bluuebunny/crossref_metadata_2025 huggingface dataset:
Poly(vinyl alcohol)-tannic Acid Cryogel Matrix as Antioxidant and Antibacterial Material
Authors: Ari Betul , Sahiner Mehtap , Demirci Sahin , Sahiner Nurettin
Published: December 2021

The biocompatible, viscoelastic properties of poly(vinyl alcohol) (PVA) in combination with the antimicrobial and antioxidant natural polyphenolic, tannic acid (TA), and the natural flavonoid and antioxidant curcumin (Cur), were used in the preparation of PVA:TA and PVA:TA:Cur cryogel composites using cryotropic gelation to combine the individually beneficial properties. The effect of TA content on the antioxidant and antimicrobial activities of PVA:TA cryogel composites and the antioxidant activities of PVA:TA:Cur cryogel composites was determined using Trolox equivalent antioxidant capacity (TEAC) and total phenol content (TPC) assays, and were compared. The PVA:TA:Cur cryogel composite showed the highest antioxidant activity, with a TEAC value of 2.10 ± 0.24 and a TPC value of 293 ± 12.00. The antibacterial capacity of the PVA:TA and PVA:TA:Cur 1:1:0.1 cryogel composites was examined against two different species of bacteria, E. coli and S. aureus. It was found that the minimum inhibition concentration (MIC) value of the PVA:TA:Cur 1:1:0.1 cryogel composites varied between 5 and 10 mg/mL based on the type of microorganism, and the minimum bactericidal concentration (MBC) value was 20 mg/mL irrespective of the type of microorganism. Furthermore, the hemocompatibility of the PVA:TA cryogel composites was evaluated by examining their hemolytic and coagulation behaviors. PVA:TA 1:1 cryogels with a value of 95.7% revealed the highest blood clotting index value amongst all of the synthesized cryogels, signifying the potential for blood contacting applications. The release of TA and Cur from the cryogel composites was quantified at different pH conditions, i.e., 1.0, 7.4, and 9.0, and additionally in ethanol (EtOH) and an ethanol–water (EtOH:Wat) mixture. The solution released from the PVA:TA cryogels in PBS was tested for inhibition capability against α-glucosidase (E.C. 3.2.1.20). Concentration-dependent enzyme inhibition was observed, and 70 µL of 83 µg/mL PVA:TA (1:1) cryogel in PBS inhibited α-glucosidase enzyme solution of 0.03 unit/mL in 70 µL by 81.75 ± 0.96%.
The Surface Characteristics, Microstructure and Mechanical Properties of PEEK Printed by Fused Deposition Modeling with Different Raster Angles
Authors: Gao Sasa , Liu Ruijuan , Xin Hua , Liang Haitao , Wang Yunfei , Jia Junhong
Published: December 2021

Additive manufacturing provides a novel and robust way to prepare medical product with anatomic matched geometry and tailored mechanical performance. In this study, the surface characteristics, microstructure, and mechanical properties of fused deposition modeling (FDM) prepared polyether-ether-ketone (PEEK) were systematically studied. During the FDM process, the crystal unit cell and thermal attribute of PEEK material remained unchanged, whereas the surface layer generally became more hydrophilic with an obvious reduction in surface hardness. Raster angle has a significant effect on the mechanical strength but not on the failure mechanism. In practice, FDM fabricated PEEK acted more like a laminate rather than a unified structure. Its main failure mechanism was correlated to the internal voids. The results show that horizontal infill orientation with 30° raster angle is promising for a better comprehensive mechanical performance, and the corresponding tensile, flexural, and shear strengths are (76.5 ± 1.4) MPa, (149.7 ± 3.0) MPa, and (55.5 ± 1.8) MPa, respectively. The findings of this study provide guidelines for FDM-PEEK to enable its realization in applications such as orthopedic implants.
Evaluation of the Validity of Digital Optical Microscopy in the Assessment of Marginal Adaptation of Dental Adhesive Interfaces
Authors: Daher René , Krejci Ivo , di Bella Enrico , Marger Laurine
Published: December 2021

Analysis of marginal adaptation of dental adhesive interfaces using scanning electron microscopy has proven to be a powerful nondestructive method to evaluate the quality of adhesion. This methodology is, however, time-consuming and needs expensive equipment. The purpose of this study was to evaluate the possibility and efficiency of using a digital optical microscope (DOM) to perform marginal analysis and to compare it with the scanning electron microscope (SEM) analysis. Fifteen defect-free molars were selected for this study. Class V cavities were prepared and restored with resin composite, and epoxy replicas were obtained from silicone impressions of the restored teeth. Custom-made image analysis software was then used to measure the percentage of the noncontinuous margins (NCM) of each sample. To compare the DOM to the gold standard, SEM, each sample was analyzed 10 times using the DOM and three times using the SEM, by the same experienced operator. The repeatability coefficient and concordance were evaluated, and a Bland and Altman analysis was used for comparison of the two methods of measurements. To validate the DOM analysis method, an ANOVA approach (Gage R R) was used. Repeatability and reproducibility, which are two components of precision to validate the DOM analysis system, were calculated. For this, the same restorations were analyzed by two additional operators three times with the DOM. The duration of each step of the analysis using both methods was also recorded as a secondary outcome. Regarding the repeatability of each method, the Friedman test showed no statistically significant difference within the repetitions of measurements by SEM and DOM (p = 0.523 and p = 0.123, respectively). Moreover, the Bland-Altman analysis revealed a bias of 0.86 and concluded no statistically significant difference between the two methods, DOM and SEM. ANOVA evaluated DOM measurement system variation including the variances of repeatability and reproducibility that represented, respectively, 0.3% and 4% of the variance components. Reproducibility or inter-operator variability represented the principal source of variability with a statistically significant difference (p = 0.024). The time required for analysis with SEM was almost double that of DOM. The use of digital optical microscopy appears to be a valid alternative to the SEM for the analysis of marginal adaptation of dental adhesive interfaces. Further studies to evaluate the effect of training of operators in digital optical microscopy could reveal higher accuracy for this method and inter-operator agreement when experience is gained.
Detection of Contaminants in Hydrogen Fuel for Fuel Cell Electrical Vehicles with Sensors—Available Technology, Testing Protocols and Implementation Challenges
Authors: Arrhenius Karine , Bacquart Thomas , Schröter Karin , Carré Martine , Gozlan Bruno , Beurey Claire , Blondeel Claire
Published: December 2021

Europe’s low-carbon energy policy favors a greater use of fuel cells and technologies based on hydrogen used as a fuel. Hydrogen delivered at the hydrogen refueling station must be compliant with requirements stated in different standards. Currently, the quality control process is performed by offline analysis of the hydrogen fuel. It is, however, beneficial to continuously monitor at least some of the contaminants onsite using chemical sensors. For hydrogen quality control with regard to contaminants, high sensitivity, integration parameters, and low cost are the most important requirements. In this study, we have reviewed the existing sensor technologies to detect contaminants in hydrogen, then discussed the implementation of sensors at a hydrogen refueling stations, described the state-of-art in protocols to perform assessment of these sensor technologies, and, finally, identified the gaps and needs in these areas. It was clear that sensors are not yet commercially available for all gaseous contaminants mentioned in ISO14687:2019. The development of standardized testing protocols is required to go hand in hand with the development of chemical sensors for this application following a similar approach to the one undertaken for air sensors.
The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda
Authors: Mbonyinshuti Francois , Nkurunziza Joseph , Niyobuhungiro Japhet , Kayitare Egide
Published: December 2021

Today’s global business trends are causing a significant and complex data revolution in the healthcare industry, culminating in the use of artificial intelligence and predictive modeling to improve health outcomes and performance. The dataset, which was referred to is based on consumption data from 2015 to 2019, included approximately 500 goods. Based on a series of data pre-processing activities, the top ten (10) essential medicines most used were chosen, namely cotrimoxazole 480 mg, amoxicillin 250 mg, paracetamol 500 mg, oral rehydration salts (O.R.S) sachet 20.5 g, chlorpheniramine 4 mg, nevirapine 200 mg, aminophylline 100 mg, artemether 20 mg + lumefantrine (AL) 120 mg, Cromoglycate ophthalmic. Our study concentrated on the application of machine learning (ML) to forecast future trends in the demand for essential drugs in Rwanda. The following models were created and applied: linear regression, artificial neural network, and random forest. The random forest was able to predict 10 selected medicines with an accuracy of 88 percent with the train set and 76 percent with the test set, and it can thus be used to forecast future demand based on past consumption data by inputting a month, year, district, and medicine name. According to our findings, the random Forest model performed well as a forecasting model for the demand for essential medicines. Finally, data-driven predictive modeling with machine learning (ML) could become the cornerstone of health supply chain planning and operational management.
A Sustainable Process to Produce Manganese and Its Alloys through Hydrogen and Aluminothermic Reduction
Authors: Safarian Jafar
Published: December 2021

Hydrogen and aluminum were used to produce manganese, aluminum–manganese (AlMn) and ferromanganese (FeMn) alloys through experimental work, and mass and energy balances. Oxide pellets were made from Mn oxide and CaO powder, followed by pre-reduction by hydrogen. The reduced MnO pellets were then smelted and reduced at elevated temperatures through CaO flux and Al reductant addition, yielding metallic Mn. Changing the amount of the added Al for the aluminothermic reduction, with or without iron addition led to the production of Mn metal, AlMn alloy and FeMn alloy. Mass and energy balances were carried out for three scenarios to produce these metal products with feasible material flows. An integrated process with three main steps is introduced; a pre-reduction unit to pre-reduce Mn ore, a smelting-aluminothermic reduction unit to produce metals from the pre-reduced ore, and a gas treatment unit to do heat recovery and hydrogen looping from the pre-reduction process gas. It is shown that the process is sustainable regarding the valorization of industrial waste and the energy consumptions for Mn and its alloys production via this process are lower than current commercial processes. Ferromanganese production by this process will prevent the emission of about 1.5 t CO2/t metal.
Explicit-pH Coarse-Grained Molecular Dynamics Simulations Enable Insights into Restructuring of Intestinal Colloidal Aggregates with Permeation Enhancers
Authors: Hossain Shakhawath , Parrow Albin , Kabedev Aleksei , Kneiszl Rosita Carolina , Leng Yuning , Larsson Per
Published: December 2021

Permeation enhancers (PEs) can increase the bioavailability of drugs. The mechanisms of action of these PEs are complex, but, typically, when used for oral administration, they can transiently induce the alteration of trans- and paracellular pathways, including increased solubilization and membrane fluidity, or the opening of the tight junctions. To elucidate these mechanistic details, it is important to understand the aggregation behavior of not only the PEs themselves but also other molecules already present in the intestine. Aggregation processes depend critically on, among other factors, the charge state of ionizable chemical groups, which is affected by the pH of the system. In this study, we used explicit-pH coarse-grained molecular dynamics simulations to investigate the aggregation behavior and pH dependence of two commonly used PEs—caprate and SNAC—together with other components of fasted- and fed-state simulated intestinal fluids. We also present and validate a coarse-grained molecular topology for the bile salt taurocholate suitable for the Martini3 force-field. Our results indicate an increase in the number of free molecules as a function of the system pH and for each combination of FaSSIF/FeSSIF and PEs. In addition, there are differences between caprate and SNAC, which are rationalized based on their different molecular structures and critical micelle concentrations.
Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data
Authors: Pen Chin-Lin , Chang Wen-Jer , Lin Yann-Horng
Published: December 2021

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.
Valorization of Vine Prunings by Slow Pyrolysis in a Fixed-Bed Reactor
Authors: Calcan Suzana Ioana , Pârvulescu Oana Cristina , Ion Violeta Alexandra , Răducanu Cristian Eugen , Bădulescu Liliana , Dobre Tănase , Egri Diana , Moț Andrei , Popa Vlad , Crăciun Mihaela Emanuela
Published: December 2021

The paper aimed at studying the slow pyrolysis of vine pruning waste in a fixed bed reactor and characterizing the pyrolysis products. Pyrolysis experiments were conducted for 60 min, using CO2 as a carrier gas and oxidizing agent. The distribution of biochar and bio-oil was dependent on variations in heat flux (4244–5777 W/m2), CO2 superficial velocity (0.004–0.008 m/s), and mean size of vegetal material (0.007–0.011 m). Relationships among these factors and process performances in terms of yields of biochar (0.286–0.328) and bio-oil (0.260–0.350), expressed as ratio between the final mass of pyrolysis product and initial mass of vegetal material, and final value of fixed bed temperature (401.1–486.5 °C) were established using a 23 factorial design. Proximate and ultimate analyses, FT-IR and SEM analyses, measurements of bulk density (0.112 ± 0.001 g/cm3), electrical conductivity (0.55 ± 0.03 dS/m), pH (10.35 ± 0.06), and water holding capacity (58.99 ± 14.51%) were performed for biochar. Water content (33.2 ± 1.27%), density (1.027 ± 0.014 g/cm3), pH (3.34 ± 0.02), refractive index (1.3553 ± 0.0027), and iodine value (87.98 ± 4.38 g I2/100 g bio-oil) were measured for bio-oil. Moreover, chemical composition of bio-oil was evaluated using GC-MS analysis, with 27 organic compounds being identified.
Influence of Graphene Nano Fillers and Carbon Nano Tubes on the Mechanical and Thermal Properties of Hollow Glass Microsphere Epoxy Composites
Authors: D Kumar , Shahapurkar Kiran , Venkatesh C. , R Muruganandhan , Tirth Vineet , Manivannan Chandru , Alarifi Ibrahim M. , Soudagar Manzoore Elahi M. , El-Shafay Ahmed S.
Published: December 2021

The present work aimed to analyze the roll of carbon nano tubes and graphene nano fillers on the mechanical and thermal characteristics of hollow glass microsphere reinforced epoxy composites. Composites with varying content of hollow glass microballoons (2, 4, 6, 8, and 10 wt %) reinforced in epoxy matrix were fabricated. Additionally, two more types of composites, one with graphene nano fillers and the other with carbon nano tube at a constant 0.5 wt %, were fabricated with varying weight percentages of hollow glass microballoons (2, 4, 6, 8, and 10%). The composites were fabricated using an open mold casting process. Composites were tested for thermal and mechanical properties. The tensile and flexural moduli were found to rise as the HGM concentration increased. Graphene-filled HGM/epoxy composites revealed the highest modulus compared with HGM/epoxy and HGM/CNT/epoxy composites. The impact strength of all composite types decreased as the HGM content increased. Neat epoxy specimens revealed low response as compared with all the composites tested. Further, the thermal conductivity of HGM/epoxy composites was lower as compared with other compositions and neat epoxy. Scanning electron microscopy was used to analyze the surface morphological behavior of the composites subjected to flexural test. It was found that HGM/G/E composites with 10% of HGM and 0.5% of graphene by weight in epoxy matrix were the optimum.
Combination of Autohydrolysis and Catalytic Hydrolysis of Biomass for the Production of Hemicellulose Oligosaccharides and Sugars
Authors: Vilcocq Léa , Crepet Agnès , Jame Patrick , Carvalheiro Florbela , Duarte Luis C.
Published: December 2021

Three different types of biomass sourced from forestry waste (eucalyptus residues), agricultural waste (wheat straw), and energy crop (miscanthus) were used as starting materials to produce hemicellulosic sugars, furans (furfural and hydroxymethylfurfural), and oligosaccharides. A two-step hybrid process was implemented; biomass was first autohydrolysed without any additive to extract hemicelluloses and dissolve it in water. Then, the hydrolysate was treated with a solid acid catalyst, TiO2-WOx, in order to achieve hydrolysis and produce monomeric sugars and furans. This article investigates the role of the biomass type, autohydrolysis experimental conditions, polymerisation degree and composition of hemicelluloses on the performance of the process coupling autohydrolysis and catalytic hydrolysis. The highest global yields of both oligosaccharides and monomeric sugars were obtained from Eucalyptus (37% and 18%, respectively).
A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong
Authors: Kwong Ivan H. Y. , Wong Frankie K. K. , Fung Tung , Liu Eric K. Y. , Lee Roger H. , Ng Terence P. T.
Published: December 2021

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.
Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset
Authors: Kuang Jianming , Ng Alex Hay-Man , Ge Linlin
Published: December 2021

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.
Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks
Authors: Reder Stefan , Mund Jan-Peter , Albert Nicole , Waßermann Lilli , Miranda Luis
Published: December 2021

The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs.
Usage Experience and Capabilities of the VEGA-Science System
Authors: Loupian Evgeny , Burtsev Mikhail , Proshin Andrey , Kashnitskii Alexandr , Balashov Ivan , Bartalev Sergey , Konstantinova Anna , Kobets Dmitriy , Radchenko Maxim , Tolpin Vladimir , Uvarov Ivan
Published: December 2021

Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new methods based on modern web technologies providing access to very large distributed data archives are gaining increasing importance. Furthermore, these new data handling solutions should provide not just access but also analysis and processing features, similar to desktop solutions. This paper describes the VEGA-Science web GIS—an open-access novel tool for satellite data processing and analysis. The overview of its architecture and basic technical components is given, but most attention is paid to examples of actual system application for various applied and research tasks. In addition, an overview of projects using the system is given to illustrate its versatility and further development directions are considered.
Lightweight Multilevel Feature Fusion Network for Hyperspectral Image Classification
Authors: Liang Miaomiao , Wang Huai , Yu Xiangchun , Meng Zhe , Yi Jianbing , Jiao Licheng
Published: December 2021

Hyperspectral images (HSIs), acquired as a 3D data set, contain spectral and spatial information that is important for ground–object recognition. A 3D convolutional neural network (3DCNN) could therefore be more suitable than a 2D one for extracting multiscale neighborhood information in the spectral and spatial domains simultaneously, if it is not restrained by mass parameters and computation cost. In this paper, we propose a novel lightweight multilevel feature fusion network (LMFN) that can achieve satisfactory HSI classification with fewer parameters and a lower computational burden. The LMFN decouples spectral–spatial feature extraction into two modules: point-wise 3D convolution to learn correlations between adjacent bands with no spatial perception, and depth-wise convolution to obtain local texture features while the spectral receptive field remains unchanged. Then, a target-guided fusion mechanism (TFM) is introduced to achieve multilevel spectral–spatial feature fusion between the two modules. More specifically, multiscale spectral features are endowed with spatial long-range dependency, which is quantified by central target pixel-guided similarity measurement. Subsequently, the results obtained from shallow to deep layers are added, respectively, to the spatial modules, in an orderly manner. The TFM block can enhance adjacent spectral correction and focus on pixels that actively boost the target classification accuracy, while performing multiscale feature fusion. Experimental results across three benchmark HSI data sets indicate that our proposed LMFN has competitive advantages, in terms of both classification accuracy and lightweight deep network architecture engineering. More importantly, compared to state-of-the-art methods, the LMFN presents better robustness and generalization.
Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations
Authors: Bracci Alessandro , Baldini Luca , Roberto Nicoletta , Adirosi Elisa , Montopoli Mario , Scarchilli Claudio , Grigioni Paolo , Ciardini Virginia , Levizzani Vincenzo , Porcù Federico
Published: December 2021

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods.
Enhanced Warming in Global Dryland Lakes and Its Drivers
Authors: Wang Siyi , He Yongli , Hu Shujuan , Ji Fei , Wang Bin , Guan Xiaodan , Piccolroaz Sebastiano
Published: December 2021

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade−1) than those in semi-humid and humid regions (0.19 °C decade−1) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions.
Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea
Authors: Tilstone Gavin H. , Pardo Silvia , Simis Stefan G. H. , Qin Ping , Selmes Nick , Dessailly David , Kwiatkowska Ewa
Published: December 2021

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (Rrs) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with <23 relative % difference at 443, 490 and 560 nm compared to in situ Rrs and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated Rrs by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate Rrs and Chl a products and OC time series for the Baltic Sea.
Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios
Authors: Kälin Ursula , Staffa Louis , Grimm David Eugen , Wendt Axel
Published: December 2021

To validate the accuracy and reliability of onboard sensors for object detection and localization for driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle during test maneuvers within a reference environment (e.g., car during parking maneuvers), independent of the onboard sensors. One requirement is a 6 degree of freedom (DoF) pose with position uncertainty below 5 mm (3σ), orientation uncertainty below 0.3° (3σ), at a frequency higher than 20 Hz, and with a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, synchronization via a Precision Time Protocol (PTP) and system interoperability to a robot operating system (ROS) are achieved. The developed system combines motion capture cameras mounted in a 360° panorama view setup on the vehicle, measuring retroreflective markers distributed over the test site with known coordinates, while robotic total stations measure a prism on the vehicle. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is visualized. The results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met while allowing a flexible installation in different environments.
Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles
Authors: Liu Meijie , Yan Ran , Zhang Jie , Xu Ying , Chen Ping , Shi Lijian , Wang Jin , Zhong Shilei , Zhang Xi
Published: December 2021

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.
Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks
Authors: Santos Adão F. , Lacerda Lorena N. , Rossi Chiara , Moreno Leticia de A. , Oliveira Mailson F. , Pilon Cristiane , Silva Rouverson P. , Vellidis George
Published: December 2021

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.
A Truncated Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction
Authors: Lu Lu , Gao Meiguo
Published: December 2021

Interrupted sampling repeater jamming (ISRJ) is becoming more widely used in electronic countermeasures (ECM), thanks to the development of digital radio frequency memory (DRFM). Radar electronic counter-countermeasure (ECCM) is much more difficult when the jamming signal is coherent with the emitted signal. Due to the intermittent transmission feature of ISRJ, the energy accumulation of jamming on the matched filter shows a ‘ladder’ characteristic, whereas the real target signal is continuous. As a consequence, the time delay and distribution of the jamming slice can be obtained based on searching the truncated-matched-filter (TMF) matrix. That is composed of pulse compression (PC) results under matched filters with different lengths. Based on the above theory, this paper proposes a truncated matched filter method by the reconstruction of jamming slices to suppress ISRJ of linear frequency modulation (LFM) radars. The numerical simulations indicate the effectiveness of the proposed method and validate the theoretical analysis.
A Wearable Biofeedback Device to Increase Gait Swing Time Could Have Positive Effects on Gait among Older Adults
Authors: Giraldo-Pedroza Alexandra , Lee Winson Chiu-Chun , Lam Wing-Kai , Coman Robyn , Alici Gursel
Published: December 2021

Older adults walk with a shorter stride length, reduced hip range of motion (ROM) and higher cadence. These are signs of reductions in walking ability. This study investigated whether using a wireless smart insole system that monitored and provided biofeedback to encourage an extension of swing time could increase stride length and hip flexion, while reducing the cadence. Seven older adults were tested in this study, with and without the biofeedback device, in an outdoor environment. Gait analysis was performed by using GaitRite system and Xsens MVN. Repeated measures analysis demonstrated that with biofeedback, the swing time increased by 6.45%, stride length by 4.52% and hip flexion by 14.73%, with statistical significance. It also decreased the cadence significantly by 5.5%. This study has demonstrated that this smart insole system modified positively the studied gait parameters in older adults and has the potential to improve their walking ability.
Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing
Authors: Ali Abid , Iqbal Muhammad Munawar , Jamil Harun , Akbar Habib , Muthanna Ammar , Ammi Meryem , Althobaiti Maha M.
Published: December 2021

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.
Detection of Typical Compensatory Movements during Autonomously Performed Exercises Preventing Low Back Pain (LBP)
Authors: Sellmann Asaad , Wagner Désirée , Holtz Lucas , Eschweiler Jörg , Diers Christian , Williams Sybele , Disselhorst-Klug Catherine
Published: December 2021

With the growing number of people seeking medical advice due to low back pain (LBP), individualised physiotherapeutic rehabilitation is becoming increasingly relevant. Thirty volunteers were asked to perform three typical LBP rehabilitation exercises (Prone-Rocking, Bird-Dog and Rowing) in two categories: clinically prescribed exercise (CPE) and typical compensatory movement (TCM). Three inertial sensors were used to detect the movement of the back during exercise performance and thus generate a dataset that is used to develop an algorithm that detects typical compensatory movements in autonomously performed LBP exercises. The best feature combinations out of 50 derived features displaying the highest capacity to differentiate between CPE and TCM in each exercise were determined. For classifying exercise movements as CPE or TCM, a binary decision tree was trained with the best performing features. The results showed that the trained classifier is able to distinguish CPE from TCM in Bird-Dog, Prone-Rocking and Rowing with up to 97.7% (Head Sensor, one feature), 98.9% (Upper back Sensor, one feature) and 80.5% (Upper back Sensor, two features) using only one sensor. Thus, as a proof-of-concept, the introduced classification models can be used to detect typical compensatory movements in autonomously performed LBP exercises.
Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry
Authors: Henrichs Elia , Noack Tanja , Pinzon Piedrahita Ana María , Salem María Alejandra , Stolz Johnathan , Krupitzer Christian
Published: December 2021

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.