Search Results (860)

In this study, we outline a modified harmonic balance method for solving non-homogenous integrable dispersionless equations and obtaining the corresponding periodic solutions, a research field which shows limited investigation. This study is the first to solve this nonlinear problem, based on a recently developed harmonic balance method combined with Vieta’s substitution technique. A set of analytical formulas are generated from the modified harmonic balance method and used to compute the approximate periodic solutions of the dispersionless equations. The main advantage of this method is that the computation effort required in the solution procedure can be smaller. The results of the modified harmonic balance method show reasonable agreement with those obtained using the classic harmonic balance method. Our proposed solution method can decouple the nonlinear algebraic equations generated in the harmonic balance process. We also investigated the effects of various parameters on nonlinear periodic responses and harmonic convergence. Full article

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns. Full article

Reverse osmosis (RO) concentrated water can be effectively treated with catalytic ozone oxidation technology, an effective advanced oxidation process. In order to provide a thorough reference for the safe treatment and reuse of RO concentrated water, this paper examines the properties of RO concentrated water, such as its high salt content, high levels of organic pollutants, and low biochemistry. It also examines the mechanism of its role in treating RO concentrated water and combs through its applications in municipal, petrochemical, coal chemical, industrial parks, and other industries. The study demonstrates that ozone oxidation technology can efficiently eliminate the organic matter that is difficult to break down in RO concentrated water and lower treatment energy consumption; however, issues with free radical inhibitor interference, catalyst recovery, and stability still affect its use. Future research into multi-technology synergistic processes, the development of stable and effective non-homogeneous catalysts, and the promotion of their use at the “zero discharge” scale for industrial wastewater are all imperative. Full article

Coal-fired power plants exacerbate hazy weather under low winter temperatures, while sulphate ions (SO₄²⁻) in condensable particulate matter (CPM) emitted from ultra-low emission coal-fired power plants accelerate sulphate formation. The transformation of gaseous precursors (SO₂, NOx, NH₃) is the main pathway for sulphate formation by homogeneous or non-homogeneous reactions. For the sustainability of the world, in this paper, the effects of condensation temperature, H₂O, NO_X and NH₃ on the SO₄²⁻ generation characteristics under low-temperature rapid condensation conditions are investigated. With lower temperatures, especially from 0 °C cooling to −20 °C, the concentration of SO₄²⁻ was as high as 26.79 mg/m³. With a greater proportion of H₂SO₄ in the aerosol state, and a faster rate of sulphate formation, H₂O vapour condensation can provide a reaction site for sulphuric acid aerosol generation. SO₄²⁻ in CPM is mainly derived from the non-homogeneous reaction of SO₂. SO₃ is an important component of CPM and provides a reaction site for the formation of SO₄²⁻. SO₂ and SO₃, in combination with Stefan flow, jointly play a synergistic role in the generation of SO₄²⁻. The content of SO₄²⁻ was as high as 36.18 mg/m³. While NO_X sometimes inhibits the formation of SO₄²⁻, NH₃ has a key role in the nucleation process of CPM. NH₃, SO₂ and NO_X have been found to rapidly form sulphate with particle sizes up to 5 µm at sub-zero temperatures and promote the formation of sulphuric acid aerosols. Full article

Time series data are fundamental for analyzing temporal dynamics and patterns, enabling researchers and practitioners to model, forecast, and support decision-making across a wide range of domains, such as finance, climate science, environmental studies, and signal processing. In the context of high-dimensional time series, the Vector Autoregressive model (VAR) is widely used, wherein each variable is modeled as a linear combination of lagged values of all variables in the system. However, the traditional VAR framework relies on the assumption of stationarity, which states that the autoregressive coefficients remain constant over time. Unfortunately, this assumption often fails in practice, especially in systems subject to structural breaks or evolving temporal dynamics. The Time-Varying Vector Autoregressive (TV-VAR) model has been developed to address this limitation, allowing model parameters to vary over time and thereby offering greater flexibility in capturing non-stationary behavior. In this study, we propose an enhanced modeling approach for the TV-VAR framework by incorporating minimization solvers in generalized additive models and one-sided kernel smoothing techniques. The effectiveness of the proposed methodology is assessed using simulations based on non-homogeneous Markov chains, accompanied by a detailed discussion of its advantages and limitations. Finally, we illustrate the practical utility of our approach using an application to real-world financial data. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Grey models have attracted considerable attention as a time series forecasting tool in recent years. Nevertheless, the linear characteristics of the differential equations on which traditional grey models rely frequently result in inadequate predictive accuracy and applicability when addressing intricate nonlinear systems. This study introduces a conformable fractional order unbiased kernel-regularized nonhomogeneous grey model (CFUKRNGM) based on statistical learning theory to address these limitations. The proposed model initially uses a conformable fractional-order accumulation operator to derive distribution information from historical data. A novel regularization problem is then formulated, thereby eliminating the bias term from the kernel-regularized nonhomogeneous grey model (KRNGM). The parameter estimation of the CFUKRNGM model requires solving a linear equation with a lower order than the KRNGM model, and is automatically calibrated through the Bayesian optimization algorithm. Experimental results show that the CFUKRNGM model achieves superior prediction accuracy and greater generalization performance compared to both the KRNGM and traditional grey models. Full article

An improved adaptive constant false alarm rate (CFAR) detector based on fuzzy theory is proposed to address the issue of poor detection performance and robustness of the variability index (VI) class CFAR detectors due to the misjudgment of the background environment and other reasons. The integration of the order statistic threshold adjustable detection algorithm (OSTA) into the adaptive CFAR detector has the potential to address the aforementioned issue. Therefore, in a clutter edge environment, the ratio of the means of the leading and lagging windows is calculated separately, and the differences between these mean ratios and predefined thresholds are used as inputs to the fuzzy inference machine, and the background clutter estimation of the OSTA is determined based on the fuzzy output, which can extend the range of values of the background clutter estimation, and improve the detection performance of the OSTA in this environment. The Kaigh–Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. Therefore, KLQ is used to detect targets in this environment, further improving the detection performance of the detector. The experimental results show that in multiple-target environments with an average misjudgment rate of 27.48%, the proposed detector increases the detection probability by 15.58% compared to the recently proposed variability index heterogeneous clutter estimate modified ordered statistics CFAR detector (VIHCEMOS-CFAR), and in a clutter edge environment, the false alarm rate of the proposed detector was reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Additionally, the effectiveness of the proposed detector is also validated by real clutter data. It can be seen that the proposed adaptive CFAR detector has better robustness to the misjudgment of the background environment and better overall detection performance regardless of the environment. Full article

The synthesis of realistic fog in images is critical for applications such as autonomous navigation, augmented reality, and visual effects. Traditional methods based on Koschmieder’s law or GAN-based image translation typically assume homogeneous fog distributions and rely on oversimplified scattering models, limiting their physical realism. In this paper, we propose a physics-driven approach to fog synthesis by discretizing the Radiative Transfer Equation (RTE). Our method models spatially inhomogeneous fog and anisotropic multi-scattering, enabling the generation of structurally consistent and perceptually plausible fog effects. To evaluate performance, we construct a dataset of real-world foggy, cloudy, and sunny images and compare our results against both Koschmieder-based and GAN-based baselines. Experimental results show that our method achieves a lower Fréchet Inception Distance ($-10\%$ vs. Koschmieder, $-42\%$ vs. CycleGAN) and a higher Pearson correlation ($+4\%$ and $+21\%$, respectively), highlighting its superiority in both feature space and structural fidelity. These findings highlight the potential of RTE-based fog synthesis for physically consistent image augmentation under challenging visibility conditions. However, the method’s practical deployment may be constrained by high memory requirements due to tensor-based computations, which must be addressed for large-scale or real-time applications. Full article

Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the conditional distribution of a gap time given the previous recurrence time to be deduced. Under this approach, some parametric rate models have been proposed, differing in their distributional assumptions on gap times. In particular, the extended exponential–Poisson, Weibull and extended Chen–Poisson distributions have been considered. Alternatively, a flexible rate model using restricted cubic splines is proposed here to capture complex non-monotonic rate shapes. Moreover, a comprehensive comparison of parametric rate models is presented. The maximum likelihood method is applied for parameter estimation in the presence of right-censoring. It is shown that some models include important special cases that allow testing of the independence assumption between a gap time and the previous recurrence time. The likelihood ratio test, as well as two information criteria, are discussed for model selection. Model fit is assessed using Cox–Snell residuals. Applications to two well-known clinical data sets illustrate the comparative performance of both the existing and proposed models, as well as their practical relevance. Full article

This paper mainly investigates a class of third-order semilinear delay differential equations with a nonhomogeneous term ${\left({\left[x^{″}\left(t\right)\right]}^{\alpha }\right)}^{\prime }+q\left(t\right)x^{\alpha }\left(\sigma \left(t\right)\right)+f\left(t\right)=0,t\ge t_0.$ Under the oscillation criteria, we propose a sufficient condition to ensure that all solutions for the equation exhibit oscillatory behavior when $\alpha$ is the quotient of two positive odd integers, supported by concrete examples to verify the accuracy of these conditions. Furthermore, for the case $\alpha =1,$ a sufficient condition is established to guarantee that the solutions either oscillate or asymptotically converge to zero. Moreover, under these criteria, we demonstrate that the global oscillatory behavior of solutions remains unaffected by time-delay functions, nonhomogeneous terms, or nonlinear perturbations when $\alpha =1.$ Finally, numerical simulations are provided to validate the effectiveness of the derived conclusions. Full article

Background: The rise in antimicrobial-resistant (AMR) strains of Neisseria gonorrhoeae is internationally recognised as a critical public health concern, with limited treatment options available. The urgency of this issue prompted the European Centre for Disease Prevention and Control to establish ‘EURO-GASP’ to monitor trends in resistance and address developments. Comprehensive data on AMR strains in people living with HIV (PLHIV) is limited, especially in Cyprus. Objectives: To analyse trends in rates of resistant N. gonorrhoeae infections and identify any correlations between patient factors that may contribute to such in PLHIV in The Republic of Cyprus. Methods: We conducted a retrospective chart review study on N. gonorrhoea resistance among PLHIV from the Gregorios HIV reference clinic in Larnaca, Cyprus, between 2015 and 2023. Antimicrobial susceptibility was assessed via disc diffusion or gradient strip method on GC II agar against a non-homogenous panel of antibiotic preparations, based on standard laboratory practice variation. Demographic and clinical data, including antibiograms, treatments and test of cure, were recorded. Statistical analysis was performed using Stata v16, with significance set at p < 0.05. The study received approval from the Cyprus National Bioethics Committee. Results: A total of 45 isolates from 39 patients were analysed, with 62% of these demonstrating resistance to at least one antibiotic. Resistance rates were not shown to change over time. We identified a statistically significant linear association between a person having a history of an STI and the number of antibiotics which the isolate is resistant to (β = 1.2; p: 0.004). Notably, a single isolate demonstrated resistance to ceftriaxone, the first-line treatment currently recommended in both Europe and the United States. This finding is particularly alarming given the critical role of ceftriaxone in the management of gonorrhoea. Conclusions: Whilst there has been no increase in resistance rates over time, the detection of ceftriaxone-resistant N. gonorrhoeae is a significant public health concern. Given that having a history of an STI makes a person more likely to develop a resistant infection, PLHIV or those who engage in risky sexual behaviours are particularly vulnerable. There is a pressing need to enhance surveillance and implement routine susceptibility testing in Cyprus, given the country’s role as a major international hub for travel and migration. Molecular analysis can further improve our understanding. Additionally, the global public health community must urgently prioritise the development of novel therapeutic agents for the treatment of gonorrhoea. Full article

We examine the use of a Geometric Distribution for pre-calculating the publication deadlines of blocks in a simulation of proof-of-work Blockchain. Specifically, we focus on Discrete-Event Simulation, where the simulator identifies events to be simulated, calculates their deadlines, places them in an Event Queue ordered by deadline, and processes them sequentially. In Blockchain, these events include the publication and reception of a block by each Miner. While a Geometric Distribution allows the calculation of block publication deadlines in the absence of Difficulty updates, in the case of evolving Difficulty, it must be extended to a non-homogeneous Geometric Distribution. To address this issue, we introduce the Geometric Distribution with a Variable Probability, a non-homogeneous Geometric Distribution that enables the calculation of block publication deadlines in the presence of Difficulty Regulation—a distinctive feature of proof-of-work Blockchain. We then present the architecture and operating principles of a Discrete-Event Simulator based on this distribution, along with simulation results that validate our approach. Full article

As the development of oilfields in China enters its middle-to-late stage, the old oilfields still occupy a dominant position in the production structure. The seepage process of reservoirs in the high Water Content Period (WCP) presents significant nonlinear and non-homogeneous evolution characteristics, and the traditional seepage-modeling methods are facing the double challenges of accuracy and adaptability when dealing with complex dynamic scenarios. In recent years, Deep Learning technology has gradually become an important tool for reservoir seepage field prediction by virtue of its powerful feature extraction and nonlinear modeling capabilities. This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). Key processes based on Deep Learning, such as feature engineering, network structure design, and physical constraint integration mechanisms, are further explored. Based on the summary of the existing results, this paper proposes future development directions including real-time prediction and closed-loop optimization, multi-source data fusion, physical consistency modeling and interpretability enhancement, model migration, and online updating capability. The research aims to provide theoretical support and technical reference for the intelligent development of old oilfields, the construction of digital twin reservoirs, and the prediction of seepage behavior in complex reservoirs. Full article

With the aim of the limitations of the existing hydroelectric simulation experiment methods under non-homogeneous reservoir conditions, this paper investigates the seepage characteristics and production capacity laws of complex structural wells by designing hydroelectric simulation experiments of horizontal wells and planar multi-branch wells under non-homogeneous reservoir conditions, based on the hydroelectric similarity principle. The experiments use a CuSO₄ solution and gel to simulate homogeneous and non-homogeneous reservoirs, respectively, and combine with similarity theory to construct the correspondence between the seepage field and the electric field, and to analyze the pressure distribution and the change in production. The results show the following: non-homogeneity significantly alters seepage paths, leading to a reduction in the actual control area; the superimposed effects of branching interference of planar multi-branching wells, and the non-homogeneity of the reservoir, increase the effectiveness of mobilizing the low-permeability area between the branches; the daily gas production of the horizontal wells and the planar multi-branching wells under non-homogeneous conditions are 37.6 × 10⁴ m³/d and 70.9 × 10⁴ m³/d, respectively; and the production gap widened with the increase in the pressure function difference as compared to the homogeneous conditions. This study provides an experimental basis for the development of non-homogeneous gas reservoirs, and it has reference value for the study of seepage mechanism and optimization of well design. Full article