bluuebunny/crossref_metadata_2025 huggingface dataset:
Interfaces Based on Laser-Structured Arrays of Carbon Nanotubes with Albumin for Electrical Stimulation of Heart Cell Growth
Authors: Gerasimenko Alexander Yu. , Kitsyuk Evgeny , Kurilova Uliana E. , Suetina Irina A. , Russu Leonid , Mezentseva Marina V. , Markov Aleksandr , Narovlyansky Alexander N. , Kravchenko Sergei , Selishchev Sergey V. , Glukhova Olga E.
Published: May 2022

Successful formation of electronic interfaces between living cells and electronic components requires both good cell viability and performance level. This paper presents a technology for the formation of nanostructured arrays of multi-walled carbon nanotubes (MWCNT) in biopolymer (albumin) layer for higher biocompatibility. The layer of liquid albumin dispersion was sprayed on synthesized MWCNT arrays by deposition system. These nanostructures were engineered using the nanosecond pulsed laser radiation mapping in the near-IR spectral range (λ = 1064 nm). It was determined that the energy density of 0.015 J/cm2 provided a sufficient structuring of MWCNT. The structuring effect occurred during the formation of C–C bonds simultaneously with the formation of a cellular structure of nanotubes in the albumin matrix. It led to a decrease in the nanotube defectiveness, which was observed during the Raman spectroscopy. In addition, laser structuring led to a more than twofold increase in the electrical conductivity of MWCNT arrays with albumin (215.8 ± 10 S/m). Successful electric stimulation of cells on the interfaces with the system based on a culture plate was performed, resulting in the enhanced cell proliferation. Overall, the MWCNT laser-structured arrays with biopolymers might be a promising material for extended biomedical applications.
Equilibrium Biosorption of Zn2+ and Ni2+ Ions from Monometallic and Bimetallic Solutions by Crab Shell Biomass
Authors: Morales-Barrera Liliana , Cristiani-Urbina Eliseo
Published: May 2022

This work explored the technical feasibility of using crab shell (CS) as a promising, low-cost biosorbent to individually and simultaneously remove Zn2+ and Ni2+ from aqueous solutions. It was found that in both monometallic and bimetallic systems, Zn2+ and Ni2+ biosorption by CS was strongly dependent on the solution pH, with the optimum biosorption occurring at a pH of 6.0 for both heavy metals. The obtained isotherms for Zn2+ and Ni2+ biosorption onto CS in monometallic and bimetallic systems demonstrated that CS has a higher affinity for Zn2+ than for Ni2+. The experimental equilibrium data for the bimetallic system revealed that when one heavy metal is present in the system, there is a decrease in the equilibrium biosorption capacity for the other heavy metal; therefore, the combined action of Zn2+ and Ni2+ was antagonistic. The Sips and Redlich–Peterson isotherm models best fitted the equilibrium biosorption data for Zn2+ and Ni2+ in the monometallic systems, while the modified Sips model best fitted the binary biosorption equilibrium data. DRIFTS analyses indicated that carbonate ion, chitin, and proteins are mainly involved in the biosorption of Zn2+ and Ni2+ by CS from aqueous solutions, as confirmed using a range of analytical techniques.
Microwave-Driven Electrodeless Ultraviolet Lamp Based on Coaxial Slot Radiator
Authors: Huang Yuqing , Zhong Nanya , Zhu Huacheng , Huang Kama
Published: May 2022

Microwave-driven electrodeless ultraviolet (UV) lamps have the advantages of high efficiency and high power. However, the conventional microwave system is slightly oversized, which restricts the use of the lamp in a narrow space. A miniaturized microwave-driven electrodeless UV lamp based on a coaxial slot antenna was developed in this study. First, the structure of slots was optimized using a finite-difference time-domain algorithm such that high efficiency of radiated energy could be achieved. Second, a complex model based on the Drude model and the electromagnetic theory was established to simulate the interaction between the microwave and UV lamps. The efficiency and uniformity of the UV lamps were analyzed. Finally, an experimental system was built, and the computed results agreed well with the simulation results. The efficiency of the miniaturized microwave-driven electrodeless UV lamp reached 91.8%.
Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula
Authors: Rai Vikrant , Agrawal Devendra K.
Published: May 2022

Arteriovenous fistulas (AVFs), created for hemodialysis in end-stage renal disease patients, mature through the outward remodeling of the outflow vein. However, early thrombosis and chronic inflammation are detrimental to the process of AVF maturation and precipitate AVF maturation failure. For the successful remodeling of the outflow vein, blood flow through the fistula is essential, but early arterial thrombosis attenuates this blood flow, and the vessels become thrombosed and stenosed, leading to AVF failure. The altered expression of various proteins involved in maintaining vessel patency or thrombosis is regulated by genes of which the expression is regulated by transcription factors and microRNAs. In this study, using thrombosed and stenosed arteries following AVF creation, we delineated transcription factors and microRNAs associated with differentially expressed genes in bulk RNA sequencing data using upstream and causal network analysis. We observed changes in many transcription factors and microRNAs that are involved in angiogenesis; vascular smooth muscle cell proliferation, migration, and phenotypic changes; endothelial cell function; hypoxia; oxidative stress; vessel remodeling; immune responses; and inflammation. These factors and microRNAs play a critical role in the underlying molecular mechanisms in AVF maturation. We also observed epigenetic factors involved in gene regulation associated with these molecular mechanisms. The results of this study indicate the importance of investigating the transcriptional and epigenetic regulation of AVF maturation and maturation failure and targeting factors precipitating early thrombosis and stenosis.
Religious Literacy in National Curricula of Estonia
Authors: Sooniste Aleksandra , Schihalejev Olga
Published: May 2022

Religious literacy may be seen as a prerequisite for religious freedom. This article analyzes how the development of religious literacy is supported in the curricula of Estonian primary and secondary schools and the extent to which these schools guarantee religious freedom. We assume that developing religious literacy is not limited to the lessons of Religious Education but can also be achieved in schools in which Religious Education is not taught. We analyze the national curricula for both basic and upper secondary schools to understand how religious literacy is represented, whether implicitly or explicitly. We are particularly interested in how the competencies of religious literacy are supported in the curricula and how freedom of religion is ensured in state-funded schools. The texts are analyzed according to the core curriculum analysis method and the religious literacy model, which identifies four stages in the development of religious literacy: examining religion as a category, engaging with a disposition towards religions, building up relevant knowledge, and promoting skills to interact well in multicultural society. This analysis shows that the dominant discourse related to religious literacy in the curricula focuses on social skills for future citizens, but religious literacy itself, a vital skill for operating in the modern multicultural world, is scarcely mentioned. Instead, it is present primarily as an implicit concept, and religion is portrayed as distant both in time and in space. Thus, this approach to education contributes insufficiently to the freedom of religion needed in a contemporary multicultural society.
Caring for Elderly People with Severe Conditions: Do Religious Education and Values Make a Difference?
Authors: Oviedo Lluis , Torralba Josefa , Ripoll Jose Luis
Published: May 2022

Western societies are experiencing a demographic shift towards an increased elderly population that is often affected by limiting conditions. This growth is not always balanced by a greater concern and assistance from younger cohorts, which could perceive that demand as a heavy burden, especially under conditions where children often move to work far away from their families. The present research tries to understand this challenging situation by studying the attitudes of preadolescents and adolescents towards the elderly. This study has explored the reactions of students between 12 and 17 years old towards images and experiences around the oldest and most frail. The collected data indicate the positive role of religion and spirituality in their concern, and the moderating factor of empathy, with gender and age being less significant in their attitudes towards the elderly in their social environment.
Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
Authors: Luo Jiawei , Zhang Yongwei , Yang Jianyu , Zhang Donghui , Zhang Yongchao , Zhang Yin , Huang Yulin , Jakobsson Andreas
Published: May 2022

The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from OK3 to OK2. Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
Authors: Shang Xinyi , Cheng Xiao , Zheng Lei , Liang Qi , Chi Zhaohui
Published: May 2022

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km2, which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems.
Flood Management, Characterization and Vulnerability Analysis Using an Integrated RS-GIS and 2D Hydrodynamic Modelling Approach: The Case of Deg Nullah, Pakistan
Authors: Ahmad Ijaz , Wang Xiuquan , Waseem Muhammad , Zaman Muhammad , Aziz Farhan , Khan Rana Zain Nabi , Ashraf Muhammad
Published: May 2022

One-dimensional (1D) hydraulic models have been extensively used to conduct flood simulations for investigating flood depth and extent maps. However, the 1D models cannot simulate many other flood characteristics, such as flood velocity, duration, arrival time and recession time when the flow is not restricted within the channel. These flood characteristics cannot be disregarded as they play an important role in developing flood mitigation and evacuation strategies. This study formulates a two-dimensional (2D) hydrodynamic model combined with remote sensing (RS) and geographic information system (GIS) approach to generate additional flood characteristic maps that cannot be produced with 1D models. The model was applied to a transboundary river of Deg Nullah in Pakistan to simulate an extreme flood event experience in 2014. The flood extent images from the moderate resolution imaging spectroradiometer (MODIS) and observed flood extents were used to evaluate the model performance. Moreover, an entropy distance-based approach was proposed to facilitate the integrated multivariate flood vulnerability classification. The simulated 2D flood modeling results showed a good agreement with the flood extents registered by MODIS and the observed ones. The northwest parts of Deg Nullah near Seowal, Dullam Kahalwan and Zafarwal were the most vulnerable areas due to high flood depths and prolonged flooding duration. Whereas high flood velocities, short flood arrival time, prolonged flood duration and recession times were observed in the upper reach of Deg Nullah thereby making it the most susceptible, critical and vulnerable region to flooding events.
Analyses of a Lake Dust Source in the Middle East through Models Performance
Authors: Hamzeh Nasim Hossein , Ranjbar Saadat Abadi Abbas , Chel Gee Ooi Maggie Chel Gee , Habibi Maral , Schöner Wolfgang
Published: May 2022

Drying lakes have become a new source of dust, causing severe problems in surrounding areas. From 2000 to 2017, a statistical study was conducted on Lake Urmia in Iran in the Middle East. The results indicated a significant increase in the annual number of dusty days in stations around the lake and the mean annual aerosol optical depth (AOD) at 550 nm. The sharp decrease in annual snowfall rate over the Lake Urmia area since 2007 has been linked to the lake’s decreasing water level and drying. During a dust storm event from 27 October to 31 October 2017, a local dust storm originated from Lake Urmia before another large-scale dust storm originated from the An-Nafud desert. According to MODIS true-color images, dust particles were lifted from Lake Urmia and transported eastward to the Caspian Sea and the HYSPLIT model. The comparison of the four models under the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) revealed that the models overestimated surface dust concentrations compared to ground-based PM10 measurements. Nevertheless, the NOAA/WRF-Chem and DREAMABOL models simulated higher dust concentrations during the dust period. More emphasis should be placed on the development of dust models for SDS-WAS models in Lake Urmia.
SAR-BagNet: An Ante-hoc Interpretable Recognition Model Based on Deep Network for SAR Image
Authors: Li Peng , Feng Cunqian , Hu Xiaowei , Tang Zixiang
Published: May 2022

Convolutional neural networks (CNNs) have been widely used in SAR image recognition and have achieved high recognition accuracy on some public datasets. However, due to the opacity of the decision-making mechanism, the reliability and credibility of CNNs are insufficient at present, which hinders their application in some important fields such as SAR image recognition. In recent years, various interpretable network structures have been proposed to discern the relationship between a CNN’s decision and image regions. Unfortunately, most interpretable networks are based on optical images, which have poor recognition performance for SAR images, and most of them cannot accurately explain the relationship between image parts and classification decisions. Based on the above problems, in this study, we present SAR-BagNet, which is a novel interpretable recognition framework for SAR images. SAR-BagNet can provide a clear heatmap that can accurately reflect the impact of each part of a SAR image on the final network decision. Except for the good interpretability, SAR-BagNet also has high recognition accuracy and can achieve 98.25% test accuracy.
Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan
Authors: Saleem Farhan , Arshad Arfan , Mirchi Ali , Khaliq Tasneem , Zeng Xiaodong , Rahman Md Masudur , Dilawar Adil , Pham Quoc Bao , Mahmood Kashif
Published: May 2022

Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region.
Variations in Flow Patterns in the Northern Taiwan Strait Observed by Satellite-Tracked Drifters
Authors: Lu Ching-Yuan , Hsu Po-Chun , Zheng Quanan , Ho Chung-Ru
Published: May 2022

This study investigates the variations in flow patterns in the northern Taiwan Strait in summer using high-frequency (HF) radar measurements, satellite-tracked drifter trajectories and numerical models. There is an obvious interaction between intra-diurnal tides and ocean currents in northwestern Taiwan. When the tide changes between high tide and low tide, the change in direction of the nearshore flow occurs before the change in the offshore flow. Drifter trajectories show that there are three different drifting paths in the Taiwan Strait in summer. One path is along the west coast of Taiwan from the southwest coast to the northeast coast. Another path is the same as the first one but leads northward to the East China Sea instead of eastward to the northeast coast of Taiwan. The other path exists along the west coast of Taiwan, some distance out, after being deflected by the bottom ridge. The regional ocean modeling system model was used in this study to clarify the influencing factors that lead to these three paths. The results of multiple simulations and HF radar data indicate that the bifurcation of the first two drift paths in northwestern Taiwan is caused by ebb and flood tide transitions. The different routes of the latter two paths are due to the significant speed difference between the nearshore current and the offshore current approximately 45 km from the coast.
Thaw Settlement Monitoring and Active Layer Thickness Retrieval Using Time Series COSMO-SkyMed Imagery in Iqaluit Airport
Authors: Ma Deying , Motagh Mahdi , Liu Guoxiang , Zhang Rui , Wang Xiaowen , Zhang Bo , Xiang Wei , Yu Bing
Published: May 2022

Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone.
Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images
Authors: Li Zhuqiang , Chen Shengbo , Meng Xiangyu , Zhu Ruifei , Lu Junyan , Cao Lisai , Lu Peng
Published: May 2022

The quantity and quality of cropland are the key to ensuring the sustainable development of national agriculture. Remote sensing technology can accurately and timely detect the surface information, and objectively reflect the state and changes of the ground objects. Using high-resolution remote sensing images to accurately extract cropland is the basic task of precision agriculture. The traditional model of cropland semantic segmentation based on the deep learning network is to down-sample high-resolution feature maps to low resolution, and then restore from low-resolution feature maps to high-resolution ideas; that is, obtain low-resolution feature maps through a network, and then recover to high resolution by up-sampling or deconvolution. This will bring about the loss of features, and the segmented image will be more fragmented, without very clear and smooth boundaries. A new methodology for the effective and accurate semantic segmentation cropland of high spatial resolution remote sensing images is presented in this paper. First, a multi-temporal sub-meter cropland sample dataset is automatically constructed based on the prior result data. Then, a fully convolutional neural network combined with contextual feature representation (HRNet-CFR) is improved to complete the extraction of cropland. Finally, the initial semantic segmentation results are optimized by the morphological post-processing approach, and the broken spots are ablated to obtain the internal homogeneous cropland. The proposed method has been validated on the Jilin-1 data and Gaofen Image Dataset (GID) public datasets, and the experimental results demonstrate that it outperforms the state-of-the-art method in cropland extraction accuracy. We selected the comparison of Deeplabv3+ and UPerNet methods in GID. The overall accuracy of our approach is 92.03%, which is 3.4% higher than Deeplabv3+ and 5.12% higher than UperNet.
Multi-Rotor UAV-Borne PolInSAR Data Processing and Preliminary Analysis of Height Inversion in Urban Area
Authors: Lv Zexin , Qiu Xiaolan , Cheng Yao , Shangguan Songtao , Li Fangfang , Ding Chibiao
Published: May 2022

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) has many useful applications, especially in forest areas. With the development of SAR miniaturization technology, researchers can install PolInSAR on small unmanned aerial vehicles (UAV), which can reduce flight costs. Limited by size and power, UAV-borne SAR usually works in a high-frequency band, which restricts its application to such things as vegetation height inversion. While on the other hand, the high resolution acquired under a short wavelength promises its application in urban areas. However, there are fewer studies on the application of PolInSAR in urban areas compared with that in forest areas. In this paper, we propose a processing method for a Ku-band multi-rotor-UAV-borne PolInSAR and provide a preliminary analysis of height inversion results on its data from the Fudan campus in Shanghai. We obtain the digital surface model (DSM) of different polarization modes and the DSM of polarimetric interferometry optimal decomposition in this area, whose RMSE is 2.88 m. On this basis, the elevation inversion results of targets such as buildings, lampposts, and trees are compared and analyzed. We preliminarily explore and analyze the reasons for the different results of different targets. To this end, we propose a mathematical derivation of the relationship between the interferometric phase between PolInSAR and InSAR of Pauli decomposition. We also perform a simulation to analyze the relationship between the phase center height of Pauli decomposition and PolInSAR under different cases. It provides a reference for the application of small UAV-borne PolInSAR in urban areas.
Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020
Authors: Baqa Muhammad Fahad , Lu Linlin , Chen Fang , Nawaz-ul-Huda Syed , Pan Luyang , Tariq Aqil , Qureshi Salman , Li Bin , Li Qingting
Published: May 2022

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.
NNetEn2D: Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping
Authors: Velichko Andrei , Wagner Matthias P. , Taravat Alireza , Hobbs Bruce , Ord Alison
Published: May 2022

Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method parameters and image rotation. To overcome these difficulties, this study proposes a new method for estimating two-dimensional neural network entropy (NNetEn2D) for evaluating the regularity or predictability of images using the LogNNet neural network model. The method is based on an algorithm for converting a 2D kernel into a 1D data series followed by NNetEn2D calculation. An artificial test image was created for the study. We demonstrate the advantage of using circular instead of square kernels through comparison of the invariance of the NNetEn2D distribution after image rotation. Highest robustness was observed for circular kernels with a radius of R = 5 and R = 6 pixels, with a NNetEn2D calculation error of no more than 10%, comparable to the distortion of the initial 2D data. The NNetEn2D entropy calculation method has two main geometric parameters (kernel radius and its displacement step), as well as two neural network hyperparameters (number of training epochs and one of six reservoir filling techniques). We evaluated our method on both remote sensing and geophysical mapping images. Remote sensing imagery (Sentinel-2) shows that brightness of the image does not affect results, which helps keep a rather consistent appearance of entropy maps over time without saturation effects being observed. Surfaces with little texture, such as water bodies, have low NNetEn2D values, while urban areas have consistently high values. Application to geophysical mapping of rocks to the northwest of southwest Australia is characterized by low to medium entropy and highlights aspects of the geology. These results indicate the success of NNetEn2D in providing meaningful entropy information for 2D in remote sensing and geophysical applications.
Improved Clear Sky Model from In Situ Observations and Spatial Distribution of Aerosol Optical Depth for Satellite-Derived Solar Irradiance over the Korean Peninsula
Authors: Kim Chang Ki , Kim Hyun-Goo , Kang Yong-Heack
Published: May 2022

In solar resource assessment, the climatological environment of the target area is objectively quantified by the cloudiness or clear sky index, which is defined as the ratio of global horizontal irradiance to clear sky solar insolation. The clear sky model calculates incoming solar irradiance on the ground surface considering several atmospheric parameters such as water vapor and aerosol optical depth. This study investigated the importance of aerosol optical depth for deriving clear sky irradiance in radiative transfer models and examined its viability in a universal or community model for public use. The evaluation was conducted based on ground observations at the Korea Institute of Energy Research (KIER) station from January to December 2021. The original simulation was performed using the monthly mean of aerosol optical depth obtained from the Aerosol Robotic Network station; the mean absolute error was 29.9 W m−2. When the daily mean of in situ observations at KIER was incorporated into the clear sky model, the mean absolute error was reduced to 9.7 W m−2. Our results confirm that the clear sky model using gridded datasets of aerosol optical depth is suitable for use as a universal or community model.
Using Remote Sensing Data and Species–Environmental Matching Model to Predict the Potential Distribution of Grassland Rodents in the Northern China
Authors: Lu Longhui , Sun Zhongxiang , Qimuge Eerdeng , Ye Huichun , Huang Wenjiang , Nie Chaojia , Wang Kun , Zhou Yantao
Published: May 2022

An increase in grassland rodent pests in China has seriously affected grassland ecological environments and the development of husbandry. Here, we used remote sensing data and a species–environmental matching model to predict the potential spatial distribution of the five major rodent pest species (Microtus, Citellus, Myospalax, Meriones, Ochotona) in northern China, and examined how the predicted suitability of the area depends on environmental variables. The results were consistent and significant, better than random, and close to optimal. Meriones and Microtus had the largest areas of High Suitability and Moderate Suitability with regard to environmental conditions. The combination analysis of areas of Moderate Suitability and High Suitability showed that for 66% of the total area, conditions were suitable for just one rodent species, while conditions suitable for two and three kinds of rodents accounted for 31% and 3%, respectively. Altitude, land surface temperature in winter (November, December, February) and summer (May, June, July), vegetation cover in summer (July, August), and precipitation from spring to summer (April, May, June) determined the spatial distribution of grassland rodents. Our findings provide a powerful and useful methodological tool for tracking the five major rodent pest species in northern China and for future management measures to ensure grassland ecological environment security.
Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images
Authors: Curtarelli Marcelo Pedroso , Kurtz Diego Jacob , Salgueiro Taisa Pereira
Published: May 2022

In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions.
The Use of Sentinel-3/OLCI for Monitoring the Water Quality and Optical Water Types in the Largest Portuguese Reservoir
Authors: Rodrigues Gonçalo , Potes Miguel , Penha Alexandra Marchã , Costa Maria João , Morais Maria Manuela
Published: May 2022

The Alqueva reservoir is essential for water supply in the Alentejo region (south of Portugal). Satellite data are essential to overcome the temporal and spatial limitations of in situ measurements, ensuring continuous and global water quality monitoring. Data between 2017 and 2020, obtained from OLCI (Ocean and Land Color Instrument) aboard Sentinel-3, were explored. Two different methods were used to assess the water quality in the reservoir: K-means to group reflectance spectra into different optical water types (OWT), and empirical algorithms to estimate water quality parameters. Spatial (in five different areas in the reservoir) and temporal (monthly) variations of OWT and water quality parameters were analyzed, namely, Secchi depth, water turbidity, chlorophyll a, and phycocyanin concentrations. One cluster has been identified representing the typical spectra of the presence of microalgae in the reservoir, mainly between July and October and more intense in the northern region of the Alqueva reservoir. An OWT type representing the area of the reservoir with the highest transparency and lowest chlorophyll a concentration was defined. The methodology proposed is suitable to continuously monitor the water quality of Alqueva reservoir, constituting a useful contribution to a potential early warning system for identification of critical areas corresponding to cyanobacterial algae blooms.
Comparison of the Spatial and Temporal Variability of Cloud Amounts over China Derived from Different Satellite Datasets
Authors: Wang Yuxi , Lin Zhaohui , Wu Chenglai
Published: May 2022

Various cloud cover products have been developed over the past few decades, but their uncertainties have not been sufficiently assessed, especially at a regional scale, which is vital for the application of satellite products to climate studies. In this study, we compare the spatial–temporal variability of the cloud amount over China from the 11 datasets provided by the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project at a horizontal resolution of 1° × 1° from the 1980s to 2000s, using the site data as a reference. The differences among these datasets are quantified in terms of the standard deviations and the correlation coefficients between different datasets. Most of the datasets show a similar spatial distribution of total cloud amounts (TCAs), but their magnitudes differ. The standard deviations of the annual, winter, and summer mean TCA are approximately 9–18% for the regional mean TCAs over the four typical regions of China, including the northwestern region (NW), northeastern region (NE), Tibetan Plateau region (TP), and southern China region (SC), with the largest standard deviations of 13–18% in the TP. By analyzing the factors that influence the satellite inversion data, such as the observation instrument, inversion algorithm, and observation time, we found that the difference caused by the observation instrument or algorithm is greater than the effect of the observation time, and the satellite cloud datasets with better recognition capability for cloud types show lower uncertainties when compared with the station observation. In terms of seasonal cycle, except HIRS and MODIS-ST, most satellite datasets can reproduce the observed seasonal cycle with the largest TCA in summer and the smallest TCA in autumn and winter. For the interannual variation, ISCCP-D1, MODIS-CE, and MODIS-ST are most consistent with the site data for the annual mean TCA, and two of the remaining datasets (PATMOSX and TOVSB) show more consistent temporal variations with the site observation in summer than in winter, especially over NW and NE regions. In general, MODIS-CE shows the best performance in reproducing the spatial pattern and interannual variation of TCA amongst the 11 satellite datasets, and PATMOSX, MODIS-ST, CALIPSO-GOCCP, and CALIPSO-ST also show relatively good performance.
Oriented Ship Detector for Remote Sensing Imagery Based on Pairwise Branch Detection Head and SAR Feature Enhancement
Authors: He Bokun , Zhang Qingyi , Tong Ming , He Chu
Published: May 2022

Recently, object detection in natural images has made a breakthrough, but it is still challenging in oriented ship detection for remote sensing imagery. Considering some limitations in this task, such as uncertain ship orientation, unspecific features for locating and classification in the complex optical environment, and multiplicative speckle interference of synthetic aperture radar (SAR), we propose an oriented ship detector based on the pairwise branch detection head and adaptive SAR feature enhancement. The details are as follows: (1) Firstly, the ships with arbitrary directions are described with a rotated ground truth, and an oriented region proposal network (ORPN) is designed to study the transformation from the horizontal region of interest to the rotated region of interest. The ORPN effectively improved the quality of the candidate area while only introducing a few parameters. (2) In view of the existing algorithms that tend to perform classification and regression prediction on the same output feature, this paper proposes a pairwise detection head (PBH) to design parallel branches to decouple classification and locating tasks, so that each branch can learn more task-specific features. (3) Inspired by the ratio-of-average detector in traditional SAR image processing, the SAR edge enhancement (SEE) module is proposed, which adaptively enhances edge pixels, and the threshold of the edge is learned by the channel-shared adaptive thresholds block. Experiments were carried out on both optical and SAR datasets. In the optical dataset, PBH combined with ORPN improved recall by 5.03%, and in the SAR dataset, the overall method achieved a maximum F1 score improvement of 6.07%; these results imply the validity of our method.
Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model
Authors: Chen Peng , Zhou Hui , Li Ying , Liu Bingxin , Liu Peng
Published: May 2022

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.
A Novel Transformer-Based Attention Network for Image Dehazing
Authors: Gao Guanlei , Cao Jie , Bao Chun , Hao Qun , Ma Aoqi , Li Gang
Published: May 2022

Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance.
The Innovative Component of the Technological Structure of the Economy of Russia and Foreign Countries: International Comparative Analysis
Authors: Guren T. V. , Romanov A. A.
Published: May 2022

The article considers the characteristics of technological structures that reflect a certain level of development of productive forces and industrial relations, the science, and the emergence and development of new energy sources in Russia and some foreign countries taking the lead in the global innovation space. The main factors that determine the innovative performance of the state are identified: from laying the foundation for its creative basis (intellectual potential) to the production effectiveness and adoption intensity of innovations.

Based on a representative volume of information sources published in official statistical collections (both domestic and international) and some research findings, the authors assessed core indicators of the innovative development of Russia in comparison with foreign countries. The paper considers Russia's position in international rankings (the Global Innovation Index, the Networked Readiness Index and the Global Competitiveness Index).

Analysis results obtained using statistical methods allowed the authors to formulate several crucial conclusions regarding the development level of the technological structure of the Russian economy, its place in international rankings, and the global innovation space.