bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlled Delivery of BET-PROTACs: In Vitro Evaluation of MZ1-Loaded Polymeric Antibody Conjugated Nanoparticles in Breast Cancer
Authors: Cimas Francisco J. , Niza Enrique , Juan Alberto , Noblejas-López María del Mar , Bravo Iván , Lara-Sanchez Agustín , Alonso-Moreno Carlos , Ocaña Alberto
Published: October 2020

Bromo and extraterminal domain (BET) inhibitors-PROteolysis TArgeting Chimera (BETi-PROTAC) is a new family of compounds that induce proteasomal degradation through the ubiquitination of the tagged to BET inhibitors Bromodomain proteins, BRD2 and BRD. The encapsulation and controlled release of BET-PROTACs through their vectorization with antibodies, like trastuzumab, could facilitate their pharmacokinetic and efficacy profile. Antibody conjugated nanoparticles (ACNPs) using PROTACs have not been designed and evaluated. In this pioneer approach, the commercial MZ1 PROTAC was encapsulated into the FDA-approved polymeric nanoparticles. The nanoparticles were conjugated with trastuzumab to guide the delivery of MZ1 to breast tumoral cells that overexpress HER2. These ACNPs were characterized by means of size, polydispersity index, and Z-potential. Morphology of the nanoparticles, along with stability and release studies, completed the characterization. MZ1-loaded ACNPs showed a significant cytotoxic effect maintaining its mechanism of action and improving its therapeutic properties.
Evaluation of CpG-ODN-Adjuvanted Toxoplasma gondii Virus-Like Particle Vaccine upon One, Two, and Three Immunizations
Authors: Kang Hae-Ji , Chu Ki-Back , Kim Min-Ju , Park Hyunwoo , Jin Hui , Lee Su-Hwa , Moon Eun-Kyung , Quan Fu-Shi
Published: October 2020

Successful vaccines against specific pathogens often require multiple immunizations and adjuvant usage. Yet, assessing the protective efficacy of different immunization regimens with adjuvanted Toxoplasma gondii vaccines remains elusive. In this study, we investigated the vaccine efficacy induced by CpG-ODN-adjuvanted T. gondii virus-like particles (VLPs) after challenge infection with T. gondii (ME49) in mice (BALB/c) upon one, two, and three immunizations. Immunization with adjuvanted T. gondii VLPs induced higher levels of T. gondii-specific IgG and/or IgA antibody responses, germinal center (GC) B cells, total B cells, and CD4+ and CD8+ T cells compared with unadjuvanted VLPs. Increasing the number of immunizations was strongly correlated with enhanced protective immunity against T. gondii in mice, with the highest protection being demonstrated in mice thrice-immunized with either adjuvanted T. gondii VLPs or VLPs alone. Notably, lesser bodyweight reductions and cerebral cyst counts were observed in mice receiving multiple immunizations with the adjuvanted VLPs, thereby confirming the effectiveness of adjuvanted boost immunizations. These results demonstrated that multiple immunizations with T. gondii VLPs is an effective approach, and the CpG-ODN can be developed as an effective adjuvant for T. gondii VLP vaccines.
Polyethylene-Based Knee Spacer for Infection Control: Design Concept and Pre-Clinical In Vitro Validations
Authors: Chang Yuhan , Lee Mel S. , Liau Jiann-Jong , Liu Yu-Liang , Chen Wen-Chuan , Ueng Steve W. N.
Published: October 2020

Antibiotic-loaded polymethyl methacrylate (PMMA) has been widely applied in the treatment of knee periprosthetic joint infections. However, problems with antibiotic-loaded PMMA-based spacers, such as structural fracture and implant dislocation, remain unresolved. A novel polyethylene-based spacer, designed with an ultra-congruent articulating surface and multiple fenestrations, was introduced in the current study. Validation tests for biomechanical safety, wear performance, and efficacy of antibiotic cement were reported. During cycle fatigue testing, no tibial spacer failures were observed, and less wear debris generation was reported compared to commercial PMMA-based spacers. The volumetric wear of the novel spacer was within the safety threshold for osteolysis-free volumetric wear. An effective infection control was demonstrated despite the application of lesser antibiotic cement in the 30-day antibiotic elution test. The tube dilution test confirmed adequate inhibitory capabilities against pathogens with the loaded antibiotic option utilized in the current study. The novel polyethylene-based knee spacer may offer sufficient biomechanical safety and serve as an adequate carrier of antibiotic-loaded cement for infection control. Further clinical trials shall be conducted for more comprehensive validation of the novel spacer for practical application.
Effect of Different Compatibilization Systems on the Rheological, Mechanical and Morphological Properties of Polypropylene/Polystyrene Blends
Authors: Seier Martina , Stanic Sascha , Koch Thomas , Archodoulaki Vasiliki-Maria
Published: October 2020

The influence of reactive processing, non reactive and reactive copolymers on immiscible polypropylene (PP)–polystyrene (PS) blends with varying PS concentrations (10 wt.% and 25 wt.%) was evaluated by mechanical (tensile and tensile impact), rheological (melt flow rate, extensional and dynamic rheology) and morphological (scanning electron microscopy) analysis. As an extended framework of the study, the creation of a link to industrial applicable processing conditions as well as an economically efficient use of compatibilzing agent were considered. For radical processed blends, a high improvement in melt strength was observed while non reactive copolymers exhibited a pronounced increase in toughness and ductility correlated with overall best phase homogeneity. Conversely, the influence of the reactive copolymer was quite different for the varied PS concentrations not allowing the assumption of a specific trend for resulting blend properties, but nevertheless in the case of a lower PS concentration the tensile impact strength exceeded the value of virgin PP. Since PS and PP are widely used, the findings of this work could not only be relevant for the generation of more versatile blends compared to virgin components but also for recycling purposes, allowing the enhancement of specific properties facilitating the production of more valuable secondary materials.
Economic Evaluation and Techno-Economic Sensitivity Analysis of a Mass Integrated Shrimp Biorefinery in North Colombia
Authors: Zuorro Antonio , Moreno-Sader Kariana Andrea , González-Delgado Ángel Darío
Published: October 2020

The high freshwater consumption requirements in shrimp biorefinery approaches represents one of the major drawbacks of implementing these technologies within the shrimp processing industry. This also affects the costs associated with the plant operation, and consequently, the overall economic performance of the project. The application of mass integration tools such as water pinch analysis can reduce frewshwater consumption by up to 80%, contributing to shrimp biorefinery sustainability. In this work, the economic evaluation and the techno-economic sensitivity analysis for a mass integrated approach for shrimp biorefinery were performed to determine the economic feasibility of the project when located in the North-Colombia region and to identify the critical techno-economic variables affecting the profitability of the process. The integrated approach designed to process 4113.09 tons of fresh shrimp in Colombia reaches a return on investment (%ROI) at 65.88% and a net present value (NPV) at 10.40 MM USD. The process supports decreases of up to 28% in capacity of production and increases of 12% and 11% in the cost of raw materials and variable operating costs without incurring losses, respectively. These findings suggest that the proposed design of the water recycling network coupled to a shrimp biorefinery approach is attractive from an economic point of view.
Pros and Cons of Plastic during the COVID-19 Pandemic
Authors: de Sousa Fabiula Danielli Bastos
Published: October 2020

Since the beginning of the first cases of the new coronavirus, opinions and laws on the use of plastic materials have been questioned around the world. Their importance in the manufacture of hospital devices and personal protective equipment (PPE) is unquestionable, as they contribute largely to the reduction of the virus spread, helping health systems from all edges of the world and, most importantly, saving lives. However, the same material that is a protector, becomes a polluter when inadequately disposed of in the environment, generating or worsening socio-environmental problems, such as pollution of water bodies by plastic. A critical overview of the role of plastic during the COVID-19 pandemic is provided in this paper. A future panorama is attempted to be outlined. The real possibility of the virus spread from the use of plastic is discussed, as well as the recycling of plastic during the pandemic, correlating its use with problems that it may cause.
Rhizomatic Religion and Material Destruction in Kham Tibet: The Case of Yachen Gar
Authors: Oostveen Daan
Published: October 2020

This article looks at the Tibetan Buddhism revitalization in China in particular, in Kham Tibet, and the way how it was both made possible and obstructed by the Chinese state. As a case, we look at the Yachen Gar monastery in the West of Sichuan. The Yachen Gar monastery became the largest Buddhist university in China in the past decades, but recently, reports of the destruction of large parts of the Buddhist encampment have emerged. This article is based on my observations during my field trip in late 2018, just before this destruction took place. I will use my conceptual framework of rhizomatic religion, which I developed in an earlier article, to show how Yachen Gar, rather than the locus of a “world religion”, is rather an expression of rhizomatic religion, which is native to the Tibetan highlands in Kham Tibet. This rhizomatic religion could emerge because Yachen is situated both on the edges of Tibet proper, and on the edges of Han Chinese culture, therefore occupying an interstitial space. As has been observed before, Yachen emerges as a process which is the result of the revival of Nyingmapa Tibetan Buddhist culture, as a negotiation between the Tibetan communities and the Chinese state.
CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan
Authors: Tariq Aqil , Shu Hong
Published: October 2020

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R2 = 0.8962 and R2 = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km2 and from 108.53 to 122.61 km2 in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city.
Lowland Rice Mapping in Sédhiou Region (Senegal) Using Sentinel 1 and Sentinel 2 Data and Random Forest
Authors: Fiorillo Edoardo , Di Giuseppe Edmondo , Fontanelli Giacomo , Maselli Fabio
Published: October 2020

In developing countries, information on the area and spatial distribution of paddy rice fields is an essential requirement for ensuring food security and facilitating targeted actions of both technical assistance and restoration of degraded production areas. In this study, Sentinel 1 (S1) and Sentinel 2 (S2) imagery was used to map lowland rice crop areas in the Sédhiou region (Senegal) for the 2017, 2018, and 2019 growing seasons using the Random Forest (RF) algorithm. Ground sample datasets were annually collected (416, 455, and 400 samples) for training and testing yearly RF classification. A procedure was preliminarily applied to process S2 scenes and yield a normalized difference vegetation index (NDVI) time series less affected by clouds. A total of 93 predictors were calculated from S2 NDVI time series and S1 vertical transmit–horizontal receive (VH) and vertical transmit–vertical receive (VV) backscatters. Guided regularized random forest (GRRF) was used to deal with the arising multicollinearity and identify the most important predictors. The RF classifier was then applied to the selected predictors. The algorithm predicted the five land cover types present in the test areas, with a maximum accuracy of 87% and kappa coefficient of 0.8 in 2019. The broad land cover maps identified around 12,500 (2017), 13,800 (2018), and 12,800 (2019) ha of lowland rice crops. The study highlighted a partial difficulty of the classifier to distinguish rice from natural herbaceous vegetation (NHV) due to similar temporal patterns and high intra-class variability. Moreover, the results of this investigation indicated that S2-derived predictors provided more valuable information compared to VV and VH backscatter-derived predictors, but a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs. An example is finally provided that illustrates how the maps obtained can be combined with ground observations through a ratio estimator in order to yield a statistically sound prediction of rice area all over the study region.
Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification
Authors: Dang Lanxue , Pang Peidong , Lee Jay
Published: October 2020

The neural network-based hyperspectral images (HSI) classification model has a deep structure, which leads to the increase of training parameters, long training time, and excessive computational cost. The deepened network models are likely to cause the problem of gradient disappearance, which limits further improvement for its classification accuracy. To this end, a residual unit with fewer training parameters were constructed by combining the residual connection with the depth-wise separable convolution. With the increased depth of the network, the number of output channels of each residual unit increases linearly with a small amplitude. The deepened network can continuously extract the spectral and spatial features while building a cone network structure by stacking the residual units. At the end of executing the model, a 1 × 1 convolution layer combined with a global average pooling layer can be used to replace the traditional fully connected layer to complete the classification with reduced parameters needed in the network. Experiments were conducted on three benchmark HSI datasets: Indian Pines, Pavia University, and Kennedy Space Center. The overall classification accuracy was 98.85%, 99.58%, and 99.96% respectively. Compared with other classification methods, the proposed network model guarantees a higher classification accuracy while spending less time on training and testing sample sites.
Evidence That Reduced Air and Road Traffic Decreased Artificial Night-Time Skyglow during COVID-19 Lockdown in Berlin, Germany
Authors: Jechow Andreas , Hölker Franz
Published: October 2020

Artificial skyglow, the brightening of the night sky by artificial light at night that is scattered back to Earth within the atmosphere, is detrimental to astronomical observations and has an impact on ecosystems as a form of light pollution. In this work, we investigated the impact of the lockdown caused by the COVID-19 pandemic on the urban skyglow of Berlin, Germany. We compared night sky brightness and correlated color temperature (CCT) measurements obtained with all-sky cameras during the COVID-19 lockdown in March 2020 with data from March 2017. Under normal conditions, we expected an increase in night sky brightness (or skyglow, respectively) and CCT because of the transition to LED. This is supported by a measured CCT shift to slightly higher values and a time series analysis of night-time light satellite data showing an increase in artificial light emission in Berlin. However, contrary to this observation, we measured a decrease in artificial skyglow at zenith by 20% at the city center and by more than 50% at 58 km distance from the center during the lockdown. We assume that the main cause for the reduction of artificial skyglow originates from improved air quality due to less air and road traffic, which is supported by statistical data and satellite image analysis. To our knowledge, this is the first reported impact of COVID-19 on artificial skyglow and we conclude that air pollution should shift more into the focus of light pollution research.
A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics
Authors: Liu Yiyi , Lyu Shuqiang , Hou Miaole , Gao Zhenhua , Wang Wanfu , Zhou Xiao
Published: October 2020

Background: Hyperspectral technology has made it possible to perform completely non-invasive investigations on pigment analysis, in particular, on pigment identification. The most commonly used method of pigment identification is to compare the spectral similarity between ones of unknown target and ones in spectral library, which requires a comprehensive and complete spectral library and is based on overall shape of the spectrum. To a certain extent, it may ignore some of the key absorption characteristics of the spectrum. Methods: A novel spectral matching method was proposed based on the spectrum divided into subsections for identification according to the main ion absorption characteristics. Main works: (1) establishing a spectral library suitable for typical pigment identification of painting; (2) discussing the main components, as well as the absorption positions of the ions and functional groups contained in pigments frequently used by artists; (3) presenting a novel spectral matching algorithm carried on spectral subsections for pigment identification; (4) verifying the feasibility and applicability of proposed method by a Chinese painting and a fresco. Conclusions: The proposed method can correctly identify the main pigments or components contained in the mixed area, which is better than the traditional method and more convenient than the unmixing method, except for some limitations in detecting white and black pigments.
The Role of Mean Sea Level Annual Cycle on Extreme Water Levels Along European Coastline
Authors: Fernández-Montblanc Tomás , Gómez-Enri Jesús , Ciavola Paolo
Published: October 2020

The knowledge of extreme total water levels (ETWLs) and the derived impact, coastal flooding and erosion, is crucial to face the present and future challenges exacerbated in European densely populated coastal areas. Based on 24 years (1993–2016) of multimission radar altimetry, this paper investigates the contribution of each water level component: tide, surge and annual cycle of monthly mean sea level (MMSL) to the ETWLs. It focuses on the contribution of the annual variation of MMSL in the coastal flooding extreme events registered in a European database. In microtidal areas (Black, Baltic and Mediterranean Sea), the MMSL contribution is mostly larger than tide, and it can be at the same order of magnitude of the surge. In meso and macrotidal areas, the MMSL contribution is <20% of the total water level, but larger (>30%) in the North Sea. No correlation was observed between the average annual cycle of monthly mean sea level (AMMSL) and coastal flooding extreme events (CFEEs) along the European coastal line. Positive correlations of the component variance of MMSL with the relative frequency of CFEEs extend to the Central Mediterranean (r = 0.59), North Sea (r = 0.60) and Baltic Sea (r = 0.75). In the case of positive MMSL anomalies, the correlation expands to the Bay of Biscay and northern North Atlantic (at >90% of statistical significance). The understanding of the spatial and temporal patterns of a combination of all the components of the ETWLs shall improve the preparedness and coastal adaptation measures to reduce the impact of coastal flooding.
Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation
Authors: Liu Tiange , Miao Qiguang , Xu Pengfei , Zhang Shihui
Published: October 2020

Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps.
Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks
Authors: Cao Yueqian , Barros Ana
Published: October 2020

Ensemble predictions of the seasonal snowpack over the Grand Mesa, CO (~300 km2) for the hydrologic year 2016–2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time-scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8–10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of the ensemble mean backscatter with SWE (R2 > 0.95) depends on weather conditions (e.g., 5–6 cm/dB/month in January; 2–2.5 cm/dB/month in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snow at Ku-band. The emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer), and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of high-elevation SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models extended to include snow-soil and snow-vegetation interactions.
Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards
Authors: Santos-Rufo Antonio , Mesas-Carrascosa Francisco-Javier , García-Ferrer Alfonso , Meroño-Larriva Jose Emilio
Published: October 2020

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.
Accuracy Evaluation of Four Greenland Digital Elevation Models (DEMs) and Assessment of River Network Extraction
Authors: Xing Ziyang , Chi Zhaohui , Yang Ying , Chen Shiyi , Huang Huabing , Cheng Xiao , Hui Fengming
Published: October 2020

Digital Elevation Models (DEMs) of Greenland provide the basic data for studying the Greenland ice sheet (GrIS), but little research quantitatively evaluates and compares the accuracy of various Greenland DEMs. This study uses IceBridge elevation data to evaluate the accuracies of the the Greenland Ice Map Project (GIMP)1 DEM, GIMP2 DEM, TanDEM-X, and ArcticDEM in their corresponding time ranges. This study also analyzes the impact of DEM accuracy and resolution on the accuracy of river network extraction. The results show that (1) within the time range covered by each DEM, TanDEM-X with an RMSE of 5.60 m has higher accuracy than the other DEMs in terms of absolute height accuracy, while GIMP1 has the lowest accuracy among the four Greenland DEMs, with an RMSE of 14.34 m. (2) Greenland DEMs are affected by regional errors and interannual changes. The accuracy in areas with elevations above 2000 m is higher than that in areas with elevations below 2000 m, and better accuracy is observed in the north than in the south. The stability of the ArcticDEM product is higher than those of the other three DEM products, and its RMSE standard deviation over multiple years is only 0.14 m. Therefore, the errors caused by the applications of DEMs with longer time spans are smaller. GIMP1 performs in an opposite manner, with a standard deviation of 2.39 m. (3) The river network extracted from TanDEM-X is close to the real river network digitized from remote sensing images, with an accuracy of 50.78%. The river network extracted from GIMP1 exhibits the largest errors, with an accuracy of only 8.83%. This study calculates and compares the accuracy of four Greenland DEMs and indicates that TanDEM-X has the highest accuracy, adding quantitative studies on the accuracy evaluation of various Greenland DEMs. This study also compares the results of different DEM river network extractions, verifies the impact of DEM accuracy on the accuracy of the river network extraction results, and provides an explorable direction for the hydrological analysis of Greenland as a whole.
Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data
Authors: Cabezas Mariano , Kentsch Sarah , Tomhave Luca , Gross Jens , Caceres Maximo Larry Lopez , Diez Yago
Published: October 2020

Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results.
Cross-Validation of Radio-Frequency-Interference Signature in Satellite Microwave Radiometer Observations over the Ocean
Authors: Wu Ying , Li Meixin , Bao Yansong , Petropoulos George P.
Published: October 2020

Radio-frequency-interference (RFI) signals have gradually become a more serious problem in active and passive microwave remote sensing. However, currently, there is no reliable RFI source distribution data to evaluate the accuracy of existing RFI identification methods. In this study, a simplified generalized RFI detection method (GRDM) is proposed to detect RFI applied to the ocean surface. Two RFI detection methods, the GRDM and the double-principal component analysis (DPCA) method, are used for cross-validation to obtain RFI recognition thresholds of DPCA in the Advanced Microwave Scanning Radiometer 2 (AMSR2) ocean data. In addition, in the present work the source and distribution characteristics of RFI over the ocean surface are also analyzed. The results show that the proposed scheme can effectively identify RFI signals from AMSR2 data, and only 7.3, 10.65, and 18.7 GHz channels are contaminated by RFI over the ocean surface. There are strong 7.3 GHz interference signals over the waters of East Asia (with the value of ΔTBH mostly between 5 and 30 K and ΔTBv mostly between 5 and 40 K), Europe (with the value of ΔTBH mostly between 5 and 40 K and ΔTBv mostly between 5 and 30 K), and North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 30 K). The RFI signals in 10.65 GHz data are mainly distributed over the Mediterranean and other European waters (with the value of ΔTBH mostly between 5 and 35 K and ΔTBv mostly between 5 and 20 K). The RFI signals at 18.7 GHz are mainly present over the offshore marine areas of North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 40 K), with the strongest RFI distributed near the Great Lakes of America, and the RFI magnitudes over the east and west coasts are stronger than over the south coast. Satellite-borne microwave observations over the ocean suffer from interference mainly from stationary communication/television satellites. Due to the reflection of the sea surface, the range and intensity of RFI are strongly dependent on the relative geometric positions of stationary satellites and space-borne passive instruments. Therefore, RFI coverage area changes every day over the ocean in one 16-day period, which is very different from that over the land.
Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data
Authors: Halubok Maryia , Yang Zong-Liang
Published: October 2020

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.
Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration
Authors: Chen Chuanfa , Yang Shuai , Li Yanyan
Published: October 2020

Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.
Multi-Constellation Software-Defined Receiver for Doppler Positioning with LEO Satellites
Authors: Farhangian Farzan , Landry René
Published: October 2020

A Multi-Constellation Software-Defined Receiver (MC-SDR) is designed and implemented to extract the Doppler measurements of Low Earth Orbit (LEO) satellite’s downlink signals, such as Orbcomm, Iridium-Next, Globalstar, Starlink, OneWeb, SpaceX, etc. The Doppler positioning methods, as one of the main localization algorithms, need a highly accurate receiver design to track the Doppler as a measurement for Extended Kalman Filter (EKF)-based positioning. In this paper, the designed receiver has been used to acquire and track the Doppler shifts of two different kinds of LEO constellations. The extracted Doppler shifts of one Iridium-Next satellite as a burst-based simplex downlink signal and two Orbcomm satellites as continuous signals are considered. Also, with having the Two-Line Element (TLE) for each satellite, the position, and orbital elements of each satellite are known. Finally, the accuracy of the designed receiver is validated using an EKF-based stationary positioning algorithm with an adaptive measurement matrix. Satellite detection and Doppler tracking results are analyzed for each satellite. The positioning results for a stationary receiver showed an accuracy of about 132 m, which means 72% accuracy advancements compared to single constellation positioning.
Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants
Authors: Pinheiro Cristiana , Figueiredo Joana , Magalhães Nuno , Santos Cristina P.
Published: October 2020

The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot interaction-based biofeedback is proposed to improve patient-exoskeleton compliance regarding the interaction torque’s direction (joint motion strategy) and magnitude (user participation strategy) through auditory and vibrotactile cues during assisted gait training, respectively. Parallel physiotherapist-oriented strategies are also proposed such that physiotherapists can follow in real-time a patient’s motor performance towards effective involvement during training. A preliminary pre-post controlled study was conducted with eight healthy participants to conclude about the biofeedback’s efficacy during gait training driven by an ankle-foot exoskeleton and guided by a technical person. For the study group, performance related to the interaction torque’s direction increased during (p-value = 0.07) and after (p-value = 0.07) joint motion training. Further, the performance regarding the interaction torque’s magnitude significantly increased during (p-value = 0.03) and after (p-value = 68.59 × 10−3) user participation training. The experimental group and a technical person reported promising usability of the biofeedback and highlighted the importance of the timely cues from physiotherapist-oriented strategies. Less significant improvements in patient–exoskeleton compliance were observed in the control group. The overall findings suggest that the proposed biofeedback was able to improve the participant-exoskeleton compliance by enhancing human-robot interaction; thus, it may be a powerful tool to accelerate post-stroke ankle-foot deformity recovery.
A Shape Approximation for Medical Imaging Data
Authors: Huang Shih-Feng , Wen Yung-Hsuan , Chu Chi-Hsiang , Hsu Chien-Chin
Published: October 2020

This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson’s disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ± 0.04 classification accuracy, 0.87 ± 0.06 sensitivity, and 0.88 ± 0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification.
Sustainability Driven by Agriculture through Digital Transformation
Authors: Hrustek Larisa
Published: October 2020

Economic, environmental and social sustainability is increasingly gaining the attention of academia and commitment in the policies of national economies. Global warming and climate change, weather disasters and disruptions are just some of the challenges that can drastically disrupt stable business models, and thus jeopardize sustainability. Agriculture, as an activity at the center of the value chain, is facing these problems, and, at the same time, contributes to feeding the growing world population and is responsible for conserving resources and ensuring sustainability. Process automation, data analysis and processing, control and management of agricultural operations have transformed the agricultural business and contributed to sustainability, survival and development within supply chains. This article analyzes the literature in the field of sustainability and agriculture led by digital technologies of Industry 4.0. The analysis of the research field showed the high importance of the topic in academic and national circles, given the number of published articles. Secondly, the concepts of sustainable agriculture and sustainability driven agriculture in the context of digital transformation were analyzed and it was shown that transformed agriculture can successfully cope with today’s challenges. Finally, guidelines for sustainable development driven by agriculture through the determinants of digital transformation are defined.
Untact: Customer’s Acceptance Intention toward Robot Barista in Coffee Shop
Authors: Sung Hye Jin , Jeon Hyeon Mo
Published: October 2020

Restaurants have been using robots to meet the increasing consumer demand for food customization and safety and contactless service operations. This study identified the antecedent factors influencing a customer’s attitude and acceptance intention toward a robot barista. To this end, we conducted a questionnaire survey from 10 to 24 January 2020, on a sample of 317 Korean consumers who purchased coffee prepared by a robot barista. We based the analysis on the following determinant factors of the extension of the technology acceptance model (ETAM): perceived enjoyment, perceived usefulness, need for interaction, perceived risk, and perceived innovativeness. The results showed that perceived usefulness had the highest impact on acceptance intention, followed by perceived enjoyment and the need for interaction. Perceived usefulness and innovativeness positively influenced acceptance intention. These results confirmed the significance of the determinant factors in inducing customers’ attitude and acceptance intention toward a robot barista. This study extends the research on the application of artificial intelligence and the fourth industrial revolution technologies in the food service industry, and hence contributes toward the preparations for the post-Covid-19 period. It also offers practical implications for sustainable coffee shop management.
We Learnt a Lot: Challenges and Learning Experiences in a Southern African—North European Municipal Partnership on Education for Sustainable Development
Authors: Sonesson Kerstin , Nordén Birgitta
Published: October 2020

This study investigates a reciprocal partnership between two cities in Namibia and Sweden to deepen the understanding of challenges and learning outcomes in a project on education for sustainable development. Since 2008, two municipalities have developed a strong partnership via The Municipal Partnership Programme at the Swedish International Centre for Local Democracy. Municipal partnerships are results-oriented collaborations in joint projects on sustainability. The purpose is to describe how eight team members in the mutual South-North project, by addressing similar problems in different contexts, experienced challenges in the implementation of the project plan, solutions and learning processes. Semi-structured interviews were conducted at the end of the second project year. Transcripts and field notes were analysed using a phenomenographic approach and contextual analysis. Five main categories of description based on collective statements and three dimensions of learning were recognised in the research data. The analysis identifies strategies for critical knowledge formation and capability building to support mutual learning in South-North Municipal Partnerships. The concluding discussion spots the learning dimensions—how sharing experiences by justifying non-formal and transformational learning promotes organisations’ readiness for knowledge formation by conducting mutual global learning towards sustainable development goals.