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New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.

Systems thinking and attention to the relationships between a system’s variables on all spatial and temporal scales is an effective strategy in biological conservation and wildlife management. This study presents a case in a sensitive ecosystem to show how the future state of the jungle cat as an umbrella species depends on local, national, and international components. To this aim, all variables affecting the state of jungle cat were identified by an expert panel. Cross-impact analysis was applied to the identified variables in two stages using MICMAC, followed by Kane’s simulation (KSIM). The MICMAC method was used to detect the most important variables (i.e., variables with more influence and less dependency), and forecasting the future state of jungle cat was implemented by KSIM on variables screened by MICMAC. MICMAC showed that among the 22 identified variables, climate change, increased construction of dams in Afghanistan, water scarcity, and decline of agricultural lands under cultivation were the most important variables for management of jungle cat. KSIM showed declining trends for all variables in the future. Therefore, the predicted decreasing trend will continue as long as management remains unchanged on the local, national, and international scales.

This paper presents a comparison of the vertical total electron content (vTEC) estimated over Italy using two different approaches: the GPS Global Ionosphere Maps (GIMs) and the so-called “calibration technique” developed by Ciraolo in 2007. The study has been carried out at a regional level by considering three Italian dual-frequency stations of the GPS permanent network “Rete Integrata Nazionale GPS (RING)”. The GPS receivers are permanently installed at Madesimo (geographical coordinates: 46.5 N, 9.4 E), Rome (geographical coordinates: 41.8 N, 12.5 E) and Resuttano (geographical coordinates: 37.7 N, 14.1 E), respectively in the north, center and south of Italy. Time windows selected for the analysis include periods of both low (July 2008 to June 2009) and high (September 2013 to August 2014) solar activity. The two datasets have also been studied considering both quiet and disturbed geomagnetic activity conditions. Moreover, the effects of an extreme geomagnetic storm have been investigated in March 2015 when the well-known St. Patrick storm occurred. Overall, GIM estimated values are always higher than those calibrated by the Ciraolo procedure for all the considered datasets. The differences between the two methods increase as the latitude decreases, and they increase as the solar activity intensifies. The outcomes of this study shall be helpful when applying GlMs at a regional level.

The FormoSat-7/Constellation Observing System for Meteorology, Ionosphere, and Climate II (FS7/COSMIC2) program has acquired over three hundred thousand equatorial plasma bubble (EPB) observations from 2019 to 2023 in the equatorial and near low-latitude regions. The huge FS7/COSMIC2 database offers an opportunity to perform statistical inspections of the proposed hypothesis on seasonal versus longitudinal variability of EPB occurrence rates relevant to the Rayleigh–Taylor (R-T) instability. The detected EPBs are distributed along the magnetic equator with a half width of ~20° in geomagnetic latitude. The obtained EPB occurrence rates in local time (LT) rose rapidly after sunsets, and could be deconstructed into two overlapped Gaussian distributions resembling a major peak around 23:00 LT and a minor peak around 20:20 LT. The two groups of Gaussian-distributed EPBs in LT were classified as first- and second-type EPBs, which could be caused by different mechanisms such as sporadic E (Es) instabilities and pre-reversal enhancement (PRE) fields. The obtained seasonal–longitudinal distributions of both types of EPBs presented two diffused traces of high occurrence rates, which happened near the days and longitudes when and where the angle between the two lines of magnetic declination and solar terminator at the magnetic equator was equal to zero. Finally, we analyzed the climatological and seasonal–longitudinal variability of EPB occurrences and compared the results with the physical R-T instability model controlled by Es instabilities and/or PRE fields.

In this study, a low-cost, software-defined Global Positioning System (GPS) and Satellite-Based Augmentation System (SBAS) Reflectometry (GPS&SBAS-R) system has been built and proposed to measure ocean-surface wave parameters on board the research vessel New Ocean Researcher 1 (R/V NOR-1) of Taiwan. A power-law, ocean-wave spectrum model has been used and applied with the Small Perturbation Method approach to solve the electromagnetic wave scattering problem from rough ocean surface, and compared with experimental seaborne GPS&SBAS-R observations. Meanwhile, the intensity scintillations of high-sampling GPS&SBAS-R signal acquisition data are thought to be caused by the moving of rough surfaces of the targeted ocean. We found that each derived scintillation power spectrum is a Fresnel-filtering result on ocean-surface elevation fluctuations and depends on the First Fresnel Zone (FFZ) distance and the ocean-surface wave velocity. The determined ocean-surface wave speeds have been compared and validated against nearby buoy measurements.

The basic theory and experimental results of amplitude scintillation from GPS/GNSS radio occultation (RO) observations on sporadic E (Es) layers are reported in this study. Considering an Es layer to be not a “thin” irregularity slab on limb viewing, we characterized the corresponding electron density fluctuations as a power-law function and applied the Ryton approximation to simulate spatial spectrum of amplitude fluctuations. The scintillation index S4 and normalized signal amplitude standard deviation S2 are calculated depending on the sampling spatial scale. The theoretical results show that both S4 and S2 values become saturated when the sampling spatial scale is less than the first Fresnel zone (FFZ), and S4 and S2 values could be underestimated and approximately proportional to the logarithm of sampled spatial wave numbers up to the FFZ wave number. This was verified by experimental analyses using the 50 Hz and de-sampled FormoSat-3/Constellation Observing System for Meteorology, Ionosphere and Climate (FS3/COSMIC) GPS RO data in the cases of weak, moderate, and strong scintillations. The results show that the measured S2 and S4 values have a very high correlation coefficient of >0.97 and a ratio of ~0.5 under both complete and undersampling conditions, and complete S4 and S2 values can be derived by dividing the measured undersampling S4 and S2 values by a factor of 0.8 when using 1-Hz RO data.

Urinary incontinence remains a pressing problem, particularly for women. So this study was conducted to assess risk factors for stress, urge, mixed urinary incontinence and overactive bladder (OVB). Three hundred and thirty women aged 15–49, non-pregnant, non-breastfeeding who were referred to gynecologic clinics were surveyed. A questionnaire was used to collect data. Women with no symptoms related to urinary incontinence (UI) and OVB served as the reference group. The risk of all types of UI and OVB increased with constipation. Posterior pelvic organ prolapse was associated with stress and urge incontinence. Vaginal delivery was a predictor of stress, urge and mixed incontinence. BMI and PID were predictors of OVB. Pelvic muscle strength was a predictor of stress incontinence. Vaginal length was associated with mixed incontinence. Optimal weight gain, having a healthy lifestyle, treatment of constipation and pelvic organ prolapse, and improving pelvic floor muscle strength can be suggested as preventive measures against UI and OVB. Pelvic measurement can be included in evaluation of UI.

Accurate prediction of longitudinal dispersion coefficient (K) is a key element in studying of pollutant transport in rivers when the full cross sectional mixing has occurred. In this regard, several research studies have been carried out and different equations have been proposed. The predicted values of K obtained by different equations showed a great amount of uncertainty due to the complexity of the phenomenon. Therefore, there is still a need to make an improvement on the existing predictive models. In this study, a multi-objective particle swarm optimization (PSO) technique was used to derive new equations for predicting longitudinal dispersion coefficient in natural rivers. To do this, extensive field data, including hydraulic and geometrical characteristics of different rivers were applied. The results of this study were compared with those of the previous studies using the statistical error measures. The comparison revealed that the proposed model is superior to the previous ones. According to this study, PSO algorithm can be applied to improve the performance of the predictive equations by finding optimum values of the coefficients. The proposed model can be successfully applied to estimate the longitudinal dispersion coefficient for a wide range of rivers’ characteristics.

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.