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O projeto “Ciência na Fronteira: Expandindo os Limites do Conhecimento” tinha como objetivo instigar nos jovens o interesse pelas Ciências da Natureza por meio de atividades experimentais envolvendo tópicos avançados como Buracos Negros e Relatividade, apresentados em linguagem acessível a estudantes da Educação Básica. Fomentado por edital do Instituto Federal do Mato Grosso do Sul, Campus Corumbá (IFMS-CB, 2020), seu público-alvo eram estudantes da Educação Básica do Estado, matriculados entre o oitavo ano do Ensino Fundamental e a segunda série do Ensino Médio. Todas as atividades foram mediadas por monitoras bolsistas do Ensino Médio Técnico Integrado do IFMS-CB. Concebido inicialmente como presencial, o grande desafio encontrado foi a transposição de sua proposta de abordagem interativa e colaborativa para o ambiente virtual. Isso foi conseguido com o uso de recursos digitais variados que permitiam a manipulação de mídias e criações colaborativas, e o sucesso desta adaptação foi verificado por meio da avaliação dos participantes, que apontaram a interatividade como o principal ponto positivo de todo o projeto. Outro aprendizado foi o uso da rede social Instagram para divulgação das inscrições. O modelo inicial usava as mídias institucionais do IFMS-CB e teve pouco resultado. O projeto mostrou que é possível tanto ensinar Ciências através de tópicos avançados quanto promover um ambiente virtual de aprendizado interativo eficiente.

Background Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impacts. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate. Aims and methods In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections. Hourly time series are created by combining VIIRS estimates with Fire Radiative Power (FRP) estimates from GOES-17 (Geostationary Operational Environmental Satellite) data. Conclusions, key results and implication We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 to 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors

Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades, the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for the modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative, when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1–2 cm for locations (GNSS stations) already used in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural networks for high-resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland (1–2 cm in terms of RMS), showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research., Acta Astronautica, 204, ISSN:0094-5765

During the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) study, the NASA DC-8 carried out in situ chemical measurements in smoke plumes emitted from wildfires and agricultural fires in the contiguous United States. The DC-8 payload included a modified proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) for the fast measurement of gaseous ammonia (NH3) and a high-resolution time-of-flight aerosol mass spectrometer (AMS) for the fast measurement of submicron particulate ammonium (NH4+). We herein report data collected in smoke plumes emitted from 6 wildfires in the Western United States, 2 prescribed grassland fires in the Central United States, 1 prescribed forest fire in the Southern United States, and 66 small agricultural fires in the Southeastern United States. Smoke plumes contained double to triple digit ppb levels of NH3. In the wildfire plumes, a significant fraction of NH3 had already been converted to NH4+ at the time of sampling (≥2 h after emission). Substantial amounts of NH4+ were also detected in freshly emitted smoke from corn and rice field fires. We herein present a comprehensive set of emission factors of NH3 and NHx, with NHx=NH3+NH4+. Average NH3 and NHx emission factors for wildfires in the Western United States were 1.86±0.75 g kg−1 and 2.47±0.80 g kg−1 of fuel burned, respectively. Average NH3 and NHx emission factors for agricultural fires in the Southeastern United States were 0.89±0.58 and 1.74±0.92 g kg−1, respectively. Our data show no clear inverse correlation between modified combustion efficiency (MCE) and NH3 emissions. The observed NH3 emissions were significantly higher than measured in previous laboratory experiments in the FIREX FireLab 2016 study.

The latest estimate of the United State Energy Information Administration (US-EIA) shows that Nigeria is the largest producer of oil in Africa with largest natural gas reserves on the continent and the fifth–largest exporter of liquefied natural gas (LNG) in the World. In 2017, Nigeria's generation capacity was 12,664 megawatts (MW), with 10,522 MW representing 83% from fossil fuels, 2,110 MW representing 17% from hydropower, and 32 MW accounting for 1% coming from solar, wind, biomass, and other sources. The fossil power plants constitutes the major driver in greenhouse gas emission, therefore reduction in its operation will reduce the rate of carbon emission. To achieve carbon neutrality in line with the Paris agreement of 2015, the need for nuclear and other low carbon emission renewable electricity generation technologies is vital. This study utilizes the International Atomic Energy Agency (IAEA) Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) code in modelling energy supply needs for Nigeria using various electricity generation technologies. By placing the upper limit on carbon emission constraints, discount rate of 11.5% and other electricity generation data obtained from EIA. Using the modelling period of forty-five (45) years with a base year of 2015 the results shows that the contribution of fossil is bound below the upper limit while that of hydro continue to increase from 2016 to the end of the modelling year. Contribution from nuclear power plant starts from 2029, and continue to increase throughout the modeling period which suppressing contribution from fossil below the upper limit of CO2, The negligible contribution from solar and wind been suppressed by nuclear and hydro power plants. The results shows that nuclear and other low carbon energy technologies are the preeminent alternative for future electricity production in achieving carbon neutrality target.

Forms of promoting reading in the context of cultural and social functions Of a library on the example of the Ignacy Krasicki Public Library in Przemyśl Contemporary public libraries are centers of social life, places of meeting and exchanging thoughts, which contribute to consolidating social bonds. In recent years, the changes in culture and education made librarians face new challenges and the development of information society brought changes to the image of library institutions. Libraries are changing, influenced by the environment and the needs of their users; they eliminate barriers, add alternative forms of cultural and educational activity to their offer, as well as performing communication and entertainment functions. In order to promote readership and develop reading culture they get involved in carrying out social and culture-making tasks. Also the Public Library in Przemyśl is following contemporary changes and development tendencies, contributing to the rich cultural and social history of Przemyśl. It cooperates with the local community, observes the needs of the users, creates information, educational and cultural offer, extending and diversifying it depending on the readers’ needs. The Library in Przemyśl consciously creates its image. It is an open institution which plays an important role in the cultural and social sphere of the town and region

El objetivo principal de este artículo es explorar la organización política del espacio, las formas en que el espacio y la interacción humana en el espacio se estructuran para cumplir funciones políticas, en relación con el tema central de la geografía moderna: la organización espacial de la sociedad humana. Este es un tema que, a pesar de su evidente importancia, ha recibido relativamente poca atención directa y sistemática por parte de los geógrafos u otros científicos sociales. La geografía política permanece firmemente anclada en la tradición “estatista” dentro de la geografía, con su énfasis en la diferenciación de áreas y la descripción de características únicas. Este estudio no pretende sustituir una nueva ortodoxia por una antigua. Tampoco pretende abarcar toda la amplitud de interés en la geografía política. Su objetivo fundamental es explorar algunos caminos nuevos y sugerir un marco de conceptos y temas que posiblemente puedan vincular la geografía política de manera más efectiva con los desarrollos metodológicos y filosóficos recientes en la propia geografía y en las demás ciencias sociales. Quizás lo más importante, su objetivo final es involucrar al lector, hacerlo consciente de la organización política del espacio, de la forma en que él mismo percibe estas organizaciones y cómo afecta su propia actividad y comportamiento.

With the introduction of the operating system Android 7 Nougat in the year 2016, it became possible to access Global Navigation Satellite System (GNSS) code and carrier phase observations. These observations can be processed with the state-of-the-art GNSS processing software packages, which allows an in-depth evaluation of the smartphone's GNSS performance. The availability of carrier phase observations enables sub-decimeter-level positioning. A few years ago, smartphones wearing dual-frequency GNSS chipsets hit the mass market. In this study, we investigate the capability of such a device for the estimation of tropospheric delays. Static measurements carried out over the period of two weeks are performed using a Google Pixel 4 XL smartphone. The measurements are processed using relative positioning methods with a baseline length of about 33 kilometers, where a continuously operating reference station (CORS) acts as a base. The estimated differential zenith tropospheric wet delay (dZWD), obtained for the smartphone are then combined with absolute values computed at the reference station, in order to obtain time series of Zenith Total Delay (ZTD). Using this method, we demonstrate that high-precision ZTDs can be successfully determined from smartphone GNSS observations. When comparing the estimated tropospheric delays with those determined at a nearby geodetic receiver to assess the accuracy of the acquired time series of ZTD, differences in the range of few millimeters to a centimeter are visible. We examine the impact of various error sources, such as antenna phase center variations and residual effects of the ionosphere. Given that the obtained accuracies are at the level of a centimeter and below, the suggested method shows the potential to resolve small-scale tropospheric structures in near real-time, and thus, could be an interesting data source for numerical weather prediction models or related GNSS crowdsourcing projects., Measurement Science and Technology, 34 (9), ISSN:0957-0233, ISSN:1361-6501

Software developed for Manuscript ResLearner ***In case of using any part of the code make sure to cite our paper*** Please first unzip venv.zip, igpsrapid.zip, and images.zip and put the folders in the same folder as other files. Available alsoat Gitlab:https://gitlab.ethz.ch/mkiani/gps-rapid-eop-data-improvement.git&nbsp

Software developed for Manuscript ResLearner ***In case of using any part of the code make sure to cite our paper*** Please first unzip venv.zip, igpsrapid.zip, and images.zip and put the folders in the same folder as other files. Available alsoat Gitlab:https://gitlab.ethz.ch/mkiani/gps-rapid-eop-data-improvement.git&nbsp

Illegal mining activities has become rampant in Adamawa State Nigeria, contaminating the soil with radionuclides which can be transferred to the food crops farmed in the host communities. Ingestion of such plants could cause harmful effects. In this study, radionuclide transfer factor from soil to plants and internal exposure dose around mining sites in Adamawa State were evaluated. 30 (15 soil, 15 plant) samples were analyzed for activity concentrations of 226Ra, 232Th and 40K. The transfer factor from soil to plant was calculated and the ingestion dose was also calculated using the Integrated Module for Bioassay Analysis (IMBA). Result shows that the mean activity concentrations of 226Ra, 232Th and 40K in the soil are 106.32Bq/kg, 84.34Bq/kg and 466.14Bq/kg, while for plants are 98.42Bq/kg, 72.69Bq/kg and 274.07Bq/kg respectively. These values were above the world average by United Nations Scientific Community on the Effects of Atomic Radiation (UNSCEAR) 35Bq/kg, 30Bq/kg and 400Bq/kg except for 40K which was lower in plant samples. Mean transfer factors were 0.942, 0.919 and 1.149 respectively, corresponding to a mean ingestion dose of 1.02E-01mSv/y, indicating high transfer factor in all the mining sites and high ingestion doses. Even though the mean ingestion dose was low, ingestion of such plants may pose radiation risk to the host communities in a long time. Therefore, proper policy by regulatory authorities must be put in place to protect the host community and the public from high ingestion doses that accounts for internal radiation exposure especially mining sites A, B and C.

Aims: To analyse the radiological hazard indices from mining sites in Adamawa State, Nigeria. Study Design: Experimental study design using Gamma ray spectroscopy with a well calibrated Sodium Iodide (NaI) detector. Place and Duration of Study: Adamawa State, Nigeria and Department of Physics, Nasarawa State University Keffi, Nigeria, and Centre for Energy Research and Training (CERT) Laboratory, Ahmadu Bello University Zaria, Nigeria between November 2019 and August 2020. Methodology: Fifteen composite samples of soil from four mining sites collected using the systematic sampling techniques were analysed for activity concentrations of Ra-226, Th-232 and K-40 and Gamma Absorbed Dose Rate, Radium Equivalent Activity, External Hazard Index, Annual Effective Dose Rate and Excessive Life Cancer Risk were calculated. Results: Mean activity concentrations Ra-226 (107.60Bq/kg), Th-232 (84.89Bq/kg), and K-40 (475.34Bq/kg) were all above the world average values 35Bq/kg, 30Bq/kg and 400Bq/kg recommended by UNSCEAR. Mean Gamma Absorbed Dose Rate, Radium Equivalent Activity, Annual Effective Dose Rate, External Hazard Index, and Excessive Life Cancer Risk were 120.31nGy/h, 265.469Bq/kg, 0.148mSv/y, 0.401, and 0.369 respectively, against recommended values 59nGy/h, 370Bq/kg, 1mSv/y, 0.45, and 0.29 according to UNSCER, NEA-OECD and ICRP. Conclusion: High values of Activity Concentrations, Gamma Absorbed Dose, and Excessive Life Cancer Risk poses significant threat to the host community, especially around the 3 mining sites SA, SB and SC. Therefore, safety distance from mining areas is recommended by the competent Authority responsible for radiation protection matters in Nigeria. General awareness to enlighten the public about the possible dangers of undue radiation exposure and the risk of residing close to mining vicinity is required, for adequate protection of the host community.

Data uncertainty plays an important role in the field of geodesy. Even though deep learning is becoming increasingly important for geodetic applications due to its high accuracy, it typically does not consider the data uncertainty. As we demonstrate in this study, we propose to include the uncertainty of data in deep neural network architectures to achieve a better generalization. This is advantageous for big data applications as well as for small datasets. Inspired by weighted and total least squares, we formulate the problem for both input and target uncertainties, and combine it with the Bayesian learning method. This results in a new form of the loss function in machine learning. As an alternative approach, we consider data uncertainties by including them as additional features. For comparison purposes, we use models without the consideration of data uncertainty as a benchmark. To show the efficacy of the proposed method, we apply it to the prediction of Earth Orientation Parameters (EOPs, namely polar motion, dUT1, and LOD) and Global Navigation Satellite System (GNSS) station coordinate time series. We demonstrate that the least-squares-inspired method outperforms both the benchmark and the feature-inspired method for both the studies. In the EOPs study, the improvement can be more than 50% in the study interval. In the study of GNSS station coordinate time series, which is presented for 1000 stations across the globe, the improvement on an average basis is around 12%. The results demonstrate the advantage of using uncertainty information in the machine learning algorithms, when applied to geodetic time series., Advances in Space Research, 70 (3), ISSN:0273-1177, ISSN:1879-1948

The proliferation of spaceborne radio frequency interference (RFI) is threatening the operation of VLBI. In particular, mega-constellations, such as Starlink, OneWeb, and Amazon Kuiper, pose a significant threat since some of these satellites will emit signals in the upper-frequency range of the VLBI Global Observing System (VGOS). The power of these signals may saturate the amplifiers of VLBI antennas, causing nonlinearities within the observations. Furthermore, some future InSAR satellites, such as the NISAR mission, might emit signals strong enough to permanently damage the highly sensitive VLBI hardware. We will present our approach for an active mitigation strategy that modifies the current VLBI observing strategy to avoid observations in the direction of spaceborne RFI. However, active avoidance means additional constraints during the observation planning. We will present results of the investigation the impact of these constraints w.r.t. the precision of the geodetic parameters using simulations and compare it to a situation (1) with active avoidance, (2) without active avoidance, where affected observations are lost and simply removed from the simulations, and (3) without active avoidance and no spaceborne RFI being present. Our simulations are based on different VGOS networks and satellite mega-constellation expansion stages. We will discuss to which extent our approach can help to avoid spaceborne RFI but also highlight its limitations and necessary development steps toward operational use. Finally, we will discuss the practical requirements of active mitigation of spaceborne RFI based on scheduling. Here, the accuracy of the orbit prediction seems to play a crucial role. With the current observation strategy of submitting a schedule seven days prior to observation, significant error margins must be applied to the satellite orbits. We will also present recent developments in storing and providing satellite orbit information in the form of two-line-elements (TLE) that will be used in the current scheduling pipelines.

As new sources of proteins, edible insects may be excellent additives in a new generation of environmentally friendly food products that are nutritionally valuable, safe, sustainable, and are needed in today’s world. The aim of this study was to determine the effect of the application of cricket flour on extruded wheat-corn-based snack pellets’ basic composition, fatty acids profile, nutritional value, antioxidant activity and selected physicochemical properties. Results showed that the application of cricket flour had a significant impact on the composition and properties of snack pellets based on wheat-corn blends. In newly developed products, the enhanced level of protein and almost triple increase in crude fiber was found as an insect flour supplementation reached 30% level in the recipe. The level of cricket flour and the applied processing conditions (various moisture contents and screw speeds) significantly affect the water absorption and water solubility index and texture and color profile. Results revealed that cricket flour application significantly increased the total polyphenols content in the assessed samples in comparison to plain wheat-corn bases. Antioxidant activity was also noted to be elevated with increasing cricket flour content. These new types of snack pellets with cricket flour addition may be interesting products with high nutritional value and pro-health properties.

Jerzy Soja,1,2 Karolina Górka,1,2 Iwona Gross-Sondej,1,2 Bogdan Jakieła,2 Sławomir Mikrut,3 Krzysztof Okoń,4 Adam Ćmiel,5 Piotr Sadowski,4 Wojciech Szczeklik,6 Anna Andrychiewicz,7 Tomasz Stachura,1,2 Grażyna Bochenek,1,2 Stanisława Bazan-Socha,2 Krzysztof Sładek1,2 1Department of Pulmonology and Allergology, University Hospital, Kraków, Poland; 2 2nd Department of Internal Medicine, Jagiellonian University Medical College, Kraków, Poland; 3Faculty of Mining, Surveying and Environmental Engineering, AGH University of Science and Technology, Kraków, Poland; 4Department of Pathology, Jagiellonian University Medical College, Kraków, Poland; 5Department of Applied Mathematics, AGH University of Science and Technology, Kraków, Poland; 6Centre for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Kraków, Poland; 7Department of Endoscopy, University Hospital, Kraków, PolandCorrespondence: Jerzy Soja, Department of Internal Medicine, Faculty of Medicine, Jagiellonian University Medical College, Jakubowskiego 2, Krakow, 30-688, Poland, Email jerzy.soja@uj.edu.plBackground: Bronchial thermoplasty (BT) is an interventional endoscopic treatment for severe asthma leading to the clinical improvement, but morphologic changes of bronchial wall related to the procedure and predictors of a favorable response to BT remain uncertain. The aim of the study was to validate an endobronchial ultrasound (EBUS) in assessing the effectiveness of BT treatment.Methods: Patients with severe asthma who met the clinical criteria for BT were included. In all patients clinical data, ACT and AQLQ questionnaires, laboratory tests, pulmonary function tests and bronchoscopy with radial probe EBUS and bronchial biopsies were collected. BT was performed in patients with the thickest bronchial wall L2 layer representing ASM. These patients were evaluated before and after 12 months of follow-up. The relationship between baseline parameters and clinical response was explored.Results: Forty patients with severe asthma were enrolled to the study. All 11 patients qualified to BT successfully completed the 3 sessions of bronchoscopy. BT improved asthma control (P=0.006), quality of life (P=0.028) and decreased exacerbation rate (P=0.005). Eight of the 11 patients (72.7%) showed a clinically meaningful improvement. BT also led to a significant decrease in the thicknesses of bronchial wall layers in EBUS (L1 decreased from 0.183 to 0.173 mm, P=0.003; L2 from 0.207 to 0.185 mm, P = 0.003; and L3– 5 from 0.969 to 0.886 mm, P=0.003). Median ASM mass decreased by 61.8% (P=0.002). However, there was no association between baseline patient characteristics and the magnitude of clinical improvement after BT.Conclusion: BT was associated with a significant decrease in the thickness of the bronchial wall layers measured by EBUS including L2 layer representing ASM and ASM mass reduction in bronchial biopsy. EBUS can assess bronchial structural changes related to BT; however, it did not predict the favorable clinical response to therapy.Keywords: airway remodeling, airway smooth muscle, bronchial thermoplasty, bronchial wall layers, endobronchial ultrasound

The densification of high-quality, permanent GNSS stations in Europe enables a large-scale investigation of deformation processes on the Earth’s surface. This work aims to interpolate the horizontal and vertical GNSS station velocities and thus produce velocity fields showing the land motion for Switzerland, the Alps and Europe. The GNSS station velocities are provided by the EUREF Working Group on European Dense Velocities. The data set contains horizontal (east, north) and vertical velocities for around 8000 stations in Europe. Five interpolation methods are implemented and compared, namely, Inverse Distance Weighting (IDW), Ordinary Kriging, K - Nearest Neighbors (KNN), Random Forest and Multilayer Perceptron (MLP). Latitude and longitude of the station locations are used as input features for the interpolation. Additional input features will be engineered for Random Forest and MLP. Generally, the performance of all five interpolation methods with latitude and longitude as features evaluated on the test data by Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) is comparable for all velocity components in Switzerland, the Alps and Europe. RMSE and MAE vary among the methods by hundredths of a mm/year. The only exceptions are the horizontal velocity components for the extent of Europe, where MLP and Ordinary Kriging perform slightly worse than the other methods. The key findings of the qualitative analysis are that MLP and Ordinary Kriging produce the smoothest velocity fields, while IDW, KNN and Random Forest produce artifacts due to their mode of operation. All methods interpolate similar velocity fields where the station data is dense and greater differences when it is sparse. Especially for extrapolation areas where no data is available their performance is not verified. The interpolation of the GNSS station velocities in Switzerland, the Alps and Europe for this work shows that it is possible to produce velocity fields with accuracy level below 1 mm/year and the different phenomena of land motion can be clearly identified.

In gravimetry satellite missions GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (GRACE Follow-On), accelerometer measurements from both satellites are necessary for the gravity field recovery. The accelerometer provides accurate measurements of the non-gravitational forces acting on the spacecraft, such as atmospheric drag, solar radiation pressure and albedo. These measurements are required to separate any non-gravitational effect from the sought-after gravitational perturbations on the spacecraft motion. Therefore, the quality of accelerometer data, denoted as ACC products, significantly affects the quality of gravity field models.Near the end of the GRACE mission, due to the reduced battery capacity, the on-board accelerometer of the GRACE-B was turned off and its measurements were replaced by synthetic accelerometer data, called transplant data. The transplant data are generated by a series of adjustments to the GRACE-A ACC data. A similar approach was also employed for the GRACE-FO mission, when the GRACE-D ACC data degraded and were required to be replaced with synthetic data as well. Using the transplant data in both missions is one of the main challenges of providing high-quality gravity field models.We investigate the feasibility of Machine Learning (ML) algorithms for the recovery of GRACE-B ACC based on GRACE-A measurements and orbital data such as shadow factor and β angle. Taking advantage of ~14 years of GRACE-B measurements, this work aims to develop a model which can predict the missing accelerometer data under different orbital conditions. Two different architectures are implemented to forecast GRACE-B accelerometer data: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). The performance is evaluated using the Root Mean Square Error (RMSE) and by comparing the predicted data with the calibrated real data in the evaluated period. Furthermore, the ML-based ACC products will be compared to the transplant products and their impact on the gravity field will be discussed.

High-precision global ionospheric modeling is important for radio communication, navigation, or studies on space weather. Traditional spatial ionospheric modeling approaches include spherical harmonics and trigonometric B-splines. The Ionospheric Associated Analysis Centers (IAAC) of the International GNSS Service (IGS) use these methods to model vertical total electron content (VTEC) globally, and generate Global Ionospheric Maps (GIMs). Due to the limitations of spatial modeling approaches, conventional GIMs cannot comprehensively describe the spatial feature of the ionosphere. With the capability of capturing complex and non-linear relationships of diverse data, machine learning (ML) has been increasingly applied to ionospheric modeling. Currently, most of the existing ML-related studies focused on temporal prediction of ionospheric states and rarely considered the aspect of the spatial modeling of VTEC. Although some studies predicted global ionosphere maps using machine learning, they used conventional GIMs as inputs, implying that the precision of the ML-based spatial modeling could be limited by traditional methods and quantity of input GNSS observations utilized to generate GIMs.The goal of this study is the spatial interpolation of VTEC using ML methods for the generation of ML-based GIMs. We first determine VTEC using carrier-to-code levelling through Kalman filter and based on geometry-free multi-GNSS observations from GNSS stations of the IGS network. The derived satellite-specific VTEC time series are then used to train the ML models. Several algorithms, such as extreme gradient boosting and random forest, are applied and their performance is evaluated. Moreover, VTEC from satellite altimetry is used as an additional means to assess the quality of the generated ML models. Finally, we compare the acquired ML-based GIMs with conventional GIMs to investigate the advantage of using the proposed approach for global VTEC modeling.

The by-products of abattoirs may become valuable resources for nutrient recycling and energy generation by including pyrolysis and biogas production in the value creation chain. This study investigated the potential of bone chars as sorbents for ammonium in order to produce a soil amendment useful for fertilizing purposes. Ammonium enriched from the digestate by membrane distillation or from pure ammonium sulphate solutions accommodated the nitrogen sorption to the bone chars. The plant availability of the sorbed nitrogen was studied by a standardized short-term plant test with rye (Secale cereale L.).The results showed that ammonium, both from biogas digestate of the abattoir and from pure salt solutions, could be sorbed successfully to the bone chars post-pyrolysis and increased the nitrogen concentration of the chars (1.6±0.3 %) by 0.2-0.4 %. This additional nitrogen was desorbed easily and supported plant growth (+17 to +37 %) and plant nitrogen uptake (+19 to 74 %). The sorption of ammonium to the bone chars had a positive effect on the reversal of pure bone char phytotoxicity and on nitrogen availability. In summary, this study showed that abattoir wastes are useful pyrolysis input materials to produce bone chars and to use biogas digestates as ammonium source for nitrogen sorption to the chars. This innovation offers the possibility to produce nitrogen-enriched bone chars as a new type of fertilizer that upgrades the known value of bone char as phosphorus fertilizer by an additional nitrogen fertilizer effect. The study also shows that abattoirs, too, may become contributors to circular economy by facilitating the recovery of nitrogen and phosphorus.

The proliferation of spaceborne radio frequency interference (SRFI) is threatening the operation of Very Long Baseline Interferometry (VLBI). Most SRFI is caused by the excessive number of newly launched satellites, in particular from mega-constellations such as Starlink, OneWeb, or Amazon Kuiper. Some of these satellites will emit signals in the upper-frequency range that is currently observed by the next-generation VLBI system, the VLBI Global Observing System (VGOS). The power of these signals may saturate the amplifiers of VLBI antennas causing nonlinearities within the observations. Besides, some planned InSAR satellites might emit signals strong enough to permanently damage the highly sensitive VLBI hardware. Within this work, we will investigate an active mitigation approach that modifies the current VLBI observing strategy to avoid observations in the direction of SRFI. However, active avoidance means additional constraints during the observation planning. We will investigate the impact of these constraints w.r.t. the precision of the geodetic parameters using simulations and compare it to a situation without active avoidance, where affected observations are simply removed from the simulations, and to a situation without any SRFI being present. The simulations will be conducted based on current and future VGOS networks and current and future satellite constellation expansion stages. We will demonstrate that active avoidance has the potential to pose a solution to avoid SRFI but also highlight its limitations.

Global Navigation Satellite System (GNSS) is an essential tool for troposphere monitoring. Currently, GNSS meteorology depends mainly on the data from geodetic receivers of global or regional networks. However, these geodetic-grade GNSS stations are costly, and thus cannot be densely deployed, especially in less developed regions. Since the release of the Android 7 operating system in 2016, Android smartphones can be used to collect raw GNSS data. Considering that nowadays there are about 3 billion Android smartphones worldwide, a smartphone GNSS data crowdsourcing campaign was launched on March 17th 2022 as a part of the CAMALIOT project. About 5 TB of raw GNSS observations were collected around the world by more than 12 thousand users of the CAMALIOT Android application. In this contribution, we highlight the results related to the dedicated pipeline developed to process the crowdsourced smartphone GNSS data. Firstly, all the collected data were classified by a machine learning-based model to disregard observations of low quality. It was found that only about 2% of the collected data could potentially be used for troposphere delay estimation. The high-quality observations were then processed in the relative-positioning mode by forming baselines with the nearby geodetic stations. Several crowdsourced data sets were used to demonstrate that the zenith wet delays (ZWD) derived from smartphone data could achieve an accuracy of better than 10 mm. However, uncalibrated phase center variations of the smartphone antennas and multipath errors are still the main limitations to further improve the ZWD estimation. Overall, our study indicates that crowdsourced smartphone GNSS data is promising to densify the existing GNSS networks in terms of troposphere monitoring.

The emergence of low-cost micro-electro-mechanical system inertial measurement units, cameras and Global Navigation Satellite System (GNSS) receivers has promoted the research of multi-sensor fusion for positioning. With the rapid development of chip technology, it has become a trend for low-cost GNSS chips to provide multi-frequency carrier phase observations while supporting multiple constellations. To enhance the positioning performance of multi-frequency and multi-system precise point positioning (PPP) in the complex urban environment, this paper proposes a tightly coupled system of monocular visual-inertial odometry (MVIO) and uncombined PPP (UC-PPP) based on factor graph optimization. The initialization of the integrated system includes two parts: one is for monocular vision and inertial navigation systems, and another is for MVIO and UC-PPP. The initialization of MVIO and UC-PPP adopts a coarse-to-fine approach to correct online the transformation of local and global frames. In addition, the sliding window and marginalization methods are adopted to retain the constraints between adjacent observations and eliminate useless observations in the window. The pedestrian and vehicle tests in the complex urban environment verify the performance of the proposed method. Compared with open-source software GVINS, the positioning accuracy of the proposed method has been further improved due to the use of carrier phase observations with higher measurement accuracy. Compared with PPP alone, the improvement of the proposed method for the low-speed and short-distance pedestrian test in the east, north, and up directions is 73.3, 54.8 and 62.7%, respectively, while the improvement for the high-speed and long-distance vehicle test is 63.0, 59.3 and 70.5%, respectively. Experiment results show that the proposed method has better positioning accuracy and performance in complex urban environments.

Rapid provision of Earth Orientation Parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency, but the highest accuracy. To reduce these discrepancies, we focus on a data-driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically-constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semi-diurnal, diurnal, and long-period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed and El Ni\~{n}o Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS) we achieve improvements as high as 60\%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of European Space Agency, on which ResLearner presents comparable improvements.

Earth orientation parameters (EOPs) are essential in geodesy, linking the terrestrial and celestial reference frames. Due to the time needed for data processing and combining different space geodetic techniques, EOPs of the highest quality suffer latencies from several days to several weeks. However, real-time EOPs are needed for multiple geodetic and geophysical applications. Predictions of EOPs in the ultra-short term can overcome the latency of EOP products to a certain extent. Traditionally, predictions are performed using statistical methods. With the rapid expansion of computing capacity and data volume, the application of deep learning in geodesy has become increasingly promising in recent years. In particular, the Long Short-Term Memory (LSTM) neural networks, one of the most popular Recurrent Neural Network varieties, are promising for geodetic time series prediction. In this study, we investigate the potential of using LSTM to predict daily length of day (LOD) variations up to ten days in advance, accounting for the contribution of effective angular momentum (EAM). The data are first preprocessed to obtain residuals by combining physical and statistical models. Then, we employ LSTM networks to predict the LOD residuals using both LOD and EAM residuals as input features. Our methods outperform all other state-of-the-art methods in the first eight days with an improvement of up to 43% under the first EOP Prediction Comparison Campaign conditions. In addition, we assess the performance of LOD predictions using more extended time series to consider the improvements of EOP products over the last decade. The results show that extending data volume significantly increases the performance of the methods., Journal of Geodesy, 97 (5), ISSN:0949-7714, ISSN:1432-1394

Funding Acknowledgements Type of funding sources: None. Background Recent guidelines recommend centralisation of post-resuscitation care in out-of-hospital cardiac arrest (OHCA) patients in dedicated Cardiac Arrest Centres (CACs) aiming to improve outcome. Purpose To describe the process of implementation of the centralisation policy and impact on 30-day mortality rate. Methods This nationwide study included all adult OHCA patients (>18 years) based on the Danish Cardiac Arrest Registry. Centralisation was introduced in 2009, 2010, and 2012 in four regions (A, B, C, D), and one region has not yet implemented the policy (control). Parallel trends assumption was made and a difference-in-difference model with repeated cross-sections was used to estimate the average treatment effect on the treated patients. The 30-day mortality was compared between patients living in regions that introduced centralisation versus control region without centralisation. Results A total of 20,566 patients with OHCA were identified between 2007 and 2020. The majority were between 50 to 75 years (53.7%) and 67.8% male. Bystander cardiopulmonary resuscitation was performed in 55.3%. Shockable rhythm was present in 34.6% of the patients. The policy of centralisation of OHCA in CACs has been gradually and not yet completely implemented (Figure 1A). Patients with shockable rhythms (72%), and younger patients < 50 years (64%) were more frequently transported to CACs. The 30-day mortality was lower among patients transported to CACs (Figure 1B). Centralisation significantly improved survival by 6% (95% CI: 2%-10%) percentage points compared to the control region after controlling for all known covariates (sex, age, Charlson Comobidity Index, shockable rhythm, and bystander cardiopulmonary resuscitation). Conclusions In this nationwide study, centralisation of post-resuscitation care of OHCA in dedicated CACs has been gradually implemented over time. Although, OHCA patients with favorable prognostic factors were more frequently transported to CACs, centralisation increased survival by 6 absolute percentage points.

Background and aims: It is unclear to what extent genetic testing improves the ability to diagnose familial hypercholesterolaemia (FH). We investigated the percentage with FH among individuals referred to Danish lipid clinics, and evaluated the impact of genetic testing for a diagnosis of FH.Methods: From September 2020 through November 2021, all patients referred for possible FH to one of the 15 Danish lipid clinics were invited for study participation and >97% (n = 1488) accepted. The Dutch Lipid Clinical Network criteria were used to diagnose clinical FH. The decision of genetic testing for FH was based on local practice.Results: A total of 1243 individuals were referred, of whom 25.9% were diagnosed with genetic and/or clinical FH. In individuals genetically tested (n = 705), 21.7% had probable or definite clinical FH before testing, a percentage that increased to 36.9% after genetic testing. In individuals with unlikely and possible FH before genetic testing, 24.4% and 19.0%, respectively, had a causative pathogenic variant.Conclusions: In a Danish nationwide study, genetic testing increased a diagnosis of FH from 22% to 37% in patients referred with hypercholesterolaemia suspected of having FH. Importantly, approximately 20% with unlikely or possible FH, who without genetic testing would not have been considered having FH (and family screening would not have been undertaken), had a pathogenic FH variant. We therefore recommend a more widespread use of genetic testing for evaluation of a possible FH diagnosis and potential cascade screening. Background and aims: It is unclear to what extent genetic testing improves the ability to diagnose familial hypercholesterolaemia (FH). We investigated the percentage with FH among individuals referred to Danish lipid clinics, and evaluated the impact of genetic testing for a diagnosis of FH. Methods: From September 2020 through November 2021, all patients referred for possible FH to one of the 15 Danish lipid clinics were invited for study participation and >97% (n = 1488) accepted. The Dutch Lipid Clinical Network criteria were used to diagnose clinical FH. The decision of genetic testing for FH was based on local practice. Results: A total of 1243 individuals were referred, of whom 25.9% were diagnosed with genetic and/or clinical FH. In individuals genetically tested (n = 705), 21.7% had probable or definite clinical FH before testing, a percentage that increased to 36.9% after genetic testing. In individuals with unlikely and possible FH before genetic testing, 24.4% and 19.0%, respectively, had a causative pathogenic variant. Conclusions: In a Danish nationwide study, genetic testing increased a diagnosis of FH from 22% to 37% in patients referred with hypercholesterolaemia suspected of having FH. Importantly, approximately 20% with unlikely or possible FH, who without genetic testing would not have been considered having FH (and family screening would not have been undertaken), had a pathogenic FH variant. We therefore recommend a more widespread use of genetic testing for evaluation of a possible FH diagnosis and potential cascade screening.

In this study, the Indian medicinal herb Homalium zeylanicum benth was tested for its ability to cure wounds. A variety of solvents have been used to extract the leaves of the plant. Leaf extract was used for wound healing research both in the laboratory and in animals. HaCaT cells were used to investigate the cytotoxicity of plant extracts, and the IC50 values for the various leaf ethyl acetate extracts were determined. All doses of the test samples indicated reduced cytotoxicity on the tested cell line. Scratch assays were used to measure the migration rates of HaCaT cells. During an in vivo study, animals treated with ethyl acetate leaf extract had higher percentages of wound contraction power and tensile strength.. According to this study, the plant's leaf extract may have wound-healing properties, which is in line with its traditional medicinal uses.

Mining activities is one of the most significant sources of radiation exposure from long-lived naturally occurring radioactive materials (NORMs), and have resulted in unjustifiable public exposure doses which contravene radiation protection standards. As a result, estimating the extent of such exposure is essential for keeping the recommended public dose limit. This study therefore aims at estimating the public radiation dose around mining areas and its environs in some selected cities across Nigeria. Data on measured activity concentration of NORMs radionuclides comprising of U-238, Ra-226, Th-232 and K-40 from soil round mining areas in some selected cities in Nigeria were extracted from previous literatures and are used for public dose estimation using RESRAD computer code. From the calculated mean activity concentration, the results from each mining locations shows the maximum dose are lower than International Commission on Radiological Protection (ICRP), commended public dosage boundary of 1 mSv/yr with highest dose of 0.91 mSv/yr from Ra-226, Th-232 and K-40 over the period of 47.9 years reported from Gura Top mining site in Jos, Plateau State, while the lowest dose of 0.09 mSv/yr each over the period of 14.06 and 20.27 years occurred at Itagunmodi and Arufu sites for U-238, Th-232 and K-40. The highest dose recorded in Gura Top was due to numerous mining activities in the region. According to the ICRP, there is no safe level of radiation exposure. Therefore, there is need for competent authorities to conduct periodic assessment of radiation exposure from mining sites to ensure that all exposure emanating as a result of such activities are kept below the prescribed dose limit in accordance to the principle of As Low As Reasonably Achievable (ALARA), thereby ensuring public protection from unjustified radiation exposure.

Biochar, as a carbon-rich material, may have a notable influence on carbon balance, especially that in soil mediums. The oxidation of biochar modifies the biochar’s effects on the soil’s carbon dynamics. To evaluate the alteration in soil carbon storage, biochars derived from wheat straw (WS) and wood residues (WR) produced at 350, 450, and 550 °C (marked BWS350, BWS450, BWS550, BWR350, BWR450, and BWR550) were oxidized with hydrogen peroxide (H2O2) and applied on a loamy soil (2% d.m.) for a 180-day greenhouse incubation period. The highest organic carbon (OC) concentration and carbon pool index (CPI) were obtained from the oxidized BWS550, with 154% and 70% increases, respectively, compared to the unamended control. For both the WS and WR biochars, applying oxidation significantly improved the soil’s aggregation indices, i.e., the mean weight diameter (MWD), water stable aggregates (WSA), and fractal dimension (D). BWS350, BWS450, and BWS550 showed significantly higher WSAs, with percentages of 68, 74, and 76% compared to the control (41%). The fractal dimensions decreased with an increasing pyrolysis temperature in both the biochar types. All the biochar treatments significantly decreased the soil bulk density (BD), while for both the pristine and oxidized biochars, the lowest BD was related to the biochars produced at high temperatures. The structural qualities of the biochars were enhanced by oxidation, particularly their specific surface areas and porosities, and this had a substantial impact on the soil structure and carbon status. The wheat straw biochar was more effective than the wood residue biochar and a higher pyrolysis temperature was more effective than lower ones for supporting the enhancement of the soil carbon pool.

Abattoirs produce by-products that may become valuable resources for nutrient recycling and energy generation by including pyrolysis and biogas production in the value creation chain. This study investigated the potential of bone chars as sorbents for ammonium in order to produce a soil amendment useful for fertilizing purposes. Ammonium enriched from the digestate by membrane distillation or from pure ammonium sulphate solutions accommodated the nitrogen sorption to the bone chars. The plant availability of the sorbed nitrogen was studied by a standardized short-term plant test with rye (Secale cereale L.). The results showed that ammonium, both from biogas digestate and from pure salt solutions, could be sorbed successfully to the bone chars post-pyrolysis and increased the nitrogen concentration of the chars (1.6 ± 0.3%) by 0.2–0.4%. This additional nitrogen was desorbed easily and supported plant growth (+17 to +37%) and plant nitrogen uptake (+19–74%). The sorption of ammonium to the bone chars had a positive effect on the reversal of pure bone char phytotoxicity and on nitrogen availability. In summary, this study showed that abattoir wastes are useful pyrolysis input materials to produce bone chars and to provide ammonium source for sorption to the chars. This innovation offers the possibility to produce nitrogen-enriched bone chars as a new type of fertilizer that upgrades the known value of bone char as phosphorus fertilizer by an additional nitrogen fertilizer effect.

Background A key decision in the treatment of atrial fibrillation is choosing between a rhythm control strategy or a rate control strategy as the main strategy. When choosing rate control, the optimal heart rate target is uncertain. The Danish Atrial Fibrillation trial is a randomized, multicenter, two-group, superiority trial comparing strict rate control versus lenient rate control in patients with either persistent or permanent atrial fibrillation at inclusion. To prevent bias arising from selective reporting and data-driven analyses, we developed a predefined description of the statistical analysis. Methods The primary outcome of this trial is the physical component score of the SF-36 questionnaire. A total of 350 participants will be enrolled based on a minimal important difference of 3 points on the physical component score of the SF-36 questionnaire, a standard deviation of 10 points, a statistical power of 80% (beta of 20%), and an acceptable risk of type I error of 5%. All secondary, exploratory, and echocardiographic outcomes will be hypothesis-generating. The analyses of all outcomes will be based on the intention-to-treat principle. We will analyze continuous outcomes using linear regression adjusting for “site,” type of atrial fibrillation at inclusion (persistent/ permanent), left ventricular ejection fraction (≥ 40% or p-value of 0.05 and assessments of clinical significance will be based on the anticipated intervention effects defined in the sample size and power estimations. Thresholds for both statistical and clinical significance will be assessed according to the 5-step procedure proposed by Jakobsen and colleagues. Discussion This statistical analysis plan will be published prior to enrolment completion and before any data are available and is sought to increase the validity of the DANish Atrial Fibrillation trial. Trial registration Clinicaltrials.gov NCT04542785. Registered on Sept 09, 2020.

Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for better understanding our climate system. Global TWSAs can be simulated by hydrological models with high spatial resolution but limited accuracy or measured by the Gravity Recovery and Climate Experiment (GRACE) satellite mission with opposite characteristics. In this study, we designed a self-supervised data assimilation algorithm with a novel loss function, which combines the advantages of different TWSA sources. The determined global TWSAs have the same spatial resolution as the hydrological simulations (0.5°) and preserve the large-scale accuracy inherited from the GRACE measurements. They contribute to monitoring natural hazards locally and show potential for better understanding the impacts of natural and anthropogenic activities on the water cycle. We anticipate our approach to be generally applicable to other TWSA data sources and the resulting products to be valuable for the geoscience community and society.

Asthma heterogeneity complicates the search for targeted treatment against airway inflammation and remodeling. We sought to investigate relations between eosinophilic inflammation, a phenotypic feature frequent in severe asthma, bronchial epithelial transcriptome, and functional and structural measures of airway remodeling. We compared epithelial gene expression, spirometry, airway cross-sectional geometry (computed tomography), reticular basement membrane thickness (histology), and blood and bronchoalveolar lavage (BAL) cytokines of n = 40 moderate to severe eosinophilic (EA) and non-eosinophilic asthma (NEA) patients distinguished by BAL eosinophilia. EA patients showed a similar extent of airway remodeling as NEA but had an increased expression of genes involved in the immune response and inflammation (e.g., KIR3DS1), reactive oxygen species generation (GYS2, ATPIF1), cell activation and proliferation (ANK3), cargo transporting (RAB4B, CPLX2), and tissue remodeling (FBLN1, SOX14, GSN), and a lower expression of genes involved in epithelial integrity (e.g., GJB1) and histone acetylation (SIN3A). Genes co-expressed in EA were involved in antiviral responses (e.g., ATP1B1), cell migration (EPS8L1, STOML3), cell adhesion (RAPH1), epithelial–mesenchymal transition (ASB3), and airway hyperreactivity and remodeling (FBN3, RECK), and several were linked to asthma in genome- (e.g., MRPL14, ASB3) or epigenome-wide association studies (CLC, GPI, SSCRB4, STRN4). Signaling pathways inferred from the co-expression pattern were associated with airway remodeling (e.g., TGF-β/Smad2/3, E2F/Rb, and Wnt/β-catenin).

Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System (GNSS) data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: they are either too complicated for implementation or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modelling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial-domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total five short baselines were formed and multipath errors were extracted from the posfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data. We conclude that the ML-based multipath mitigation method is effective, easy-to-use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio (SNR), during model training.

In this study, we experimentally and numerically investigate the evolution of the tensile material properties of Discontinuous Fiber Composites (DFCs) with an increasing average number of platelets through the thickness for two different platelet widths. The results show that both the number of platelets and the platelet width have significant effects on the tensile modulus and strength. We find that not only the average mechanical properties but also their coefficients of variation change according to the different DFC mesostructures. To understand the relationship between material morphology at the mesoscale and corresponding material properties, we developed a random platelet mesostructure generation algorithm combined with explicit finite element models. Leveraging the computational tools, we find that moduli and strength increase with increasing average number of platelets through the thickness. The increasing trend continues until reaching an asymptotic limit at about 45 layers through the thickness for the narrow platelets and 27 layers for the square platelets. In the study, we address the importance of having accurate simulations of the mesostructure to match not only the average modulus and strength but also their associated coefficients of variation. We show that it is possible to accurately predict the tensile material properties of DFCs, including their B-basis design values. This is a quintessential condition for the adoption of DFCs in structural applications.

Very Long Baseline Interferometry (VLBI) experiments are organized within the geodetic, astrometric and astronomical communities for different applications, requiring different observation strategies adopted in scheduling. Currently, the next-generation geodetic and astrometric VLBI Global Observing System (VGOS) is being established. Over the last years, evidence was presented that the delays introduced by the angular structure of the geodetic radio sources contribute significantly to the VGOS observable error budget. Consequently, correcting these structure delays through imaging will play an important part in the future, requiring a different scheduling approach. Within this work, a new source-centric VLBI scheduling approach is presented for improved imaging capabilities of geodetic observations. The algorithm is tested for a seven- and nine-station network and compared with classical geodetic schedules. Monte Carlo simulations are utilized to determine the expected geodetic and astrometric parameter precision, and two independent processing pipelines are used to assess the potential for astronomical source imaging. Based on the simulation results, it is revealed that with the new scheduling approach twice as many sources can be properly imaged. Furthermore, the precision of the Earth orientation parameter estimates is improved on average by 15 % , while the source position coordinate estimates are on average improved by 50 %. Tests with two VGOS networks of twelve and 29 antennas further reveal that the scheduling approach is also applicable to future VGOS networks., Journal of Geodesy, 97 (2), ISSN:0949-7714, ISSN:1432-1394

Microfluidic paper-based analytical devices (microPADs) are emerging as simple-to-use, low-cost point-of-care testing platforms. Such devices are mostly fabricated at present by creating hydrophobic barriers using wax or photoresist patterning on porous paper sheets. Even though devices fabricated using these methods are used and tested with a wide variety of analytes, still they pose many serious practical limitations for low-cost automated mass fabrication for their widespread applicability. We present an affordable and simple two-step process - cut and heat (CH-microPADs) - for the selective fabrication of hydrophilic channels and reservoirs on a wide variety of porous media such as tissue/printing/filter paper and cloth types, such as cotton and polyester, by a lamination process. The technique presents many advantages as compared to existing commonly used methods. The devices possess excellent mechanical strength against bending, folding and twisting, making them virtually unbreakable. They are structurally flexible and show good chemical resistance to various solvents, acids and bases, presenting widespread applicability in areas such as clinical diagnostics, biological sensing applications, food processing, and the chemical industry. Fabricated paper media 96 well-plate CH-microPAD configurations were tested for cell culture applications using mice embryonic fibroblasts and detection of proteins and enzymes using ELISA. With a simple two-step process and minimal human intervention, the technique presents a promising step towards mass fabrication of inexpensive disposable diagnostic devices for both resource-limited and developed regions.

The study was carried out to document the medicinal plants used by the traditional healers in Mayannur Forest, Thrissur District, Kerala, India. In the present study, 107 plant species belonging to 46 families were documented in the Mayannur forest, Kerala. The informants of an age group ranging from 50-80 were selected for collecting data of the plants in the forest. The medicinal plants such as herbs (36%), shrub (19%), trees (27%) and climbers (18%) were mainly used by traditional healers for the treatment of fever, wound healing, skin diseases and menstrual problems. Saraca asoca was mainly in the traditional medicines to cure diseases and for treating menstrual problems. Medicinal plants used by the traditional healers were documented along with their scientific name, common name and medicinal uses. The present study shows that, the plant material mainly used was leaves for the treatment of diseases. Among the 107 plant species Rare Endangered Threatened (RET) plant species were also documented. Documentation of knowledge and conservation of the endangered plants helped for the sustainable development.

The study showed novel findings about changes in the fate and bioavailability of conazole fungicides (CFs) after biochar (BC) addition to soil. Two contrasting soils (low- and high-sorbing of CF; L soils, H soils) were amended by three BCs (low-, moderate-, and high-sorbing of CF; L-BC, M-BC, H-BC) at 0.2% and 2% doses. Epoxiconazole (EPC) and tebuconazole (TBC) were then added to the soil–BC mixtures, and their degradation, bioaccumulation in earthworms (Eisenia andrei), and bioconcentration in lettuce (Lactuca sativa) were studied for three months. Also, stir bar sorptive extraction (SBSE) was performed to determine CF (bio)accessibility. The EPC and TBC degradation in the soil–BC mixtures followed usually the first-order decay kinetics. The BC addition prevalently decreased the pesticides degradation in the L soil mixtures but often increased it in the H soil mixtures. In general, EPC degraded less than TBC. BC type and dose roles in the pesticides degradation were unclear. The BC addition significantly reduced pesticide uptake to the earthworms in the L soil mixtures (by 37–96%) and in the H soil mixtures (by 6–89%) with 2% BC. The BC addition reduced pesticide uptake to the lettuce roots and leaves significantly—up to two orders of magnitude, and this reduction was strong in H soil mixtures at 2% of BC. The BC addition reduced the CF (bio)accessibility measured by SBSE in all L soil mixtures and some H soil mixtures with 2% BC. Although not significant, it also seems that the pesticide bioaccumulation, bioconcentration, and (bio)accessibility were decreasing according to the BC type (L-BC > M-BC > H-BC). The pesticide concentrations in the earthworms and lettuce correlated significantly to the SBSE results, which indicates this technique as a possible predictor of biotic uptake. Our results showed that the interactions were hard to predict in the complex soil–BC–pesticide system.

Air quality models are used to assess the impact of smoke from wildland fires, both prescribed and natural, on ambient air quality and human health. However, the accuracy of these models is limited by uncertainties in the parametrisation of smoke plume injection height (PIH) and its vertical distribution. We compared PIH estimates from the plume rise method (Briggs) in the Community Multiscale Air Quality (CMAQ) modelling system with observations from the 2013 California Rim Fire and 2017 prescribed burns in Kansas. We also examined PIHs estimated using alternative plume rise algorithms, model grid resolutions and temporal burn profiles. For the Rim Fire, the Briggs method performed as well or better than the alternatives evaluated (mean bias of less than ±5–20% and root mean square error lower than 1000 m compared with the alternatives). PIH estimates for the Kansas prescribed burns improved when the burn window was reduced from the standard default of 12 h to 3 h. This analysis suggests that meteorological inputs, temporal allocation and heat release are the primary drivers for accurately modelling PIH.