Radar altimetry provides valuable measurements to characterize the state and the evolution of the Antartica and Greenland ice sheet cover. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential capacity of complementing the dedicated radar altimeters incrementing the temporal and spatial resolution of the surface height measurements. In this work we perform an study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission, designed primarily to provide sea state information, like sea surface roughness or wind, but not altimetric products. The data has been analyzed with altimetric methodologies, already proved in aircraft based experiments, to extract signal delay observables to be used to infer the topography of the Greenland cover. The penetration depth of the GNSS signals into ice has also considered. The topographic signal obtained is consistent with those obtained with other passive or active microwave sensors. The main conclusion derived from this work is that GNSS-R also provides valuable measurements of the ice sheet cover and, as taken at a variety of geometries and at least two frequency bands, they prospect different depths into the ice. They have thus potential to complement our understanding of the ice firn and its evolution.

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

The term deviation frequency denotes the boundary between the variable part of the amplitude and phase scintillation spectrum and the part of uninformative noises. We suggested the concept of the “characteristic deviation frequency” during the observation period which is defined as the most probable value of the deviation frequency under current local conditions. This work is a case study of the characteristic deviation frequency (fd) registered for GPS, GLONASS, Galileo and SBAS signals under quiet and weakly disturbed geomagnetic conditions in April 2021 at the mid-latitude GNSS station. Our results demonstrated that the fd value for all signal components of GPS, GLONASS and GALILEO signals varied within 15-22 Hz. The characteristic deviation frequency was 20 Hz for the mentioned GNSS signals. In difference, the deviation frequency was limited within 13-20 Hz for SBAS with the lower characteristic deviation frequency at 18 Hz. We assume that the concept of the characteristic deviation frequency can be used to determine the optimal sampling rate of the GNSS carrier phase data for the ionospheric studies. The characteristic deviation frequency can also characterize the state of the regular trans-ionospheric radio channel.

The application of signal-to-noise ratio (SNR) observations from ground-based GNSS Reflectometry is becoming an operational tool for coastal sea-level altimetry. As in all data analyses, systematic influences must be reduced here too, to achieve reliable results. A prominent influence results from atmospheric refraction. Different approaches exist to describe or to correct for this influence. In our contribution we will revise the latest developments and suggest a simple atmospheric interferometric delay model that takes into account ray bending as well as along-path propagation delay. The model takes into account a spherical reflector and can therefore be applied for data from very low elevation angles, too. The findings are double-checked by numerical experiments based on a step-by-step raytracing procedure.

The signal-to-noise ratio (SNR) data is part of the global navigation satellite systems (GNSS) observables. In a marine environment, the oscillation of the SNR data can be used to derive reflector heights. Since the attenuation of the SNR oscillation is related to the roughness of the sea surface, the significant wave height (SWH) of the water surface can be calculated from the analysis of the attenuation. The attenuation depends additionally on the relation between the coherent and the incoherent part of the scattered power. The latter is a function of the correlation length of the surface waves. Because the correlation length changes with respect to the direction of the line of sight relative to the wave direction, the attenuation must show an anisotropic characteristic. In this work, we present a method to derive the wave direction from the anisotropy of the attenuation of the SNR data. The method is investigated based on simulated data as well by the analysis of experimental data from a GNSS station in the North Sea.

Abstract: Bulgaria is famous for its oil-bearing rose. R. damascena Mill. and R. alba L. are mainly cultivated in the country, but a recent survey of industrial plantations in 2020 revealed that R. centifolia L. and hybrids of R. damascena Mill. x R. gallica L. are also common in the rose valley. Although their essential oil cannot be compared in quality with the classic, they are preferred by farmers with high yields of flowers and resistance to diseases and pests. All these roses are also used to produce rose water and extracts. The aim of this investigation was to compare the yield and chromatographic fingerprints of seven rose oils and hydrosols produced in Bulgaria. The quantitative composition of the main components of the oils was compared with the norms of the world standard. Our study showed that the yield of essential oil from these roses was in the range of 0.015 - 0.048%. The main group in the chemical composition is terpene alcohols, which vary in the range: geraniol (17.60 - 34.02 %), citronellol (8.38 - 28.7 %), and nerol (4.8 - 11.90 %), but with a different ratio. Hydrocarbons are represented by saturated aliphatic homologs with an odd number of carbon atoms, the main ones being: nonadecane (8.10 - 22.67 %), heneicosane (4.37 - 10.21 %), heptadecane (1.07 - 2.98 %) and triclosan (0.81 - 5.90 %). In contrast, the chemical profile of the hydrosols was performed by phenyl ethyl alcohol (27.45 - 69.88 %), geraniol (13.72 - 28.67 %), and citronelol+nerol (4.56 - 17.37 %). The results show that the presence of plantations with a genotype different from that of R. damascena implies differences in the quality of rose oils and hydrosols. This predetermines their properties of use.

High-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. For the shortcomings of GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehi-cle positioning method of GNSS/INS/OD combination based on lateral distance measurement and lane constraint. Firstly, lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurement, odometer, and lane markings. Experimental results demonstrate that for a 4.6 km tunnel, the average absolute error of lateral positioning is 0.294 meters, and the longitudinal positioning error is no more than 0.6 meters, which can effectively meet practical operational requirements.

The technology for determining the point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for 5 control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas and satellite determinations allows the creation of a high accurate altitude network for mountainous areas.

GNSS is nowadays a standard tool for monitoring and studying the Earth’s dynamic environment. However, the development of dense GNSS measurements remains limited in many experiments by the cost of geodetic-class equipments allowing to achieve the high precision required by many applications. Recently, multi-constellation, multi-frequency, low-power and, above all, less expensive GNSS electronic chips have become available. We present a prototype of a low-cost, open-source multi-GNSS station. Our prototype comprises a dual-frequency GNSS chip, a calibrated antenna, a Raspberry Pi card and a 4G key for data transmission. The system is easy to deploy in the field and allows precise positioning in real time and post-processing. We assess the performance of our prototype in terms of raw data quality, and quality of the obtained high rate and daily position one-year-long time series. Our results demonstrate a quality equivalent to geodetic-class equipment and better quality than other low-cost systems proposed so far.

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS-reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites, and double difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalogue for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

An ionospheric response at middle latitudes to geomagnetic storms is not yet very well understood. Total electron content (TEC) variations associated with eight strong geomagnetic storms between 2015 and 2022 obtained from GNSS receivers in the western area of the North Atlantic (Portuguese continental and insular territory) are studied in an attempt to fill this gap. It was found that for most of the studied geomagnetic storms TEC variations are synchronous for the longitudinal ranges from 27ºW and 9ºW. In the southern part of the studied region (around 32ºN) the amplitude of TEC variations is, in general, significantly higher than in the northern part (around 39ºN). Some of the studied geomagnetic storms were associated with TEC variations that we interpret as effects of post-sunset equatorial plasma bubbles that travelled well north from their habitual region. Also, while most of the studied storms were accompanied by reports on different kind of malfunction of GNSS systems (GPS; GALILEO and other), there is no clear pattern in their appearance in dependence on the geomagnetic/ionospheric storms’ strength, commencement time, and its characteristics, in general.

In this paper, we propose a system to create high-precision maps using UAV-LiDAR and to determine the location of individual fruit trees (apple trees) on the maps. The system is based on a UAV-LiDAR system that flies over an actual orchard. A UAV was flown over an actual orchard, and the point cloud of the onboard LiDAR and the location information of RTK-GNSS were obtained. The system records the LiDAR point cloud and RTK-GNSS position information. Automated software processes point cloud data offline and Automated software processes point cloud data offline and automatically segments each apple tree in the map. The RTK-GNSS position information is used for the segmented trees. The positional information obtained from RTK-GNSS was georeferenced to the segmented trees without using ground evaluation points. As a sample, location information was obtained from trees using the Quasi-Zenith Satellite System (QZSS) MICHIBIKI. The positional accuracy of the trees was evaluated using the positional information obtained from the Quasi-Zenith Satellite System MICHIBIKI as a reference. As a result, the alignment accuracy was sufficient to identify individual fruit trees.

The CoDRIVE solution builds on R&D in the development of connected and autonomous vehicles (CAVs). The mainstay of the system is a low-cost GNSS receiver integrated with a MEMS grade IMU powered with CoDRIVE algorithms and high precision data processing software. The solution integrates RFID (radio-frequency identification) localisation information derived from tags installed in the roads around the University of Nottingham. This aids the positioning solution by correcting the long-term drift of inertial navigation technology in the absence of GNSS. The solution is informed of obscuration of GNSS through city models of skyview and elevation masks derived from 360-degree photography. The results show that predictive intelligence of the denial of GNSS and RFID aiding realises significant benefits compared to the inertial only solution. According to the validation, inertial only solutions drift over time, with an overall RMS accuracy over a 300 metres section of GNSS outage of 10 to 20 metres. After deploying the RFID tags on the road, experiments show that the RFID aided algorithm is able to constrain the maximum error to within 3.76 metres, and with 93.9% of points constrained to 2 metres accuracy overall.

It is common knowledge that estimating the height component of GNSS stations in general is much more problematic than estimating the horizontal position. Many different effects, such as tectonic signals, non-tectonic signals, atmospheric delay, noise, etc., are known to affect the height component of GNSS stations more than the horizontal component. However, the height component of GNSS stations is still poorly estimated. In this study, the height time series of 37 continuous GNSS stations covering the 2014–2019 date range is used from the Turkish National Permanent GNSS Network-Active (TUSAGA-Active). Since it is easier to interpret the effects of the height component due to its topographic features and seasonal changes being more effective than in the rest of the country, stations were chosen in the Eastern Anatolia region of Turkey. The daily coordinates of the GNSS stations were obtained as a result of the GAMIT/GLOBK software solution. By applying time series analysis to the daily coordinate values of the stations, statistically significant trends, periodic and stochastic components of the stations were determined. As a result of the analysis, the vertical velocities of the GNSS stations and the standard deviations of the vertical velocities were determined. Furthermore, when the height components of continuous GNSS stations were examined, it was seen that there were seasonal effects, and it was investigated whether the height components were related to meteorological parameters. For that, simple linear regression analysis was performed to determine how dependent the height components of the continuous GNSS stations were on meteorological parameters. As a result of the analysis, the height components of the continuous GNSS stations are dependent on meteorological parameters such as temperature, pressure, relative humidity, wind speed, and precipitation. In addition, height component time series analysis of continuous GNSS stations was performed by using Autoregressive Moving Average (ARMA) models from linear time series methods. As a result of the study, the performance of the ARMA modeling results again indicated the dependence of the height component of the continuous GNSS stations on the meteorological parameters.

To ensure the safety in aircraft flying, we aim use of the deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection posed. The use of the Fast Region-based Convolutional Neural Networks (Fast R-CNN) driven model seeks to augment and improve existing automated Non-Destructive Testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aeronautics engine defect data samples can thus pose another problem in training model tackling multiple detections perform accuracy. To overcome this issue, we employ a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall the achieve result get more then 90% accuracy based on the AE-RTISNet retrained with 8 types of defect detection. Caffe structure software to make networks tracking detection over multiples Fast R-CNN. We consider the AE-RTISNet provide best results to the more traditional multiple Fast R-CNN approaches simpler translate to C++ code and installed in the Jetson™ TX2 embedded computer. With the use of LMDB format, all images using input images of size 640 × 480 pixel. The results scope achieves 0.9 mean average precision (mAP) on 8 types of material defect classifiers problem and requires approximately 100 microseconds.

Radio Occultation (RO) is pivotal for profiling the neutral and ionized atmosphere, with the PlanetiQ mission, via its GNOMES satellites, striving to establish an advanced atmospheric observing system. However, an assessment of the spatiotemporal distributions of PlanetiQ observations and comparisons with reliable datasets are lacking. This study addresses this gap by examining the temporal and spatial distribution of RO observations from PlanetiQ during its initial 198 operational days in 2023, alongside comparisons with COSMIC and Numerical Weather Prediction (NWP) models. Data from GN02, GN03, and GN04 satellites, yielding 1099, 1313, and 1843 RO events per day respectively, were analyzed. The satellite constellation's observations demonstrate a generally well-distributed pattern, albeit minor deficiencies in equatorial and polar regions. Single-profile comparisons with COSMIC data reveal strong correlations for pressure, temperature, Water Vapor Pressure (WVP), and refractivity profiles, with temperature exhibiting larger variations (RMSE = 1.24°C). Statistical analyses confirm statistically insignificant differences between PlanetiQ and COSMIC profiles at the same spatio-temporal coordinates. Comparisons with NWP models show slight differences with GFS, with overall RMSE values of 0.23 mb (WVP), 0.6 mb (pressure), 1.3 (refractivity), and 1.5°C (temperature). However, assessments against GFS/ECMWF models indicate overall compatibility, with insignificant differences between PlanetiQ profiles and models observations.

Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3DMA GNSS-positioning system based on state-of-the-art 3DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system’s recorded data in New York City. The real-time forward processed FGO can provide a root-mean-square error (RMSE) with about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV.

GNSS receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure deformations. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS estimated deformations on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in the Sardinia Island. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. This encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and maintenance of the global coordinate framework. Long Short-Term Memory (LSTM), a deep learning model has been widely applied in the field of high-precision time series prediction especially when combined with Variational Mode Decomposition (VMD) to form the VMD-LSTM hybrid model. To further improve the prediction accuracy of the VMD-LSTM model, this paper proposes a dual variational modal decomposition long short-term memory (DVMD-LSTM) model to effectively handle the noise in GNSS time series prediction. This model extracts fluctuation features from the residual terms obtained after VMD decomposition to reduce the prediction errors associated with residual terms in the VMD-LSTM model. Daily E, N, and U coordinate data recorded at multiple GNSS stations between 2000 and 2022 are used to validate the performance of the proposed DVMD-LSTM model. The experimental results demonstrate that compared to the VMD-LSTM model, the DVMD-LSTM model achieves significant improvements in prediction performance across all measurement stations. The average RMSE is reduced by 9.86%, and the average MAE is reduced by 9.44%. Furthermore, the average accuracy of the optimal noise model for the predicted results is improved by 36.50%, and the average speed accuracy of the predicted results is enhanced by 33.02%. These findings collectively attest to the superior predictive capabilities of the DVMD-LSTM model, thereby enhancing the reliability of the predicted results.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using UAV-acquired imagery. However, with the increased availability of UAVs with onboard GNSS RTK, georeferencing without GCPs is a promising alternative. However, systematic elevation error remains a problem of this technique. We aimed to analyze the reasons for this systematic error and propose strategies for the elimination of this error. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100m with vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° a 15°). Each of these flights was performed twice to produce a full double grid. Models were calculated from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights from the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS-RTK data and the deviation in the determined internal orientation parameters (focal length). Besides, we have shown that a combination of two flights with different image acquisition axis can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

The Apennine sector formed by Sannio and Irpinia is one of the most important seismic districts in Italy, representing a good case of study due to the plenty of recorded earthquakes that have therein occurred from Roman times up to nowadays. We have merged the historical record and the new satellite techniques that allow a precise determination of ground movements, and then derived physical dimensions like strain rate. In this way, we have verified that in Irpinia the hazard of new strong shocks forty years after one of the strongest known seismic events in the district is still high. This aspect must be considered very important from many points of view, particularly for Civil Protection plans, as well as civil engineering and urban planning development.

Advances in technology have introduced remarkable capabilities, particularly in the realm of gathering and accessing vast amounts of data, which has led to the emergence of the "Big Data" era, presenting exciting opportunities to harness the wealth of the generated information. Traffic management centers comprise a stakeholder that can exploit such data towards improving road network operation. This research, explores the utilization of real traffic data, collected by distinct sources on an interurban motorway: static location based aggregate traffic data and probe vehicle data. The first is collected by traffic detectors, while the second involves patrol vehicles’ GNSS data. The traffic quantity analysed is vehicle speed, while specific emphasis has been given on the ap-plication of heatmaps to represent daily traffic patterns and the utilization of k-means clustering. The performed analysis demonstrates the importance of considering spatiotemporal variables as a unified entity when analysing speed in transportation networks. The limitations of using patrol vehicles as estimators of ambient speed, especially in low and heavy traffic conditions, are deline-ated, while the dual purpose of data gathered from these vehicles towards enhancing driving performance and enabling detector maintenance is emphasized. Valuable traffic insights are offered through comprehensive spatiotemporal speed behavior analysis and data combination. The inte-gration of these techniques enhances decision-making for a streamlined and secure transportation system

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in the recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundary for positioning. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude and longitude coordinate to determine the precise location of the fisheye image. The results are then compared with the conventional GNSS and advanced 3DMA GNSS positioning methods. The aims of the proposed algorithm are to increase positioning and heading accuracy in a rich urban environment.

In the last years, the development of low-cost GNSS sensors allowed monitoring in a continuous way movement related to natural processes like landslides with increasing accuracy and limited efforts. In this work, we present the first results of an experimental low-cost GNSS continuous monitoring applied to the unstable slope affecting the Madonna del Sasso Sanctuary (NW Italy). The courtyard of Sanctuary is built of two unstable blocks delimited by high cliff. Previous studies and non-continuous monitoring showed that blocks suffer a seasonal cycle of thermal expansion and long-term trend to downslope of few millimeters per year. The presence of continuous monitoring solution, could be an essential help to better understand the kinematics of unstable slope and to recognize the beginning of a possible paroxysm phase that could end with a failure of the unstable area. We tested the accuracy of the instruments and the first year of experimental measurements are presented. We also propose a methodological approach that considers the use of automatized procedures for the identification of anomalous trends and a risk communication strategy based on monitoring data.

In this paper, we explore a new norm on L^p-space called r-norm, and hence producing a new normed space called r-normed space. Measure theoretic and functional analytic properties are covered such as the relation between r-norm and the classical p-norm. An application of r-normed space in probability theory and statsitics is also presented, notably, its role as a rigorous framework in describing the coefficient of determination.

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data is generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using the predictive relationship indicates that for simplistic river scenes, the SNR over the river is predictive of the river width to within +/-5m. The presence of clutter (surrounding water bodies) within ~500m of the river causes perturbations in the SNR measured over the river that can render the river width retrievals unreliable. The results of this study indicate that under idealized conditions, GNSS-R data is able to measure river widths as narrow as 160m with high accuracy.

This paper shows the results of applying high-resolution Unmanned Aircraft System (UAS) photogrammetric surveying on a large landslide. A real case study, where permanently installing GCPs could be complex, where natural shaped and formed land pose severe limitations in deploying ground targets with optimal geometric configuration. We analysed performances in terms of survey accuracy obtained by performing photogrammetric surveys through the Zenmuse P1 DJI optical camera and Phantom 4 Pro 2. In combination with DJI Matrice 300 UAS, the P1 camera allows direct georeferencing through GNSS observations in RTK mode. Photogrammetric surveys, performed through different georeferencing methods, have been compared. Several targets have been permanently installed on the ground over the maximum vegetation height to guarantee long-lasting reference over the years in the area, which is characterised by a diffuse short vegetation coverage. Multitemporal UAS surveys have been then compared using Digital Image Correlation (DIC) algorithms, and deformation maps have been produced. Afterwards, DIC results were compared with observations made by the GNSS ground-based permanent receivers resulting in a standard deviation of 0,077 m. Through results analysis, good accordance between ground-based GNSS observations and DIC analysis on the photogrammetric surveys have been identified for the same time span. To conclude, this type of landslide presents a moderate deformation speed; in such a case, effective deformations monitoring could be achieved using pseudo-direct georeferencing, which permitted a 0.24 m accuracy on the whole tested area.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

In this article, we give a brief review of the origin of the neutrino mass in some interesting non-linear supersymmetric models with R-symmetry. These models are able to address and solve the most important problems of the particle physics and provide mechanisms for neutrino mass generation and their mixing parameters in agreement with the current experimental data. Their prediction could be experimentally tested in the near future by collider experiments.

Herein, a chemophenetic significance, based on phenolic metabolite profiling of three Senecio (S. hercynicus, S. ovatus and S. rupestris) and two Jacobaea species (J. pancicii and J. maritima) coupled to morphometric data, is presented. A set of twelve morphometric characters were recorded from each plant species and used as predictor variables in a Linear Discriminant Analysis (LDA) model. From a total 75 observations (15 from each of the five species), the model correctly assumed their species' membership, except 2 observations. Among the studied species, S. hercynicus and S. ovatus presented the greatest morphological similarity. A phytochemical profiling of phenolic specialized metabolites by UHPLC-Orbitrap-HRMS revealed 46 hydroxybenzoic, hydroxycinnamic, acylquinic acids and their derivatives, 1 coumarin, and 21 flavonoids. Hierarchical and PCA clustering applied to the phytochemical data corroborated the similarity of S. hercynicus and S. ovatus, observed in the morphometric analysis. This study contributes to the phylogenetic relationships between the tribe Senecioneae taxa and highlights the chemophenetic similarity/dissmilarity of the studied species belonging to Senecio and Jacobaea genera.

Neuroinflammation, defined as inflammatory reactions mediated by cytokines, chemokines, reactive oxygen species, and secondary messengers in the central nervous system (CNS) including the brain and spinal cord is the basis of many neurological disorders. Recently, erythropoietin (EPO) has been considered and studied as a modulator of neuroinflammation. On this article minireview of pathophysiology of neuroinflammation and the neuroprotective effects of EPO is discussed and a case of subacute huge subdural hematoma with double mydriasis operated urgently, treated with low daily dose (vs high dose once or twice a month in the literature) of EPO and recovered fully and discharged home with good consciousness is reported. In addition, the probable unfavorable outcome of erythropoietin administration in patients with neuroinflammation in COVID-19 is considered.

ITRF input data which are integrated by GNSS, SLR, VLBI, DORIS combination centers are considered to be relatively high-quality and accurate solutions. However, when utilizing these inputs, one still needs to identify outliers, rescale inaccurate covariance matrix and evaluate the precision of the observed datum information. To achieve the above-mentioned objectives, we propose a terrestrial reference frame (TRF) stacking approach to establish the single technical reference frameworks for the ITRF2014 and ITRF2020 datasets of all four technologies. As a result, roughly 0.5% or less of the SLR observations are identified as outliers, while the ratio of DORIS, GNSS, and VLBI observations are below 1%, around 2%, and ranging from 1% to 1.2%, respectively. The post-rescaling covariance scale factors are 25.07, 27.25, 18.84, 6.98 for GNSS, SLR, VLBI, and DORIS in ITRF2014 datasets, and 8.95, 14.9, 16.8, 7.78 in ITRF2020 datasets, respectively. It is shown that the consistency between the SLR scale and ITRF has improved, increasing from around -5mm in ITRF2014 datasets to approximately -1mm in ITRF2020 datasets. The scale velocity derived from fitting the VLBI scale parameter series with all epochs in ITRF2020 datasets differs by approximately 0.21mm/year from the velocity obtained by fitting the data up to 2013.75 because of the scale drift of VLBI at around 2013. The decreasing standard deviations of the Polar motion parameter (XPO, YPO) offsets between Stacking TRFs and 14C04 (20C04) indicating an improvement in the precision of polar motion observations for all of the four techniques. From the perspective of the Weighted Root Mean Square in station coordinates, the measurement precision of GNSS, SLR, and DORIS techniques has improved, while VLBI shows no significant change.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF tracking loops. Firstly, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Secondly, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other receivers in complex dynamic environments.

To enhance system reliability and mitigate vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute position and reducing data gaps. To address shortcomings of traditional Kalman Filter (KF) such as sensor error, imperfect nonlinear system model, and KF estimation error, a GRU-aided ESKF architecture is proposed to enhance positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to priories and identify the potential faults in the urban environment facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association error and navigation environment error during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting higher efficiency in complex environments.

Noise model selection criteria has a significant impact on identifying the stochastic noise proper-ties of any GNSS daily coordinate time series. The low-frequency random walk noise existing in these time series could lead to overestimation of the tectonic rate, so it is of great significance to accurately detect the random walk component. This study focuses on noise model estimation cri-terion (BIC_tp) derived from the AIC and the BIC by introducing 2π factors. It is more sensitive to abnormal steps (random jumps). Using observation data from 72 GNSS stations from 1992 to 2022 and simulated data, four combined noise models are used to explore the impacts of time se-ries lengths (ranging from2 to 24 years) and data loss (between 2% and 30%) on noise models and velocity estimation. The results show that as the time length increased, the selected optimal noise model, and the estimated uncertainty of the tectonic trend with different data gap, gradually con-verge. When the time length is short (less than 8 years), it could lead to the FNRWWN, FNWN, and PLWN models being mistakenly estimated as GGMWN models, thereby affecting the accura-cy of determining the station velocity parameters. When the time length is 12 years, the RW noise component is more probably detected, As the time length increases, the impact of RW on velocity uncertainty is weakened. Finally, we conclude that for a time series with a minimum time length of 12 years, both the selection of the optimal stochastic noise model and the estimation of the ve-locity parameters are reliable.

There is crescent demand for knowledge improvement of the integrated water vapor (IWV) distribution in regions affected by heat islands that are associated with extreme rainfall events such as in the metropolitan area of Rio de Janeiro (MARJ). This work assessed the suitability and distribution of IWV in the MARJ using products from the Global Navigation Satellite Systems (GNSS), MODerate Resolution Imaging Spectroradiometer (MODIS), and radiosonde. GNSS data were collected by the tracking station named RDJN, from the cooperation of the International GNSS Monitoring and Assessment System (iGMAS) and the National Observatory of Brazil (Observatório Nacional - ON), and the tracking stations ONRJ, RIOD, and RJCG belonging to the Brazilian Network for Continuous Monitoring (RBMC) in the period of January 2015–August 2018. High variability of the near surface air temperature (T) and relative humidity (RH) were observed among eight meteorological sites considered. The mean T differences between sites, up to 4.4 °C, led to mean differences as high as 3.1 K for weighted mean temperature (Tm) and hence 0.83 mm for IWV differences. The performance of the MODIS MOD07 and MYD07 products provided a reasonably good representation of the mean spatial distribution of IWV, especially during the daylight passages of the satellites TERRA and AQUA. Local grid points of MODIS IWV estimates had relatively good agreement with the GNSS-derived IWV, with mean differences from -2.4–1.1 mm considering only daytime passages of the satellites TERRA and AQUA. During nighttime, MODIS underestimated IWV (from -9–-3 mm) with respect to GNSS, due to attenuation of IR radiation by clouds. A contrasting behavior was found in the radiosonde IWV estimates compared with the estimates from GNSS. There were dry biases of 1.4 mm (3.7% lower than expected) by radiosonde IWV during the daytime considering that all other estimates were unbiased and the differences between IWV GNSS and IWV RADS were consistent. Based on the IWV comparisons between radiosonde and GNSS at nighttime, the atmosphere over the radiosonde site is about 1.2 (2.3) mm wetter than over RIOD (RDJN) station. The long time series of the comparisons between IWVRDJN and IWVRIOD showed that the highest values of IWV occurred from the afternoon to nocturnal hours. Further, the atmosphere over the site RIOD was consistently about 1 mm wetter than over RDJN. These results showed the feasibility of the iGMAS RDJN station data compared with the RBMC, MODIS, and radiosonde data to investigate IWV in a region with occurrence of heat islands, and the peculiar physiographic and meteorological characteristics as in the MARJ. This work recommended the usage of complete meteorological station data collocated near every GNSS receiver aiming improvements of local GNSS IWV estimates and serving as additional support for operational numerical assimilation, weather forecast, and nowcast of extreme rainfall events.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

Introduction: EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous and famous add-ins along with global, widespread communications and online free training, its popularity increased every year. Method: To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar for the main EEGLAB reference since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. Results: The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author’s keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12 %, international Co-authorship is 37.27 % and Co-authors per document is s 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. Other bibliometric analyses such as co-occurrence networks and thematic maps of abstracts, titles, and keywords are estimated and presented. Conclusions: EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

This research aims to obtain empirical evidence of the influence of the masculinity of male CEOs on the of research & development, this research seeks to identify the influence of the face of mas-culinity of male CEO’s on the of research & development. The study used a quantitative approach with population and research samples using companies listed on the Indonesia Stock Exchange from 2016 to 2021. The study collected images of faces identified as male CEOs from data from the Indonesia Stock Exchange website and company as utilizing Google searches. The data analysis method in this study used Regression Ordinary Least Square (OLS) with Stata Software. Stata software is one of the regression completion procedures that has a high degree of flexibility in re-search that connects theories, concepts and data that can be done on variables in research. The findings empirically explain that the higher the face value of male CEO masculinity has an impact on reducing research & development costs, and vice versa the lower the face value of male CEO masculinity has an impact on increasing research & development. The practical implications of the research results can help the Indonesian Association of Accountants in developing Financial Accounting Standard No. 19 in Indonesia. The theoretical implications of the research results can explain the Theory of Agency and the Theory of Consistency of Behavior. The policy implications of the results of the study can provide empirical evidence that the higher the facial value of male CEO masculinity has an impact on reducing research & development, while also the lower the face value of male CEO masculinity has an impact on increasing research & development.

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

In this work, we explore a the different forms of a new type of modified gravity, namely f(φ) gravity. We construct the Big Rip type for the energy density and the curvature of the universe. We show that dark energy is a result of the transformation of the field φ mass (dark matter) to energy. In addition, we provide that Ω_{m}≈0,050, Ω_{DM}≈0,2, Ω_{DE}≈0,746, is in excellent agreement with observation data. We explore a generalized formalism of braneworld modified gravity. We also construct a new field equations, which generalize the Einstein field equations. We provide a relation between the extra dimension in 3-brane with the vacuum energy density. We show that the energy density of matter depends directly on the number of dimensions. We manage to find the value of the Gauss-Bonnet coupling α=1/4 which is a good agreement with the results in the literature, this correspondence creates a passage between f(R) gravity and Gauss-Bonnet gravity, this comparison leads to a number of bulk dimensions equal to D=10¹²¹+4.

The study focuses on the application of R programming language towards marine geological research with a case study of Mariana Trench. Due to its logical and straightforward syntax, multi-functional standard libraries, R is especially attractive to the geologists for the scientific computing. Using R libraries, the unevenness of various factors affecting Mariana Trench geomorphic structure has been studied. These include sediment thickness, slope steepness, angle aspect, depth at the basement and magmatism of the nearby areas. Methods includes using following R libraries: {ggplot2} for regression analysis, Kernel density curves, compositional charts; {ggalt} for Dumbbell charts for data comparison by tectonic plates, ranking dot plots for correlation analysis; {vcd} for mosaic plots, silhouette plots for compositional similarities among the bathymetric profiles, association plots; {car} for ANOVA. Bathymetric GIS data processing was dome in QGIS and LaTeX. The innovativeness of the work consists in the multi-disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data.

The positional deviation of the Automatic Emergency Call System (AECS) under collision conditions brings difficulties for post-rescue operations. Currently, there is a lack of analysis on the system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on the data deviation of the simulated GNSS system. We also propose environmental limits applicable to comprehensive AECS tests, which can objec-tively evaluate the reliability of AECS and provide data support for AECS bench test. Through the satellite signal-based semi-physical simulation, AECS is subjected to bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibra-tion and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the AECS test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature dif-ference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of satellite signal simulation, nec-essary environmental conditions for AECS test are obtained, including the temperature consistent with the maximum operating temperature of the vector generator and the vibration Power Spectral Density (PSD) lower than 1.2gRMS.

This paper concerns a new methodology for accuracy assessment of global positioning system verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of GPS (Global Positioning System) positioning of an autonomous driving vehicle within a lane on the road is one of the key safety considerations. Safety is addressed as a geometry of the problem, where the aim is to maintain knowledge that the vehicle (its bounding box) is within its lane. Accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. For this reason, a new methodology is proposed. Methodology is composed of six elements: 1) Mobile mapping system minimal setup, 2) Global positioning data processing, 3) LiDAR data processing, 4) Alignment algorithm, 5) Accuracy assessment confirmation and 6) Autonomous driving safety analysis. The research challenge is to assess positioning accuracy of moving cars taking into account the constraints of the coverage of limited access highways in the United States of America. The available coverage limits the possibility of repeatable measurements and introduces an important challenge being the lack the ground truth data. State-of-the-art methods are not applicable for this particular application, therefore a novel approach is proposed. The method is to align all the available LiDAR car trajectories to confirm the GNSS+INS (Global Navigation Satellite System + Inertial Navigation System) accuracy. For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. SLAM implementation used state-of-the-art observation equations and the Weighted Non-Linear Least Square optimization technique that enables integration of the required constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, LiDAR Velodyne HDL32) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32785 trajectories with total length of 1,159,956.9~km and of total $186.4*10^{9}$~optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016--2019 period. It is demonstrated that the alignment improves the trajectories, thus final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99\% of the assessed data satisfies the safety requirements (driving within lanes of 3.6~m) for Mid-Size (width 1.85~m, length 4.87~m) vehicle and 95\% for 6-Wheel Pickup (width 2.03--2.43~m, length 5.32--6.76~m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

The purpose of the work is an underwater positioning safety study that used the GNSS-like underwater navigation systems. In the process of research, we used the methods of software modeling of underwater spoofing processes. The spoofing problem consists of three stages: design of spoofers, design of spoofing detection systems, and design of anti-spoofing systems. This article discusses some methods of spoofing detection. We briefly describe the known methods of underwater positioning systems. Unlike GNSS, currently only LNSS (Local Navigation Satellite System) can be considered in this case. Spoofing detection systems with one hydrophone are of great practical importance, as they allow for use of standard hydroacoustic equipment. However, detection of spoofing is not possible in static mode, which is with underwater vehicle at rest. In case of two hydrophones the detection of spoofing in static mode is possible. We discuss the navigation based on the use of an acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position using the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service area, GNSS-like UPS-es are divided into global, regional, zonal and local systems. In this article, we take into account only the local class of GNSS-like UPS. The acoustic signal generator transmits a simulation of several buoy signals. If the level of the simulated signal exceeds the signal strength of actual buoys, the UPS receiver will “lock onto” the fake signal and then calculate a false position basing on it. The development of further research should be focused on the creation of hardware and software systems for conducting physical experiments at depths up to 400 m.

The formal transfer of GNSS under water is not possible. It probably makes sense to talk only about the transformation of GPS into LPS, that is, in the Local Position System. However, the basic methods that are used to solve the problem of Spoofing Detection above water can be used under water. It should be understood that engineering problems are significantly different, since the nature of the propagation of acoustic waves in water and electromagnetic waves in the atmosphere are fundamentally different. In this article, we will limit the navigation with acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position based on the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service areas, GNSS-like UPS are divided into global, regional, zonal and local system. In this article, we will limit ourselves to considering only local GNSS-like UPS. The acoustic signal generator transmits a signal simulation of several satellites. If the level of the simulated signal exceeds the signal strength of real satellites, the receiver of UPS will “capture” the fake signal and calculate the false position based on it.

A Friedmann-Lemaitre-Robertson-Walker space-time model with all curvatures k=0,±1 is explored in f(R,T) gravity. The solutions are obtained via the parametrization of the scale factor that leads to a model transiting from a decelerated universe to an accelerating one. The physical features of the model are discussed and analysed.

When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.

The purpose of this study was to determine the income, costs and revenue of the Gontor Putri 3 bakery. Want to know the efficiency of the bakery business in Karangbanyu Regency. The method used in this research is analytical descriptive by conducting a survey. The data used in this research is primary data. Data collection techniques use observation, interviews and documentation, while the process of analysis is quantitative descriptive. Using the R/C ratio, a value of 1.72 per month is obtained. This value indicates that the bakery factory in Karangbanyu is efficient because the value is more than one.

Revenue-sharing (RS) contracts are a common approach to incentivize innovation of upstream suppliers by addressing the uneven profit distribution between upstream and downstream firms. Considering the possible overconfidence characterizing decision makers in the supply chain, we investigate the effect of the RS contract and the tendency of overconfidence of supply chain members on the investment in R&D of key components of products in the context of an upstream supplier that is the leader in the R&D and production of key components. We find that regardless of the bargaining power of either party, a RS contract can increase the R&D investment in key components. Regarding the effects of overconfidence of either downstream manufacturer or up-stream supplier, it can increase the R&D investment in key components. Supplier (manufacturer) overconfidence can harm their own profits but increase the profits of the manufacturer (supplier), and when the level of overconfidence is below a certain threshold, the damage to their own profits is less than the increase in each other's profits, thus benefiting the whole supply chain. In addition, we also find a joint effect of RS contracts and overconfidence: when the bargaining power of the supplier is low, the RS contract has a certain compensatory effect on the loss of their own profits caused by overconfidence.

The circular economy (CE) has emerged as a viable alternative for tackling the problems caused by rising consumption, including solid waste (SW). The present study conducted a meta-analysis of research published between 2012 and 2022 on CE and solid waste management (SWM). A bibliometric analysis was used from the Web of Science (WoS) and Scopus databases. By studying the articles, we briefly discussed the connections between them and how one contributes to the evolution of the other. Some findings point to greater collaboration between Italy and Bolivia, followed by China and Malaysia. However, they move away from the traditional axes as the United States and Canada. In addition, it was found that the adoption of reuse and selective collection, in fact, allow the reduction of the volume of discarded materials. In this way, it was possible to conclude that a greater connection between CE and GRS makes it possible to optimize the useful life of landfills through the treatment of organic waste and recycling, to encourage waste picker cooperatives and selective collection programs, and to reduce the financial costs of the system as a whole, making it more durable and sustainable.

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese-Kazakh (Latin script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively.

The aim of this study, for determination of verteporfin in real samples (simulated body fluid, and simulated tears, 0.9% isotonic sodium chloride solution, Lactated Ringer IV solution for infusion, 5% dextrose IV solution for infusion, lemon juice and drinking water) was the method validation and to examined by HPLC-DAD-UV. Metod validation parameters such as specificity, linearity, precision, accuracy, robustness, limit of detection (LOD) and limit of quantitation (LOQ) for verteporfin were validated and developed according to the International Conference on Harmonization (ICH) Q2 R1 guidelines. The LOD and LOQ for verteporfin were found 0.06 µg/L and 0.2 µg/L, respectively. The recovery values of the optimization and validation for verteporfin were found in the range of 97.5-100.7%. The relative standard deviations (RSD) for vertepofin were <1%. The developed method was successfully applied to real samples with high accuracy and the recoveries (%) from real samples were 99.9, 100, 98.2, 99.2, 99.4, 98.8 and 99.4, respectively.

Crocodilepox virus (CRV) belongs to the Poxviridae family and mainly infects hatchling and juvenile Nile crocodiles. Most poxviruses encode inhibitors of the host antiviral protein kinase R (PKR), which is activated by viral double-stranded (ds) RNA formed during virus replication, resulting in the phosphorylation of eIF2 and subsequent shutdown of general mRNA translation. Because CRV lacks orthologs of known poxviral PKR inhibitors, we experimentally characterized one candidate (CRV157), which contains a predicted dsRNA-binding domain. Bioinformatic analyses indicated that CRV157 evolved independently from other poxvirus PKR inhibitors. CRV157 bound to dsRNA, co-localized with PKR in the cytosol, and inhibited PKR from various species. To analyze whether CRV157 could inhibit PKR in the context of a poxvirus infection, we constructed recombinant vaccinia virus strains that contain either CRV157 or a mutant CRV157 deficient in dsRNA binding in a strain that lacks PKR inhibitors. The presence of wild type CRV157 rescued vaccinia virus replication, while the CRV157 mutant did not. The ability of CRV157 to inhibit PKR correlated with virus replication and eIF2alpha phosphorylation. The independent evolution of CRV157 demonstrates that poxvirus PKR inhibitors evolved from a diverse set of ancestral genes in an example of convergent evolution.

In this article we have presented a new perception of herd immunity threshold (HIT) which considers that only a “band of population” are susceptible to any pathogenic infection. This is termed as the “effective herd immunity threshold” (EHIT) and the progression of the disease (caused by this pathogenic infection) is mainly determined by this EHIT value. We have argued here that this EHIT value (considering the immunity band picture in the population) will be substantially lower than the estimated canonical HIT values obtained from various existing models. We propose that the actual prediction of the disease progression should now be calculated using the EHIT values.

Inspired by the work of S. D. Odintsov and V. K. Oikonomou, Phys. Rev. D 92, 024016 (2015) [1], the present study reports a reconstruction scheme for f (R) gravity with the scale factor a(t) µ (t _* - t) _c²²describing the pre-bounce ekpyrotic contraction, where t is the big crunch time. The reconstructed f (R) is used to derive expressions for density and pressure contributions and the equation of state parameter resulting from this reconstruction is found to behave like "quintom". It has also been observed that the reconstructed f (R) has satisfied a sufficient condition for a realistic model. In the subsequent phase the reconstructed f (R) is applied to the model of chameleon scalar field and the scalar field f and the potential V(f) are tested for quasi-exponential ex pansion. It has been observed that although the reconstructed f (R) satisfies one of the sufficient conditions for realistic model, the quasi-exponential expansion is not available due to this reconstruction. Finally, the consequences pre-bounce ekpyrotic inflation i n f (R) gravity are compared to the background solution for f (R) matter bounce.

The crux of the paper is to present a detailed analysis of COVID-19 data which is available on global basis. This analysis is performed using some specific package of R software. It provides various insights from the data and help to understand the current status of this pandemic in India so that effective measures can be formulated by policymakers. These insights include global summary of this disease, growth rate of this pandemic and performance of SIR model for the given global data. The analysis has been presented in different tables and graphs to understand the outputs of the problem in a more detailed point of view.

Collisions of low energy electrons with molecules are important for understanding many aspects of the environment and technologies. Understanding the processes that occur in these types of collisions can give insights into plasma etching processes, edge effects in fusion plasmas, radiation damage to biological tissues and more. A radical update of the previous expert system for computing observables relevant to these processes, Quantemol-N, is presented. The new Quantemol Electron Collision (QEC) expert system simplifyies the user experience, improving reliability and implements new features. The QEC GUI interfaces the Molpro quantum chemistry package for molecular target setups and to the sophisticated UKRmol+ codes to generate accurate and reliable cross-sections. These include elastic cross-sections, super elastic cross-sections between excited states, electron impact dissociation, scattering reaction rates, dissociative electron attachment, differential cross-sections, momentum transfer cross-sections, ionization cross sections and high energy electron scattering cross-sections. With this new interface we will be implementing dissociative recombination estimations, vibrational excitations for neutrals and ions, and effective core potentials in the near future.

This paper introduces an application of R programming language for geostatistical data processing with a case study of the Mariana Trench, Pacific Ocean. The formation of the Mariana Trench, the deepest among all hadal oceanic depth trenches, is caused by complex and diverse geomorphic factors affecting its development. Mariana Trench crosses four tectonic plates: Mariana, Caroline, Pacific and Philippine. The impact of the geographic location and geological factors on its geomorphology has been studied by methods of statistical analysis and data visualization using R libraries. The methodology includes following steps. Firstly, vector thematic data were processed in QGIS: tectonics, bathymetry, geomorphology and geology. Secondly, 25 cross-section profiles were drawn across the trench. The length of each profile is 1000-km. The attribute information has been derived from each profile and stored in a table containing coordinates, depths and thematic information. Finally, this table was processed by methods of the statistical analysis on R. The programming codes and graphical results are presented. The results include geospatial comparative analysis and estimated effects of the data distribution by tectonic plates: slope angle, igneous volcanic areas and depths. The innovativeness of this paper consists in a cross-disciplinary approach combining GIS, statistical analysis and R programming.

We present a methodology to enable users to interact with statistical information owned by an institution and stored in a cloud infrastructure. Mainly based on R, this approach was developed following the open-data philosophy. Also, since we use R, the implementation is mainly based on open-source software. R gives several advantages from the point of view of data management and acquisition, as it becomes a common framework that can be used to structure the processes involved in any statistical operation. This simplifies the access to the data and enable to use all the power of R in the cloud information. This methodology was applied successfully to develop a tool to manage the data of the Centre d’Estudis d’Opinió, but it can be applied to other institutions to enable open access to its data. The infrastructure also was deployed to a cloud infrastructure, to assure the scalability and a 24/7 access.

The exceptional increase in molecular DNA sequence data in open repositories is mirrored by an ever-growing interest among evolutionary biologists to harvest and use those data for phylogenetic inference. Many quality issues, however, are known and the sheer amount and complexity of data available can pose considerable barriers to their usefulness. A key issue in this domain is the high frequency of sequence mislabelling encountered when searching for suitable sequences for phylogenetic analysis. These issues include the incorrect identification of sequenced species, non-standardised and ambiguous sequence annotation, and the inadvertent addition of paralogous sequences by users, among others. Taken together, these issues likely add considerable noise, error or bias to phylogenetic inference, a risk that is likely to increase with the size of phylogenies or the molecular datasets used to generate them. Here we present a software package, phylotaR, that bypasses the above issues by using instead an alignment search tool to identify orthologous sequences. Our package builds on the framework of its predecessor, PhyLoTa, by providing a modular pipeline for identifying overlapping sequence clusters using up-to-date GenBank data and providing new features, improvements and tools. We demonstrate our pipeline’s effectiveness by presenting trees generated from phylotaR clusters for two large taxonomic clades: palms and primates. Given the versatility of this package, we hope that it will become a standard tool for any research aiming to use GenBank data for phylogenetic analysis.

We developed two human induced pluripotent stem cell (hiPSC)/human embryonic stem cell-specific glycan-recognizing mouse antibodies, R-10G and R-17F, using the Tic (JCRB1331) hiPSC line as an antigen. R-10G recognizes a low-sulfate keratan sulfate, and R-17F recognizes lacto-N-fucopentaose-1. To evaluate the general characteristics of stem cell glycans, we used the hiPSC line 201B7 (HPS0063), a prototype iPSC line. Using an R-10G affinity column, an R-10G-binding protein was isolated. The protein yielded a single but very broad band from 480 to 1,236 kDa by blue native gel electrophoresis. After trypsin digestion, the protein was identified as podocalyxin by liquid chromatography/mass spectrometry. According to Western blotting, the protein reacted with R-10G and R-17F. The R-10G positive band was resistant to digestion with glycan-degrading enzymes, including peptide N-glycanase, but the intensity of the band was decreased significantly by digestion with keratanase, keratanase II, and endo-β-galactosidase, suggesting the R-10G epitope to be a keratan sulfate. These results suggest that keratan sulfate-type epitopes are shared by hiPSCs. However, the keratan sulfate from 201B7 cells contained a polylactosamine disaccharide unit (Galβ1-4GlcNAc) at a significant frequency, whereas that from Tic cells consisted mostly of keratan sulfate disaccharide units (Galβ1-4GlcNAc(6S)). In addition, the abundance of the R-10G epitope was significantly lower in 201B7 cells than in Tic cells.

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Multimission satellite altimetry (e.g. ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic view of ocean variations in the past decades, including sea-level rise and mesoscale circulations. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared with its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise in coastal and lake observations. In this study, we validate Sentinel-3 Level-2 products in Baltic Sea coastal areas and two lakes in Estonia. Moreover, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level anomaly data and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis model outcomes are compared with measurements from a tide gauge network. A dense in situ water level network deployed along the coast for geodetic observation was utilised to provide ground truths for validating altimetry results. Three validation methods were used for Level-2 data: (i) collocated Sentinel-3 and GNSS ship measurements; (ii) a national geoid model (EST-GEOID2017) with sea-level anomaly correction; (iii) collocated Sentinel-3 and buoy measurements. The validations were carried out in seven Sentinel-3A/B overpasses in 2019. Our results show that the uncertainty of the Sentinel-3 Level-2 altimetry product is below decimetre level on the Estonian coast and the targeted lakes. Results from CMEMS Level-3 showed a correlation of 0.8 (RMSE 0.19 m) and 0.91 (RMSE 0.27 m) when compared against tide gauge measurements and NEMO model, respectively.

Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a new passive radar imaging system. However, compared with conventional SAR, range resolution of GNSS-SAR is significantly lower. To improve range resolution of GNSS-SAR is an interested topic for investigation. In this paper, a novel range compression algorithm for enhancing range resolutions of GNSS-SAR is proposed. In the proposed scheme, at first, range compression is conducted by correlating the received reflected GNSS signal of intermediate frequency (IF) with the synchronized direct baseband GNSS signal in range domain. Then spectrum equalization is applied to the compressed results to suppress side lobes. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR can be obtained by the proposed range compression algorithm, compared to the conventional range compression algorithm.

During the period January 2014 – October 2018, four strong earthquakes occurred in the Ionian Sea, Greece. A rich aftershock sequence followed each event of them. More analytically, according to the manual solutions of National Observatory of Athens, the first event (K1), occurred on 26 January 2014 in Kefallinia Island with magnitude M_L = 5.8, which was followed by another in the same region (K2) on 3 February 2014 with magnitude M_L = 5.7. The third event occurred on 17 November 2015, M_L = 6.0 in Lefkas Island (L1) and the last on 25 October 2018, M_L = 6.6 in Zande Island (Z1). The first three of these earthquakes caused moderate structural damages mainly in houses and produced particular unrest to the local population. This work presents first the calculation of the source parameters of the strong events as well as for all earthquakes with magnitude M_L > 4.0, using the methodology of the Moment tensor inversion and secondary data from permanent GPS stations were analyzed to confirm the findings from seismological data and to investigate the displacement due to the earthquakes.

A Brewer Spent Grain (BSG), the most abundant by-product of the brewing process, is mainly used in animal feed and biogas production. The aim of the present work is BSG valorization by yeast fermentation to produce carotenoids. R. mucilaginosa CBS316 was cultured on BSG syrups, supplemented or not with YE and PEP (s-Fsa and Fsa). The biomass and carotenoid production were optimized in flasks and then scaled-up in bioreactor. In flask tests, the maximum biomass concentration was 5.3 and 6.2 g/L for Fsa and s-Fsa cultures, respectively. The maximum carotenoid production was 833 μg/L in Fsa and 1159 μg/L in s-FSA. Considering that the highest specific productivity value (P 7.16 μg/g*h) was obtained with Fsa syrup, this medium was selected for the process scale-up. In bioreactor tests, the maximum concentrations of biomass (4.29 g/L) and carotenoid (396 ug/L) was reached at 72 hours of fer-mentation, 24 h later than in flask. The total carotenoid produced was 524 and 150 ug in bioreactor and flask cultures, respectively. The carotenoids production by R. mucilaginosa was obtained both in Fsa and s-Fsa syrups. The results confirm the possibility to use the syrup as it is, Fsa, thus limiting the process costs.

This study aims to analyse whether Chinese companies' R&amp;D investment affects management innovation performance. Also, this dissertation defines management innovation as diversification strategies such as technology and industry diversification and analyzes the impact of these diversification strategies. In particular, this paper analyzes whether R&amp;D investment leads to technology and industry diversification and whether technology diversification creates a mediating effect in the process of R&amp;D investment leading to industry diversification. The analysis results can be summarized as follows: First, R&amp;D investment showed a significant positive relationship with industry diversification. Second, R&amp;D investment shows a significantly positive relationship with technology diversification. Third, technology and industry diversification showed a positive relationship. Fourth, this study examined the mediating role of technology diversification, and it was confirmed that technology diversification plays a mediating role in the effect of R&amp;D investment on industry diversification. R&amp;D investment and business innovation play a key role in the sustainable development of enterprises, and the findings of this paper have important reference value for enterprises' R&amp;D investment decision-making. The results of this study provide the following implications that Chinese companies improve business innovation performance by increasing their R&amp;D investment, promoting innovative R&amp;D, and improving technology diversification and industry diversification strategies.

We investigate the impact of incoming nuclear data on the predictions of the neutron star (NS) stripping model for short gamma-ray bursts. The main area to which we pay attention is the NS crust. We show that the uncertain properties of the NS equation of state can significantly influence the stripping time tstr, the main dynamical parameter of the model. Based on the known time delay (tstr≈1.7s) between the peak of the gravitational wave signal GW170817 and the detection of gamma photons from GRB170817A, we obtain new restrictions on the nuclear matter parameters, in particular, the symmetry energy slope parameter: L&lt;114.5MeV. In addition, we study the process of nucleosynthesis in the outer and inner crusts of a low-mass NS. We show that the nucleosynthesis is strongly influenced by both the incoming nuclear data and the equation of state of the NS matter.

We analyze the behavior of relativistic spherical objects within the context of modified $f(R,T)$ gravity considering Tolman VI spacetime, where gravitational lagrangian is a function of Ricci scalar(R) and trace of energy momentum tensor(T) i.e,$ f(R,T)= R+ 2\beta T$, for some arbitrary constant $\beta$. For developing our model, we have chosen $\pounds_{m} = -p$, where $ \pounds_{m}$ represents matter lagrangian. For this investigation, we have chosen three compact stars namely PSR J1614-2230 [Mass=(1.97$\pm$ 0.4)M$_\odot$; Radius= 9.69$_{-0.02} ^{+0.02}$ Km] ,Vela X-1 [Mass=(1.77$\pm$ 0.08)M$_\odot$; Radius= 9.560$_{-0.08} ^{+0.08}$ Km] and 4U 1538-52 [Mass=(9.69)M$_\odot$; Radius= 1.97 Km]. In this theory the equation of pressure isotropy is identical to standard Einstein's theory. So all known metric potential solving Einstein's equations are also valid here. In this paper, we have investigated the effort of coupling parameter ($\beta$) on the local matter distribution. Sound of speed and adiabatic index are higher with grater values of $\beta$ while on contrary mass function and gravitational redshift are lower with higher values of $\beta$ . For supporting the theoretical results, graphical representation are also employed to analyze the physical viability of the compact stars.

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. Therefore, the restoration of degraded landscapes is essential to maintain the functionality of ecosystems, especially those with greater social, economic and environmental vulnerability. Nevertheless, policy-makers are frequently challenged by deciding on where to prioritize restoration actions, which usually includes to deal with multiple and complex needs under an always short budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. To help decision-makers taking informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called "RePlant alpha" which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis, with at least six indicators and for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

New setting is introduced to study the global powerful alliance. Global powerful alliance is about a set of vertices which are applied into the setting of neutrosophic graphs. Neighborhood has the key role to define this notion. Also, neighborhood is defined based on strong edges. Strong edge gets a framework as neighborhood and after that, too close vertices have key role to define global powerful alliance based on strong edges. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs excluding empty, path, star, and wheel and containing complete, cycle and r-regular-strong are investigated in the terms of set, minimal set, number, and neutrosophic number. Neutrosophic number is used in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number. It’s called “modified neutrosophic number”. Summation of three values of vertex makes one number and applying it to a set makes neutrosophic number of set. This approach facilitates identifying minimal set and optimal set which forms minimal-global-powerful-alliance number and minimal-global-powerful-alliance-neutrosophic number. Two different types of sets namely global-powerful alliance and minimal-global-powerful alliance are defined. Global-powerful alliance identifies the sets in general vision but minimal-global-powerful alliance takes focus on the sets which deleting a vertex is impossible. Minimal-global-powerful-alliance number is about minimum cardinality amid the cardinalities of all minimal-global-powerful alliances in a given neutrosophic graph. New notions are applied in the settings both individual and family. Family of neutrosophic graphs has an open avenue, in the way that, the family only contains same classes of neutrosophic graphs. The results are about minimal-global-powerful alliance, minimal-global-powerful-alliance number and its corresponded sets, minimal-global-powerful-alliance-neutrosophic number and its corresponded sets, and characterizing all minimal-global-powerful alliances, minimal-t-powerful alliance, minimal-t-powerful-alliance number and its corresponded sets, minimal-t-powerful-alliance-neutrosophic number and its corresponded sets, and characterizing all minimal-t-powerful alliances. The connections amid t-powerful-alliances are obtained. The number of connected components has some relations with this new concept and it gets some results. Some classes of neutrosophic graphs behave differently when the parity of vertices are different and in this case, cycle, and complete illustrate these behaviors. Two applications concerning complete model as individual and family, under the titles of time table and scheduling conclude the results and they give more clarifications and closing remarks. In this study, there’s an open way to extend these results into the family of these classes of neutrosophic graphs. The family of neutrosophic graphs aren’t study deeply and with more results but it seems that analogous results are determined. Slight progress is obtained in the family of these models but there are open avenues to study family of other models as same models and different models. There’s a question. How can be related to each other, two sets partitioning the vertex set of a graph? The ideas of neighborhood and neighbors based on strong edges illustrate open way to get results. A set is global powerful alliance when two sets partitioning vertex set have uniform structure. All members of set have more amount of neighbors in the set than out of set and reversely for non-members of set with less members in the way that the set is simultaneously t-offensive and(t-2)-defensive. A set is global if t=0. It leads us to the notion of global powerful alliance. Different edges make different neighborhoods but it’s used one style edge titled strong edge. These notions are applied into neutrosophic graphs as individuals and family of them. Independent set as an alliance is a special set which has no neighbor inside and it implies some drawbacks for these notions. Finding special sets which are well-known, is an open way to purse this study. Special set which its members have only one neighbor inside, characterize the connected components where the cardinality of its complement is the number of connected components. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation

Identification and segmentation of rock particles is very important in geology, mining, engineering construction, and environmental monitoring. Traditional methods for recognizing the rock particle characteristics are time-consuming, labor-intensive, and limited by the geographical scope of the sampling sites, which makes it more difficult to represent the spatial distribution of the rock particles accurately. Recent advancements in deep learning techniques offer promising solutions to automate this process. This study aims at utilizing state-of-the-art (SOTA) Mask R-CNN deep learning algorithm from the Detectron2 framework, to capture global features of rocks that will enable the recognition of rock particles more efficiently, just from having the rock images. These images are subject to image processing techniques such as a Gaussian filter, to denoise and smoothen the image, and an Illumination Adaptive Transformer (IAT) framework, to adjust the lighting exposure; all of which add to improve the quality of the image dataset. Afterward, the Mask R-CNN and Detectron2 models are utilized in training the images using transfer learning. Experimental results from evaluating the performance of the proposed algorithms showcase the effectiveness of the approach, thereby highlighting its potential to revolutionize rock particle segmentation across various domains.

Objective: The study aimed to evaluate the effects of monoclonal antibodies (mAbs) acting on the calcitonin gene-related peptide (CGRP) or its receptor (anti-CGRP/R mAbs) on comorbid symptoms of depression, anxiety, and fatigue in migraine patients resistant to traditional prophylaxis. Methods: The study was an open-label prospective study assessing comorbidities in patients with high frequency (HFEM) and chronic migraine (CM), medication overuse headache (MOH), and resistance to traditional prophylaxis treated with anti-CGRP/R mAbs for 3 months. Results: 77 patients were enrolled with either HFEM (21%) or CM (79%) with or without MOH (56% and 44% respectively). We identified 21 non-responders (27%) and 56 responders (73%), defined on the reduction ≥ 50% of headache frequency. The two groups were highly homogeneous for investigated comorbidities. Disease severity in terms of headache frequency, migraine-related disability, and affective comorbid symptoms were reduced in both groups with different thresholds; allodynia and fatigue were ameliorated only in responders. Conclusion: anti-CGRP/R antibodies improve pain together with affection, fatigue, and sensory sensitization in migraine patients.

This article explores the relationship between convex functions defined on integers ($\mathbb{Z}$) and their extension to real numbers ($\mathbb{R}$). We introduce key definitions and investigate the hypothesis that there exists a unique convex curve within this family of functions, leading to a proof by contradiction. Our findings highlight the preservation of convexity as functions transition from integers to real numbers.

Disgust evolved as a way to protect one’s self from illness. DS-R measures disgust propensity of three kinds of disgust (Core, Animal Reminder and Contamination). Although the DS-R scale was refined mainly with young and largely female student population its impact on educational orientation has not been assessed. In the present study we examined the DS-R scoring and the choice of postgraduate studies in medical (n= 94) and psychology (n= 97) students. They responded to an anonymous web-based survey and completed the DS-R and a questionnaire on their demographics and plans for postgraduate studies. Female students outnumbered males (3:1) and scored higher in Total DS-R score (median: 59 vs. 50, p<0.05). Psychology students scored higher in all three kinds of disgust (p<0.05), indicating a higher level of disease avoidance. Medical students willing to follow Internal Medicine scored higher in Core Disgust (p<0.05) while psychology students willing to study Experimental Psychology scored lower in Animal Reminder subscale (p<0.001). Also, the higher the psychology students scored in Core Disgust scale the higher was the probability to choose Experimental Psychology. In conclusion, disgust propensity as rated by DS-R differentiates medical from psychology students and is also related to orientation preferences in postgraduate studies.

We propose an electrocardiogram (ECG) signal-based algorithm to estimate the respiratory rate is a significant informative indicator of physiological state of a patient. The consecutive ECG signals reflect the information about the respiration because inhalation and exhalation make transthoracic impedance vary. The proposed algorithm extracts the respiration-related signal by finding out the commonality between the frequency and amplitude features in the ECG pulse train. The respiration rate can be calculated from the principle components after the procedure of the singular spectrum analysis. We achieved 1.7569 breaths per min of root-mean-squared error and 1.7517 of standard deviation with a 32-seconds signal window of the Capnobase dataset, which gives notable improvement compared with the conventional Autoregressive model based estimation methods.

Partial negative allosteric modulators (NAM) of the mGlu5 receptor are an excellent alternative to full antagonists and NAMs because they retain some therapeutic effects and, at the same time, have a much broader therapeutic window. Here, we investigated whether partial mGlu5 NAM, M-5MPEP induced a fast and sustained antidepressant-like effect, characteristic of rapid-acting antidepressant drugs (RAADs) like ketamine, in C57BL/6J mice. M-5MPEP caused a rapid, dose-dependent antidepressant-like action in the tail suspension test (TST) 60 min after injection. This effect was antagonized by an AMPA receptor antagonist (NBQX) and a TrkB receptor antagonist (ANA-12) but not by 5HT1A and 5HT2A/2C receptor antagonists. Furthermore, M-5MPEP (30 mg/kg) induced sustained antidepressant-like effects 24 h after the last of four administrations. These effects were revealed in the splash test, designed to measure apathy-like state, and in the TST. Western blot and ELISA analyses indicated the involvement of the TrkB/BDNF pathway in the sustained M-5MPEP effects. Additionally, prolonged effects of M-5MPEP were entirely reversed by the TrkB receptor antagonist ANA-12 in both the splash test and the TST, confirming the critical role of TrkB receptor activation in the sustained antidepressant-like effect of M-5MPEP. Importantly, M-5MPEP enhanced the subthreshold dose of (R)-ketamine in the TST, indicating both substances' convergent mechanisms of action and the possibility of their practical use in treating depression.

Infant electrocardiograms (ECG) and heart rates (HR) are very useful biosignals for psychological research and clinical work, but can be hard to analyze properly, particularly long form (≥5 minutes) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open-source ECG approaches are tested on infant datasets. The best performing open-source method is then modified to maximize its performance on infant data (e.g., including a 15Hz high pass filter, adding local peak correction). The HR signal is then subsequently analyzed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HR. A Signal Quality Index (SQI) for HR is also developed, providing insight into where a signal is recoverable and where it is not, allowing for more confidence in analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for future analysis of infant ECG and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world large datasets.

We introduce a special vector field ω on a Riemannian manifold (N^m, g), such that the Lie derivative of the metric g with respect to ω is equal to ρRic, where Ric is the Ricci curvature of (N^m, g) and ρ is a smooth function on N^{m} and call this vector field a ρ-Ricci vector field. We use ρ-Ricci vector field on a Riemannian manifold (N^m, g) and find two characterizations of m-sphere S^m(α). In first result, we show that an m-dimensional compact and connected Riemannian manifold (N^m, g) with nonzero scalar curvature admits a ρ-Ricci vector field ω such that ρ is nonconstant function and the integral of Ric(ω,ω) has a suitable lower bound is necessary and sufficient for (N^m, g) to be isometric to m-sphere S^m(α). In second result, we show that an m-dimensional complete and simply connected Riemannian manifold (N^m, g) of positive scalar curvature admits a ρ-Ricci vector field ω such that ρ is a nontrivial solution of Fischer-Marsden equation and the squared length of the covariant derivative of ω has an appropriate upper bound, if and only if, (N^m, g) to be isometric to m-sphere S^m(α).

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins (1947) to the r-complete convergence introduced by Tartakovsky (1998). We also establish its relation to the r-quick convergence first introduced by Strassen (1967) and extensively studied by Lai (1976). Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models.

In this paper, we investigated the trajectory of the massive particle in the vicinity of a general spherical symmetric black hole. Also, in the framework of general sphericalily symmetric black hole, Pseudo-Newtonian potential (PNP) and effective potentials has been investigated. As an example, static spherically symmetric black hole in f(R) gravity is considered and presented the brief discussion on the structure of spacetime and horizons. We calculated energy and angular momentum in the framework of general relativity as well as in Pseudo-Newtonian theory. A graphical comparison of angular momentum in this both framework has been studied.

Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R can acquire images every 15 minutes at 2km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

Background: Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) affects human respiratory function causing COVID-19 disease. Safe natural products with potential antiviral phytochemicals with benefits to control high-altitude sickness could be adopted as adjunct therapy for COVID-19. The red petals of Rhododendron arboreum, commonly available and consumed in the Himalayan region may have phytochemicals with potential antiviral properties against COVID-19 targets.Purpose: This study was aimed to profile the secondary metabolites of R. arboreum petals, to assess their absorption, distribution, metabolism and elimination (ADME) properties and evaluate their antiviral potential by docking against COVID-19 targets such as SARS-CoV-2 main protease (M^pro PDB ID: 6LU7) and Human Angiotensin Converting Enzyme 2 (ACE2) receptor (PDB ID: 1R4L) that mediates the viral replication and entry into the host respectively.Methods: The phytochemicals of R. arboreum petals were mainly profiled using Gas Chromatography-Mass Spectroscopy (GC-MS) and ¹H-NMR. In addition, the phytochemicals reported from the literature were tabulated. The ADME properties of the phytochemicals were predicted using SwissADME tool. Molecular docking simulation of the phytochemicals against SARS-CoV-2 main protease (M^pro PDB ID: 6LU7) and Human Angiotensin converting enzyme 2 (ACE2) receptor (PDB ID: 1R4L) were carried out using PyRx.Results: R. arboreum petals were found to be rich in appreciable proportions of secondary metabolites such as Quinic acid, 3-Caffeoyl-quinic acid, 5-O-Coumaroyl-D-quinic acid, 5-O-Feruloylquinic acid, 2,4-Quinolinediamine, Coumaric acid, Caffeic acid, Epicatechin, Catechin, 3-Hydroxybenzoic acid, Shikimic acid, Protocatechuic acid, Epicatechin gallate, Quercetin, Quercetin-O-pentoside, Quercetin-O-rhamnoside, Kaempferol-O-pentoside and Kaempferol. Several of these phytochemicals were reported to exhibit inhibitory activities against a range of viruses. From the molecular docking studies, 5-O-Feruloylquinic acid, 3-Caffeoyl-quinic acid, 5-O-Coumaroyl-D-quinic acid, Epicatechin and Catechin showed strong binding affinity with SARS-CoV-2 M^pro and human ACE2 receptor.Conclusion: This report showed that R. arboreum petals are rich in several antiviral phytochemicals that also docked against SARS-CoV-2 M^Pro and Human ACE2 receptor. This is the first report highlighting R. arboreum petals as a reservoir of antiviral phytochemicals with potential for synergetic activities. The outcomes merit further in vitro, in vivo and clinical studies on R. arboreum phytochemicals to develop natural formulations against COVID-19 disease for therapeutic benefits.

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

Alteration in heparan sulfate (HS) biosynthesis has been described to impact a range of pivotal processes involved in cancer progression. HS 3-O-sulfotransferase 3B1 (HS3ST3B1) catalyzes the reaction of 3-O-sulfation within HS chains. Over the past few years, a growing number of studies has reported a tumor-promoting effect of this HS-modifying enzyme in various cancers. Consistent with these studies, we previously reported that HS3ST3B1 overexpression increased survival and resistance to apoptosis in MDA-MB-231 breast cancer (BrCa) cells. In the present study, we further investigated the biological and clinical significance of HS3ST3B1 expression in BrCa. Interrogation of public databases revealed that high expression levels of HS3STB1 are associated with invasive and mesenchymal-like phenotype in BrCa cells and predict poor clinical outcome in BrCA patients with chemotherapy. In vitro, HS3ST3B1 overexpression increased the invasive properties of MDA-MB-231 cells and enhanced chemo-resistance to conventional anticancer drugs. Mechanistically, we found that the advantage given by HS3ST3B1 was linked to the expression and activation of PDGF-Rβ. Silencing the expression of this receptor reversed the effects driven by HS3ST3B1 overexpression. Altogether, our results suggest that HS3ST3B1 may play an important role in the progression of aggressive BrCa and offer a potentially druggable target for therapeutic application.

With the use of linear regression modeling, this study sought to forecast and analyze rainfall patterns in Ethiopia. The dataset included historical rainfall data as well as pertinent independent variables. The R-squared score and mean squared error (MSE) were used to assess the effectiveness of the linear regression model. The results showed that the model's ability to forecast and analyze rainfall patterns was not very good. Only around 5.3% of the variance in rainfall could be explained by the chosen independent variables, according to the R-squared score of 0.053. This low number indicated that there was little correlation between the selected variables and rainfall, and that a sizable percentage of the variation in the dependent variable was not captured by the model. The model's accuracy in forecasting rainfall was shown to be quite low by the MSE error, with significant squared discrepancies between the expected and actual amounts. These results support the notion that the linear regression model as it is now designed is not appropriate for studying or forecasting Ethiopia's rainfall patterns. Alternative modeling approaches, the inclusion of other pertinent factors, and the assurance of the availability of high-quality data that faithfully captures the intricate dynamics of rainfall in the area all require more investigation.

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

Studies on the neurobiological causes of anxiety disorders suggest that the GABA system in-creases synaptic concentration and enhances the affinity of GABAA (type A) receptors for ben-zo-diazepine ligands. Flumazenil antagonizes the benzodiazepine binding site of the GABA (γ-aminobutyric acid) type /benzodiazepine receptor (BZR) complex in the central nervous sys-tem (CNS). The integration of the metabolites of flumazenil by LC-tandem mass spectrometry will complete understanding of the in vivo metabolism of flumazenil and accelerate radio-pharmaceutical inspection and registration. The main goal of our study was to investigate using reversed-phase HPLC (PR-HPLC) coupled with electrospray ionization triple quadrupole tan-dem mass spectrometry (ESI-QqQ MS) for the identification of flumazenil and its metabolites in a hepatic matrix. And a carrier-free nucleophilic fluorination with automatic synthesizer for [18F]flumazenil which applied to in vivo nano-positron emission tomography (Nano-PET)/computed tomography (CT) imaging and ex vivo bio-distribution used to analyze in normal rats. The study showed that 50% of the flumazenil was bio-transformed at 60 min by the rat liver homogenate, while one metabolite (M1) was a methyl transesterification product of flumazenil. In a rat liver microsomes system, two metabolites were identified (M2 and M3) as its carboxylic acid and hydroxylated ethylester forms, respectively. [18F]flumazenil in vivo nanoPET/CT imag-ing and ex vivo bio-distribution assay also showed significant effects on GABAA receptor availa-bility in the amygdala, prefrontal cortex, cortex, and hippocampus in the rat brain, worriless about the formation of metabolites. We showed completion of the bio-transformed course of flumazenil by the hepatic system; and [18F]flumazenil can be a good ligand and serve as a PET agent for determination of GABAA/BZR complex for multiplex neurological syndromes in the clinical stage.

This study investigated whether export performance is correlated with innovation. We explicitly considered five types of innovation: a) process innovation; b) product innovation; c) process and product innovation; d) product innovation new to the firm; and e) product innovation new to the market. We en-larged the findings of R&D-export and R&D-investment relations using a vast sample of cross-European firms. We considered innovation as endogenous and determined by R&D through a process characterized by the role of the regional technological environment. Our analysis enabled us to propose an integrated model incorporating R&D, innovation and export, in a framework of simultaneous equations which consider their mutual correlations. To the best of our knowledge, this work is the first in recent years to empirically compute the relation between R&D and tangible investment at the micro level. Econometric results showed that product and process innovation are positively linked to the export intensity of European manufacturing firms. The results also showed complementarity effects between the two kinds of innovation. The av-erage effect on export intensity from carrying out process innovation is larger than that found for product innovation, except in cases where the product is new to the market. When both types of innovation have been carried out, a larger effect results than that found for product or process innovation alone. Furthermore, the average marginal effect on export intensity from innovations of any kind among those studied here is highly positive and significant.

We obtain exact solutions to the field equations for 5 dimensional LRS Bianchi type-I spacetime in $f(R,T)$ theory of gravity where specifically the following three cases are considered: (i) $f(R,T)=\mu(R+T)$, (ii) $f(R,T)=R \mu + R T \mu^2$ and (iii) $f(R,T)=R+\mu R^2+\mu T$ where $R$ and $T$ respectively the Ricci scalar and trace of the energy-momentum tensor. It is found that the equation of state (EOS) parameter $w$ is governed by the parameter $\mu$ involved in the $f(R,T)$ expressions. We fine-tune the parameter $\mu$ to obtain effect of phantom energy in the model, however we also restrict this parameter to obtain a stable model of the universe. It is noted that the model isotropizes at finite cosmic time.

This paper is related to the fractional view analysis of Helmholtz equations, using innovative analytical techniques. The fractional analysis of the proposed problems has been done in terms of Caputo-operator sense. In the current methodology, first, we applied the r-Laplace transform to the targeted problem. The iterative method is then implemented to obtain the series form solution. After using the inverse transform of the r-Laplace, the desire analytical solution is achieved. The suggested procedure is verified through specific examples of the fractional Helmholtz equations. The present method is found to be an effective technique having a closed resemblance with the actual solutions. The proposed technique has less computational cost and a higher rate of convergence. The suggested methods are therefore very useful to solve other systems of fractional order problems.

G-quadruplexes are noncanonical nucleic acid structures formed from stacked guanine tetrads. They are frequently used as building blocks and functional elements in fields such as synthetic biology and also thought to play widespread biological roles. G-quadruplexes are often studied as monomers but can also form a variety of higher-order structures. This increases the structural and functional diversity of G-quadruplexes, and recent evidence suggests that it could also be biologically important. In this review we describe the types of multimeric topologies adopted by G-quadruplexes and highlight what is known about their sequence requirements. We also summarize the limited information available about potential biological roles of multimeric G-quadruplexes and suggest new approaches that could facilitate future studies of these structures.

The aim of this study was to analyze whether the respiratory variation in ECG standard lead II R-wave amplitude (ΔRDII) could be used to assess intravascular volume status following inferior vena cava (IVC) clamping. This clamping causes an acute decrease in cardiac output during liver transplantation (LT). We retrospectively compared ΔRDII and related variables before and after IVC clamping in 34 recipients. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were used to derive a cutoff value of ΔRDII for predicting pulse pressure variation (PPV). After IVC clamping, cardiac output significantly decreased while ΔRDII significantly increased (P = 0.002). The cutoff value of ΔRDII for predicting a PPV >13% was 16.9% (AUC: 0.685) with a sensitivity of 57.9% and specificity of 77.6% (95% confidence interval 0.561 – 0.793, P = 0.015). Frequency analysis of ECG also significantly increased in the respiratory frequency band (P = 0.016). Although significant changes in ΔRDII during vena cava clamping were found at norepinephrine doses < 0.1 μg/kg/min (P = 0.014), such changes were not significant at norepinephrine doses > 0.1 μg/kg/minP = 0.093). ΔRDII could be a noninvasive dynamic parameter in LT recipients presenting with hemodynamic fluctuation. Based on our data, we recommended cautious interpretation of ΔRDII may be requisite according to vasopressor administration status.

There are no studies that quantitatively compare life histories among scorpion species. Statistical procedures applied to 94 scorpion species indicate that those with larger bodies do not necessarily have larger litters or longer life cycles, opposite to some theoretical predictions.

The intercomparison among different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde, radio occultation (e.g. GNSS-RO) and atmospheric model outputs (e.g. ERA5 reanalysis). Since instruments and/or measures are not perfectly collocated, the miss-collocation uncertainty must be considered in the related intercomparison uncertainty budgets.