Search Results (47)

Accurate real-time positioning of Unmanned Aerial Vehicles (UAVs) is critical for navigation and mapping but remains challenging in complex environments due to signal blockages and multipath effects. This study presents a comparative framework for real-time error prediction of the Global Navigation Satellite System (GNSS), evaluating two machine learning models (Random Forest and XGBoost) and a deep learning model (Long Short-Term Memory network) against an Extended Kalman Filter baseline. A high-precision total station provides ground-truth coordinates, enabling the derivation of positioning error labels from synchronized GNSS raw data. Among the evaluated models, the tree-based XGBoost model achieves a significantly lower Mean Squared Error (MSE) and a considerably higher Coefficient of Determination ($R^2$) score than other models in predicting positioning deviations. The high-accuracy error predictions from the optimal model establish the core of a software-only solution for positioning integrity. The framework demonstrates that reliable, real-time error estimates can be derived directly from observation data, providing the essential input required for future compensation systems without necessitating additional hardware. Full article

The Global Navigation Satellite System (GNSS) is commonly used for outdoor positioning. However, its effectiveness diminishes in urban canyons and indoor environments attributed to signal blockage. This study aims to explore the potential of GNSS signals penetrating indoor spaces through windows and to enhance indoor positioning with Three-Dimensional Mapping-Aided (3DMA) GNSS, a concept generally applied outdoors. The research employs a 3D model of a corridor with manually labeled window locations to predict satellite visibility within indoor areas. The study integrates Pedestrian Dead Reckoning (PDR) with an indoor Shadow-matching (I-SM) technique, utilizing an Extended Kalman Filter (EKF) to improve positioning accuracy. One of the findings indicates that the proposed method significantly enhances positioning performance and its availability, achieving a root mean square error (RMSE) that is 2 m better than using PDR alone or single epoch I-SM. The study concludes that integrating GNSS with I-SM technique and PDR can optimize an indoor positioning solution and highlights the potential for improved navigation solutions in complex urban environments. Full article

Supercritical CO₂ modifies deep coal reservoirs through the coupled effects of adsorption-induced deformation and geochemical dissolution. CO₂ adsorption causes coal matrix swelling and facilitates micro-fracture propagation, while CO₂–water reactions generate weakly acidic fluids that dissolve minerals such as calcite and kaolinite. These synergistic processes remove pore fillings, enlarge flow channels, and generate new dissolution pores, thereby increasing the total pore volume while making the pore–fracture network more heterogeneous and structurally complex. Such reservoir restructuring provides the intrinsic basis for CO₂ injectivity and subsequent CH₄ displacement. Both adsorption capacity and volumetric strain exhibit Langmuir-type growth characteristics, and permeability evolution follows a three-stage pattern—rapid decline, slow attenuation, and gradual rebound. A negative exponential relationship between permeability and volumetric strain reveals the competing roles of adsorption swelling, mineral dissolution, and stress redistribution. Swelling dominates early permeability reduction at low pressures, whereas fracture reactivation and dissolution progressively alleviate flow blockage at higher pressures, enabling partial permeability recovery. Injection pressure is identified as the key parameter governing CO₂ migration, permeability evolution, sweep efficiency, and the CO₂-ECBM enhancement effect. Higher pressures accelerate CO₂ adsorption, diffusion, and sweep expansion, strengthening competitive adsorption and improving methane recovery and CO₂ storage. However, excessively high pressures enlarge the permeability-reduction zone and may induce formation instability, while insufficient pressures restrict the effective sweep volume. An optimal injection-pressure window is therefore essential to balance injectivity, sweep performance, and long-term storage integrity. Importantly, the enhanced methane production and permanent CO₂ storage achieved in this study contribute directly to greenhouse gas reduction and improved sustainability of subsurface energy systems. The multi-field coupling insights also support the development of low-carbon, environmentally responsible CO₂-ECBM strategies aligned with global sustainable energy and climate-mitigation goals. The integrated experimental–numerical framework provides quantitative insight into the coupled adsorption–deformation–flow–geochemistry processes in deep coal seams. These findings form a scientific basis for designing safe and efficient CO₂-ECBM injection strategies and support future demonstration projects in heterogeneous deep coal reservoirs. Full article

The performance of integrated Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) navigation often declines in complex urban environments due to frequent GNSS signal blockages. This poses a significant challenge for autonomous driving applications that require continuous and reliable positioning. To address this limitation, this paper presents a novel hybrid framework that combines a deep learning architecture with an adaptive Kalman Filter. At the core of this framework is a Temporal Convolutional Network and Bidirectional Long Short-Term Memory (TCN-BiLSTM) model, which generates accurate pseudo-GNSS measurements from raw INS data during GNSS outages. These measurements are then fused with the INS data stream using an Adaptive Noise-Regulated Iterated Extended Kalman Filter (ANR-IEKF), which enhances robustness by dynamically estimating and adjusting the process and observation noise statistics in real time. The proposed ANR-IEKF + TCN-BiLSTM framework was validated using a real-world vehicle dataset that encompasses both straight-line and turning scenarios. The results demonstrate its superior performance in positioning accuracy and robustness compared to several baseline models, thereby confirming its effectiveness as a reliable solution for maintaining high-precision navigation in GNSS-denied environments. Validated in 70 s GNSS outage environments, our approach enhances positioning accuracy by over 50% against strong deep learning baselines with errors reduced to roughly 3.4 m. Full article

Emerging technologies such as urban air mobility and autonomous vehicles increasingly rely on Global Navigation Satellite Systems (GNSS) for accurate positioning. However, GNSS alone suffers from severe degradation in complex environments, particularly due to multipath effects caused by reflections from surrounding structures. These effects distort pseudo-range measurements and, in combination with signal attenuation and blockage, lead to significant positioning errors. To address this challenge, this study proposes a loosely integrated navigation framework that combines GNSS, ultra-wideband (UWB), and inertial navigation system (INS) data. UWB enables high-precision ranging, and we further extend its application to estimate the locations of UWB anchors themselves. This approach alleviates a major technical limitation of UWB systems, which typically require anchor positions near buildings to be precisely surveyed beforehand. Field experiments were conducted in multipath-prone outdoor environments using a drone equipped with GNSS, UWB, and INS sensors. The results demonstrate that the proposed GNSS/UWB/INS integration reduces positioning errors by up to approximately 90% compared with GNSS/INS integration. Moreover, in areas surrounded by UWB anchors (UWB-Anchored Area), submeter-level positioning accuracy was achieved. These findings highlight the robustness of the proposed method against multipath interference and its potential to overcome anchor-dependency issues, thereby contributing to safe and reliable navigation solutions for future urban applications such as eVTOL operations. Full article

Although the hub blockage effect is generally disregarded for large-sized horizontal axis wind machines, it can significantly affect the performance of small-sized turbines whose ratio between the hub and rotor radii can attain values up to 25–30%. This article proposes a generalisation of the Blade-Element/Momentum Theory (BE/M-T), accounting for the effects of the hub presence on the rotor performance. The new procedure relies on the quantitative evaluation of the radial distribution of the axial velocity induced by the hub all along the blade span. It is assumed that this velocity is scarcely influenced by the magnitude and type of the rotor load, and it is evaluated using a classical CFD approach applied to the bare hub. The validity and accuracy of the modified BE/M-T model are tested by comparing its results with those of a more advanced CFD-actuator-disk (CFD-AD) approach, which naturally and duly takes into account the hub blockage, the rotor presence, an and the wake divergence and rotation, and the results are validated against experimental data. The comparison shows that the correction for the hub blockage effects in the BE/M-T model significantly reduces the differences with the results of the reference method (CFD-AD) both in terms of global (power coefficient) and local (thrust and torque per unit length) quantities. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

Plastic remains a cheap material for numerous uses in households, industries, and engineering; however, it disintegrates in aquatic ecosystems to form smaller particles termed microplastics. Microplastics (MPs) have become a cause for concern due to their persistence and potential effects on freshwater ecosystems. Moreover, the toxicity of microplastics can be achieved through different mechanisms, including physical blockage and additive leaching, or they can function as vectors for other chemical pollutants. Microplastics were found to provide a growing surface for microbial communities, forming a biofilm termed the plastisphere. Microplastic pollution seems to need urgent attention globally; however, the comparability of results becomes a challenge due to the different techniques employed by different researchers. Moreover, the complete removal of MPs has proven to be an impossible task. This review explored MP occurrence in freshwater ecosystems, the role of microbial communities in the dynamics of microplastics, removal techniques, strategies for reduction in the environment, and their effect on freshwater ecosystems. Moreover, techniques to reduce microplastic release, such as recycling, plastic–fuel conversion, and biodegradable plastics, are explored. The review provides recommendations for reducing microplastic release and removal in freshwater ecosystems. This review stresses existing gaps to explore going forward in addressing microplastic pollution and possible removal techniques. Full article

Plastic pollution and its environmental consequence are on the rise globally. In Nigeria, the proliferation of plastic bottle and sachet water (PBSW) manufacturing companies in various parts of the country has led to an increase in plastic waste generation. Existing studies have identified challenges and the critical need for the adoption of sustainable solutions to mitigate its adverse environmental impact, especially for developing countries. Therefore, the motivation for this study stems from the urgent need for a progressive shift in the studies focused on feasible solutions to the common challenges and strategies for implementation. This study aims to investigate the identified challenges of a lack of awareness and waste management of single-use plastics in Nigeria, towards achieving a circular economy of plastic waste whilst considering its socio-economic context. This study used a mixed method approach combining quantitative and qualitative data through interviews and questionnaires to investigate awareness on the impact of plastic pollution amongst key stakeholders in plastic waste management in the UNN. The potential of introducing a DRS in the sustainable collection of single-use plastic bottle and sachet water waste was also explored. The result reveals the perceived consequence of plastic pollution is short-term, at the level of mesoplastics, physically observed as plastic litter (68.2%), leading to a blockage of canals (65.0%), an excessive rate of flooding (19.1%) and other related issues; effective channels of creating awareness and educating the public on plastic pollution are social media (48.3%), school education (23.3%), mass media (21.7%), and others (6%). An implementation framework for sustainable plastic waste collection was developed from the research findings, adapting the Norwegian Deposit Return Scheme (DRS) to suit the current socio-economic context of the population. Additionally, awareness can be increased through targeted government policies that reward sustainable plastic waste management practices, public awareness campaigns, and the use of social media. Full article

Tyre wear particles (TWPs), generated from tyre-road abrasion, are a pervasive and under-regulated environmental pollutant, accounting for a significant share of global microplastic contamination. Recent estimates indicate that 1.3 million metric tons of TWPs are released annually in Europe, dispersing via atmospheric transport, stormwater runoff, and sedimentation to contaminate air, water, and soil. TWPs are composed of synthetic rubber polymers, reinforcing fillers, and chemical additives, including heavy metals such as zinc (Zn) and copper (Cu) and organic compounds like polycyclic aromatic hydrocarbons (PAHs) and N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD). These constituents confer persistence and bioaccumulative potential. While TWP toxicity in aquatic systems is well-documented, its ecological impacts on terrestrial environments, particularly in agricultural soils, remain less understood despite global soil loading rates exceeding 6.1 million metric tons annually. This review synthesizes global research on TWP sources, environmental fate, and ecotoxicological effects, with a focus on soil–plant systems. TWPs have been shown to alter key soil properties, including a 25% reduction in porosity and a 20–35% decrease in organic matter decomposition, disrupt microbial communities (with a 40–60% reduction in nitrogen-fixing bacteria), and induce phytotoxicity through both physical blockage of roots and Zn-induced oxidative stress. Human exposure occurs through inhalation (estimated at 3200 particles per day in urban areas), ingestion, and dermal contact, with epidemiological evidence linking TWPs to increased risks of respiratory, cardiovascular, and developmental disorders. Emerging remediation strategies are critically evaluated across three tiers: (1) source reduction using advanced tyre materials (up to 40% wear reduction in laboratory tests); (2) environmental interception through bioengineered filtration systems (60–80% capture efficiency in pilot trials); and (3) contaminant degradation via novel bioremediation techniques (up to 85% removal in recent studies). Key research gaps remain, including the need for long-term field studies, standardized mitigation protocols, and integrated risk assessments. This review emphasizes the importance of interdisciplinary collaboration in addressing TWP pollution and offers guidance on sustainable solutions to protect ecosystems and public health through science-driven policy recommendations. Full article

In view of the problems of data pollution, incomplete feature extraction, and poor multi-network parameter sharing and transmission under the federated learning framework of deep learning, this article proposes an improved asynchronous federated learning algorithm of multi-model fusion based on data injection pollution. Through data augmentation, the existing dataset is preprocessed to enhance the algorithm’s ability to identify the noise data. In our approach, the residual network is used to extract the static information of the image, the capsule network is used to extract the spatial dependence among the internal structures of the image, several layers of convolution are used to reduce the dimensions of both features, and the two extracted features are fused. In order to reduce the transmission overhead of parameters shared between the residual network and capsule network, we adopt an asynchronous parameter transmission between the global trainer and the local trainer. When the global trainer broadcasts the parameters to each local trainer, several trainers are randomly selected to avoid communication link blockage. Finally, through conducting various experiments, the results show that our alogrithm can effectively extract the pathological features in the image and achieve higher accuracy, outperforming the current mainstream algorithms. Full article

Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit (GRU)–Transformer model (GRU-T). The GRU–Transformer hybrid dynamically adjusts the process noise covariance matrix within an error-state Extended Kalman Filter (ES-EKF) framework to address non-stationary noise and signal outages. Forest field tests demonstrate that GRU-T significantly improves positioning accuracy. Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. The GRU-T model effectively captures both short- and long-term temporal dependencies and meets real-time, high-frequency sampling requirements. These results indicate that the GRU–Transformer hybrid model enhances the accuracy and robustness of GNSS/INS navigation in complex orchard environments, offering technical support for high-precision positioning in intelligent agricultural machinery systems. Full article

Managed aquifer recharge (MAR) is widely used globally. However, clogging events remain a major obstacle to long-term operations. This study investigated the effects of natural biofilms on the migration and deposition of suspended particles (SPs) at varying concentrations using column experiments and multiple analytical methods. At 74 h, the K′ in the high-concentration group (HT) with biofilm coating decreased by 77.3%, while, in the high-concentration group (HTCK) without biofilm coating, the K′ decreased by 68.5%. Within the same recharge time, the K′ in the medium-concentration group without biofilm coating decreased by 59.9%. The results show that the biofilm covering the porous medium promotes the clogging of suspended particles. Compared with the high-concentration group, the development of porous medium blockage was slower in the low-concentration suspended particle group. SEM and CT analyses revealed that the biofilms altered the surface roughness of the porous media, thereby promoting SP deposition. The study also confirmed that the interactions between SPs and biofilms in recharge water, including electrostatic interactions, hydrophobic interactions, and extracellular polymer flocculation, collectively exacerbated the clogging process in MAR. XDLVO analysis indicated that the biofilm-coated porous media reduced the electrostatic interaction potential energy and energy barrier between SPs and quartz sand, thereby facilitating kaolin deposition in saturated porous media. Correlation and significance analyses identified hydrophobic interactions as the primary mechanism driving SP–biofilm combined with clogging. Moreover, the reduced SP concentrations in the recharge water increased the SP migration distance in porous media, slowing the clogging progression in low-SP groups. These findings offer valuable insights into the prevention and management of MAR clogging caused by the coexistence of biofilms and SPs. Full article

Global navigation satellite systems are the best means of navigation for dynamic platforms. However, interference, line-of-sight blockages, and multipath are destructive to receiver operations. Advanced receiver architectures like vector tracking loops have been shown to be more resilient in tracking during degraded signal environments and dynamic scenarios. Additionally, controlled reception pattern antennas can be used to steer the effective antenna gain pattern to resist interference. This work introduces algorithms for a software-defined radio that combines vector tracking loops with a phased antenna array to digitally steer beams for the amplification of signals of interest so that the effects of signal degradation and multipath can be reduced. The proposed receiver design was tested on dynamic live sky data in multipath-rich environments and compared against traditional scalar receivers with and without beamforming as well as robust commercial receivers. The results showed that beam steering receivers were obtaining the expected amplification and that the vector tracking with beam steering was able to provide better positioning and signal tracking performance than the other implemented software receivers and provide continuous measurements where the commercial receiver failed to track degraded signals. Full article

The safe operation of unmanned ground vehicles (UGVs) demands fundamental and essential requirements for continuous and reliable positioning performance. Traditional coupled navigation systems, combining the global navigation satellite system (GNSS) with an inertial navigation system (INS), provide continuous, drift-free position estimation. However, challenges like GNSS signal interference and blockage in complex scenarios can significantly degrade system performance. Moreover, ultra-wideband (UWB) technology, known for its high precision, is increasingly used as a complementary system to the GNSS. To tackle these challenges, this paper proposes a novel tightly coupled INS/UWB/GNSS-RTK integrated positioning system framework, leveraging a variational Bayesian adaptive Kalman filter based on the maximum mixture correntropy criterion. This framework is introduced to provide a high-precision and robust navigation solution. By incorporating the maximum mixture correntropy criterion, the system effectively mitigates interference from anomalous measurements. Simultaneously, variational Bayesian estimation is employed to adaptively adjust noise statistical characteristics, thereby enhancing the robustness and accuracy of the integrated system’s state estimation. Furthermore, sensor measurements are tightly integrated with the inertial measurement unit (IMU), facilitating precise positioning even in the presence of interference from multiple signal sources. A series of real-world and simulation experiments were carried out on a UGV to assess the proposed approach’s performance. Experimental results demonstrate that the approach provides superior accuracy and stability in integrated system state estimation, significantly mitigating position drift error caused by uncertainty-induced disturbances. In the presence of non-Gaussian noise disturbances introduced by anomalous measurements, the proposed approach effectively implements error control, demonstrating substantial advantages in positioning accuracy and robustness. Full article