Search Results (30,028)

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency of directional operation and the accuracy of wellbore trajectory control, this paper presents an improved Sparrow Search algorithm by integrating the multi-strategy model and Constant-Toolface models to calculate the single-stand control scheme for single-bent-housing motors in directional drilling. To evaluate the performance of the algorithm, the Particle Swarm algorithm, the Sparrow Search algorithm, and the improved Sparrow Search algorithm (LCSSA) are used to optimize the process parameters for each drilling, respectively. Numerical tests based on drilling data show that all three algorithms can predict the drilling parameters. In contrast, the LCSSA exhibits the fastest convergence and the smallest error after optimizing single-stand control, attaining an average convergence time of 0.08 s. It accurately back-calculated theoretical model parameters with high accuracy and met engineering requirements when applied to actual drilling data. In field applications, the LCSSA reduces the deviation from the planned trajectory by over 25%, restricting the deviation to within 0.005 m per stand; additionally the total drilling time was reduced by at least 18% compared to previous methods. The integration of the LCSSA with the drilling system significantly enhances drilling operations by optimizing trajectory accuracy and boosting efficiency and serves as an advanced tool for designing process parameters. Full article

Cycle slip detection in the carrier phase is important for achieving precise relative positioning with global navigation satellite systems (GNSSs) in urban environments. The false alarm rate (FAR) and missing detection rate (MDR) of the detection approach will significantly affect the positioning error. This paper proposes a novel cycle slip detection approach based on inter-frequency cross-validation (IFCV), and designs corresponding test statistics and thresholds. In contrast to conventional cycle slip detection methods, the proposed IFCV approach has the advantages of higher accuracy and efficiency, which is validated by the airborne dynamic test. For cycle slips of specific proportions (such as five-cycle and four-cycle slips), the IFCV method significantly reduces the MDR from 3.89% to 0% and decreases the relative positioning error from 4.69 cm to 3.17 cm. In tests involving randomly injected cycle slips, the IFCV method reduces the MDR from 0.02% to 0%, reduces the relative positioning error from 3.83 cm to 3.02 cm, and decreases the average computational time from 1.02 ms to 0.53 ms. Full article

An activated biochar was produced from post-coagulation sludge (also called water treatment residuals or water treatment sludge) in the pyrolysis process at 800 °C in a nitrogen atmosphere and chemical activation using NaOH. The produced adsorption material was characterised by an S_BET surface area of 439 m²/g, a total volume of pores of 0.301 cm³/g, and an average pore size of 1.4 nm. FTIR analysis reveals the presence of primarily C-H, C-O, N-H, C-N, and O-H groups on the activated biochar surface. The batch adsorption process was conducted for three dyes: Acid Red 18, Acid Green 16, and Reactive Blue 81. In the study, the effect of pH, contact time, adsorption kinetics, and adsorption isotherm was determined. The studies showed that, for all dyes, the highest efficiency of the process was achieved at a pH of 2. The results indicate the occurrence of a chemical adsorption process, as evidenced by the best fit to the experimental results obtained with the pseudo-second-order kinetics model and the Elovich model. In the case of the adsorption isotherm, the SIPS model best describes the adsorption for Acid Red 18 and Reactive Blue 81, and the Jovanovic model describes the adsorption of Acid Green 16. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

The construction of cascade dam systems profoundly reshapes river hydrological processes, yet the analysis of their spatial heterogeneity effects has long been constrained by the mechanistic deficiencies and interpretability limitations of traditional mechanistic models. Focusing on the middle-lower Han River (a 652 km reach regulated by seven dams) as a representative case, this study develops an LSTM-Attention-SHAP interpretable framework to achieve, for the first time, intelligent decoupling of dam-induced hydrological effects and mechanistic analysis of spatial differentiation. Key findings include the following: (1) The LSTM model demonstrates exceptional predictive performance of water level and flow rate in intensively regulated reaches (average Nash–Sutcliffe Efficiency, NSE = 0.935 at Xiangyang, Huangzhuang, and Xiantao stations; R² = 0.988 for discharge at Xiantao Station), while the attention mechanism effectively captures sensitive factors such as the abrupt threshold (>560 m³/s) in the Tangbai River tributary; (2) Shapley Additive exPlanations (SHAP) values reveal spatial heterogeneous dam contributions: the Cuijiaying Dam increases discharge at Xiangyang station (mean SHAP +0.22) but suppresses water level at Xiantao station (mean SHAP −0.15), whereas the Wangfuzhou Dam shows a stable negative correlation with Xiangyang water levels (mean SHAP −0.18); (3) dam operations induce cascade effects through altered channel storage capacity. These findings provide spatially adaptive strategies for flood risk zoning and ecological operations in globally intensively regulated rivers such as the Yangtze and Mekong. Full article

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

Wildfires are increasing in frequency and severity, with Australia’s 2019–2020 Black Summer burning over 18 million hectares. Accurate prediction of wildfire behavior is essential for effective risk assessment and emergency response. This study presents a machine learning framework for predicting wildfire dynamics across Australia’s seven regions using the IBM wildfire dataset. Various Machine Learning (ML) models were evaluated to forecast three key indicators: Fire Area (km²), Fire Brightness Temperature (K), and Fire Radiative Power (MW). Lasso Regression consistently outperformed the other models, achieving an average RMSE of 0.04201 and R² of 0.29355. Performance varied across regions, with stronger results in areas like New South Wales and Queensland, likely influenced by differences in topography, microclimate, and vegetation. However, limitations include the exclusion of ignition sources such as lightning and human activity, which are critical for capturing the environment accurately and improving predictive accuracy. Future work will integrate these factors alongside more detailed weather and vegetation data. Practical implementation may face challenges related to real-time data availability, system integration, and response coordination, but this approach offers promising potential for operational wildfire decision support. Full article

Objectives: Fluorescein break-up time (FBUT) is commonly used to assess tear film stability. However, the instillation of fluorescein destabilises the tear film, impacting validity and clinical applicability, while the subjective nature and variation in volume and concentration reduces repeatability. Non-invasive break-up time (NIBUT) offers an alternative method with less potential bias. Normal tear break-up time is conventionally accepted as 10 seconds (s); however, FBUT is expected to be lower than NIBUT. This study was designed to compare FBUT and NIBUT values in a pre-operative refractive surgery population, where diagnosis of dry eye disease may alter the risk–benefits ratio and contraindicate surgical procedure(s). Improved understanding of the relationship between these two methods will aid appropriate pre-operative patient counselling and consent. Methods: Data from consecutive participants presenting to a private ophthalmology clinic, for initial refractive surgery pre-operative assessment, were analysed. NIBUT and FBUT were performed. Paired and unpaired comparisons were made using the Wilcoxon signed-rank and Mann–Whitney U tests, respectively, and relationships with demographics were explored using Spearman’s rank correlation coefficient. Results: Median and interquartile range (IQR) for the first NIBUT was 12.5 s (7.0–18.0 s) and 14.2 s (9.4–18.0 s) for the right and left eyes, respectively. Median and IQR for the average NIBUT was 14.0 s (6.9–18.0 s) and 14.6 s (10.1–18.0 s) for the right and left eyes, respectively. Median and IQR for FBUT was 7 s (5–8 s) and 6 s (5–8 s) for the right and left eyes, respectively. There was a statistically significant difference between NIBUT and FBUT (p < 0.001). Conclusions: The findings suggest that the commonly used diagnostic threshold of 10 s cannot be uniformly applied to both FBUT and NIBUT, as FBUT systematically underestimates tear stability. Full article

This study presents the design, implementation, and experimental validation of a self-contained earthquake early warning system (EEWS) based on real-time frequency-domain analysis of ground motion. The proposed system integrates a low-noise triaxial micro-electro-mechanical system (MEMS) accelerometer with a high-performance microcontroller, enabling autonomous seismic event detection without dependence on external communications or centralized infrastructure. The characteristic period of ground motion (τ_c) is estimated using a spectral moment method applied to the first three seconds of vertical acceleration following P-wave arrival. Event triggering is based on a short-term average/long-term average (STA/LTA) algorithm, with alarm logic incorporating both spectral and amplitude thresholds to reduce false positives from low-intensity or distant events. Experimental validation was conducted using a custom-built uniaxial shaking table, replaying 10 real earthquake records (Mw 4.1–7.7) in 20 repeated trials each. Results show high repeatability in τ_c estimation and strong correlation with event magnitude, demonstrating the system’s reliability. The findings confirm that modern embedded platforms can deliver rapid, robust, and cost-effective seismic warning capabilities. The proposed EEW solution is well-suited for deployment in critical infrastructure and resource-limited seismic regions, supporting scalable and decentralized early warning applications. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article

Three-dimensional (3D) printing offers new opportunities in surgical planning and medical education, yet high costs and technological complexity often limit its widespread use, especially in low-resource settings. This study presents a personalized, cost-effective, and anatomically accurate liver model designed using open-source tools and affordable 3D-printing techniques. Segmentation of hepatic CT images was performed in 3D Slicer using a region-growing method, and the resulting models were optimized and exported as STL files. The external mold was printed with Fused Deposition Modeling (FDM) using PLA+, while internal structures such as vessels and tumors were fabricated via Liquid Crystal Display (LCD) printing using PLA Pro resin. The final assembly was cast in food-grade gelatin to mimic liver tissue texture. The complete model was produced for under USD 50, with an average total production time of under 128 h. An exploratory pedagogical evaluation with five medical trainees yielded high Likert scores for anatomical understanding (4.6), spatial awareness (4.4), planning confidence (4.2), and realism (4.4). This model demonstrated utility in preoperative discussions and training simulations. The proposed workflow enables the fabrication of low-cost, realistic hepatic phantoms suitable for education and surgical rehearsal, promoting the integration of 3D printing into everyday clinical practice. Full article

In turbine blade environments, the combination of blade curvature and accelerating flow gives rise to streamwise pressure gradients (SPGs), which substantially impact coolant–mainstream interactions. This study investigates the effect of SPGs on film cooling performance using Large Eddy Simulation (LES) for a shaped cooling hole at a density ratio of $\mathrm{DR}=1.5$ under two blowing ratios: $M=0.5$ and $M=1.6$. Both favorable pressure gradient (FPG) and zero pressure gradient (ZPG) conditions are examined. LES predictions are validated against experimental data in the high blowing ratio case, confirming the accuracy of the numerical method. Comparative analysis of the time-averaged flow fields indicates that, at $M=1.6$, FPG enhances wall attachment of the coolant jet, reduces boundary layer thickness, and suppresses vertical dispersion. Counter-rotating vortex pairs (CVRPs) are also compressed in this process, leading to improved downstream cooling. At $M=0.5$, however, the ZPG promotes greater lateral coolant spread near the hole exit, resulting in superior near-field cooling performance. Instantaneous flow structures are also analyzed to further explore the unsteady dynamics governing film cooling. The Q criterion exposes the formation and evolution of coherent vortices, including hairpin vortices, shear-layer vortices, and horseshoe vortices. Compared to ZPG, the FPG case exhibits a greater number of downstream hairpin vortices identified by density gradient, and this effect is particularly pronounced at the lower blowing ratio. The shear layer instability is evaluated using the local gradient $Ri$ number, revealing widespread Kelvin–Helmholtz instability near the jet interface. In addition, Fast Fourier Transform (FFT) analysis shows that FPG shifts disturbance energy to lower frequencies with higher amplitudes, indicating enhanced turbulent dissipation and intensified coolant mixing at a low blowing ratio. Full article

Nomex honeycomb composites (NHCs) are highly sensitive to the abnormal wear state of disc tools during cutting, leading to poor product quality. This paper proposes a real-time anomaly detection method combining a novel CNN–GRU–Attention (CGA) deep learning model with an Exponentially Weighted Moving Average (EWMA) control chart to monitor sensor data from the disc tool. The CGA model integrates an improved CNN layer to extract multidimensional local features, a GRU layer to capture long-term temporal dependencies, and a multi-head attention mechanism to highlight key information and reduce error accumulation. Trained solely on normal operation data to address the scarcity of abnormal samples, the model predicts cutting force time series with an RMSE of 0.5012, MAE of 0.3942, and R² of 0.9128, outperforming mainstream time series data prediction models. The EWMA control chart applied to the prediction residuals detects abnormal tool wear trends promptly and accurately. Experiments on real NHC cutting datasets demonstrate that the proposed method effectively identifies abnormal machining conditions, enabling timely tool replacement and significantly enhancing product quality assurance. Full article

Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning models often fail to accurately predict future trends in complex financial data due to inherent limitations. To address these challenges, this study introduces a WOA-BiLSTM-ARIMA hybrid forecasting model leveraging parameter optimization. Specifically, the whale optimization algorithm (WOA) optimizes hyperparameters for the Bidirectional Long Short-Term Memory (BiLSTM) network, overcoming parameter tuning challenges in conventional approaches. Due to its strong capacity for nonlinear feature extraction, BiLSTM excels at modeling nonlinear patterns in financial time series. To mitigate the shortcomings of BiLSTM in capturing linear patterns, the Autoregressive Integrated Moving Average (ARIMA) methodology is integrated. By exploiting ARIMA’s strengths in modeling linear features, the model refines BiLSTM’s prediction residuals, achieving more accurate and comprehensive financial time series forecasting. To validate the model’s effectiveness, this paper applies it to the prediction experiment of future spread data. Compared to classical models, WOA-BiLSTM-ARIMA achieves significant improvements across multiple evaluation metrics. The mean squared error (MSE) is reduced by an average of 30.5%, the mean absolute error (MAE) by 20.8%, and the mean absolute percentage error (MAPE) by 29.7%. Full article