Search Results (43)

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

This paper addresses the semi-global polynomial synchronization (SGPS) problem for a class of high-order bidirectional associative memory neural networks (HOBAMNNs) with multiple proportional delays. The time-delay-dependent semi-global polynomial stability criterion for error systems was established via a direct approach. The derived stability conditions are formulated as several simple inequalities that are readily solvable, facilitating direct verification using standard computational tools (e.g., YALMIP). Notably, this method can be applied to many system models with proportional delays after minor modifications. Finally, a numerical example is provided to validate the effectiveness of the theoretical results. Full article

To mitigate the limited accuracy of the Simplified General Perturbations 4 (SGP4) model in predicting medium-orbit satellite trajectories, we propose an enhanced methodology integrating deep learning with traditional algorithms. The developed BiLSTM-TS forecasting framework comprises a Bidirectional Long Short-Term Memory (BiLSTM) network, trend analysis module (T), and seasonal decomposition module (S). This architecture effectively captures sequential dependencies, trend variations, and periodic patterns within time series data, thereby improving prediction interpretability. In our experimental validation, we chose Beidou-2 M6 (C14), GSAT0203 (GALILEO 7), and the Global Positioning System (GPS) satellite named GPS BIIR-13 (PRN 02) as representative satellites. Satellite position data derived from conventional orbital models were input into the BiLSTM-TS framework for statistical learning to predict orbital deviations. These predicted errors were subsequently combined with SGP4 model outputs obtained through Two-Line Element set (TLE) data analysis to minimize overall trajectory inaccuracies. Using BeiDou-2 M6 (C14) as a case study, results indicated that the BiLSTM-TS implementation achieved significant error reduction; mean-squared error along the X-axis was reduced to 0.0309 km², with mean absolute error of 0.1245 km, and maximum absolute error was constrained to 0.4448 km. Full article

Sheep and goat pox (SGP) are animal diseases of important economic impact which have been emerging into new geographic areas, including occasional incursions in disease free countries. The main objective of this study is to observe and analyse the global distribution of SGP during an 18-year period (2005–2022). Countries’ SGP epidemiology was characterised by classifying them according to the frequency of reporting years. A negative binomial regression model was used to test for associations between the economic status of a country, the sheep and goat populations, the continent, and the likelihood of an SGP outbreak occurring. A change-point analysis was used to determine significant change points of outbreaks for 18 years. Countries which presented high endemic status were mostly located in the North African region, the Middle East, and Asia, in particular India and China. Economic status was found to be significant for outbreak occurrence in endemic countries, in contrast to countries with outbreaks occurring where other socio-economic factors influence the disease occurrence. The total sheep and goat population was found to be significantly associated with countries and regions. The change-point analysis showed that changes in outbreak occurrence were observed when countries with most reported outbreaks controlled the diseases. While the husbandry and social conditions that exist in certain regions, particularly of Africa and Asia, make the prospect of SGP eradication highly unlikely, an effective implementation of vaccination strategies and control policies would decrease the incidence of SGP, improving animal health and economics in affected countries. Full article

With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis—strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure. Full article

Wind turbine blade leading edge erosion (LEE) reduces energy production and increases wind energy operation and maintenance costs. Degradation of the blade coating and ultimately damage to the underlying blade structure are caused by collisions of falling hydrometeors with rotating blades. The selection of optimal methods to mitigate/reduce LEE are critically dependent on the rates of coating fatigue accumulation at a given location and the time variance in the accumulation of material stresses. However, no such assessment currently exists for the United States of America (USA). To address this research gap, blade coating lifetimes at 883 sites across the USA are generated based on high-frequency (5-min) estimates of material fatigue derived using a mechanistic model and robust meteorological measurements. Results indicate blade coating failure at some sites in as few as 4 years, and that the frequency and intensity of material stresses are both highly episodic and spatially varying. Time series analyses indicate that up to one-third of blade coating lifetime is exhausted in just 360 5-min periods in the Southern Great Plains (SGP). Conversely, sites in the Pacific Northwest (PNW) exhibit the same level of coating lifetime depletion in over three times as many time periods. Thus, it may be more cost-effective to use wind turbine deregulation (erosion-safe mode) for damage reduction and blade lifetime extension in the SGP, while the application of blade leading edge protective measures may be more appropriate in the PNW. Annual total precipitation and mean wind speed are shown to be poor predictors of blade coating lifetime, re-emphasizing the need for detailed modeling studies such as that presented herein. Full article

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO₂). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO₂ sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments. Full article

Inherent constant Q attenuation can be described using fractional Laplacian operators. Typically, the fractional Laplacian viscoacoustic or viscoelastic wave equations are addressed utilizing the staggered-grid pseudo-spectral (SGPS) method. However, this approach results in time numerical dispersion errors due to the low-order finite difference approximation. In order to address these time-stepping errors, a k-space-based temporal compensating scheme is established to solve the first-order viscoacoustic wave equation. This scheme offers the advantage of being nearly free from grid dispersion for homogeneous media and enhances simulation stability. Numerical examples indicate that the proposed k-space scheme aligns well with analytical solutions for homogeneous media. Additionally, this method demonstrates excellent applicability for complex models and is more efficient due to its ability to adopt a larger time step compared with conventional staggered-grid pseudo-spectral methods. Full article

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO₂ concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R², MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R² = 0.94 (T), R² = 0.94 (RH), R² = 0.63 (CO₂), R² = 0.84 (TVOC, based on the SGP30 sensor), and R² = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO₂ prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments. Full article

For large infrastructure projects, such as high-voltage underground cables or for evaluating the very shallow geothermal potential (vSGP) of small-scale horizontal geothermal systems, large-scale geothermal collector systems (LSCs), and fifth generation low temperature district heating and cooling networks (5GDHC), the thermal conductivity (λ) of the subsurface is a decisive soil parameter in terms of dimensioning and design. In the planning phase, when direct measurements of the thermal conductivity are not yet available or possible, λ must therefore often be estimated. Various empirical literature models can be used for this purpose, based on the knowledge of bulk density, moisture content, and grain size distribution. In this study, selected models were validated using 59 series of thermal conductivity measurements performed on soil samples taken from different sites in Germany. By considering different soil texture and moisture categories, a practicable guideline in the form of a decision tree, employed by empirical models to calculate the thermal conductivity of unconsolidated soils, was developed. The Hu et al. (2001) model showed the smallest deviations from the measured values for clayey and silty soils, with an RMSE value of 0.20 W/(m∙K). The Markert et al. (2017) model was determined to be the best-fitting model for sandy soils, with an RMSE value of 0.29 W/(m∙K). Full article

In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-intensive. Consequently, conducting real experiments at new input configurations might be impractical. To address this challenge, data-driven surrogate models have emerged as a cost-effective and time-efficient alternative. They provide simplified mathematical representations that approximate the output of interest. Models based on Gaussian Processes (GPs) have gained popularity in aerodynamics due to their ability to provide accurate predictions and quantify uncertainty while maintaining tractable execution times. To handle large datasets, sparse approximations of GPs have been further investigated to reduce the computational complexity of exact inference. In this paper, we revisit and adapt two classic sparse methods for GPs to address the specific requirements frequently encountered in aerodynamic applications. We compare different strategies for choosing the inducing inputs, which significantly impact the complexity reduction. We formally integrate our implementations into the open-source Python toolbox SMT, enabling the use of sparse methods across the GP regression pipeline. We demonstrate the performance of our Sparse GP (SGP) developments in a comprehensive 1D analytic example as well as in a real wind tunnel application with thousands of training data points. Full article

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance. Full article

The entry of SARS-CoV-2 into the host cell is mediated by its S-glycoprotein (SGP). Sulfated glycans bind to the SGP receptor-binding domain (RBD), which forms a ternary complex with its receptor angiotensin converting enzyme 2. Here, we have conducted a thorough and systematic computational study of the binding of four oligosaccharide building blocks from novel marine sulfated glycans (isolated from Pentacta pygmaea and Isostichopus badionotus) to the non-glycosylated and glycosylated RBD. Blind docking studies using three docking programs identified five potential cryptic binding sites. Extensive site-targeted docking and molecular dynamics simulations using two force fields confirmed only two binding sites (Sites 1 and 5) for these novel, highly charged sulfated glycans, which were also confirmed by previously published reports. This work showed the structural features and key interactions driving ligand binding. A previous study predicted Site 2 to be a potential binding site, which was not observed here. The use of several molecular modeling approaches gave a comprehensive assessment. The detailed comparative study utilizing multiple modeling approaches is the first of its kind for novel glycan–SGP interaction characterization. This study provided insights into the key structural features of these novel glycans as they are considered for development as potential therapeutics. Full article

The water balance in the very shallow subsurface can be influenced by capillary rise due to a high groundwater table. Since moisture content is an important factor for the thermal conductivity of soils, this can also have an influence on the very shallow geothermal potential (vSGP). For this reason, the effect of spatial and seasonal variations in groundwater tables on moisture content in essential depth layers was investigated at a large-scale geothermal collector system (LSC) in Bad Nauheim, Germany. Quasi-one-dimensional simulations using the FEFLOW^® finite-element simulation system were employed to determine site-dependent and seasonally varying moisture contents, from which thermal conductivities were derived. The model setup was previously validated based on recorded moisture contents. The simulations resulted in groundwater-related maximum seasonal and spatial differences in thermal conductivity of 0.14 W/(m∙K) in the LSC area. Larger differences of up to 0.21 W/(m∙K) resulted for different soil textures at the same depth due to different thermal properties. The results indicate that an efficient design of LSCs requires a sufficiently detailed subsurface exploration to account for small-scale variations in grain size distribution and groundwater level. Full article

The objective of this work was to investigate, for the first time, the antioxidant effect of a mixture of natural antimicrobials in an Enterocytozoon hepatopenaei (EHP) shrimp-gut model of infection and the biological mechanisms involved in their way of action. The study approach included investigations, firstly, in vitro, on shrimp-gut primary (SGP) epithelial cells and in vivo by using EHP-challenged shrimp. Our results show that exposure of EHP spores to 0.1%, 0.5%, 1%, and 2% AuraAqua (Aq) significantly reduced spore activity at all concentrations but was more pronounced after exposure to 0.5% Aq. The Aq was able to reduce EHP infection of SGP cells regardless of cells being pretreated or cocultured during infection with Aq. The survivability of SGP cells infected with EHP spores was significantly increased in both scenarios; however, a more noticeable effect was observed when the infected cells were pre-exposed to Aq. Our data show that infection of SGP cells by EHP activates the host NADPH oxidases and the release of H₂O₂ produced. When Aq was used during infection, a significant reduction in H₂O₂ was observed concomitant with a significant increase in the levels of CAT and SOD enzymes. Moreover, in the presence of 0.5% Aq, the overproduction of CAT and SOD was correlated with the inactivation of the NF-κB pathway, which, otherwise, as we show, is activated upon EHP infection of SGP cells. In a challenge test, Aq was able to significantly reduce mortality in EHP-infected shrimp and increase the levels of CAT and SOD in the gut tissue. Conclusively, these results show, for the first time, that a mixture of natural antimicrobials (Aq) can reduce the EHP-spore activity, improve the survival rates of primary gut-shrimp epithelial cells and reduce the oxidative damage caused by EHP infection. Moreover, we show that Aq was able to stop the H₂O₂ activation of the NF-κB pathway of Crustins, Penaeidins, and the lysozyme, and the CAT and SOD activity both in vitro and in a shrimp challenge test. Full article