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A non-periodic "step-like" variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. [ABSTRACT FROM AUTHOR]

Expansive soils pose a significant challenge in geotechnical engineering, especially in coastal areas. While research has mainly focused on their elastic properties, this study explores the overlooked aspect of inelastic subsidence during prolonged droughts, utilizing decade‐long GPS datasets from the University of Houston Coastal Center. Our findings reveal substantial subsidence, approximately one to two dm, during the summer droughts of 2018, 2020, 2022, and 2023, due to compaction within the upper 4 m of expansive soils. Inelastic subsidence constitutes roughly 10% of the total subsidence, resulting in step‐like permanent land elevation loss over time. Notably, drought‐induced subsidence is prominent in open‐field areas with expansive soils but is minor in built‐up areas or in non‐expansive soil regions. The occurrence of inelastic subsidence challenges traditional assessments of relative sea‐level rise and coastal flooding, emphasizing the need to consider it in coastal infrastructure planning for enhanced resilience against climate uncertainties. Plain Language Summary: Expansive soils, often found in coastal regions, are known for causing issues like land shifts and unstable buildings. Our research adds a new dimension: prolonged droughts can lead to significant, irreversible sinking of expansive soils, permanently lowering the elevation of open land areas. Using a decade of GPS data, we found that during droughts, these soils can sink considerably and will not fully recover. Interestingly, in developed regions where pavement covers the soils, thereby minimizing moisture loss, this sinking is observed to be minimal. In the Galveston coastal area, drought‐induced sinking can reach one to two dm, with irreversible subsidence making up about 10% of the total subsidence. This becomes a growing concern as droughts become more frequent due to climate change, especially in coastal areas. Additionally, our results suggest that current methods for estimating sea‐level rise may be missing a key factor: we have underestimated the speed at which uninhabited coastal lands are sinking because we did not account for this irreversible sinking due to droughts. Key Points: Prolonged droughts cause up 2 dm of subsidence in shallow expansive soils (<4 m deep) in Galveston, TX, 10% of which is inelasticProlonged droughts lead to notable subsidence in open fields with expansive soils, but less in built‐up areas or non‐expansive soil areasRecurring droughts lead to permanent land elevation losses in open‐field coast, possibly skewing sea‐level rise and flood risk projections [ABSTRACT FROM AUTHOR]

For real-time (RT) precise point positioning (PPP), the state space representation (SSR) information is often delayed due to possible communication delays and specific broadcast intervals. In this case, the positioning results will diverge and re-converge due to the increase of SSR products extrapolation errors. In addition, RT orbit and clock offset accuracy, as well as their extrapolation errors, will vary in different systems and satellites. We propose a PPP with ambiguity resolution (PPP-AR) method that combines a time-differenced carrier phase (TDCP) model, in which the characteristics of the orbit and clock are considered. Under normal communication, the PPP-AR solution is obtained by fixing satellites with small SSR product errors. When the communication is abnormal, the TDCP model is utilized to extrapolate user coordinates by considering different extrapolation error characteristics of satellites. The experimental results show that GPS and Galileo SSR products have better accuracy than BDS, with signal-in-space user ranger errors (SISREs) of 2.7, 2.2, and 8.6 cm, respectively. Optimizing the PPP stochastic model based on SISREs can effectively reduce the convergence time. Under 5 min SSR delay, SISREs caused by clock and orbit extrapolation for GPS/Galileo/BDS are 3.5, 1.4, and 2.6 cm, respectively. After optimizing the TDCP stochastic model based on extrapolation errors, the horizontal and vertical positioning accuracies can be maintained at 0.7 cm and 5.0 cm. For multi-GNSS, the combination of the TDCP and PPP-AR can overcome the influence of short delay. After optimizing the stochastic model, the GPS/Galileo/BDS positioning accuracy can be maintained at about 2.4 cm under 3 min delay, showing an accuracy improvement rate of 59.3% compared with the traditional method using only PPP. Additionally, the rapid PPP convergence results can be obtained by inheriting previous filter state information when the communication recovers normally. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is a chronic renal disease which could eventually develop into renal failure. Though albuminuria and estimated glomerular filtration rate (eGFR) are helpful for the diagnosis of DKD, the lack of specific biomarkers reduces the efficiency of therapeutic interventions.Methods: Based on bulk-seq of 56 urine samples collected at different time points (including 11 acquired from DKD patients and 11 from healthy controls), in corporation of scRNA-seq data of urine samples and snRNA-seq data of renal punctures from DKD patients (retrieved from NCBI GEO Omnibus), urine-kidney specific genes were identified by Multiple Biological Information methods.Results: Forty urine-kidney specific genes/differentially expressed genes (DEGs) were identified to be highly related to kidney injury and proteinuria for the DKD patients. Most of these genes participate in regulating glucagon and apoptosis, among which, urinary PART1 (mainly derived from distal tubular cells) and PLA2R1 (podocyte cell surface marker) could be used together for the early diagnosis of DKD. Moreover, urinary PART1 was significantly associated with multiple clinical indicators, and remained stable over time in urine.Conclusion: Urinary PART1 and PLA2R1 could be shed lights on the discovery and development of non-invasive diagnostic method for DKD, especially in early stages. [ABSTRACT FROM AUTHOR]

Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non‐invasive diagnostic tools. We downloaded scRNA‐seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP‐Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP‐Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP‐Genes might be involved in the immune response and extracellular components. Six key EP‐Genes were identified and correlated with renal function. IMC showed that key EP‐Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non‐invasive diagnosis of FSGS. [ABSTRACT FROM AUTHOR]

Due to the multipath and signal shading effects, there exist biases in the carrier phase measurements, which degrades the reliability of ambiguity resolution in real-time kinematic (RTK) positioning and, subsequently, the positioning accuracy. Integrity monitoring is an effective way to measure, improve, and guarantee the correctness of the RTK positioning solutions, but most integrity monitoring has focused only on the pseudorange faults and further the traditional ambiguity validation methods cannot monitor faulty ambiguity resolution separately. To provide high accuracy and high reliability for safety–critical applications, this study proposes a solution separation (SS)-based method for integrity monitoring of RTK precise positioning, in which the SS failure mode and test statistics are used to monitor the reliability of fixed ambiguity solutions by detecting faulty ambiguity fixes and bound positioning errors in the position domain using protection level (PL). The vehicular test with a low-cost receiver shows that the SS method can effectively detect and exclude fault ambiguity resolutions by 98.35%, and the double-difference residuals of SS are reduced from 6.89 cm to 2.56 cm. The positioning accuracy with the SS method has gained an 83.64% improvement compared with the traditional methods. The horizontal PL and the vertical PL can also effectively bound the positioning errors, with average values of 4.49 cm and 12.25 cm, respectively. The SS PL can achieve 99.17% and 96.14% in the horizontal and vertical directions to provide normal integrity service, respectively. [ABSTRACT FROM AUTHOR]

Smartphones with the Android operating system can acquire Global Navigation Satellite System (GNSS) raw pseudorange and carrier phase observations, which can provide a new way for the general public to obtain precise position information. However, only postprocessing precise orbit and clock offset products in some older smart devices are applied in current studies. The performances of precise point positioning (PPP) with the smartphone using real-time products and newly smartphones are still unrevealed, which is more valuable for real-time applications. This study investigates the observation data quality and multi-GNSS real-time PPP performance using recent smartphones. Firstly, the observed carrier-to-noise density ratio (C/N0), number of satellites and position dilution of precision (PDOP) of GNSS observations are evaluated. The results demonstrate that the C/N0 received by Huawei Mate40 is better than that of the Huawei P40 for GPS, BDS, QZSS and Galileo systems, while the GLONASS is poorer, and the PDOP of the Huawei P40 is slightly better than that of Mate40. Additionally, a comprehensive analysis of real-time precise orbit and clock offset products performance is conducted. The experiment result expresses that the orbit and clock offset performance of GPS and Galileo is better than that of BDS-3 and GLONASS, and BDS-2 is the worst. Finally, single- and dual-frequency multi-GNSS combined PPP experiments using observations received from smartphones and real-time products are conducted; the results indicate that the real-time static PPP using a smartphone can achieve decimeter-level positioning accuracy, and kinematic PPP can achieve meter-level positioning accuracy after convergence. [ABSTRACT FROM AUTHOR]

Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides' strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value. [ABSTRACT FROM AUTHOR]

The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and timeliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7–8 min, and 24 were for GEO satellites with an average detected time of 4–5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files. [ABSTRACT FROM AUTHOR]