Search Results (16,017)

This study investigates a symmetric fractional-order epidemic model with time delays and non-monotonic incidence rates, considering two viral strains. By confirming the existence, uniqueness, and boundedness of the system’s solutions, the research ensures the model’s well-posedness, guaranteeing its mathematical soundness and practical relevance. The study calculates and evaluates the equilibrium points and the basic reproduction numbers $R_0^1$ and $R_0^2$ to understand the dynamic behavior of the model under different parameter settings. Through the application of the Lyapunov method, the research examines the asymptotic global stability of the system, determining whether it will converge to a particular equilibrium state over time. Furthermore, Hopf bifurcation theory is employed to investigate potential periodic solutions and bifurcation scenarios, highlighting how the system might shift from stability to periodic oscillations under certain conditions. By utilizing the Adams-Bashforth-Moulton numerical simulation method, the theoretical results are validated, reinforcing the conclusions and demonstrating the model’s applicability in real-world contexts. It emphasizes the importance of fractional-order models in addressing epidemiological issues related to time delays ($\tau$), individual heterogeneity (m, k), and memory effects ($\theta$), offering greater accuracy compared with traditional integer-order models. In summary, this research provides a theoretical foundation and practical insights, enhancing the understanding and management of epidemic dynamics through fractional-order epidemic models. Full article

This article presents the development of the AZTLI-NN network and the evaluation of this network as a set of evolutionary algorithms in airfoil optimization tasks. AZTLI-NN has the characteristic of predicting the aerodynamic coefficients of the airfoils in the form of images (graphs of the aerodynamic coefficients as a function of the angle of attack) from parameter vectors corresponding to the parameterization method CST. This feature allows the network to achieve good performance when generalizing the predictions of the aerodynamic coefficients, being on par with neural networks that have the aerodynamic coefficients encoded in the form of structured data, and has the ability to handle a wide range of usage airfoils in general aviation. In addition, a case of how AZTLI-NN together with an adaptive evolutionary algorithm and population size reduction methods achieve good performance in finding the airfoil that provides the highest possible endurance value is shown, so this work is considered as an option in the early stages of the design for the selection of airfoils in the design of large-endurance UAVs. Full article

The effect of 3D printing parameters by Fused Filament Fabrication (FFF) on the translucency of polylactic acid (PLA) parts was investigated. Six different printing parameters were studied: object orientation, layer height, nozzle temperature, fan speed, extrusion multiplier, and printing speed. The commercially available Plasty Mladeč PLA filament and the Original Prusa MK4 3D printer were used for the experiments. The translucency of the printed samples of 50 × 25 × 1 mm dimensions was measured using a luxmeter in an integrating sphere. A total of 32 sample combinations were created. Each sample was printed ten times. The results show that all investigated parameters significantly affect the optical properties of PLA parts. The best measured translucency values were obtained when printing in portrait mode, with a layer height of 0.30 mm, nozzle temperature of 240 °C, fan speed of 100%, 0.75 set extrusion multiplier, and a speed of 40 mm/s. ANOVA was used to statistically evaluate the effect of each parameter on translucency, and statistically significant differences were found between different combinations of parameters (p < 0.05). Scanning Electron Microscope (SEM) analysis provided detailed images of the surface structure of the printed samples, allowing for a better discussion of the microscopic properties affecting the translucency. The best print setting has an efficiency of 88% compared to the default setting of 65%. The ability of visible light to pass through the print (translucency) improved by 23%. Full article

Background: Transoral robotic surgery (TORS) is nowadays considered a valuable minimally invasive approach to treat oropharyngeal squamous cell carcinoma (OPSCC). The aim of this technique is to improve functional preservation and reduce morbidity with excellent oncologic outcomes compared to the traditional transoral approach and chemoradiotherapy (CRT). The purpose of this systematic review is to assess an exhaustive overview of functional outcomes of TORS for OPSCC by evaluating several parameters reported in the available literature, such as the prevalence and dependence of tracheotomy, feeding tubes (FTs) and percutaneous endoscopic gastrostomy (PEG), the length of hospitalization, swallowing scores, speech tests and quality of life (QoL) questionnaires. Methods: A systematic literature review has been performed following the PRISMA 2020 checklist statement. A computer-aided search was carried out using an extensive set of queries on the Embase/PubMed, Scopus and Web of Sciences databases relating to papers published from 2007 to 2024. Results: A total of 28 papers were systematically reviewed, reporting 1541 patients’ data. The mean time of hospitalization was 6 days. A planned tracheotomy was performed in 8% of patients with a mean time of removal of 8 days. The prevalence and dependence of FT was 60% and 10%, respectively. Moreover, the presence of a high-stage T tumor with the contextual requirement of adjuvant therapies, the involvement of base tongues and the patient’s age being >55 years increased the risk of requiring an FT and PEG. Swallowing and long-term QoL outcomes highlight the superiority of the TORS approach alone compared to TORS with adjuvant therapies. Conclusions: TORS presented various favorable functional outcomes compared to other surgical approaches and primary CRT. However, adjuvant therapies after TORS strongly reduced the advantage of the robotic procedure, thus suggesting that T1 and T2 tumors may benefit mainly from TORS alone. Full article

Integrated Computational Materials Engineering (ICME) is a set of methodologies utilized by researchers and engineers assisting the study of material behaviour during production processes and/or service. ICME aligns with societal efforts for the twin green and digital transitions while improving the sustainability and cost efficiency of relevant products/processes. A significant link of the ICME chain, especially for metallic materials, is the crystal plasticity (CP) formulation. This review examines firstly the progress CP has made since its conceptualization and secondly the relevant thematic areas of its utilization and portraits them in a concise and condensed manner. CP is a proven tool able to capture complex phenomena and to provide realistic results, while elucidating on the material behaviour under complex loading conditions. To this end, a significant number of formulations falling under CP, each with their unique strengths and weaknesses, is offered. It is a developing field and there are still efforts to improve the models in various terms. One of the biggest struggles in setting up a CP simulation, especially a physics-based one, is the definition of the proper values for the relevant parameters. This review provides valuable data tables with indicative values. Full article

This paper presents an EPANET pressure-dependent analysis-based method for analyzing bursts in every pipe in a water supply system (WSS) and applies the method to large Finnish WSSs. EPANET is enhanced with the per-junction required and minimum pressures, a flow- and pressure-controlled pump battery component and a full control system model to accurately capture the dynamic behavior of the whole system, including the effect of control system parameters and settings. The results are combined with population and income data, and the correlations of the various physical and hydraulic parameters affecting the burst effects are analyzed. Full article

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability. Full article

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model’s adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model’s potential as a reliable tool for real-time RUL prediction in industrial applications. Full article

This paper addresses the problem of mitigating unknown partial path overlaps in communication systems. This study demonstrates that by utilizing the front-end insight of communication systems along with the sum–product algorithm applied to factor graphs, it is possible not only to track these overlapping components accurately, but also to detect all multipath channel impairments simultaneously. The proposed methodology involves discretizing channel parameters, such as channel paths and attenuation coefficients, to ensure the most accurate computation of means of Gaussian observations. These parameters are modeled as Bernoulli random variables with priors set to $0.5$. A notable aspect of the algorithm is its integration of the received signal power into the calculation of noise variance, which is critical for its performance. To further reduce the receiver complexity, a novel implementation strategy, based on provided pre-defined look up tables (LOTs) to the reciver, is introduced. The simulation results, covering both distributed and concentrated pilot scenarios, reveal that the algorithm performs almost equally under both conditions and surpasses the established upper bound in performance. Full article

Background: Acute sarcopenia refers to the swift decline in muscle function and mass following acute events such as illness, surgery, trauma, or burns that presents significant challenges in hospitalized older adults. Methods: narrative review to describe the mechanisms and management of acute sarcopenia. Results: The prevalence of acute sarcopenia ranges from 28% to 69%, likely underdiagnosed due to the absence of muscle mass and function assessments in most clinical settings. Systemic inflammation, immune–endocrine dysregulation, and anabolic resistance are identified as key pathophysiological factors. Interventions include early mobilization, resistance exercise, neuromuscular electrical stimulation, and nutritional strategies such as protein supplementation, leucine, β-hydroxy-β-methyl-butyrate, omega-3 fatty acids, and creatine monohydrate. Pharmaceuticals show variable efficacy. Conclusions: Future research should prioritize serial monitoring of muscle parameters, identification of predictive biomarkers, and the involvement of multidisciplinary teams from hospital admission to address sarcopenia. Early and targeted interventions are crucial to improve outcomes and prevent long-term disability associated with acute sarcopenia. Full article

Modal parameters are inherent characteristics of civil structures. Due to the effect of environmental factors and ambient loads, the physical and modal characteristics of a structure tend to change over time. Therefore, the effective identification of time-varying modal parameters has become an essential topic. In this study, an instantaneous modal identification method based on an adaptive chirp mode decomposition (ACMD) technique was proposed. The ACMD technique is highly adaptable and can accurately estimate the instantaneous frequencies of a structure. However, it is important to highlight that an initial frequency value must be selected beforehand in ACMD. If the initial frequency is set incorrectly, the resulting instantaneous frequencies may lack accuracy. To address the aforementioned problem, the Welch power spectrum was initially developed to extract a high-resolution time–frequency distribution from the measured signals. Subsequently, the time–frequency ridge was identified based on the maximum energy position in the time–frequency distribution plot, with the frequencies associated with the time–frequency ridge serving as the initial frequencies. Based on the initial frequencies, the measured signals with multiple degrees of freedom could be decomposed into individual time-varying components with a single degree of freedom. Following that, the instantaneous frequencies of each time-varying component could be calculated directly. Subsequently, a sliding window principal component analysis (PCA) method was introduced to derive instantaneous mode shapes. Finally, vibration data collected under various operational scenarios were used to validate the proposed method. The results demonstrated the effective identification of time-varying modal parameters in real-world civil structures, without missing modes. Full article

Volume-phase holographic gratings are suitable for use in greenhouse gas detection imaging spectrometers, enabling the detection instruments to achieve high spectral resolution, high signal-to-noise ratios, and high operational efficiency. However, when utilized in the infrared wavelength band with high dispersion requirements, gratings struggle to meet the demands for low polarization sensitivity due to changes in diffraction performance caused by phase delays in the incidence of light waves with distinct polarization states, and current methods for designing bulk-phase holographic gratings require a large number of calculations that complicate the balance of diffraction properties. To overcome this problem, a design method for transmissive bulk-phase holographic gratings is proposed in this study. The proposed method combines two diffraction theories (namely, Kogelnik coupled-wave theory and rigorous coupled-wave theory) and establishes a parameter optimization sequence based on the influence of design parameters on diffraction characteristics. Kogelnik coupled-wave theory is employed to establish the initial Bragg angle range, ensuring that the diffraction efficiency and phase delay of the grating thickness curve meet the requirements for incident light waves in various polarization states. Utilizing rigorous coupled-wave theory, we optimize grating settings based on criteria such as a center wavelength diffraction efficiency greater than 95%, polarization sensitivity less than 10%, maximum bandwidth, and spectral diffraction efficiency exceeding 80%. The ideal grating parameters are ultimately determined, and the manufacturing tolerances for various grating parameters are analyzed. The design results show that the grating stripe frequency is 1067 lines per millimeter, and the diffraction efficiencies of TE and TM waves are 96% and 99.89%, respectively. The diffraction efficiency of unpolarized light is more than 88% over the whole spectral range with an average efficiency of 94.49%, an effective bandwidth of 32 nm, and a polarization sensitivity of less than 7%. These characteristics meet the performance requirements for dispersive elements based on greenhouse gas detection, the spectral resolution of the detection instrument is up to 0.1 nm, and the signal-to-noise ratio and working efficiency are improved by increasing the transmittance of the instrument. Full article

This research focused on the helicopter turboshaft engine dynamic model, identifying task solving in unsteady and transient modes (engine starting and acceleration) based on sensor data. It is known that about 85% of helicopter turboshaft engines operate in steady-state modes, while only around 15% operate in unsteady and transient modes. Therefore, developing dynamic multi-mode models that account for engine behavior during these modes is a critical scientific and practical task. The dynamic model for starting and acceleration modes has been further developed using on-board parameters recorded by sensors (gas-generator rotor r.p.m., free turbine rotor speed, gas temperature in front of the compressor turbine, fuel consumption) to achieve a 99.88% accuracy in identifying the dynamics of these parameters. An improved Elman recurrent neural network with dynamic stack memory was introduced, enhancing the robustness and increasing the performance by 2.7 times compared to traditional Elman networks. A theorem was proposed and proven, demonstrating that the total execution time for N Push and Pop operations in the dynamic stack memory does not exceed a certain value O(N). The training algorithm for the Elman network was improved using time delay considerations and Butterworth filter preprocessing, reducing the loss function from 2.5 to 0.12% over 120 epochs. The gradient diagram showed a decrease over time, indicating the model’s approach to the minimum loss function, with optimal settings ensuring the stable training. Full article

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N₀) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N₀ of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N₀ might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes. Full article

Predicting the key plume evolution features of groundwater contamination are crucial for assessing uncertainty in contamination control and remediation, while implementing detailed complex numerical models for a large number of scenario simulations is time-consuming and sometimes even impossible. This work develops surrogate models with an effective and practicable pathway for predicting the key plume evolution features, such as the distance of maximum plume spreading, of groundwater contamination with natural attenuation. The representative various scenarios of the input parameter combinations were effectively generated by the orthogonal experiment method and the corresponding numerical simulations were performed by the reliable Groundwater Modeling System. The PSO-SVM surrogate models were first developed, and the accuracy was gradually enhanced from 0.5 to 0.9 under a multi-objective condition by effectively increasing the sample data size from 54 sets to 78 sets and decreasing the input variables from 25 of all the considered parameters to a smaller number of the key controlling factors. The statistical surrogate models were also constructed with the fitting degree all above 0.85. The achieved findings provide effective generic surrogate models along with a scientific basis and investigation approach reference for the environmental risk management and remediation of groundwater contamination, particularly with limited data. Full article