Search Results (71)

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series. Full article

As agricultural non-point source pollution becomes increasingly severe and constitutes the primary source of water quality degradation, accurately predicting water quality in agricultural watersheds has become critical for environmental protection. In order to solve the nonlinear and non-stationary characteristics of water quality data, this paper proposes a combined model based on variational modal decomposition and genetic algorithm optimization of long short-term memory networks (VMD-GA-LSTM) for agricultural watershed water quality prediction. The VMD-GA-LSTM model utilizes the variational mode decomposition technique to decompose the time series data into multiple intrinsic mode functions and then uses the optimized LSTM network to predict each component to improve the accuracy of water quality prediction. The analysis of water quality data from the Baima River in China demonstrated that the VMD-GA-LSTM model significantly reduced prediction errors compared to other similar models. The VMD-GA-LSTM predictive model proposed in this paper effectively addresses the volatility characterizing water quality in agricultural watersheds, improves prediction accuracy, and it reveals valuable trends in water quality dynamics, providing practical solutions for sustainable agricultural practices and environmental governance. Full article

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH₄ test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data. Full article

Based on the small perturbation method, the transient pressure control equation considering real gas effects was solved, and the fitting expression for the dynamic characteristic parameters of the gas film during the start-up process was obtained. Subsequently, the influence of structural parameters of spiral-groove dry-gas seals on the dynamic tracking of the stationary ring’s motion during the non-steady-state start-up process under three-degree-of-freedom perturbations was analyzed. The results show that when the stationary and rotating rings initially separate, the stationary ring exhibits good tracking performance for both axial and angular motions of the rotating ring, although the tracking capability varies significantly. As time and film thickness increase, the tracking capability gradually weakens, and for the working film thickness, the tracking parameters tend to stabilize when the working film thickness is reached. The larger the spiral angles and the deeper the dynamic pressure grooves, the poorer the axial and angular tracking performance of the sealing ring. The number of grooves has a minimal impact on the axial and angular tracking performance of the stationary ring. A higher balance coefficient improves the axial and angular tracking performance of the stationary ring. Full article

Due to nonlinear aerodynamics, “non-traditional” flight modes may appear in longitudinal and lateral/directional dynamics once an aircraft experiences a high angle of attack and rapid maneuvers. Signal decomposition techniques are required to uncover these modes since they are hidden in flight characteristics. This study represents the Enhanced SynchroSqueezing Transform (ESST) for the extraction of “non-traditional” flight modes from flight data. Developed in the framework of the SynchroSqueezing Transform (SST), the ESST decomposes an Amplitude- and Frequency-Modulated (AMFM) signal into Intrinsic Mode Functions (IMFs). This process is optimized using the Genetic Algorithm (GA). Numerical investigations are performed to confirm the validity of the ESST. Both quantitative criteria for the fitness of the IMFs and qualitative study of the Time–Frequency Representations (TFRs) suggest that the ESST may perform better than the SST in decomposing nonlinear and non-stationary signals. Then, a method is proposed to find the instantaneous characteristics of the flight modes obtained by the ESST. The ESST analyzes an aircraft’s longitudinal and lateral flight data in post-stall maneuvers. The TFRs of flight parameters verify the existence of identical flight modes at different flight conditions. The IMFs are separated, and their instantaneous characteristics are computed. In addition, the ESST modes are compared to conventional modes. The results indicate that the ESST is capable of obtaining both classical oscillatory modes, such as Short Period (SP) and Dutch Roll (DR), and “non-traditional” modes. In the end, coupled modes are identified by comparing longitudinal and lateral IMFs. Full article

Conical diffusers are widely used in technical devices (gasifiers, turbines, combustion chambers) and technological processes (ejectors, mixers, renewable energy). The perfection of flow gas dynamics in a conical diffuser affects the intensity of heat and mass transfer processes, the quality of mixing/separation of working media and the flow characteristics of technical devices. These parameters largely determine the efficiency and productivity of the final product. This article presents an analysis of experimental data on the gas-dynamic characteristics of stationary air flows in a vertical, conical, flat diffuser under different initial boundary conditions. An experimental setup was created, measuring instruments were selected, and an automated data collection system was developed. Basic data on the gas dynamics of air flows were obtained using the thermal anemometry method. Experimental data on instantaneous values of air flow velocity in a diffuser for initial velocities from 0.4 m/s to 2.22 m/s are presented. These data were the basis for calculating and obtaining velocity fields and turbulence intensity fields of the air flow in a vertical diffuser. It is shown that the value of the initial flow velocity at the diffuser inlet has a significant effect on the gas-dynamic characteristics. In addition, a spectral analysis of the change in air flow velocity both by height and along the diffuser axis was performed. The obtained data may be useful for refining engineering calculations, verifying mathematical models, searching for technical solutions and deepening knowledge about the features of gas dynamics of air flows in vertical diffusers. Full article

Unsteady gas-dynamic phenomena in pipelines of complex configuration are widespread in heat exchange and power equipment. Therefore, studying the heat transfer level of pulsating air flows in round and triangular pipes with different turbulence intensities is a relevant and significant task for the development of science and technology. The studies were conducted on a laboratory stand based on the thermal anemometry method and an automated system for collecting and processing experimental data. Rectilinear round and triangular pipes with identical cross-sectional areas were used in the work. Flow pulsations from 3 to 15.8 Hz were generated by means of a rotating flap. The turbulence intensity (TI) of the pulsating flows varied from 0.03 to 0.15 by installing stationary flat turbulators. The working medium was air with a temperature of 22 ± 1 °C moving at a speed from 5 to 75 m/s. It was established that the presence of gas-dynamic unsteadiness leads to an increase in the TI by 47–72% in a round pipe and by 36–86% in a triangular pipe. The presence of gas-dynamic unsteadiness causes a heat transfer intensification in a round pipe by 26–35.5% and by 24–36% in a triangular pipe. It was shown that a significant increase in the TI of pulsating flows leads to an increase in the heat transfer coefficient by 11–16% in a round pipe and a decrease in the heat transfer coefficient by 7–24% in a triangular pipe. The obtained results can be used in the design of heat exchangers and gas exchange systems in power machines, as well as in the creation of devices and apparatuses of pulse action. Full article

There is a fundamental issue in the launching system with the modular charge technology, which is an unsteady gas–solid flow in the sequence-change space within a short period of time. It leads to complex particle behavior, causing the strong pulsation of particle energy released during the combustion process. As a result, a large initial pressure wave is generated, which damages the launching stability. In this work, a 3D gas–solid flow model is developed based on the computational fluid dynamics–discrete element method (CFD-DEM) model to analyze the particle behavior in the launching system with different numbers of modules. The rationality of the model is verified through the experiment. It is found that the particles near the cover of the rightmost module move out of the module rapidly and collide with the right face of the chamber, forming a retained particle layer. When particles are stationary, the distribution of particles consists of slope accumulations and horizontal accumulation. With the increase in the module number, the position changes of all tracer particles are decreased, both the thickness and the length of the horizontal shape are increased, the variation laws of the slope stack height change from exponential to linear, and the distribution of particles becomes uniform. Full article

The impact of wave-induced forces on the integrity of stationary oscillating water column (OWC) devices is essential for ensuring their structural safety. In our study, we built a three-dimensional numerical model of an OWC device using the computational fluid dynamics (CFDs) software OpenFOAM-v1912. Subsequently, the hydrodynamic performance of the numerical model is comprehensively validated. Finally, the hydrodynamic performance data are analyzed in detail to obtain meaningful conclusions. Results indicate that the horizontal wave force applied to the OWC device is approximately 6.6 to 7.9 times greater than the vertical wave force, whereas the lateral wave force is relatively small. Both the horizontal and vertical wave forces decrease as the relative water depth increases under a constant wave period and height. In addition, the highest dynamic water pressure is observed at the interface between the water surface and device, both within and outside the front wall of the gas chamber. The dynamic water pressure at different locations on the front chamber increases and subsequently decreases as the wave frequency increases. Full article

We consider a toy model for the study of monitored dynamics in many-body quantum systems. We study the stochastic Schrödinger equation resulting from continuous monitoring with a rate $\Gamma$ of a random Hermitian operator, drawn from the Gaussian unitary ensemble (GUE) at every time t. Due to invariance by unitary transformations, the dynamics of the eigenvalues ${\left\{{\lambda }_{\alpha }\right\}}_{\alpha =1}^n$ of the density matrix decouples from that of the eigenvectors, and is exactly described by stochastic equations that we derive. We consider two regimes: in the presence of an extra dephasing term, which can be generated by imperfect quantum measurements, the density matrix has a stationary distribution, and we show that in the limit of large size $n\to \infty$ it matches with the inverse-Marchenko–Pastur distribution. In the case of perfect measurements, instead, purification eventually occurs and we focus on finite-time dynamics. In this case, remarkably, we find an exact solution for the joint probability distribution of $\lambda$’s at each time t and for each size n. Two relevant regimes emerge: at short times $t\Gamma =O\left(1\right)$, the spectrum is in a Coulomb gas regime, with a well-defined continuous spectral distribution in the $n\to \infty$ limit. In that case, all moments of the density matrix become self-averaging and it is possible to exactly characterize the entanglement spectrum. In the limit of large times $t\Gamma =O\left(n\right)$, one enters instead a regime in which the eigenvalues are exponentially separated $\log ({\lambda }_{\alpha }/{\lambda }_{\beta })=O(\Gamma t/n)$, but fluctuations $\sim O\left(\sqrt{\Gamma t/n}\right)$ play an essential role. We are still able to characterize the asymptotic behaviors of the entanglement entropy in this regime. Full article

Green taxes are taxes collected to protect the environment by controlling the negative effects of certain activities and products on the environment. They are also an instrument of environmental policy and can therefore contribute to several sustainable development goals. According to the studies carried out, the green economy aims to ensure sustainable development. The main objective of this paper is to identify the existing relationships between green taxes and sustainable economic development through a dynamic panel analysis. A dynamic panel analysis was therefore carried out on the existing links between environmental taxes and charges at the European level and the indicators of the circular economy. The results of the two dynamic regressions for the two dependent variables, namely total green taxes and energy taxes, show a positive and significant correlation with the variation of GDP and with primary energy consumption, confirming the hypothesis that environmental taxes and energy taxes are closely linked to these two important indicators of sustainable development. Thus, as GDP changes, the taxes on energy production and the energy products used in both transport and stationary applications increase. As a result of the analysis, we can note that the increase in primary energy consumption and the consumption of raw materials leads to an increase in environmental and energy taxes. Energy taxes are a possible solution to reduce CO₂ emissions in third world countries and may even stimulate climate action. In contrast, we found no significant correlation between green taxes and the following variables: Human Development Index, net greenhouse gas emissions, private investment and gross value added related to circular economy sectors, the consumption of raw materials, waste generated, waste treatment, the supply, transformation, and consumption of renewable energy, public expenditure on environmental protection, and climate-related economic losses. Full article

The article describes a hard- and software controlled complex for gas-strain monitoring, consisting of stationary laser strainmeters and a laser nanobarograph, a stationary gas analyzer, and a weather station installed at Shultz Cape in the Sea of Japan; and a mobile shipboard complex, consisting of a gas analyzer and a weather station installed in a scientific research vessel. In the course of trial methodological measurements on these systems, general patterns were identified in the dynamics of greenhouse gases and deformation of the Earth’s crust in the range of diurnal and semi-diurnal tides, and also in the range of ultra-low frequencies, caused by atmospheric wave processes and, possibly, individual tones of the Earth’s eigen oscillations. Full article

This article analyzes the symmetry of two-dimensional stationary gas dynamics equations in Lagrangian coordinates, including the search for equivalence transformations, the group classification of equations, the derivation of group foliations, and the construction of conservation laws. The consideration of equations in Lagrangian coordinates significantly simplifies the procedure for obtaining conservation laws, which are derived using the Noether theorem. The final part of the work is devoted to group foliations of the gas dynamics equations, including for the nonstationary isentropic case. The group foliations approach is usually employed for equations that admit infinite-dimensional groups of transformations (which is exactly the case for the gas dynamics equations in Lagrangian coordinates) and may make it possible to simplify their further analysis. The results obtained in this regard generalize previously known results for the two-dimensional shallow water equations in Lagrangian coordinates. Full article

As greenhouse gas emissions from dairy farms are on the rise, effective monitoring of these emissions has emerged as a crucial tool for assessing their environmental impacts and promoting sustainable development. Most of the existing studies on GHGs from dairy farms involve stationary detections with long response times and high costs. In this study, a greenhouse gas detection system was constructed based on NDIR technology using a single broadband light source and a four-channel thermopile detector for the detection of CH₄, N₂O, and CO₂; the detection range of CH₄ was 0~100 ppm; that of N₂O was 0~500 ppm; and that of CO₂ was 0~20%. After the concentration calibration, the cross-interference between the gas measurement channels was studied, and the least-squares method was used to correct the interference between the three gases. The experimental results showed that the full-range deviation of the detection device was lower than 0.81%, the repeatability was lower than 0.39%, the stability was lower than 0.61%, and the response time was lower than 10 s. This study also carried out on-site testing in Luoyang Shengsheng Ranch (Luoyang, China), and the results show that the error between this device and the PTM600 portable gas analyzer is within 9.78%, and the dynamic response time of this device is within 16 s, at which point the content of greenhouse gases in dairy farms can be measured quickly and accurately. The objective of this study is to enhance the precision and effectiveness of greenhouse gas (GHG) emissions monitoring from dairy farms, thereby contributing to environmental protection and sustainable development goals. By achieving this, we aim to facilitate societal progress towards a greener and low-carbon future. Full article

The optimization of reactant and product mass transfer within fuel cells stands as a critical determinant for achieving optimal fuel-cell performance. With a specific focus on stationary applications, this study delves into the comprehensive examination of fuel-cell mass transfer properties, employing a sophisticated blend of computational fluid dynamics (CFD) and the innovative design of a double-layered wire mesh (DLWM) as a flow field and gas diffusion layer. The investigation notably contrasts a meticulously developed 3D fine mesh flow field with a numerical model of the integrated DLWM implemented on the cathode end of a proton exchange membrane fuel cell (PEMFC). Evaluations reveal that the 3D fine mesh experiences a notable threefold increase in pressure drop compared to the DLWM flow field, indicative of the enhanced efficiency achieved by the DLWM configuration. Oxygen distribution analyses further underscore the promising performance of both the 3D fine mesh and the proposed DLWM, with the DLWM showcasing additional improvements in water removal capabilities within the cell. Impressively, the DLWM attains a remarkable maximum current density of 2137.17 mA/cm² at 0.55 V, indicative of its superior performance over the 3D fine mesh, while also demonstrating the potential for cost-effectiveness and scalability in mass production. Full article