Development and Testing of Augmentations of Continuously-Operating GPS Networks to Improve Their Spatial and Temporal Resolution

Continuously-operating networks of GPS receivers (CGPS) are not capable of determining the characteristics of crustal deformation at the fine temporal or spatial scales required. Four ‘temporal densification schemes’ and two 'spatial densification schemes' to augment the CGPS networks have been developed and tested. The four ‘temporal densification schemes’ are based on the high rate Real-Time Kinematic (RTK) GPS technique, GPS multipath effects, Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR). The 'serial scheme' based on using GPS as a seismometer has been proposed. Simulated seismic signals have been extracted from the very noisy high rate RTK-GPS results using an adaptive filter based on the least-mean-square algorithm. They are in very good agreement with those of the collocated seismometers. This scheme can improve the CGPS temporal resolution to 0.1 second. The 'retro-active scheme' takes advantage of the fact that the GPS multipath disturbance is repeated between consecutive days. It can therefore provide a means of correcting multipath errors in the observation data themselves. A reduction of the standard deviations of the pseudo-range and carrier phase multipath time series to about one fourth and one half the original values respectively, has been demonstrated. The 'all-GPS parallel scheme' uses the multipath effects as a signal to monitor the antenna environment. Models relating the changes of multipath and antenna environment have been derived. The 'cross-technique parallel scheme' integrates the collocated CGPS, VLBI and SLR results, taking advantage of the decorrelation among their biases and errors. Crustal displacement signature has been extracted as a common-mode signal using data from two stations: Matera in Italy and Wettzell in Germany. Two 'spatial densification schemes' which can verify with each other have been developed and tested. The 'soft' scheme integrates CGPS with radar interferometry (InSAR). The Double Interpolation and Double Prediction (DIDP) approach combines the strengths of the high temporal resolution of CGPS and the high spatial resolution possible with the InSAR technique. This scheme can improve the spatial resolution to about 25m. The 'hard' scheme requires the deployment of single-frequency receivers to in-fill the present CGPS arrays. Alternatively some receivers may be installed at some geophysically strategic sites outside existing CGPS arrays. The former has been tested within Japan's GEONET, while the latter has been tested using a five-station array

A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR.

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost and real-time global coverage capability. In this paper, a general imaging formation algorithm was proposed for accurately and efficiently focusing GNSS-based bistatic SAR data, which avoids the interpolation processing in traditional back projection algorithms (BPAs). A two-dimensional point target spectrum model was firstly presented, and the bulk range cell migration correction (RCMC) was consequently derived for reducing range cell migration (RCM) and coarse focusing. As the bulk RCMC seriously changes the range history of the radar signal, a modified and much more efficient hybrid correlation operation was introduced for compensating residual phase errors. Simulation results were presented based on a general geometric topology with non-parallel trajectories and unequal velocities for both transmitter and receiver platforms, showing a satisfactory performance by the proposed method

EUS assisted transmural cholecystogastrostomy fistula creation as a bridge for endoscopic internal gallbladder therapy using a novel fully covered metal stent

BACKGROUND: Laparoscopic cholecystectomy (LC) has become the “gold standard” for treating symptomatic gallstones. Innovative methods, such as a scarless therapeutic procedure through a natural orifice are being introduced, and include transgastric or transcolonic endoscopic cholecystectomy. However, before clinical implementation, instruments still need modification, and a more convenient treatment is still needed. The aim of this study was to evaluate the feasibility of endoscopic internal gallbladder therapy such as cholecystolithotomy in an animal survival model. METHODS: Four pigs underwent endoscopic-ultrasound (EUS)-guided cholecystogastrostomy and the placement of a novel covered mental stent. Four weeks later the stents were removed and an endoscope was advanced into the gallbladder via the fistula, and cholecystolithotomy was performed. Two weeks later the pigs were sacrificed, and the healing of the fistulas was assessed. RESULTS: EUS-guided cholecystogastrostomy with mental stent deployment was successfully performed in all the animals. Four weeks after the procedure, the fistulas had formed and all the stents were removed. Endoscopic cholecystolithotomy was performed through each fistula. All the animals survived until they were sacrificed 2 weeks later. The fistulas were found to be completely healed. CONCLUSIONS: This study reports the first endoscopic transmural cholecystolithotomy after placement of a novel mental stent in an animal survival model

Towards a deep-learning-based framework of sentinel-2 imagery for automated active fire detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019-2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km(2) (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans

InSAR reveals land deformation at Guangzhou and Foshan, China between 2011 and 2017 with COSMO-SkyMed data

Subsidence from groundwater extraction and underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK) to understand if changes in temporal and spatial patterns of subsidence rates occurred. Using InSAR time-series analysis (TS-InSAR), we found that significant surface displacement rates occurred in the study area varying from -35 mm/year (subsidence) to 10 mm/year (uplift). The 2011-2017 TS-InSAR results were compared to two separate TS-InSAR analyses (2011-2013, and 2013-2017). Our CSK TS-InSAR results are in broad agreement with previous ENVISAT results and levelling data, strengthening our conclusion that localised subsidence phenomena occurs at different locations in Guangzhou and Foshan. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. Many local scale deformation zones have been identified related to different phenomena. The majority of deformations is related to excessive groundwater extraction for agricultural and industrial purposes but subsidence in areas of subway construction also occurred. Furthermore, a detailed analysis on the sinkhole collapse in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Roads and highways with similar subsidence phenomenon are identified. Continuous monitoring of the deforming areas identified by our analysis is important to measure the magnitude and spatial pattern of the evolving deformations in order to minimise the risk and hazards of land subsidence

Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents:a longitudinal study based on positive youth development data (2019–2022)

Background: Internet Addiction (IA) has emerged as a critical concern, especially among school age children and adolescents, potentially stalling their physical and mental development. Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test. Methods: The longitudinal data consisting of 8,824 schoolchildren from the Chengdu Positive Child Development (CPCD) survey were analyzed, where 33.3% of participants were identified with IA (Age: 10.97 ± 2.31, Male: 51.73%). IA was defined using Young’s Internet Addiction Test (IAT ≥ 40). Demographic variables such as age, gender, and grade level, along with key variables including scores of Cognitive Behavioral Competencies (CBC), Prosocial Attributes (PA), Positive Identity (PI), General Positive Youth Development Qualities (GPYDQ), Life Satisfaction (LS), Delinquent Behavior (DB), Non-Suicidal Self-Injury (NSSI), Depression (DP), Anxiety (AX), Family Function Disorders (FF), Egocentrism (EG), Empathy (EP), Academic Intrinsic Value (IV), and Academic Utility Value (UV) were examined. Chi-square and Mann–Whitney U tests were employed to validate the significance of the mentioned predictors of IA. We applied six ML models: Extra Random Forest, XGBoost, Logistic Regression, Bernoulli Naïve Bayes, Multi-Layer Perceptron (MLP), and Transformer Encoder. Performance was evaluated via 10-fold cross-validation and held-out test sets across survey waves. Feature selection and SHapley Additive exPlanations (SHAP) analysis were utilised for model improvement and interpretability, respectively. Results: ExtraRFC achieved the best performance (Test AUC = 0.854, Accuracy = 0.798, F1 = 0.659), outperforming all other models across most metrics and external validations. Key predictors included grade level, delinquent behavior, anxiety, family function, and depression scores. SHAP analysis revealed consistent and interpretable feature contributions across individuals. Conclusion: Depression, anxiety, and family dynamics are significant factors influencing IA in children. The Extra Random Forest model proves most effective in predicting IA, emphasising the importance of addressing these factors to promote healthy digital habits in children. This study presents an effective SHAP-based explainable ML framework for IA prediction in children and adolescents.</p

Sweat permeable and ultrahigh strength 3D PVDF piezoelectric nanoyarn fabric strain sensor

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices