Search Results (12,694)

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying ${\mathrm{PM}}_{2.5}$ (particulate matter of size ≤ $2.5 \mathsf{\mu }\mathrm{m}$ in diameter) pollution levels. For the most polluted day, the optimal sampling interval for ${\mathrm{PM}}_{2.5}$ was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for ${\mathrm{PM}}_{2.5}$. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. Full article

Longitudinal dispersion coefficient is a key parameter governing solute transport in porous media, with significant implications for various industrial processes. However, the impact of microfractures on the longitudinal dispersion coefficient remains insufficiently understood. In this study, pore-scale direct numerical simulations are performed to analyze solute transport in microfractured porous media during unstable miscible displacement. Spatiotemporal concentration profiles were fitted to the analytical solution of the convection–dispersion equation to quantify the longitudinal dispersion coefficient across different microfracture configurations. The results indicate that the longitudinal dispersion coefficient is highly sensitive to microfracture characteristics. Specifically, an increased projection length of microfractures in the flow direction and a reduced lateral projection length enhance longitudinal dispersion at the outlet. When Peclet number ≥1, the longitudinal dispersion coefficient follows a three-stage variation pattern along the flow direction, with microfracture connectivity and orientation dominating its scale sensitivity. Furthermore, both diffusion-dominated and mixed advective-diffusion regimes are observed. In diffusion-dominated regimes, significant channeling alters the applicability of traditional scaling laws, with the relationship between longitudinal dispersion coefficient and porosity holding only when the Peclet number is below 0.07. These results provide a comprehensive scale-up framework for CO₂ miscible flooding in unconventional reservoirs and CO₂ storage in saline aquifers, offering valuable insights for the numerical modeling of heterogeneous reservoir development. Full article

In the realm of road–rail dual-use bridges, conducting accurate vehicle–bridge coupling analysis is crucial, as the combined effects of road traffic and rail transit induce complex dynamic challenges. This study investigates a road–rail dual-use network arch bridge, highlighting the dynamic effects induced by light rail loadings. By employing a noniterative vehicle–bridge coupling analysis method, the dynamic responses of hangers caused by vehicular and light rail loads are effectively captured. Additionally, this study explores the influence of various parameters, including vehicle types, driving lanes, and road surface roughness on the responses of hangers positioned at different locations along the bridge. The findings reveal that light rail induces significantly larger dynamic effects compared to motor vehicles. When the light rail operates closer to the hanger, the responses of hangers are more pronounced. Furthermore, different road surface roughness level notably affects the amplitude of axial stress and bending moment fluctuations. Poorer road conditions amplify these dynamic effects, leading to increased stress variations. These insights underscore the necessity of integrating considerations for both road and rail traffic in the structural analysis and design of network arch bridges to ensure their reliability and serviceability. Full article

To enhance the power output performance of high-power electrolytic plating equipment and improve the dynamic response capability and disturbance rejection ability of the DC-DC converter—a core component in electrolytic plating systems—this study proposes a predictive control strategy for phase-shifted full-bridge converters based on an improved nonlinear disturbance observer. The implementation framework comprises three key technical components: Firstly, a model predictive control (MPC)-based inner-loop controller architecture is constructed to optimize the dynamic response characteristics of the current inner-loop system. Subsequently, an enhanced nonlinear disturbance observer is designed to accurately estimate parameter variations in converter electronic components and external disturbances. Finally, a feedforward compensation module is developed to mitigate the inherent duty cycle loss phenomenon in phase-shifted full-bridge converters. Simulation results demonstrate that the proposed control strategy significantly improves system dynamic performance, achieving a 25% reduction in settling time compared with conventional methods while maintaining robust disturbance rejection capabilities under ±10% voltage fluctuations. This integrated approach effectively addresses the conflicting requirements between dynamic response speed and anti-interference performance in high-power electrochemical process control systems. Full article

Total electron content (TEC) serves as a key parameter characterizing ionospheric conditions. Accurate prediction of TEC plays a crucial role in improving the precision of Global Navigation Satellite Systems (GNSS). However, existing research have predominantly emphasized spatial variations in the ionosphere, neglecting the periodic changes of the ionosphere with the diurnal cycle. In this paper, we propose a TEC prediction model, which simultaneously considers both spatial and temporal characteristics to extract spatiotemporal features of ionospheric distribution. Additionally, we integrate several space weather element datasets into the prediction model framework, allowing the generation of multiple space weather feature values that represent the influence of space weather on the ionosphere at different latitudes and longitudes. Moreover, we apply Gaussian process regression (GPR) interpolation to geomagnetic data to characterize impact on the ionosphere, thereby enhancing the prediction accuracy. We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). We also examined the effect of using different numbers of space weather feature values in these models. Our model outperforms the comparison models in terms of prediction error metrics, including mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), and the structural similarity index (SSIM). Furthermore, we analyzed the influence of different batch sizes on model training accuracy to find the best performance of each model. In addition, we investigated the model performance during geomagnetic quiet periods, where our model provided the most accurate predictions and demonstrates higher prediction accuracy in the equatorial anomaly region. We also analyzed the prediction performance of all models during space weather events. The results indicate that the proposed model is the least affected during geomagnetic storms and demonstrates superior prediction performance compared to other models. This study presents a more stable and high-performance spatiotemporal prediction model for TEC. Full article

Additive manufacturing (AM) techniques have transformed the production of parts and components with intricate geometries and customized designs, driving innovation in sustainable manufacturing practices. The additive manufacturing technology used in this work was selective laser melting (SLM), a process that uses laser energy to sinter powdered metals into solid structures. Among the various materials utilized in AM, Ti6Al4V titanium alloys are of particular interest due to their favorable mechanical properties, corrosion resistance, biocompatibility, and potential for reducing material waste. However, the machining of additively manufactured titanium parts presents challenges due to the material’s low conductivity, elastic modulus, and chemical affinity with cutting tools, which impact tool wear and surface finish quality. Milling, a commonly employed process for finishing titanium parts, often involves significant energy use and tool wear, highlighting the need for optimized and eco-conscious machining strategies. This study aims to establish correlations among four key aspects: (1) surface finish of machined Ti6Al4V AM parts, (2) cutting tool damage, (3) dry milling parameters including different cutting tools, and (4) variation of temperature at the contact surface of AM parts and tools using infrared thermography. By examining parameters such as feed per tooth (Fz), axial depth of cut (Ap), spindle trajectories (trochoidal, helicoidal, and linear), and cutting tool diameters, this work identifies conditions that enhance process efficiency while reducing environmental impact. Infrared thermography provides insights into temperature variations during milling, correlating these changes to surface roughness and critical machining parameters, thus contributing to the development of sustainable and high-performance manufacturing practices. Full article

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems. Full article

With ambitious targets set by the EU for the reduction of emissions from the energy sector by 2030, there is a need to design and develop more building projects using renewable energy sources. Even though in Europe, heating and cooling share from renewable resources is increasing, and in 2021, the total share in this sector in Croatia was at 38%, the share of heat production by heat pumps is rather low. One possibility to increase this share is to install energy piles when constructing a building, which is becoming an increasingly common practice. This case study focuses on such a system designed for a large, non-residential building in Zagreb, Croatia. The complex was designed as 13 separate dilatations, with central heating and cooling of all facilities, covered by 260 energy piles (130 pairs in serial connection), with a length of the polyethylene pipe of 20 m in a double loop inserted within the pile. The thermo-technical system was designed as a bivalent parallel system, with natural gas covering peak heating loads and a dry cooler covering cooling peak loads when the loads cannot be covered only by ground-source heat pumps. In the parallel bivalent system, the geothermal source will work with a much higher number of working hours at full load than is the case for geothermal systems that are dimensioned to peak consumption. Therefore, the thermal response test was conducted on two energy piles, connected in series, to obtain thermogeological parameters and determine the heat extraction and rejection rates. The established steady-state heat rate defines the long-term ability to extract heat energy during constant thermal load, with the inlet water temperature from the pile completely stabilized, i.e., no significant further sub-cooling is achieved in the function of the geothermal field operation time. Considering the heating and cooling loads of the building, modeling of the system was performed in such a manner that it utilized renewable energy as much as possible by finding a bivalent point where the geothermal system works efficiently. It was concluded that the optimal use of the geothermal field covers total heating needs and 70% for cooling, with dry coolers covering the remaining 30%. Additionally, based on the measured thermogeological parameters, simulations of the thermal response test were conducted to determine heat extraction and rejection rates for energy piles with various geometrical parameters of the heat exchanger pipe and fluid flow variations. Full article

Currently, Industry 4.0 creates new opportunities for analyzing data on production processes and extracting knowledge from them. With the Internet of Things, data is continuously collected from machine sensors to analyze machine health. Thanks to artificial intelligence methods and discrete simulation, it is possible to process data and dynamically adjust the operating conditions of the production line to the expected time of failure-free operation of the machine or reliable work of an employee. Recently, machine learning techniques have been used to automatically adapt the production line to changes in a given production environment. The paper presents various methods of modeling actions, i.e., forecasting the failure-free operation time of a machine or the error-free working time of an employee. The possible actions the agent can perform, the possible prediction techniques that can be selected are presented. The time between failures is described by a log-normal distribution. The asymmetric lognormal distribution is much more flexible for practical modeling compared to the “perfectly” symmetric normal distribution. In practice, the asymmetric lognormal distribution, strongly shifted to the left, can be used to describe the decreasing time between failures due to human error, as well as the time between failures of a machine in the third phase of its life cycle, which decreases as the machine ages and its components wear out. The parameters of the distribution are estimated using the maximum-likelihood approach, theempirical moments approach, the renewal-theory approach, the empirical distribution function and the method based on coefficient of variation. Numerical examples of predicting failure-free operation times described by the log-normal distribution are presented. The results are compared assuming that failure-free times are described by exponential, normal and Weibull distributions. The results are also compared with an example of the simplest learning method. Full article

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively. Full article

The impact of ambient brightness surroundings on the peak velocities of visually guided saccades remains a topic of debate in the field of eye-tracking research. While some studies suggest that saccades in darkness are slower than in light, others question this finding, citing inconsistencies influenced by factors such as pupil deformation during saccades, gaze position, or the measurement technique itself. To investigate these, we developed RELAY (Robotic EyeLink AnalYsis), a low-cost, stepper motor-driven artificial eye capable of simulating human saccades with controlled pupil, gaze directions, and brightness. Using the EyeLink 1000, a widely employed eye tracker, we assessed accuracy and precision across three illumination settings. Our results confirm the reliability of the EyeLink 1000, demonstrating no artifacts in pupil-based eye tracking related to brightness variations. This suggests that previously observed changes in peak velocities with varying brightness are likely due to human factors, warranting further investigation. However, we observed systematic deviations in measured pupil size depending on gaze direction. These findings emphasize the importance of reporting illumination conditions and gaze parameters in eye-tracking experiments to ensure data consistency and comparability. Our novel artificial eye provides a robust and reproducible platform for evaluating eye tracking systems and deepening our understanding of the human visual system. Full article

In this research, we investigated the influence of Mn²⁺ ions on the packing in stearic acid (SA) crystals through the use of Raman spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopy. The crystals investigated were obtained utilizing the slow evaporation methodology in a hexane solution under varying manganese (Mn) concentrations sourced from MnSO₄ 5H₂O (0.5, 1.0, 1.5, 2.0, 4.0, and 6.0%). XRD studies indicated that all SA crystals were grown in the B_m form (monoclinic), favoring the gauche conformation in molecular packing. Additionally, crystalline lattice modifications were observed through Raman spectral changes in the low-vibrational energy region. Variations in the intensities and Raman shifts in two lattice vibrational modes, centered at approximately 59 and 70 cm⁻¹, revealed that two types of hydrogen bonds are distinctly affected within the crystalline lattice. Furthermore, the unit cell parameters (a, b, c, and β) were determined via Rietveld refinement, and their behavior was analyzed as a function of Mn concentration. The results indicated that Mn²⁺ ions exert a strain and deformation effect on the unit cell. Lastly, differential scanning calorimetry (DSC) was employed to evaluate the thermal stability of the B_m form of SA crystals. Full article

The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness–frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability. Full article

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms^–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms^–1. Increased sensing speeds (0.4–0.5 ms^–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms^–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring. Full article

This paper seeks to establish a generalized numerical model to examine the free vibration behavior of functionally graded porous (FGP) elliptical shells and panels with various boundary types. The model is built on first-order shear deformation theory (FSDT) to express structural displacements. A segmentation technique is used to maintain continuity between shell elements, and virtual spring boundary techniques are employed to simulate arbitrary boundaries. Variable-coefficient Jacobi polynomials are introduced as admissible functions for displacement. Finally, the Ritz variational method, combined with the least-squares weighted residual method (LSWRM), is used for constructing the energy functional and solving the energy equations. Validation of the numerical model against finite element and literature results confirms its reliability and convergence properties. This study also explores the effects of geometric parameters and boundary conditions on FG elliptical shells and panels, providing a theoretical basis for future research. Full article