bluuebunny/crossref_metadata_2025 huggingface dataset:
The Injection Molding of Biodegradable Polydioxanone—A Study of the Dependence of the Structural and Mechanical Properties on Thermal Processing Conditions
Authors: Erben Jakub , Blatonova Katerina , Kalous Tomas , Capek Lukas , Behalek Lubos , Boruvka Martin , Chvojka Jiri
Published: December 2022

Recent years have observed a significant increase in the use of degradable materials in medicine due to their minimal impact on the patient and broad range of applicability. The biodegradable polymer Polydioxanone (PDO) provides a good example of the use of such one polymer that can represent the aforementioned medical materials in the field of medicine, due to its high level of biocompatibility and interesting mechanical properties. PDO is used to produce absorbable medical devices such as sutures and stents, and is also suitable for the fabrication of certain orthopedic implants. Polydioxanone can be processed using the injection molding method due to its thermoplastic nature; this method allows for the precise and easily-controllable production of medical materials without the need for toxic additives. A number of small commercial polymer implants have recently been introduced onto the market based on this processing method. It is important to note that, to date, no relevant information on the molding of PDO is available either for the scientific or the general public, and no study has been published that describes the potential of the injection molding of PDO. Hence, we present our research on the basic technological and material parameters that allow for the processing of PDO using the laboratory microinjection molding method. In addition to determining the basic parameters of the process, the research also focused on the study of the structural and mechanical properties of samples based on the thermal conditions during processing. A technological frame work was successfully determined for the processing of PDO via the microinjection molding approach that allows for the production of samples with the required homogeneity, shape stability and surface quality in a laboratory scale. The research revealed that PDO is a polymer with a major share of crystalline phases, and that it is sensitive to the annealing temperature profile in the mold, which has the potential to impact the final crystalline structure, the fracture morphology and the mechanical properties.
Modification of Commercial 3D Fused Deposition Modeling Printer for Extrusion Printing of Hydrogels
Authors: Koltsov Semyon I. , Statsenko Tatiana G. , Morozova Sofia M.
Published: December 2022

In this paper, we report a simple modification of a commercially available printer with fused deposition modeling (FDM) technology for the implementation of extrusion printing of hydrogels. The main difference between an FDM printer and a gel-extrusion printer is their material propulsion system, which has to deal with ether a solid rod or liquid. By application of plastic 3D printing on an FDM printer, specific details, namely, the plunger system and parts of the gel supply system, were produced and combined with a modified printer. Two types of printing of polymer hydrogels were optimized: droplet and filament modes. The rheological ranges suitable for printing for each method were indicated, and the resolution of the samples obtained and the algorithms for creating g-code via Python scripts were given. We have shown the possibility of droplet printing of microspheres with a diameter of 100 microns and a distance between spheres of 200 microns, as well as filament printing of lines with a thickness of 300–2000 microns, which is appropriate accuracy in comparison with commercial printers. This method, in addition to scientific groups, will be especially promising for educational tasks (as a practical work for engineering students or for the introduction of 3D printing into school classes) and industrial groups, as a way to implement 3D extrusion printing of composite polymer hydrogels in a time- and cost-effective way.
The Impact of Isocyanate Index and Filler Functionalities on the Performance of Flexible Foamed Polyurethane/Ground Tire Rubber Composites
Authors: Olszewski Adam , Kosmela Paulina , Piasecki Adam , Barczewski Mateusz , Hejna Aleksander
Published: December 2022

The structure and performance of polyurethane (PU) foams are strongly driven by the stoichiometry of the polyaddition reaction, quantitatively described by the isocyanate index. It determines the balance between isocyanate and hydroxyl groups in the reacting system and is affected by the introduction of additional functionalities originated, e.g., from applied fillers. Nevertheless, this issue is hardly taken into account in research works. Herein, the structure and performance of PU/ground tire rubber (GTR) composites differing in their isocyanate index (from 0.8 to 1.2) and prepared with and without considering the GTR functionalities in formulation development were investigated. Incorporating GTR into the PU matrix led to a reduction in average cell diameter (from 2 to 30% depending on the isocyanate index) compared to unfilled foams. However, formulation adjustments did not show a significant impact on cellular structure. The only decrease in open cell content was noted, from 10% for the 0.9 index to 40% for 1.2. Such changes were related to the increasing strength of the PU cellular structure able to maintain inside the increasing amount of carbon dioxide. On the other hand, considering hydroxyl values of GTR noticeably affected the thermomechanical performance of composites. The shift of glass transition temperature (Tg), even by 10 °C for 1.2 isocyanate index, enhanced the performance of materials, which was expressed in an 8–62% drop in the composite performance factor, pointing to the enhanced reinforcing effect resulting from filler incorporation. The stiffening of foams, related to the variations in PU segmental structure, also caused minor changes in the course of thermal degradation of PU/GTR composites due to the inferior thermal stability of hard segments. The obtained results provide important insights into the development of formulations of PU composites filled with materials containing reactive functional groups able to disrupt the stoichiometric balance of the polyaddition reaction.
Development of Co-Amorphous Loratadine–Citric Acid Orodispersible Drug Formulations
Authors: Rédai Emőke Margit , Sipos Emese , Vlad Robert Alexandru , Antonoaea Paula , Todoran Nicoleta , Ciurba Adriana
Published: December 2022

This study aimed at the preparation and characterization of co-amorphous loratadine–citric acid orally disintegrating dosage forms (ODx). A co-amorphous loratadine–citric acid was prepared by solvent evaporation method in three different molecular ratios. DSC, FTIR, and dissolution studies have been conducted for the binary system. The co-amorphous system was used to obtain oral lyophilizates and orally disintegrating tablets by direct compression. Diameter, thickness, hardness, disintegration time, uniformity of mass, and dissolution was determined for the dosage forms. DSC curves showed a lack of sharp endothermic peaks for the binary systems. FTIR spectra presented a hypsochromic modification of the characteristic peaks. Dissolution studies indicated a five-fold increase in the dissolved amount compared to pure loratadine in water. Disintegration times of direct compression ODx varied in the range of 34–41 s and for freeze-dried ODx in the range of 8–9 s. Friability was under 1% in all cases. The dissolution of loratadine in buffer solution at pH = 1 was almost complete. In conclusion binary systems of loratadine and citric acid enhance solubility and combined with the orally disintegrating pharmaceutical form also increase patient compliance.
Agent-Based and Stochastic Optimization Incorporated with Machine Learning for Simulation of Postcombustion CO2 Capture Process
Authors: Zheng Huilan , Mirlekar Gaurav , Nord Lars
Published: December 2022

In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is illustrated via the chemical absorption-based postcombustion CO2 capture process, which plays an important role in the reduction of CO2 emissions to address climate challenges. These processes simulated in a software environment are typically based on first-principle models and calculate physical properties from basic physical quantities such as mass and temperature. Employing first-principle models usually requires a long computation time, making process optimization and control challenging. To overcome this challenge, in this study, machine learning algorithms are used to simulate the postcombustion CO2 capture process. The extreme gradient boosting (XGBoost) and support vector regression (SVR) algorithms are employed to build models for prediction of carbon capture rate (CR) and specific reboiler duty (SRD). The R2 (a statistical measure that represents the fitness) of these models is, on average, greater than 90% for all the cases. XGBoost and SVR take 0.022 and 0.317 s, respectively, to predict CR and SRD of 1318 cases, whereas the first-principal process simulation model needs 3.15 s to calculate one case. The models built by XGBoost are employed in the optimization methods, such as an agent-based approach represented by the particle swarm optimization and stochastic technique indicated by the simulated annealing, to find specific optimal operating conditions. The most economical case, in which the CR is 72.2% and SRD is 4.3 MJ/kg, is obtained during optimization. The results show that computations with the data-driven models incorporated in the optimization technique are faster than first-principle modeling approaches. Thus, the application of machine learning techniques in the optimization of carbon capture technologies is demonstrated successfully.
Temperature-Related N2O Emission and Emission Potential of Freshwater Sediment
Authors: Li Shuai , Yue Ang , Moore Selina Sterup , Ye Fei , Wu Jiapeng , Hong Yiguo , Wang Yu
Published: December 2022

Nitrous oxide (N2O) is a major radiative forcing and stratospheric ozone-depleting gas. Among natural sources, freshwater ecosystems are significant contributors to N2O. Although temperature is a key factor determining the N2O emissions, the respective effects of temperature on emitted and dissolved N2O in the water column of freshwater ecosystems remain unclear. In this study, 48 h incubation experiments were performed at three different temperatures; 15 °C, 25 °C, and 35 °C. For each sample, N2O emission, dissolved N2O in the overlying water and denitrification rates were measured, and N2O-related functional genes were quantified at regular intervals. The highest N2O emission was observed at an incubation of 35 °C, which was 1.5 to 2.1 factors higher than samples incubated at 25 °C and 15 °C. However, the highest level of dissolved N2O and estimated exchange flux of N2O were both observed at 25 °C and were both approximately 2 factors higher than those at 35 °C and 15 °C. The denitrification rates increased significantly during the incubation period, and samples at 25 °C and 35 °C exhibited much greater rates than those at 15 °C, which is in agreement with the N2O emission of the three incubation temperatures. The NO3− decreased in relation to the increase of N2O emissions, which confirms the dominant role of denitrification in N2O generation. Indeed, the nirK type denitrifier, which constitutes part of the denitrification process, dominated the nirS type involved in N2O generation, and the nosZ II type N2O reducer was more abundant than the nosZ I type. The results of the current study indicate that higher temperatures (35 °C) result in higher N2O emissions, but incubation at moderate temperatures (25 °C) causes higher levels of dissolved N2O, which represent a potential source of N2O emissions from freshwater ecosystems.
Numerical Modelling and Validation of Mixed-Mode Fracture Tests to Adhesive Joints Using J-Integral Concepts
Authors: Neves Luís F. R. , Campilho Raul D. S. G. , Sánchez-Arce Isidro J. , Madani Kouder , Prakash Chander
Published: December 2022

The interest in the design and numerical modelling of adhesively-bonded components and structures for industrial application is increasing as a research topic. Although research on joint failure under pure mode is widespread, applied bonded joints are often subjected to a mixed mode loading at the crack tip, which is more complex than the pure mode and affects joint strength. Failure of these joints under loading is the objective of predictions through mathematical and numerical models, the latter based on the Finite Element Method (FEM), using Cohesive Zone Modelling (CZM). The Single leg bending (bending) testing is among those employed to study mixed mode loading. This work aims to validate the application of FEM-CZM to SLB joints. Thus, the geometries used for experimental testing were reproduced numerically and experimentally obtained properties were employed in these models. Upon the validation of the numerical technique, a parametric study involving the cohesive laws’ parameters is performed, identifying the parameters with the most influence on the joint behaviour. As a result, it was possible to numerically model SLB tests of adhesive joints and estimate the mixed-mode behaviour of different adhesives, which enables mixed-mode modelling and design of adhesive structures.
Synthesis and Characterization of Nanoformulation of the Broad-Spectrum Enzyme Inhibitor Mancozeb by Polyethylene Glycol Capping and Its Dissipation Kinetics in Water Using TiO2 Nanoparticles
Authors: Daqa Wafa Mahmoud , Alshoaibi Adil , Ahmed Faheem , Rao Tentu Nageswara
Published: December 2022

The poly(ethylene) glycol (PEG) capped mancozeb nanoformulation was prepared by the ultrasonic method using a 1% mancozeb solution and 20% capping agent, PEG-4000. The synthesized nanoformulation was characterized using UV-visible, FTIR, SEM and TEM techniques. The photolytic and photo catalytic experiments were carried out in a Borosil glass bottle in the presence of sunlight, varying the pH proportions at a single fortification level (1.0 g/mL) in ground water, under sunlight. The optimal catalyst concentration for complete degradation was observed to be 0.1 percent. The mancozeb nanoformulation in water was determined using the HPLC-PDA method, and the rate constant and the 50% degradation (DT50) values were calculated based on the results. The photolytic results show that there is no significant loss of residues due to adsorption. Titanium dioxide (TiO2) was discovered to be an excellent decontaminating catalyst in a variety of water samples. The compound survives for several days in the absence of a catalyst.
Bt and G10evo-EPSPS Protein Expressed in ZDAB3 Corn Has No Impact on Nutritional Composition and Toxicological Safety
Authors: Yu Xiaoxing , Chen Guo , Zhou Ziying , Chen Xiaoyun , He Xiaoyun , Jiao Yue , Wang Pengfei
Published: December 2022

Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins (Cry1Ab and Cry2Ab) and EPSPS protein (G10evo-EPSPS). The key nutrients and anti-nutrients of ZDAB3 maize were examined and contrasted with those of its non-transgenic control maize grown at the same locations during three planting seasons. The values for proximates, amino acids, fatty acids, minerals, vitamins, phytic acid, and trypsin inhibitor assessed for ZDAB3 were comparable to those of its non-transgenic control maize or within the range of values reported for other commercial lines. In addition, no adverse effects related to the G10evo-EPSPS protein in mammals were observed. These data indicated that the expression of Cry1Ab, Cry2Ab, and G10evo-EPSPS proteins in ZDAB3 maize does not affect the nutritional compositions, and ZDAB3 maize is equivalent to non-transgenic maize regarding those important compositions.
Evaluation of the English Version of the Smartphone Application-Based Addiction Scale (SABAS) among an Adolescent Sample
Authors: Mason Andre , Winter Taylor , Riordan Benjamin C. , Griffiths Mark D. , Scarf Damian
Published: December 2022

The increased use of smartphones among adolescents has highlighted the need to distinguish between problematic and non-problematic smartphone use. To date, there are a lack of short, easy-to-use, and valid psychometric tools to assess smartphone addiction. The primary aim of the present study was to conduct the first psychometric assessment and evaluation of the Smartphone Application-Based Addiction Scale (SABAS) among English-speaking adolescents. Participants were 1175 students recruited from across four different high schools in New Zealand, of which 1031 completed all questions and were used in the final analyses. Several psychometric tests were conducted to ascertain reliability and validity. The SABAS had high internal consistency. Consistent with earlier validation studies, the SABAS displayed weak-moderate, positive relationships with symptoms of depression (PHQ-2), anxiety (GAD-2), and sleep quality. Overall, the SABAS is a sound, unidimensional scale with robust psychometric properties and is a short and easy-to-use measure that can be used confidently among English-speaking adolescents.
Music and Spirituality in Africa: Gospel Music, Spirituality, and Everyday Meaning-Making in Nigeria
Authors: Ayorinde Oladele , Ajose Toyin Samuel
Published: December 2022

This article explores gospel music as one of the ways people negotiate spirituality and everyday meaning-making in Lagos. Beyond sonic spheres and analysis, this article provides insight into ways in which people ‘perform’ spirituality and negotiate wellbeing amidst Nigeria’s social, political, and economic uncertainty through a focus on the ‘celebrity’ character and self-fashioning of one of Nigeria’s gospel music stars, Tope Alabi. Gospel music and its infrastructures of modernity constitute one of the ways Nigerians negotiate wellbeing and respond to global economic tensions ‘from below’. We explore the nexus between gospel music and how the ‘spirituality’ it facilitates shapes people’s subjective ideas of social and economic wellbeing. We ask: what is the link between gospel music, spirituality, and people’s everyday meaning-making and self-making? Using Harry Garuba’s animist unconscious’, we explore ways in which the social life and superstar image of Nigerian ‘celebrity’ gospel musicians constitute sites where people negotiate spirituality and everyday subjective happiness, and social and economic wellbeing. We argue that spirituality, ‘being spiritual’ or the understanding thereof does not only manifest at the intersections of sound and emotion. Instead, we suggest that people’s subjective idea of spirituality or ‘being spiritual’ in a place such as Lagos can also be understood through a focus on the social life and the self-fashioning of gospel musicians. The self-fashioning and superstar image of gospel musicians become a medium through which the everyday idea of spirituality and meaning-making is negotiated, staged, and performed, and a channel through which these processes of meaning-making can be explored and understood.
Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis
Authors: Petrova Mariana , Tairov Iskren
Published: December 2022

The purpose of this paper was to investigate technologies, methods, and approaches that can be used to effectively manage smart city risks in the context of the COVID-19 pandemic. The paper was based on a review of specialized literature sources and expert statements on smart cities in times of crisis, specifically during COVID-19. A systematic literature review served as the research’s methodological foundation; this was supplemented by conceptual data analysis techniques and a modeling method. Our initial search yielded 234 research articles, 38 of which met our inclusion criteria and were included in the review. A further 32 studies fell outside of the criteria for supporting smart cities’ crisis management. The main findings showed that technologies can respond quickly to pandemic crisis risks while also ensuring the availability of urban functionality and that there are numerous risks in implementing technologies to achieve effective management. The main risks were privacy concerns, social inclusion, political bias, misinformation and fake news, and technical difficulties with education and distance employment. The practical significance of the paper lay in proposing a model based on specific technologies and policies aimed at effective risk management in the days of COVID-19.
Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing
Authors: Kim Junwoo , Kim Hwisong , Kim Duk-jin , Song Juyoung , Li Chenglei
Published: December 2022

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning.
Estimation of Urban Forest Characteristic Parameters Using UAV-Lidar Coupled with Canopy Volume
Authors: Zhang Bo , Li Xuejian , Du Huaqiang , Zhou Guomo , Mao Fangjie , Huang Zihao , Zhou Lv , Xuan Jie , Gong Yulin , Chen Chao
Published: December 2022

The estimation of characteristic parameters such as diameter at breast height (DBH), aboveground biomass (AGB) and stem volume (V) is an important part of urban forest resource monitoring and the most direct manifestation of the ecosystem functions of forests; therefore, the accurate estimation of urban forest characteristic parameters is valuable for evaluating urban ecological functions. In this study, the height and density characteristic variables of canopy point clouds were extracted as Scheme 1 and combined with the canopy structure variables as Scheme 2 based on unmanned aerial vehicle lidar (UAV-Lidar). We analyzed the spatial distribution characteristics of the canopies of different tree species, and multiple linear regression (MLR), support vector regression (SVR), and random forest (RF) models were used to estimate the DBH, AGB, and V of urban single trees. The estimation accuracy of different models was evaluated based on the field-measured data. The results indicated that the model accuracy of coupling canopy structure variables (R2 = 0.69–0.85, rRMSE = 9.87–24.67%) was higher than that of using only point-cloud-based height and density characteristic variables. The comparison of the results of different models shows that the RF model had the highest estimation accuracy (R2 = 0.76–0.85, rRMSE = 9.87–22.51%), which was better than that of the SVR and MLR models. In the RF model, the estimation accuracy of AGB was the highest (R2 = 0.85, rRMSE = 22.51%), followed by V, with an accuracy of R2 = 0.83, rRMSE = 18.51%, and the accuracy of DBH was the lowest (R2 = 0.76, rRMSE = 9.87%). The results of the study provide an important reference for the estimation of single-tree characteristic parameters in urban forests based on UAV-Lidar.
Possible Overestimation of Nitrogen Dioxide Outgassing during the Beirut 2020 Explosion
Authors: Farahat Ashraf , El-Kork Nayla , Singh Ramesh P. , Jing Feng
Published: December 2022

On 4 August 2020, a strong explosion occurred near the Beirut seaport, Lebanon and killed more than 200 people and damaged numerous buildings in the vicinity. As Amonium Nitrate (AN) caused the explosion, many studies claimed the release of large amounts of NO2 in the atmosphere may have resulted in a health hazard in Beirut and the vicinity. In order to reasonably evaluate the significance of NO2 amounts released in the atmosphere, it is important to investigate the spatio-temporal distribution of NO2 during and after the blast and compare it to the average day-to-day background emissions from vehicle and ship traffic in Beirut. In the present study, we use Sentinel-5 TROPOMI data to study NO2 emissions in the atmosphere close to the affected area prior, during, and after the Beirut explosion (28 July–8 August 2020). Analysis shows an increase in NO2 concentrations over Beirut up to about 1.8 mol/m2 one day after the explosion that was gradually dissipated in about 4 days. Seven days before the blast (on 28 July 2020) NO2 concentration was, however, observed to be up to about 4.3 mol/m2 over Beirut, which is mostly attributed to vehicle emissions in Lebanon, ships passing by the Beirut seaport and possibly the militant activities in Syria during 20–26 July. It is found that the Beirut blast caused a temporarily and spatially limited increase in NO2. The blast mostly affected the coastal areas in Lebanon, while it did not have much effect on inland regions. TROPOMI data are also analyzed for the Greater Cairo Area (GCA), Suez Canal, Egypt, and in Nicosia, Cyprus to confirm the effect of human activities, vehicles, and ship traffic on NO2 emissions in relatively high and relatively low populated zones.
Study on Radiative Flux of Road Resolution during Winter Based on Local Weather and Topography
Authors: Kwon Hyuk-Gi , Yang Hojin , Yi Chaeyeon
Published: December 2022

Large-scale traffic accidents caused by black ice on roads have increased rapidly; hence, there is an urgent need to prepare safety measures for their prevention. Here, we used local weather road observations and the linkage between weather prediction and a radiation flux model (LDAPS-SOLWEIG) to calculate prediction information regarding habitual shade areas, sky view factor (SVF), and downward shortwave radiative flux by road direction and lane. Using the LDAPS-SOLWEIG model system, a set of real-time weather prediction data (temperature, humidity, wind speed, and insolation at 1.5 km resolution) was applied, and 5 m resolution radiative flux prediction data, with road resolution blocked by local weather and topography, were calculated. We found that the habitual shaded area can be divided by the direction and lane of the road according to the height and shape of the terrain around the road. The downward shortwave radiation flux data from local meteorological observation data and that calculated from the LDAPS-SOLWEIG model system were compared. When road-freezing occurred on a case day, the RMSE was 20.41 W·m−2, MB was −5.04 W·m−2, and r was 0.78. The calculated information, habitual shaded area, and SVF can highlight road sections vulnerable to winter freezing and can be helpful in the special management of these areas.
Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests
Authors: Mueller Marlin M. , Dubois Clémence , Jagdhuber Thomas , Hellwig Florian M. , Pathe Carsten , Schmullius Christiane , Steele-Dunne Susan
Published: December 2022

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios.
Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching
Authors: Chen Chengkai , Lan Jinhui , Liu Haoting , Chen Shuai , Wang Xiaohan
Published: December 2022

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions.
Simulation of the Use of Variance Component Estimation in Relative Weighting of Inter-Satellite Links and GNSS Measurements
Authors: Kur Tomasz , Liwosz Tomasz
Published: December 2022

Inter-satellite links (ISLs) can improve the performance of the Global Navigation Satellite System (GNSS) in terms of precise orbit determination, communication, and data-exchange capabilities. This research aimed to evaluate a simulation-based processing strategy involving the exploitation of ISLs in orbit determination of Galileo satellites, which are not equipped with operational ISLs. The performance of the estimation process is first tested based on relative weighting coefficients obtained with methods of variance component estimation (VCE) varying in the complexity of the calculations. Inclusion of biases in the ISL measurements allows evaluation of the processing strategy and assessment of the impact of three different sets of ground stations: 44 and 16 stations distributed globally and 16 located in Europe. The results indicate that using different VCE approaches might lower orbit errors by up to 20% with a negligible impact on clock estimation. Depending on the applied ISL connectivity scheme, ISL range bias can be estimated with RMS between 10% to 30% of initial bias values. The accuracy of bias estimation may be associated with weighting approach and the number of ground stations. The results of this study show how introducing VCE with various simulation parameters into the processing chain might increase the accuracy of the orbit estimation.
Validating the Crop Identification Capability of the Spectral Variance at Key Stages (SVKS) Computed via an Object Self-Reference Combined Algorithm
Authors: Zhao Hailan , Meng Jihua , Shi Tingting , Zhang Xiaobo , Wang Yanan , Luo Xiangjiang , Lin Zhenxin , You Xinyan
Published: December 2022

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution acquisition. These methods are relatively insensitive to spectral shape or amplitude variation and must reconstruct a reference curve representing the entire class, possibly resulting in notable indeterminacy in the ultimate results. Few studies utilize these methods to compute the spectral variance associated with time and to define a new index for crop identification—namely, the spectral variance at key stages (SVKS)—even though this temporal spectral characteristic could be helpful for crop identification. To integrate the advantages of sensibility and avoid reconstructing the reference curve, an object self-reference combined algorithm comprising ED and SAM (CES) was proposed to compute SVKS. To objectively validate the crop-identification capability of SVKS-CES (SVKS computed via CES), SVKS-ED (SVKS computed via ED), SVKS-SAM (SVKS computed via SAM), and five spectral index (SI) types were selected for comparison in an example of maize identification. The results indicated that SVKS-CES ranges can characterize greater interclass spectral separability and attained better identification accuracy compared to other identification indexes. In particular, SVKS-CES2 provided the greatest interclass spectral separability and the best PA (92.73%), UA (100.00%), and OA (98.30%) in maize identification. Compared to the performance of the SI, SVKS attained greater interclass spectral separability, but more non-maize fields were incorrectly identified as maize fields via SVKS usage. Owning to the accuracy-improvement capability of SVKS-CES, the omission and commission errors were obviously reduced via the combined utilization of SVKS-CES and SI. The findings suggest that SVKS-CES application is expected to further spread in crop identification.
The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement
Authors: Rzonca Antoni , Twardowski Mariusz
Published: December 2022

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study.
Load Estimation Based Dynamic Access Protocol for Satellite Internet of Things
Authors: Yang Mingchuan , Xue Guanchang , Liu Botao , Yang Yupu , Su Yanyong
Published: December 2022

In recent years, the Internet of Things (IoT) industry has become a research hotspot. With the advancement of satellite technology, the satellite Internet of Things is further developed along with a new generation of information technology and commercial markets. However, existing random access protocols cannot cope with the access of a large number of sensors and short burst transmissions. The current satellite Internet of Things application scenarios are divided into two categories, one has only sensor nodes and no sink nodes, and the other has sink nodes. A time-slot random access protocol based on Walsh code is proposed for the satellite Internet-of-Things scenario with sink nodes. In this paper, the load estimation algorithm is used to reduce the resource occupancy rate in the case of medium and low load, and a dynamic Walsh code slot random access protocol is proposed to select the appropriate Walsh code length and frame length h. The simulation results show that the slotted random access protocol based on Walsh code can effectively improve the throughput of the system under high load. The introduction of load estimation in the case of medium and low load can effectively reduce the resource utilization of the system, and ensure that the performance of the access protocol based on Walsh codes does not deteriorate. However, in the case of high load, a large resource overhead is still required to ensure the access performance of the system.
Correction: Jin et al. Influence of the Nocturnal Effect on the Estimated Global CO2 Flux. Remote Sens. 2022, 14, 3192
Authors: Jin Rui , Yu Tan , Tao Bangyi , Shao Weizeng , Hu Song , Wei Yongliang
Published: December 2022

We believe that several sentences in the description of the source (sink) changes of CO2 are prone to ambiguity and are not particularly well presented [...]
Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR
Authors: Zhao Xinglei , Gao Jianfei , Xia Hui , Zhou Fengnian
Published: December 2022

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB.
An Open Surface Drifter for River Flow Field Characterization
Authors: Fuentes-Pérez Juan Francisco , Sanz-Ronda Francisco Javier , Tuhtan Jeffrey A.
Published: December 2022

The continuous observation of flows is required to assess a river’s ecological status, to allocate irrigation withdrawals, to provide sustainable hydropower production and to plan actions as well as develop adaptive management plans. Drifters have the potential of facilitating the monitoring and modeling of river behavior at a fraction of traditional monitoring costs. They are floating objects equipped with sensors able to passively follow the movements of water. During their travel, they collect and transmit information about their movement and their surrounding environment. In this paper, we present and assess a low-cost (<150 EUR) customizable drifter developed with off-the-shelf components. The open drifter is capable of handling the majority of use cases defined in the specialized literature and in addition it offers a general river flow characterization toolkit. One of the main goals of this work is to establish an open hardware and software basis to increase the use of drifters in river studies. Results show that the proposed drifter provides reliable surface velocity estimates when compared to a commercial flow meter, offering a lower cost per data point and in contrast to traditional point measurements it can be used to identify and classify large-scale surface flow patterns. The diverse sensor payload of the open drifter presented in this work makes it a new and unique tool for autonomous river characterization.
Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography
Authors: Liu Xuechao , Zhang Tao , Ye Jian’an , Tian Xiang , Zhang Weirui , Yang Bin , Dai Meng , Xu Canhua , Fu Feng
Published: December 2022

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80–50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.
Electrochemical Organophosphorus Pesticide Detection Using Nanostructured Gold-Modified Electrodes
Authors: Chang Han-Wei , Chen Chien-Lin , Chen Yan-Hua , Chang Yu-Ming , Liu Feng-Jiin , Tsai Yu-Chen
Published: December 2022

In this study, nanostructured gold was successfully prepared on a bare Au electrode using the electrochemical deposition method. Nanostructured gold provided more exposed active sites to facilitate the ion and electron transfer during the electrocatalytic reaction of organophosphorus pesticide (methyl parathion). The morphological and structural characterization of nanostructured gold was conducted using field-emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), which was further carried out to evaluate the electrocatalytic activity towards methyl parathion sensing. The electrochemical performance of nanostructured gold was investigated by electrochemical measurements (cyclic voltammetry (CV) and differential pulse voltammetry (DPV)). The proposed nanostructured gold-modified electrode exhibited prominent electrochemical methyl parathion sensing performance (including two linear concentration ranges from 0.01 to 0.5 ppm (R2 = 0.993) and from 0.5 to 4 ppm (R2 = 0.996), limit of detection of 5.9 ppb, excellent selectivity and stability), and excellent capability in determination of pesticide residue in real fruit and vegetable samples (bok choy and strawberry). The study demonstrated that the presented approach to fabricate a nanostructured gold-modified electrode could be practically applied to detect pesticide residue in agricultural products via integrating the electrochemical and gas chromatography coupled with mass spectrometry (GC/MS-MS) analysis.
Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm
Authors: Chen Ting , Chen Youjing , Li Hao , Gao Tao , Tu Huizhao , Li Siyu
Published: December 2022

With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.