and rock wool at different insulation thicknesses. The measured U-values were integrated into a dynamic building energy simulation model (DeST-h), and the simulated energy demand was subsequently evaluated through life-cycle cost (LCC) analysis to identify cost-optimal insulation configurations. The results indicate a nonlinear reduction in heating energy demand with increasing insulation thickness, with diminishing marginal returns beyond approximately 50 mm. Among the investigated materials, XPS exhibits the most favorable thermal–economic performance. For the climatic and economic conditions of Xi’an, a 50 mm XPS insulation layer minimizes total life-cycle cost while reducing annual building energy consumption by approximately 23–24% compared with the uninsulated reference case. This experimentally calibrated framework provides practical and policy-relevant guidance for insulation design and retrofit strategies in cold and dry regions. Full article

Drilling resistance (DR) measurement is a promising non-destructive technique for evaluating the mechanical properties of concrete. However, the reliability and repeatability of DR measurements are still limited by an insufficient understanding of how drill rotational speed influences the recorded drilling response. In addition, a systematic investigation of the influence of rotational speed on multiple drilling response parameters simultaneously is still lacking. This study investigates the relationship between imposed rotational speed and DR parameters—namely, rotational speed reduction, drilling force, and electrical power consumption—measured during controlled drilling tests in C30 and C50 concretes. A laboratory-developed DR testing methodology with constant feed rate and synchronized RPM, force, and power measurements was applied. Five nominal drilling speeds (in the range of 1400–2200 RPM) were examined. The results show clear, speed-dependent trends across all measurements. Strong correlations between nominal and in-hole rotational speeds were observed, while drilling force exhibited a nonlinear dependence on rotational speed. This study reveals distinct drilling behavioral signatures that differentiate concrete strength classes and clarify the mechanical origin of drilling-induced RPM reduction. The findings confirm that DR parameters, when analyzed collectively rather than individually, provide valuable diagnostic information and have strong potential for application in the non-destructive evaluation of concrete structures. Full article

This paper investigates the optimal portfolio control problem under a stochastic volatility model, whose dynamics are governed by a highly nonlinear Hamilton–Jacobi–Bellman equation. We employ a separable value function and introduce a novel exponential approximation technique to simplify the nonlinear terms of the auxiliary function. The simplified HJB equation is solved numerically using the advanced Fréchet–Newton method, which is known for its rapid convergence properties. We rigorously analyze the numerical outcomes, demonstrating that the iterative sequence converges quickly to the trivial fixed point ($g^{*}=1$) under zero risk and zero excess return conditions. This convergence is mathematically justified through rigorous functional analysis, including the principles of contraction mapping and the Kantorovich theorem, which validate the stability and efficiency of the proposed numerical scheme. The results offer theoretical insight into the behavior of the HJB equation in simplified solution spaces. Full article