Search Results (4,007)

This study introduces a theoretical and computational framework for modeling acoustic wave propagation in defective concrete, with applications to non-destructive testing and structural health monitoring. The formulation is based on a coupled system of evolutionary hyperbolic equations, where internal defects are explicitly represented as localized energetic sources or sinks. A key contribution is the definition of acoercivity coefficient, which quantifies the energetic effect of defects and enables their classification as stabilizing, neutral, or dissipative. The model establishes a rigorous relationship between defect morphology, spatial distribution, and the global energetic stability of the material. Numerical simulations performed with an explicit finite-difference time-domain scheme confirm the theoretical predictions: the normalized total energy remains above $95\%$ for stabilizing defects (${\mu }_i>0$), decreases by about $10\%$ for quasi-neutral cases (${\mu }_i\approx 0$), and drops below $50\%$ within $200\mathsf{\mu }\mathrm{s}$ for dissipative defects (${\mu }_i<0$). The proposed approach reproduces the attenuation and phase behavior of classical Biot-type and Kelvin–Voigt models with deviations below $5\%$ while providing a richer energetic interpretation of local defect dynamics. Although primarily theoretical, this study establishes a physically consistent and quantitatively validated framework that supports the development of predictive ultrasonic indicators for the energetic classification of defects in concrete structures. Full article

In this paper, we unify and extend the linear quadratic pursuit–evasion games to dynamic equations on time scales. A mixed strategy for a single pursuer and evader is studied in two settings. In the open-loop setting, the corresponding controls are expressed in terms of a zero-input difference. In the closed-loop setting, the corresponding controls require a mixing feedback term when rewriting the system in extended state form. Finally, we offer a numerical simulation. Full article

Objectives: We aimed to investigate whether AI assistance could improve the performance of pneumothorax detection on chest radiographs (CXR) by readers with varying experience from radiologists to the frontline healthcare providers, and whether AI assistance could diminish the potential confounders for readers’ detecting pneumothorax. Methods: In this retrospective, single-center, blinded, multi-reader diagnostic accuracy study, 125 CXRs were prepared from radiological information system (March 2024 to August 2024) for test. The 18 readers were composed of six groups, each had 3 persons: board-certified radiologists (Group-1), senior radiology residents (Group-2), junior radiology residents (Group-3), postgraduate year residents (Group-4), senior radiographers (Group-5), and junior radiographers (Group-6). They read the CXR independently twice, without and with AI assistance, at an interval of one month. We used receiver operating characteristic curve for performance analysis and generalized estimating equation (GEE) model for confounding factor analysis. Results: AI software alone achieved a high area under curve of 0.965 (95% CI: 0.926, 0.995). With AI assistance, the performance in all groups significantly improved (p < 0.01) especially the junior readers (the frontline healthcare providers, Group-3, 4, 6) and diminished the difference among all groups except some related to Group-1. GEE model showed that AI assistance, reader’s experience, and projection type interfere with the readers’ performance (all p < 0.05). Conclusions: AI assistance could improve the performance of pneumothorax detection by varying experience of readers, especially the frontline healthcare providers. The influence of confounders, such as reader’s experience, also be diminished by AI assistance. Full article

CWD surveillance and diagnosis are important issues in Europe since its detection in Norway, as some of its strains, like that of classical scrapie, are contagious. In addition, there are concerns as several matters about CWD are not yet known. Although diagnostic methods for the active surveillance in bovine and small ruminants have been able to detect the European CWD strains, a retrospective study on Italian wild red deer (Cervus elaphus) samples was performed to compare the results obtained from rapid screening tests, authorized according to EU Regulation 999/2001, and the RT-QuIC, a highly sensitive method in the detection of prion disease infection. A total of one hundred brainstems and medial retropharyngeal lymph nodes were selected out of those received from the CWD Italian surveillance system. Confirmed CWD-positive and -negative samples were included in the study as controls. All of the samples were first tested with the HerdChek BSE–Scrapie Antigen Test and then using the RT-QuIC. The rapid test was negative in all brainstem and lymph node samples. RT-QuIC analyses showed only one red deer brainstem sample positive for seeding activity, while all lymph nodes were negative, including the one from this case. This positive brainstem sample was then re-extracted and retested using two different recombinant prion protein substrates (Ha90-231; BV23-231) and their different batches from the first analyses. Seeding activity was consistently confirmed across both substrates and extractions, with positive signals detected down to dilutions of 10⁻⁴ using rPrP Ha90-231 and as low as 10⁻⁶ with rPrP BV23-231. The additional diagnostic investigations performed on this red deer using the alternative rapid test (TeSeE SAP Combi), Western blot, and immunohistochemistry showed negative results both in the brainstem and lymph nodes. This study showed that overall, the results obtained with the HerdChek BSE–Scrapie Antigen Test and RT-QuIC agree except in one case. Our findings highlight the potential of the RT-QuIC method to detect very low levels of PrP^Sc-associated seeding activity that may escape detection using classical methods. While seeding activity does not always equate to infectivity, only a bioassay will confirm the real disease status of this Italian case. These findings support the integration of RT-QuIC as a powerful complementary tool within existing surveillance frameworks to strengthen early detection and diagnostic accuracy. Full article

In this paper, we investigate the behavior of solutions to a nonlinear system of rational difference equations of order two, defined by $x_{n+1}={\displaystyle \frac{x_n{y}_{n-1}}{y_n(a+b{x}_n{y}_{n-1})}},y_{n+1}={\displaystyle \frac{y_n{z}_{n-1}}{z_n(c+d{y}_n{z}_{n-1})}},z_{n+1}={\displaystyle \frac{z_n{x}_{n-1}}{x_n(e+f{z}_n{x}_{n-1})}},$ where n denotes a nonzero integer; the parameters $a,b,c,d,e,f$ are real constants; and the initial conditions $x_{-1},x_0,y_{-1},y_0,z_{-1},z_0$ are nonzero real numbers. By applying a suitable variable transformation, we reduce the original coupled system to three independent rational difference equations. This allows for separate analysis using established methods for second-order nonlinear recurrence relations. We derive explicit solutions and examine the qualitative behavior, including boundedness and periodicity, under different conditions. Our findings contribute to the theory of rational difference equations and offer insights for higher-order systems in applied sciences. Full article

The replacement of synthetic preservatives with natural alternatives is increasingly important in bakery production. This study examined the antifungal activity of thyme essential oil (TEO) against bread spoilage molds and its impact on product quality and consumer acceptance. TEO was tested at concentrations from 0 to 200 ppm against Aspergillus flavus and Penicillium expansum in bread and a model system, with mold responses modeled using the Gompertz equation. Because TEO affects the sensory qualities of bread, the kinetic parameters of mold growth were used to estimate the minimum inhibitory concentration (MIC), thereby ensuring a mold-free shelf life without significantly altering sensory properties. Bread samples were analyzed for pH, moisture, water activity, texture, specific volume, and sensory attributes (odor, flavor, texture, and acceptability). Residual thymol and carvacrol (measured using GC-MS) were also evaluated. The retention of thymol and carvacrol in baked bread was 75–80%. The tested TEO concentrations did not alter the moisture content, pH, or water activity of bread, while the specific volume was reduced and the width-to-height ratio increased as the TEO concentration increased. At concentrations below 100 ppm, TEO enhanced bread softness, while higher levels (>150 ppm) slightly increased hardness. Sensory testing showed no significant differences in color or texture (p > 0.05). At 50 ppm, TEO imparted a subtle thyme aroma and flavor, improving the sensory profile. At 100 and 150 ppm, the aroma and flavor became more pronounced and were well accepted. However, at 200 ppm, the thyme aroma and flavor decreased overall acceptance. In bread, the MIC of TEO for A. flavus ranges from 104.2 ppm (200 h delay) to 120.8 ppm (250 h), and for P. expansum, from 106.6 ppm (200 h) to 123.6 ppm (250 h). The MICs (100–125 ppm) fall within sensory acceptable scores, indicating that TEO can delay mold growth while maintaining bread quality. Moderate levels of TEO extended the mold-free shelf life of bread by providing microbial control and preserving its sensory properties. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and $C_0$ complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies. Full article

The photokinetic behavior of concomitant and independent photo- and photothermal reactions exposed to monochromatic or polychromatic irradiation, has not yet been described in photochemistry literature. The occurrence of such mixtures is reported in a wide range of fields, from living species to technologically designed devices. To address the lack of investigative tools that facilitate better understanding, quantification, and control of such parallel-reaction systems, a new holistic approach is proposed in the present study. It contributes to an effort dedicated to rationalizing photokinetics along the same criteria required for thermal kinetics. The methodology builds on a previously introduced general explicit integrated rate-law formula for single-reaction systems (whose integro-differential rate-equation is not solvable). The extension of its field of applicability to multi-component photoreactive mixtures is demonstrated in the present paper. For this purpose, a large number of combinations of both photo- and photothermal individual reactions, possessing distinctly different features, were studied in binary and ternary mixtures. The data of reactions/mixtures were generated by a fourth-order Runge–Kutta numerical integration. An excellent fitting of the species’ kinetic traces by the adapted explicit formula was obtained for all mixtures. Also, the quantification of the effects of the variation in the initial concentration of one component of the mixture, and/or the presence of inert spectator molecules in the reactor, was successfully performed. The investigative photokinetic tools proposed here are shown to be handy, efficient, and useful. The findings of the present study are also thought to expand the application possibilities of reactive photothermal systems in mixtures. Full article

Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into the learning process. However, standard PINNs often suffer from training instabilities and unbalanced optimization when handling multi-term loss functions, especially in problems involving singular perturbations, fractional operators, or multi-scale behaviors. To address these limitations, we propose a novel gradient variance weighting physics-informed neural network (GVW-PINN), which adaptively adjusts the loss weights based on the variance of gradient magnitudes during training. This mechanism balances the optimization dynamics across different loss terms, thereby enhancing both convergence stability and solution accuracy. We evaluate GVW-PINN on three representative PDE models and numerical experiments demonstrate that GVW-PINN consistently outperforms the conventional PINN in terms of training efficiency, loss convergence, and predictive accuracy. In particular, GVW-PINN achieves smoother and faster loss reduction, reduces relative errors by one to two orders of magnitude, and exhibits superior generalization to unseen domains. The proposed framework provides a robust and flexible strategy for applying PINNs to a wide range of integer- and fractional-order PDEs, highlighting its potential for advancing data-driven scientific computing in complex physical systems. Full article

Amid the deep integration of China’s “dual-carbon” goals with regional coordinated development strategies, this study develops a multidimensional analytical framework of regional integration based on panel data from 41 prefecture-level cities in the Yangtze River Delta urban agglomeration from 2009 to 2023. The framework encompasses five dimensions: urban–rural integration, innovation coordination, infrastructure connectivity, ecological co-governance, and public service sharing. Using structural equation modeling (SEM), the study empirically investigates the mechanisms and pathways through which regional integration shapes low-carbon development. The results indicate that different dimensions exert differentiated impacts: urban–rural integration and infrastructure connectivity significantly promote low-carbon development, whereas public service sharing has an adverse effect due to a phenomenon known as “carbon lock-in”. By contrast, the impact of innovation coordination and ecological co-governance is not statistically significant. Moreover, substantial regional heterogeneity exists: Jiangsu Province demonstrates the leading performance in the manifest development level; Zhejiang Province shows strong systemic capacity level, but limited conversion into manifest outcomes. At the same time, most cities in Anhui Province lag in both aspects. Coordination analysis further identifies four typical development patterns: dual-high, system-driven, performance-dominant, and dual-low. Drawing on these findings, this study proposes policy recommendations across four dimensions—regional coordination, low-carbon pathway optimization, targeted empowerment, and collaborative governance—to facilitate the green and low-carbon transition of the Yangtze River Delta urban agglomeration. Full article

We study stochastic production planning in capacity-constrained manufacturing systems, where feasible operating states are restricted to a convex safe-operating region. The objective is to minimize the total cost that combines a quadratic production effort with an inventory holding cost, while automatically halting production when the state leaves the safe region. We derive the associated Hamilton–Jacobi–Bellman (HJB) equation, establish the existence and uniqueness of the value function under broad conditions, and prove a concavity property of the transformed value function that yields a robust gradient-based optimal feedback policy. From an operations perspective, the stopping mechanism encodes hard capacity and safety limits, ensuring bounded risk and finite expected costs. We complement the analysis with numerical methods based on finite differences and illustrate how the resulting policies inform real-time decisions through two application-inspired examples: a single-product case calibrated with typical process-industry parameters and a two-dimensional example motivated by semiconductor fabrication, where interacting production variables must satisfy joint safety constraints. The results bridge rigorous stochastic control with practical production planning and provide actionable guidance for operating under uncertainty and capacity limits. Full article

Plant roots influence soil infiltration by altering its properties like porosity and bulk density, which are essential for ecohydrological cycles. Moso bamboo (Phyllostachys edulis), using its well-developed underground root system, invades neighbor forest communities, thereby influencing root characteristics and soil properties. Although Moso bamboo invasion may alter soil hydrology, its specific impact on soil infiltration capacity and water flow connectivity remains unclear. This work took a fir forest (Cunninghamia lanceolata), mixed fir and bamboo forest, and a bamboo forest which represent three different degrees of invasion: uninvaded, partially invaded, and completely invaded, respectively, as study objects, using double-ring dyeing infiltration method to measure soil infiltration capacity and calculating water flow connectivity index for the root zone. To assess the effects of soil properties and root characteristics on soil infiltration capacity and water flow connectivity, we employed random forest and structural equation modeling. The analysis revealed that Moso bamboo invasion significantly enhanced soil infiltration capacity. Specifically, in partially invaded forests, the initial infiltration rate, stable infiltration rate, and average infiltration rate increased by 31.5%, 26.1%, and 28.5%, respectively. In completely invaded forests, the corresponding increases were 6.6%, 35.6%, and 28.5%. Also, Moso bamboo invasion increased water flow connectivity of root zone, compared to the uninvaded forest, the water flow connectivity index increased by 29.4% in the completely invaded forest and by 15.6% in the partially invaded forest. The marked increase in fine root biomass density (RBD1), fine root length density (RLD1), soil organic carbon (SOC), and non-capillary pores (NCP) and the decrease in soil bulk density (SBD) followed by Moso bamboo invasion effectively improved water flow connectivity and soil infiltration capacity. The analysis identified that RBD1, RLD1, NCP, and SBD as the key drivers of soil infiltration capacity, whereas the water flow connectivity index was controlled mainly by SOC, NCP, RLD1, and RBD1. These findings help clarify the mechanistic pathways of Moso bamboo’s effects on soil infiltration. Full article

The issue of modeling the personal innovativeness of project team members is determined in this study. Findings from prior research on social capital associated with innovations and innovative activities reveal that social capital factors such as trust, social networks and connections, and social values determine a person’s attitude to innovations. Different connections involved in bridging (external) and bonding (internal) social capital can create conflict between project team members in different ways. To stimulate innovation in a conflict environment, a specially configured conflict management system is required that is capable of regulating the strength and intensity of the relationship between project team members. This paper analyzes the relationship between three constructs—innovativeness, social capital, and conflict. The existence of these latent constructs, which are formed by observable indicators of employees, is proven using confirmatory factor analysis (CFA). The construct of innovativeness depends on indicators such as creativity, risk propensity, and strategicity. Social capital includes observable indicators such as trust, social networks and connections, and social norms and values. Conflict consists of observable indicators of conflict between tasks, processes, and relationships. Using structural equation modeling (SEM), the causal relationship between social capital and innovativeness is substantiated with the mediating role of conflict in project groups between its participants—innovators and adaptors. The developed sociodynamic model for measuring conflict between innovators and adapters examines the required values of the controlled parameters of intra-group and inter-group connections between innovators and adapters in order to achieve equilibrium conflict dynamics, resulting in cooperation between them. This study was conducted using data from a survey of employees of a research organization. All model constructs were tested on a sample of employees as a whole, as well as for groups of innovators and adaptors separately. Full article

River migration and anthropogenic controls on hydrological processes may play important roles in estuarine system transformations and nutrient diffusion. We used a two-dimensional shallow water equation hydrodynamic water quality model to simulate total nitrogen (TN) transport under the situations of river migration and the “Water-Sediment Regulation Scheme” (WSRS). The results showed the following: (1) River migration changed the diffusion direction of high-TN-concentration water in the YRE from the east–west diffusion in 2009 to the north–south diffusion in 2019. (2) In the years the WSRS was active, the maximum diffusion distance of high-concentration-TN water is basically the same as that of the plume edge. In 2009 and 2019, it was 30 km in the southeast of the estuary and 26.5 km in the north. Concentrations of 0.5 mg/L and 1.05 mg/L in 2009 and 2019 can be used as the threshold for judging the farthest distance of diffusion. (3) In the years without the WSRS, the TN concentration in the YRE from June to July was generally lower than the same period in 2019, and the northward diffusion distance of high-concentration-TN water in 2017 was only 10% of that during the WSRS in 2019. (4) Runoff determines the diffusion range of TN in the YRE. The average runoff during the WSRS in 2019 was 6.88 times that of the same period in 2017, and the high concentration diffusion distance of TN in 2019 was 10 times that of 2017. Changes in estuary morphology determine the diffusion direction of nutrients. The results of this paper are helpful to further understand the nutrient diffusion law of estuaries and coasts under the influence of different factors, and to provide reference for the protection of water quality safety. Full article