Search Results (319)

This paper describes the spatiotemporal distribution and risk factors of African Swine Fever (ASF) in Uganda for the period of 2010 through 2023. The study utilized a comprehensive dataset from monthly reports (2010–2023) from District Veterinary Officers (DVOs), the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF), and the Food and Agriculture Organization, Uganda. Using GPS coordinates, ASF cases were mapped using QGIS to show ASF distribution and spread in Uganda. Moran’s I analysis was used to delineate clusters of ASF. A total of 1521 ASF cases were recorded. The data show that cases of ASF were disseminated throughout the country, with more cases of ASF documented in the central region and border districts (hotspots for ASF), and few cases were reported in Acholi, Karamoja, and Lango, Ankole, West Nile, and Kigezi sub-regions. The time series analysis revealed incidences of ASF disease occurring year-round; notable peak cases were observed in some districts, and districts with ≥30,000 pigs reported higher cases of ASF. The Moran’s I (≥1) analysis showed that ASF is either aggregated (p = 0.01), especially in central districts bordering Tanzania and lake shores, or sporadic in occurrence. The disease was present in 66% of the districts, with ASF occurring throughout the year. More cases were aggregated in central and border districts and districts with large pig populations (≥30,000). Sporadic cases were reported in districts bordering the DRC, Sudan, Kenya, the lake shores, Karamoja, Acholi, and Lango sub-regions. Full article

This study investigates the feasibility of using GPS data and frequency of COVID-19-related blog words to forecast new infection trends through a linear regression analysis. By employing time series’ trend decomposition and Spearman’s rank correlation, we identify and select a set of significant variables from the GPS and blog data to construct two models: a fixed-period model and a sequential adaptive model that updates with each new wave of infections. Our findings reveal that the adaptive model more effectively captures long-term trends, achieving approximately 90% accuracy in forecasting infection rates seven days in advance. Despite challenges in forecasting exact values, this research demonstrates that combining GPS and blog data through a dynamic, wave-based learning model offers a promising direction for enhancing the forecasting accuracy of COVID-19 spread. This approach has significant implications for public health preparedness. Full article

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures. Full article

Global Navigation Satellite Systems (GNSSs) are widely used for positioning, timing, and navigation services. Such widespread usage makes them exposed to various threats including malicious attacks such as spoofing attacks. The availability of low-cost devices such as software-defined radios enhances the viability of performing such attacks. Efficient spoofing detection is of essential importance for the mitigation of such attacks. Although various methods have been proposed for that purpose it is still an important research topic. In this paper, we investigate the spoofing detection method based on the integrated usage of discrete wavelet transform (DWT) and machine learning (ML) techniques and propose efficient solutions. A series of experiments using different wavelets and machine learning techniques for Global Positioning System (GPS) and Galileo systems are performed. Moreover, the impact of the usage of different types of training data are explored. Following the computational complexity analysis, the potential for complexity reduction is investigated and computationally efficient solutions proposed. The obtained results show the efficacy of the proposed approach. Full article

This article describes a robust Gaussian Prior process state space modeling (GPSSM) approach to assess the impact of an intervention in a time series. Numerous applications can benefit from this approach. Examples include: (1) time series could be the stock price of a company and the intervention could be the acquisition of another company; (2) the time series under concern could be the noise coming out of an engine, and the intervention could be a corrective step taken to reduce the noise; (3) the time series could be the number of visits to a web service, and the intervention is changes done to the user interface; and so on. The approach we describe in this article applies to any times series and intervention combination. It is well known that Gaussian process (GP) prior models provide flexibility by placing a non-parametric prior on the functional form of the model. While GPSSMs enable us to model a time series in a state space framework by placing a Gaussian Process (GP) prior over the state transition function, probabilistic recurrent state space models (PRSSM) employ variational approximations for handling complicated posterior distributions in GPSSMs. The robust PRSSMs (R-PRSSMs) discussed in this article assume a scale mixture of normal distributions instead of the usually proposed normal distribution. This assumption will accommodate heavy-tailed behavior or anomalous observations in the time series. On any exogenous intervention, we use R-PRSSM for Bayesian fitting and forecasting of the IoT time series. By comparing forecasts with the future internal temperature observations, we can assess with a high level of confidence the impact of an intervention. The techniques presented in this paper are very generic and apply to any time series and intervention combination. To illustrate our techniques clearly, we employ a concrete example. The time series of interest will be an Internet of Things (IoT) stream of internal temperatures measured by an insurance firm to address the risk of pipe-freeze hazard in a building. We treat the pipe-freeze hazard alert as an exogenous intervention. A comparison of forecasts and the future observed temperatures will be utilized to assess whether an alerted customer took preventive action to prevent pipe-freeze loss. Full article

Indoor localization systems are gaining increasing relevance due to the limitations of traditional Global Positioning System (GPS) technology in enclosed environments. While the GPS remains widely used for navigation, its efficacy is significantly reduced indoors or in confined spaces. Given the growing societal and technological demand for precise localization and movement tracking within such environments, the development of indoor positioning systems (IPSs) has become a critical area of research. Among the available technologies, Bluetooth Low Energy (BLE) beacons have emerged as one of the most promising solutions for indoor positioning applications. This paper presents an indoor positioning system leveraging BLE beacons, specifically designed for deployment in confined environments. The system employed the Fingerprinting method for localization, and its prototype was experimentally tested within a selected healthcare facility. A series of systematic tests confirmed both the functional reliability of the proposed system and its capability to provide precise localization tailored to the spatial characteristics of the given environment. This research offers a novel application of BLE beacon technology, as it extends beyond simple presence detection to enable accurate position determination at defined time intervals and the relative positioning of multiple entities within the monitored space. Full article

This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N₂] ratio information obtained by the TIMED/GUVI, and electron density (Ne) observations from Swarm satellites. The Prophet time series forecasting model is employed to detect ionospheric anomalies. VTEC variations reveal significant daytime increases in GNSS stations such as GAMG, URUM, and CMUM after the onset of the geomagnetic storm on 1 December, indicating a dayside positive ionospheric response primarily driven by prompt penetration electric fields (PPEF). In contrast, the stations JFNG and CKSV show negative responses, reflecting regional differences. The [O]/[N₂] ratio increased significantly in the southern region between 25°N and 40°N, suggesting that atmospheric gravity waves (AGWs) induced thermospheric compositional changes, which played a crucial role in the ionospheric disturbances. Ne observations from Swarm A and C satellites further confirmed that the intense ionospheric perturbations were consistent with changes in VTEC and [O]/[N₂], indicating the medium-scale traveling ionospheric disturbance was driven by atmospheric gravity waves. Precise point positioning (PPP) analysis reveals that ionospheric variations during the geomagnetic storm significantly impact GNSS positioning precision, with various effects across different stations. Station GAMG experienced disturbances in the U direction (vertical positioning error) at the onset of the storm but quickly stabilized; station JFNG showed significant fluctuations in the U direction around 13:00 UT; and station CKSV experienced similar fluctuations during the same period; station CMUM suffered minor disturbances in the U direction; while station URUM maintained relatively stable positioning throughout the storm, corresponding to steady VTEC variations. These findings demonstrate the substantial impact of ionospheric disturbances on GNSS positioning accuracy in southern and central China during the geomagnetic storm. This study reveals the complex and dynamic processes of ionospheric disturbances over China during the 1–2 December 2023 storm, highlighting the importance of ionospheric monitoring and high-precision positioning corrections during geomagnetic storms. The results provide scientific implications for improving GNSS positioning stability in mid- and low-latitude regions. Full article

In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields. 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

The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO). Full article

With the application and promotion of space geodesy, the popularization of remote sensing technology, and the development of artificial intelligence, a more accurate and stable Terrestrial Reference Frame (TRF) has become more urgent. For example, sea level change detection, crustal deformation monitoring, and driverless cars, among others, require the accuracy of the terrestrial reference frame to be better than 1 mm in positioning and 0.1 mm/a in velocity, respectively. However, the current frequently used ITRF2014 and ITRF2020 do not satisfy such requirements. Therefore, this paper analyzes the coordinate residual time series data of linear TRFs and finds there are still some unlabeled jumps and time-dependent periodic signals, especially in the GNSS coordinate residuals, which can lead to incorrect station epoch coordinates and velocities, further affecting the accuracy and stability of the TRF. The unlabeled jumps could be detected by the sequential t-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. These nonlinear time-dependent periodic signals could be modeled better by singular spectrum analysis (SSA) with respect to least squares fitting; the fitting period is no longer composed of semi-annual and annual items, as with ITRF2014. The periods of continuous coordinate residual time series data longer than 5 years are obtained by FFT. The results show that there are no period signals for individual SLR/VLBI sites, and there are still other period terms, such as 34 weeks, 20.8 weeks and 17.3 weeks, in addition to semi-annual and annual items for some GNSS sites. Moreover, after SSA corrections, the re-calculated TRF and the corresponding EOP could be obtained, based on data from the Chinese Earth Rotation and Reference System Service (CERS) TRF and the Earth Orientation Parameter (EOPs) multi-technique determination software package (CERS TRF&EOP V2.0) developed by the Shanghai Astronomical Observatory (SHAO). Their accuracy could be evaluated with respect to the ITRF2014 and the IERS 14 C04, respectively. The results show that the accuracy and stability of the newly established a nonlinear TRF and EOP based on SSA have been greatly improved and better than a linear TRF and EOP. SSA is better than least squares fitting, especially for those coordinate residual time series with varying amplitude and phase. For GPS, comparing with the ITRF2014, the station coordinate accuracy of 10.8% is better than 1 mm, and the station velocity accuracy of 4.4% is better than 0.1 mm/year. There are 3.1% VLBI stations, for which coordinate accuracy is better than 1 mm and velocity accuracy is better than 0.1 mm/year. However, there are no stations with coordinates and velocities better than 1 mm and 0.1 mm/year for the SLR and DORIS. The WRMS values of polar motion x, polar motion y, LOD, and UT1-UTC are reduced by 2.4%, 3.2%, 2.7%, and 0.96%, respectively. The EOP’s accuracy in SOL-B, in addition to LOD, is better than that of the JPL. Full article

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R² = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience. Full article

Forecasting of time series data presents some challenges because the data’s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data processing techniques with advanced machine learning algorithms to improve time series forecasting across the sustainability domain. Specifically, this study focuses on solar irradiation forecasting in Riyadh, Saudi Arabia. Efficient and accurate forecasts of solar irradiation are important for optimizing power production and its smooth integration into the utility grid. This advancement supports Saudi Arabia in Vision 2030, which aims to generate and utilize renewable energy sources to drive sustainable development. Therefore, the proposed forecasting system has been developed to the parameters characteristic of the Riyadh region environment, including high solar intensity, dust storms, and unpredictable weather conditions. After the cleaning and filtering process, the filtered dataset was pre-processed using the standardization method. Then, the Discrete Wavelet Transform (DWT) technique has been applied to extract the features of the pre-processed data. Next, the extracted features of the solar dataset have been split into three subsets: train, test, and forecast. Finally, two different machine learning techniques have been utilized for the forecasting process: Support Vector Machine (SVM) and Gaussian Process (GP) techniques. The proposed forecasting system has been evaluated across different time horizons: one-day, five-day, ten-day, and fifteen-day ahead. Comprehensive evaluation metrics were calculated including accuracy, stability, and generalizability measures. The study outcomes present the proposed forecasting system which provides a more robust and adaptable solution for time-series long-term forecasting and complex patterns of solar irradiation in Riyadh, Saudi Arabia. Full article

The present study aimed to verify the (1) differences between players’ roles in relation to technical and tactical and time–motion indicators, and the (2) relationships between individual time–motion and technical and tactical indicators for each role in a men’s Italian football Serie A team. A total of 227 performances were analyzed (28 players: 8 forwards, FWs; 11 midfielders, MDs; 9 defenders, DFs). Technical and tactical indicators, such as ball possession (played balls, successful passes, successful playing patterns, lost balls, ball possession time), offensive play (total and successful dribbles, crosses, assists), and shooting (total shots, shots on target) were obtained by means of Panini Digital (DigitalSoccer Project S.r.l). In addition, a time–motion analysis included the total distance, distances covered at intensities of 16.0–19.8 km/h, 19.8–25.2 km/h, and over 25.2 km/h, the average recovery time between metabolic power peaks, and burst occurrence, the latter of which was performed by means of a 18 Hz GPS device (GPexe Pro2 system tool) worn by the players. Results showed role-specific differences: MDs covered more distance, while DFs had better ball possession. MDs and DFs had more successful playing patterns, and MDs and FWs performed more dribbles and shots. Strong correlations (p < 0.01, ρ > 0.8) were found between bursts and assists for FWs, high-intensity running and ball possession for MDs, and distance, dribbling, and shots for DFs. These findings highlight the importance of individual and tailored training programs to optimize role-specific performance demands. Full article

Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions—which features velocities up to 10 times those of pedestrians and higher acceleration—our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration. Full article