bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Biocompatible Polyhydroxyalkanoate/Chitosan-Tungsten Disulphide Nanocomposite for Antibacterial and Biological Applications
Authors: Mukheem Abdul , Shahabuddin Syed , Akbar Noor , Ahmad Irfan , Sudesh Kumar , Sridewi Nanthini
Published: May 2022

The unique structures and multifunctionalities of two-dimensional (2D) nanomaterials, such as graphene, have aroused increasing interest in the construction of novel scaffolds for biomedical applications due to their biocompatible and antimicrobial abilities. These two-dimensional materials possess certain common features, such as high surface areas, low cytotoxicities, and higher antimicrobial activities. Designing suitable nanocomposites could reasonably improve therapeutics and reduce their adverse effects, both medically and environmentally. In this study, we synthesized a biocompatible nanocomposite polyhydroxyalkanoate, chitosan, and tungsten disulfide (PHA/Ch-WS2). The nanocomposite PHA/Ch-WS2 was characterized by FESEM, elemental mapping, FTIR, and TGA. The objective of this work was to investigate the antimicrobial activity of PHA/Ch-WS2 nanocomposites through the time–kill method against the multi-drug-resistant model organisms Escherichia coli (E. coli) K1 and methicillin-resistant Staphylococcus aureus (MRSA). Further, we aimed to evaluate the cytotoxicity of the PHA/Ch-WS2 nanocomposite using HaCaT cell lines by using a lactate dehydrogenase (LDH) assay. The results demonstrated very significant bactericidal effects of the PHA/Ch-WS2 nanocomposite, and thus, we hypothesize that the nanocomposite would feasibly suit biomedical and sanitizing applications without causing any adverse hazard to the environment.
Effect of Chitosan Nanoparticles as Edible Coating on the Storability and Quality of Apricot Fruits
Authors: Algarni Eman H. A. , Elnaggar Ibrahim A. , Abd El-wahed Abd El-wahed N. , Taha Ibrahim M. , AL-Jumayi Huda A. , Elhamamsy Sam M. , Mahmoud Samy F. , Fahmy Alaa
Published: May 2022

Apricots are a fragile fruit that rots quickly after harvest. Therefore, they have a short shelf-life. The purpose of this work is to determine the effect of coatings containing chitosan (CH) as well as its nanoparticles (CHNPs) as thin films on the quality and shelf-life of apricots stored at room (25 ± 3 °C) and cold (5 ± 1 °C) temperatures. The physical, chemical, and sensorial changes that occurred during storage were assessed, and the shelf-life was estimated. Transmission electron microscopy was used to examine the size and shape of the nanoparticle. The nanoparticles had a spherical shape with an average diameter of 16.4 nm. During the storage of the apricots, those treated with CHNPs showed an obvious decrease in weight loss, decay percent, total soluble solids, and lipid peroxidation, whereas total acidity, ascorbic acid, and carotenoid content were higher than those in the fruits treated with CH and the untreated fruits (control). The findings of the sensory evaluation revealed a significant difference in the overall acceptability scores between the samples treated with CHNPs and the other samples. Finally, it was found that CHNP coatings improved the qualitative features of the apricots and extended their shelf-life for up to 9 days at room temperature storage and for 30 days in cold storage.
Hydrophilic Poly(glutamic acid)-Based Nanodrug Delivery System: Structural Influence and Antitumor Efficacy
Authors: Guo Yifei , Shen Yiping , Yu Bo , Ding Lijuan , Meng Zheng , Wang Xiaotong , Han Meihua , Dong Zhengqi , Wang Xiangtao
Published: May 2022

Poly(amino acids) have advanced characteristics, including unique secondary structure, enzyme degradability, good biocompatibility, and stimuli responsibility, and are suitable as drug delivery nanocarriers for tumor therapy. The isoform structure of poly(amino acids) plays an important role in their antitumor efficacy and should be researched in detail. In this study, two kinds of pH-sensitive isoforms, including α-poly(glutamic acid) (α-PGA) and γ-PGA, were selected and used as nanocarriers to prepare a nanodrug delivery system. According to the preparation results, α-PGA can be used as an ideal drug carrier. Selecting doxorubicin (DOX) as the model drug, an α-PGA/DOX nanoparticle (α-PGA/DOX NPs) with a particle size of 110.4 nm was prepared, and the drug-loading content was 66.2%. α-PGA/DOX NPs presented obvious sustained and pH-dependent release characteristics. The IC50 value of α-PGA/DOX NPs was 1.06 ± 0.77 μg mL−1, decreasing by approximately 8.5 fold in vitro against 4T1 cells after incubation for 48 h. Moreover, α-PGA/DOX NPs enhanced antitumor efficacy in vivo, the tumor inhibition rate was 67.4%, increasing 1.5 fold over DOX injection. α-PGA/DOX NPs also reduced the systemic toxicity and cardiotoxicity of DOX. In sum, α-PGA is a biosafe nanodrug delivery carrier with potential clinical application prospects.
Investigation on Rare Nuclear Processes in Hf Nuclides
Authors: Caracciolo Vincenzo , Belli Pierluigi , Bernabei Rita , Cappella Fabio , Cerulli Riccardo , Incicchitti Antonella , Laubenstein Matthias , Leoncini Alice , Merlo Vittorio , Nagorny Serge , Nisi Stefano , Wang Peng
Published: May 2022

In this work, a review of recent studies concerning rare nuclear processes in Hf isotopes is presented. In particular, the investigations using HP-Ge spectrometry and Hf-based crystal scintillators are focused; the potentiality and the results of the “source = detector” approach are underlined. In addition, a short introduction concerning the impact of such kind of research in the context of astroparticle and nuclear physics is pointed out. In particular, the study of α decay and double beta decay of 174Hf, 176Hf, 177Hf, 178Hf, 179Hf, 180Hf isotopes either to the ground state or to the lower bounded levels have been discussed. The observation of α decay of 174Hf isotope to the ground state with a T1/2=7.0(1.2)×1016 y is reported and discussed. No decay was detected for α decay of 174Hf isotope at the first excited level of daughter and of 176Hf, 177Hf, 178Hf, 179Hf, 180Hf isotopes either to the ground state or to the lower bounded levels. The T1/2 lower limits for these decays are at the level of 1016–1020 y. Nevertheless, the T1/2 lower limits for the transitions of 176Hf→172Yb (0+→0+) and 177Hf→173Yb (7/2−→5/2−) are near to the theoretical predictions, giving hope to their observation in the near future. All the other experimental limits (∼1016–1020 y) are absolutely far from the theoretical expectations. The experiments investigating the 2ϵ and ϵβ+ processes in 174Hf are also reported; the obtained half-life limits are set at the level of 1016–1018 y. Moreover, we estimate the T1/2 of 2ν2ϵ of 174Hf decay at the level of (0.3–6) × 1021 y (at now the related measured lower limit is 7.1×1016 y).
Legitimation of New Media for Religious Youth: Orthodox Elites’ Approach to Adolescent Youngsters’ Engagement with Digital Worlds
Authors: Golan Oren , Don Yaakov
Published: May 2022

The promulgation of new media has generated substantial dilemmas for religious communities in terms of its use, implementation, and impact on youth’s socialization. Previous research has echoed religious authorities’ concern regarding the widespread integration of new media yet has done little to delineate their narratives of legitimation. Ergo, the question is begged, how do religious communities legitimate the use of new media? Utilizing a case study approach, this study focuses on the social construction of new media’s legitimacy within the Jewish Religious-Zionist community in Israel, through an analysis of the community’s educational elite. To this end, 26 in-depth interviews with the community’s prominent educational leaders were conducted. Findings indicate 4 primary narratives that are employed to legitimate new media use: (1) acclaiming modernity; (2) sanctifying the new media; (3) promoting solidarity; and (4) religious study and the public’s ability to choose. Understanding these narratives of legitimation towards new media sheds light on the ways that modern ideas are incrementally being integrated into religious communities, and the ways the elite negotiate its integration through what can be seen as their most weighty tool, that of youth socialization.
Navigating Triple Consciousness in the Diaspora: An Autoethnographic Account of an Ahmadi Muslim Woman in Canada
Authors: Mian Akram Ayesha
Published: May 2022

In 1974, the Pakistani Constitution was amended to declare Ahmadi Muslims as “non-Muslim”, initiating a systematic and hegemonic structural attempt to restrict Ahmadi Muslims from professing and practicing the Islamic faith in Pakistan. This state-sanctioned exclusion led to the mass migration of Ahmadis out of Pakistan into diasporic contexts. Using autoethnography, this article examines how being an Ahmadi Muslim woman in Canada remains rooted in deeply divisive politico-religious conflicts that transcend temporal and spatial boundaries and result in multiple layers of marginalities in the diaspora. I am conscious that my self-formation is racialized, gendered, and classed across three primary intersections: as a Pakistani/South Asian; as an Ahmadi Muslim; and as a woman. This “triple consciousness”, a term coined by Black feminist scholars and Afro-Latinx scholars in the United States to extend W. E. B. Du Bois’ “double consciousness”, produces a liminal and contradictory space of belonging—one that requires further reflection and analysis in the Canadian context where the racial continues to dominate our social world and proximity to Whiteness is privileged and rewarded.
Utilizing the Theology of Religions and Human Geography to Understand the Spatial Dimension of Religion and Conflict
Authors: Harris Elizabeth J.
Published: May 2022

This paper argues for an interdisciplinary approach within the study of religion and conflict. Using a religious studies framework, it demonstrates that tools from human geography, peace studies, and the theology of religions can be used to shed light on the intractability of conflicts where religion is not “innocent”. Within human geography, the spatial dimension of individual and communal identity, most particularly the concept of “mythical space”, can illuminate the non-empirical, affective factors that condition attitudes to religious and ethnic others. Similarly, within the theology of religions, the typology of exclusivism, inclusivism, and pluralism can aid understanding of tendencies within all human communities, religious and non-religious, when faced with perceived threats from significant others. Two case studies, Sri Lanka and Israel/Palestine, are examined through this interdisciplinary approach, using illustrative “moments” within each conflict. Both highlight the affective power of primal imaginaries that are informed by narratives about religion, land, and identity. Without dismissing the importance of political and economic factors in the arising of conflict, this paper argues that it is not enough to analyse these factors alone. Other disciplines are necessary and this paper argues for two important examples: human geography and the theology of religions.
From Collectivities to Families and Back to the Individual: Religiosity, Migrations and Civic Engagement at the Beginning of the COVID Period in a Multicultural Italian City
Authors: Ricucci Roberta , Bossi Luca
Published: May 2022

Religions and religiosities are among the various social life spheres being affected by medical and political measures imposed during the health emergency. Involving a wide range of daily life dimensions and intertwining with fundamental aspects of individual and social existence, restrictions hit religions and religiosities in all those spaces where they find expression in our contemporary era. Pandemic restraints induced changes in the use of different public spaces: from school to home, from workplaces to places of worship, from prisons to squares, from hospitals to cemeteries. This also concerned the way religiosity could be performed, lived and shared in everyday life, for communities, families and individuals. In particular, during the pandemic, the role of religious environments became, once again, a place of material as well as spiritual support for migrants. And in this perspective, young people, i.e., second generations, played a prominent role, regaining prestige and recognition from adults. In fact, from being perceived as “far from religion and on the road to secularization”, as one interviewee said, young people have been able to show how it is possible to reinterpret religion in emigration, without abandoning religious values. This paper discusses the results of preliminary research on the topic conducted in the first phase of the pandemic in Turin, a city that, for its history of immigration and consolidated presence of Muslim, Catholic and Orthodox communities, is an emblematic case of the Italian multicultural context.
A Case of Pulmonary Nocardiosis Presenting with Multiple Cavitary Nodules in a Patient with Thrombocytopenia
Authors: Wang Fei , Yu Qing , Zhu Jinye , Que Chengli
Published: May 2022

Nocardiosis is an infrequent opportunistic infection that deserves more attention because of its increasing morbidity and high rate of misdiagnosis. Clinical and radiological manifestations as well as antibiograms of pulmonary nocardiosis are diverse. Herein, we report a patient with idiopathic thrombocytopenia treated with glucocorticoids, which later presented with severe pulmonary infection with widely distributed nodules on chest computed tomography. Fungal infection or tuberculosis was under initial suspicion and microbiological tests of bronchoalveolar lavage fluid eventually yielded an affirmative result of Nocardia cyriacigeorgica. The patient responded in the beginning; however, therapeutic strategies had to be altered several times due to adverse events. The patient eventually ended up with radiographic resolution at the end of six months. We wish to share some experience in dealing with this disease especially recognizing pulmonary nocardiosis in computed tomography imaging.
Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass
Authors: Xu Tianyue , Wang Fumin , Xie Lili , Yao Xiaoping , Zheng Jueyi , Li Jiale , Chen Siting
Published: May 2022

The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R2 of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.
Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets
Authors: Cao Yungang , Du Puying , Zhang Min , Bai Xueqin , Lei Ruodan , Yang Xiuchun
Published: May 2022

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.
An Optimal Transport Based Global Similarity Index for Remote Sensing Products Comparison
Authors: Tan Yumin , Shi Yanzhe , Xu Le , Zhou Kailei , Jing Guifei , Wang Xiaolu , Bai Bingxin
Published: May 2022

Remote sensing products, such as land cover data products, are essential for a wide range of scientific studies and applications, and their quality evaluation and relative comparison have become a major issue that needs to be studied. Traditional methods, such as error matrices, are not effective in describing spatial distribution because they are based on a pixel-by-pixel comparison. In this paper, the relative quality comparison of two remote sensing products is turned into the difference measurement between the spatial distribution of pixels by proposing a max-sliced Wasserstein distance-based similarity index. According to optimal transport theory, the mathematical expression of the proposed similarity index is firstly clarified, and then its rationality is illustrated, and finally, experiments on three open land cover products (GLCFCS30, FROMGLC, CNLUCC) are conducted. Results show that based on this proposed similarity index-based relative quality comparison method, the spatial difference, including geometric shapes and spatial locations between two different remote sensing products in raster form, can be quantified. The method is particularly useful in cases where there exists misregistration between datasets, while pixel-based methods will lose their robustness.
A Preliminary Study on Ionospheric Scintillation Anomalies Detected Using GNSS-R Data from NASA CYGNSS Mission as Possible Earthquake Precursors
Authors: Molina Carlos , Boudriki Semlali Badr-Eddine , Park Hyuk , Camps Adriano
Published: May 2022

Ionospheric perturbations affect the propagation of electromagnetic waves. These perturbations, besides being a problem for space communications, satellite navigation, and Earth observation techniques, could also be used as another Earth observation tool. Several recent studies showed correlations with earthquakes with ionospheric anomalies, but almost all of them use ground stations to measure the Total Electron Content (TEC) variations, and, in particular, the ones occurring after an earthquake. Here, a preliminary study is presented on how the ionospheric scintillation measured with GNSS-R instruments over oceanic regions shows a small, but detectable correlation with the occurrence of earthquakes, which in some cases occurs before the earthquakes. This study uses GNSS-R data from NASA CYGNSS Mission to measure the ionospheric amplitude scintillation (S4) for 6 months from March 2019 to August 2019, applying a statistical analysis based on confusion matrixes, and the Receiver Operating Characteristic (ROC) curves to correlate S4 anomalous variations to earthquakes. A small positive correlation is found between the ionospheric scintillation and the earthquakes during the six previous days. However, the study has some weakness because (a) a small number (∼45) of large (M > 6) earthquakes over oceanic regions are studied, (b) the region studied is close to the geomagnetic equator, where ionospheric scintillations are usual, and (c) the overall correlation is small.
Coupling and Coordination Relationships between Urban Expansion and Ecosystem Service Value in Kashgar City
Authors: Maimaiti Bumairiyemu , Chen Shuisen , Kasimu Alimujiang , Mamat Aynur , Aierken Nuermaimaiti , Chen Qianli
Published: May 2022

The growing urbanization of oasis cities in arid and semi-arid regions of Northwest China has an adverse influence on the fragile local ecological system. Therefore, improved understanding of the coupling and coordination between urban expansion (UE) and ecosystem services value (ESv) is critical to long term sustainable development. Here, we study the urbanization trend of a typical oasis city of Northwest China (Kashgar) using Landsat TM/ETM+/OLI imagery from 1990 to 2015. Land use types are classified and the spatio-temporal features of UE are analyzed; ESv of each land use types and the ecosystem services function (ESf) are determined; the driving factors of UE and the spatio-temporal change of ESv are analyzed; and the coupling and coordination relationship between UE and ESv is quantitatively determined. Results show that: (1) The land use structure has changed significantly between 1990 and 2015, with construction land (40.51 km2) showing the highest growth and farmland (28.42 km2). (2) UE values during 2000–2005 (16.65 km2) and 2010–2015 (21.09 km2) are relatively large, and during 1990–2015, the city extended from the center to the outskirts at a dynamic growth rate of 13.17% and a comprehensive expansion index of 1.54‰. (3) The total ESv was reduced by CNY 35.76 million (USD ~ 5.26 million), ranked from high to low as: waste treatment (CNY 9.94 million, USD ~1.46 million), water source conservation (CNY 7.95 million, USD ~ 1.17 million), soil formation (CNY 4.60 million, USD ~ 0.68 million), biodiversity protection (CNY 3.37 million, USD ~ 0.5 million), climate regulation (CNY 3.15 million, USD ~ 0.46 million), food production (CNY 2.83 million, USD ~ 0.42 million), gas regulation (CNY 1.96 million, USD ~ 0.29 million), entertainment and leisure (CNY 1.26 million, USD ~ 0.19 million), and raw materials (CNY 0.68 million, USD ~ 0.1 million). (4) The coupling degree between UE and ESv is relatively small (<0.5), though this value has increased yearly. The coordination degree between UE and ESv is relatively low, indicating that UE already poses a serious danger to the ecological environment. (5) The rapid growth of the population and economy and government policies are the main driving factors of intensive UE. Increasing climatic factors such as precipitation, temperature, and runoff impact ESv in some positive ways whereas UE leads to a reduction of ESv. Our results here can help to guide long-term sustainable development of arid regions, reasonable urban planning of oasis cities, and protection of the local ecological environment.
Measuring PM2.5 Concentrations from a Single Smartphone Photograph
Authors: Yao Shiqi , Wang Fei , Huang Bo
Published: May 2022

PM2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM2.5 from photographs. This paper devises an algorithm to estimate PM2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM2.5. A PM2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM2.5 sensors.
Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model
Authors: Luo Zelin , Guo Mengjing , Bai Peng , Li Jing
Published: May 2022

Evapotranspiration (ET) is an essential part of the global water cycle, and accurate quantification of ET is of great significance for hydrological research and practice. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is a commonly used remotely sensed (RS) ET model. The original PT-JPL model includes multiple vegetation variables but only requires the Normalized Difference Vegetation Index (NDVI) as the vegetation input. Other vegetation inputs (e.g., Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) are estimated by the NDVI-based empirical methods. Here we investigate whether introducing more RS vegetation variables beyond NDVI can improve the PT-JPL model’s performance. We combine the vegetation variables derived from RS and empirical methods into four vegetation input schemes for the PT-JPL model. The model performance under four schemes is evaluated at the site scale with the eddy covariance (EC)-based ET measurements and at the basin scale with the water balance-based ET estimates. The results show that the vegetation variables derived by RS and empirical methods are quite different. The ecophysiological constraints of the PT-JPL model constructed by the former are more reasonable in spatial distribution than those constructed by the latter. However, as vegetation input of the PT-JPL model, the scheme derived from empirical methods performs best among the four schemes. In other words, introducing more remotely sensed vegetation variables beyond NDVI into the PT-JPL model degrades the model performance to varying degrees. One possible reason for this is the unrealistic ET partitioning. It is necessary to re-parameterize the biophysical constraints of the PT-JPL model to ensure that the model obtains reasonable internal process simulations, that is, “getting the right results for right reasons.”
Earth-Observation-Based Services for National Reporting of the Sustainable Development Goal Indicators—Three Showcases in Bulgaria
Authors: Aleksieva-Petrova Adelina , Mladenova Irena , Dimitrova Katya , Iliev Kamen , Georgiev Atanas , Dyankova Anna
Published: May 2022

Earth Observation (EO) is used to monitor and assess the status of, and changes in, the natural and manmade environment via remote sensing technologies, usually involving satellites carrying imaging devices. EO applications provide important inputs to governments in planning, implementing, and monitoring the progress of the 2030 Agenda for Sustainable Development. Along with other countries, Bulgaria has committed to all 17 Sustainable Development Goals (SDGs) and reflected them in its strategic documents. EO is one of the priority technologies for the development of the Bulgarian space sector. This paper analyzes how EO data could significantly help Bulgarian authorities in achieving and monitoring the progress of the SDG targets based on three specific EO monitoring pilot projects’ results (showcases) focused more on the policy management approach than scientific achievement. The first project showed the opportunities of EO data for integration of a national (local) geospatial database with the existing international networks for monitoring natural disasters and accidents. The second demonstrated the time series usage of EO data for water quality monitoring. The third project integrated remote sensing data from EO and in situ measurements with ancillaries’ data to provide phenology status and crop production forecast in a common geospatial database with the aim to support the Bulgarian agriculture sector modernization.
Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity
Authors: Jia Pingping , Zhang Junhua , He Wei , Hu Yi , Zeng Rong , Zamanian Kazem , Jia Keli , Zhao Xiaoning
Published: May 2022

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R2 = 0.98) and validation (R2 = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.
Using Range Split-Spectrum Interferometry to Reduce Phase Unwrapping Errors for InSAR-Derived DEM in Large Gradient Region
Authors: Mao Wenfei , Liu Guoxiang , Wang Xiaowen , Xie Yakun , He Xiaoxing , Zhang Bo , Xiang Wei , Wu Shuaiying , Zhang Rui , Fu Yin , Pirasteh Saied
Published: May 2022

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%.
High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China
Authors: Ouyang Xiaoying , Dou Youjun , Yang Jinxin , Chen Xi , Wen Jianguang
Published: May 2022

Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R2 higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces.
XCO2 and XCH4 Reconstruction Using GOSAT Satellite Data Based on EOF-Algorithm
Authors: Lopez Franz Pablo Antezana , Zhou Guanhua , Jing Guifei , Zhang Kai , Tan Yumin
Published: May 2022

The Greenhouse Gases Observing Satellite (GOSAT) can help to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4), and how the sources and sinks of these gases vary by season, year, and location. However, the data provided by the GOSAT level 2 and 3 products have certain limitations due to their lack of spatial and temporal information; even with the application of the kriging geostatistical method on the level 2 products, the processing algorithms still need further upgrades. In this study, we apply an empirical orthogonal function (EOF)-based method on the GOSAT L3 products (137 images, from January 2010 to May 2021) to estimate the column average of carbon dioxide and methane (XCO2–XCH4) within the entire Earth. The reconstructed results are validated against the Total Carbon Column Observing Network (i.e., TCCON), with 31 in situ stations, and GOSAT L4B column-averaged data, using 107 layers. The results show an excellent agreement with the TCCON data and exhibit an R-squared coefficient of 0.95 regarding the CO2 measurements and 0.86 regarding the CH4 measurements. Therefore, this methodology can be incorporated into the processing steps used to map global greenhouse gases.
Strategies for the Storage of Large LiDAR Datasets—A Performance Comparison
Authors: Béjar-Martos Juan A. , Rueda-Ruiz Antonio J. , Ogayar-Anguita Carlos J. , Segura-Sánchez Rafael J. , López-Ruiz Alfonso
Published: May 2022

The widespread use of LiDAR technologies has led to an ever-increasing volume of captured data that pose a continuous challenge for its storage and organization, so that it can be efficiently processed and analyzed. Although the use of system files in formats such as LAS/LAZ is the most common solution for LiDAR data storage, databases are gaining in popularity due to their evident advantages: centralized and uniform access to a collection of datasets; better support for concurrent retrieval; distributed storage in database engines that allows sharding; and support for metadata or spatial queries by adequately indexing or organizing the data. The present work evaluates the performance of four popular NoSQL and relational database management systems with large LiDAR datasets: Cassandra, MongoDB, MySQL and PostgreSQL. To perform a realistic assessment, we integrate these database engines in a repository implementation with an elaborate data model that enables metadata and spatial queries and progressive/partial data retrieval. Our experimentation concludes that, as expected, NoSQL databases show a modest but significant performance difference in favor of NoSQL databases, and that Cassandra provides the best overall database solution for LiDAR data.
A Source Localization Method Using Complex Variational Mode Decomposition
Authors: Miao Qiuyan , Sun Xinglin , Wu Bin , Ye Lingyun , Song Kaichen
Published: May 2022

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources’ direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.
In Vivo Magnetic Resonance Thermometry for Brain and Body Temperature Variations in Canines under General Anesthesia
Authors: Kim Keonil , Ahn Jisoo , Yoon Kwangyong , Ko Minjung , Ahn Jiyoung , Kim Hyesung , Park Jihyeon , Lee Chulhyun , Chang Dongwoo , Oh Sukhoon
Published: May 2022

The core body temperature tends to decrease under general anesthesia. Consequently, monitoring the core body temperature during procedures involving general anesthesia is essential to ensure patient safety. In veterinary medicine, rectal temperature is used as an indicator of the core body temperature, owing to the accuracy and convenience of this approach. Some previous studies involving craniotomy reported differences between the brain and core temperatures under general anesthesia. However, noninvasive imaging techniques are required to ascertain this because invasive brain temperature measurements can cause unintended temperature changes by inserting the temperature sensors into the brain or by performing the surgical operations. In this study, we employed in vivo magnetic resonance thermometry to observe the brain temperatures of patients under general anesthesia using the proton resonance frequency shift method. The rectal temperature was also recorded using a fiber optic thermometer during the MR thermometry to compare with the brain temperature changes. When the rectal temperature decreased by 1.4 ± 0.5 °C (mean ± standard deviation), the brain temperature (white matter) decreased by 4.8 ± 0.5 °C. Furthermore, a difference in the temperature reduction of the different types of brain tissue was observed; the reduction in the temperature of white matter exceeded that of gray matter mainly due to the distribution of blood vessels in the gray matter. We also analyzed and interpreted the core temperature changes with the body conditioning scores of subjects to see how the body weight affected the temperature changes.
The Heavy-Ion Beam Diagnostic of the ISTTOK Tokamak—Highlights and Recent Developments
Authors: Malaquias A. , Nedzelskiy I. S. , Henriques R. , Sharma R.
Published: May 2022

The unique arrangement of the heavy-ion beam diagnostic in ISTTOK enables one to measure the evolution of temperature, density and pressure-like profiles in normal and AC discharges. The fast chopping beam technique provided the possibility to reduce the noise on the measurements of the plasma pressure-like profile and for the precise control of the plasma column position in real time. The consequent improvements in S/N levels allowed the observation of the effects of runaway beam magnetic energy conversion into plasma local heating. In addition, it made it possible to follow the evolution of the quiescent plasma maintained during AC transitions when the plasma current is null. The use of a new operation mode in the cylindrical energy analyzer provided an improved resolution up to five times in determining the fluctuations of the plasma potential as compared to the normal operation mode. Such analyzer is extremely compact (250 mm × 250 mm × 120 mm) and provides a unique geometry in order to cover the whole plasma diameter. The detector configuration choice gives the possibility for the simultaneous measurements of plasma poloidal magnetic field, plasma pressure-like and plasma potential profiles together with their fluctuations.
A 1.8–2.7 GHz Triple-Band Low Noise Amplifier with 31.5 dB Dynamic Range of Power Gain and Adaptive Power Consumption for LTE Application
Authors: Asl S. Ali Hosseini , Rad Reza E. , Rikan Behnam S. , Pu YoungGun , Hwang Keum Cheol , Yang Youngoo , Lee Kang-Yoon
Published: May 2022

This paper presents a multi-gain radio frequency (RF) front-end low noise amplifier (LNA) utilizing a multi-core based on the source degeneration topology. The LNA can cover a wide range of input and output frequency matching by using a receiver (RX) switch at the input and a capacitor bank at the output of the LNA. In the proposed architecture here, to avoid the saturation of RX chain, 12 gain steps including positive, 0 dB, and negative power gains are controlled by a mobile industry processor interface (MIPI). The multi-core architecture offers the ability to control the power consumption over different gain steps. In order to avoid the phase discontinuity, the negative gain steps are provided using an active amplification and T-type attenuation path that keeps the phase discontinuity below ±5 degrees between two adjacent power gain steps. Using the multi-core structure, the power consumption is optimized in different power gains. The structure is enhanced with the adaptive variable cores and reactance parameters to maintain different power consumption for different gain steps and remain the output matching in an acceptable operating range. Furthermore, auxiliary linearization circuitries are added to improve the input third intercept point (IIP3) performance of the LNA. The chip is fabricated in 65 nm complementary metal-oxide semiconductor (CMOS) silicon on insulator (SOI) process and the die area is 0.308 mm2. The proposed architecture achieves the IIP3 performance of −10.2 dBm and 8.6 dBm in the highest and lowest power gains, which are 20.5 dB and −11 dB, respectively. It offers the noise figure (NF) performance of 1.15 dB in the highest power gain while it reaches 14 dB when the power gain is −11 dB. The LNA consumes 16.8 mA and 1.33 mA current from a 1 V power supply that is provided by an on-chip low-dropout (LDO) when it operates at the highest and lowest gains, respectively.
A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
Authors: Xu Dongting , Zhang Zhisheng , Shi Jinfei
Published: May 2022

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models.