bluuebunny/crossref_metadata_2025 huggingface dataset:
Near Unity PLQY and High Stability of Barium Thiocyanate Based All-Inorganic Perovskites and Their Applications in White Light-Emitting Diodes
Authors: Adhikari Gopi Chandra , Thapa Saroj , Yue Yang , Zhu Hongyang , Zhu Peifen
Published: June 2021

All-inorganic lead halide perovskite (CsPbX3) nanocrystals (NCs) have emerged as a highly promising new generation of light emitters due to their extraordinary photophysical properties. However, the performance of these semiconducting NCs is undermined due to the inherent toxicity of lead and long-term environmental stability. Here, we report the addition of B-site cation and X-site anion (pseudo-halide) concurrently using Ba(SCN)2 (≤50%) in CsPbX3 NCs to reduce the lead and improve the photophysical properties and stability. The as-grown particles demonstrated an analogous structure with an almost identical lattice constant and a fluctuation of particle size without altering the morphology of particles. Photoluminescence quantum yield is enhanced up to near unity (~98%) by taking advantage of concomitant doping at the B- and X-site of the structure. Benefitted from the defect reductions and stronger bonding interaction between Pb2+ and SCN− ions, Ba(SCN)2-based NCs exhibit improved stability towards air and moisture compared to the host NCs. The doped NCs retain higher PLQY (as high as seven times) compared to the host NCs) when stored in an ambient atmosphere for more than 176 days. A novel 3D-printed multiplex color conversion layer was used to fabricate a white light-emitting diode (LED). The obtained white light shows a correlated color temperature of 6764 K, a color rendering index of 87, and luminous efficacy of radiation of 333 lm/W. In summary, this work proposes a facile route to treat sensitive lead halide perovskite NCs and to fabricate LEDs by using a low-cost large-scale 3-D printing method, which would serve as a foundation for fabricating high-quality optoelectronic devices for near future lighting technologies.
Differences in Nectar Traits between Ornithophilous and Entomophilous Plants on Mount Cameroon
Authors: Janeček Štěpán , Chmel Kryštof , Ewome Francis Luma , Hrubá Karolína , Klomberg Yannick , Kobe Ishmeal N. , Kouede Raissa Dywou , Mertens Jan E. J. , Njie Marcus Mokake , Tropek Robert
Published: June 2021

Despite a growing number of studies, the role of pollinators as a selection agent for nectar traits remains unclear. Moreover, the lack of data from some biogeographic regions prohibits us from determining their general importance and global patterns. We analyzed nectar carbohydrate traits and determined the main pollinators of 66 plant species in the tropical forests of Mount Cameroon (tropical West Africa). The measured nectar traits included total sugar amounts and proportions of sucrose and hexoses (i.e., glucose and fructose). We report the nectar properties for plants visited by five pollinator groups (bees, butterflies, moths, hoverflies, and specialized birds). Our results indicate that, rather than specific evolution in each of the five plant groups, there was a unique nectar-trait evolution in plants pollinated by specialized birds. The ornithophilous plants had a higher proportion of sucrose and produced larger sugar amounts than the plants pollinated by insects. We also demonstrated a significant phylogenetic signal in the nectar properties in some lineages of the studied plants.
Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat
Authors: Lv Dongyun , Zhang Chuanliang , Yv Rui , Yao Jianxin , Wu Jianhui , Song Xiaopeng , Jian Juntao , Song Pengbo , Zhang Zeyuan , Han Dejun , Sun Daojie
Published: June 2021

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39–12.26% and 20.11–27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43–6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7–8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43–6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72–9.59%, 5.62–7.75%, and 6.68–10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.
Genomic Prediction across Structured Hybrid Populations and Environments in Maize
Authors: Li Dongdong , Xu Zhenxiang , Gu Riliang , Wang Pingxi , Xu Jialiang , Du Dengxiang , Fu Junjie , Wang Jianhua , Zhang Hongwei , Wang Guoying
Published: June 2021

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.
Effect of Immersion in Water or Alkali Solution on the Structures and Properties of Epoxy Resin
Authors: Wang Bin , Li Dihui , Xian Guijun , Li Chenggao
Published: June 2021

The durability of fiber-reinforced polymer (FRP) composites is significantly dependent on the structures and properties of the resin matrix. In the present paper, the effects of physical or chemical interactions between the molecular chain of the epoxy resin matrix and water molecules or alkaline groups on the water absorption, mechanical structures, and microstructures of epoxy resin samples were studied experimentally. The results showed that the water uptake curves of the epoxy resin immersed in water and an alkali solution over time presented a three-stage variation. At different immersion stages, the water uptake behavior of the resin showed unique characteristics owing to the coupling effects of the solution concentration gradient diffusion, molecular hydrolysis reaction, and molecular segment movement. In comparison with the water immersion, the alkali solution environment promoted the hydrolysis reaction of the epoxy resin molecular chain. After the immersion in water or the alkali solution for one month, the water uptake of the resin was close to saturate, and the viscoelasticity was observed to decrease significantly. The micropore and free volume space on the surface and in the interior of the resin gradually increased, while the original large-scale free volume space decreased. The tensile strength decreased to the lowest point after the immersion in water and the alkali solution for one month, and the decrease percentages at 20 °C and 60 °C water or 60 °C alkali solution were 24%, 28%, and 22%, respectively. Afterward, the tensile strength recovered with the further extension of immersion time. In addition, it can be found that the effect of the alkali solution and water on the tensile strength of the epoxy resin was basically the same.
A Versatile Method for Preparing Polysaccharide Conjugates via Thiol-Michael Addition
Authors: Chen Junyi , Ma Xutao , Edgar Kevin J.
Published: June 2021

Polysaccharide conjugates are important renewable materials. If properly designed, they may for example be able to carry drugs, be proactive (e.g., with amino acid substituents) and can carry a charge. These aspects can be particularly useful for biomedical applications. Herein, we report a simple approach to preparing polysaccharide conjugates. Thiol-Michael additions can be mild, modular, and efficient, making them useful tools for post-modification and the tailoring of polysaccharide architecture. In this study, hydroxypropyl cellulose (HPC) and dextran (Dex) were modified by methacrylation. The resulting polysaccharide, bearing α,β-unsaturated esters with tunable DS (methacrylate), was reacted with various thiols, including 2-thioethylamine, cysteine, and thiol functional quaternary ammonium salt through thiol-Michael addition, affording functionalized conjugates. This click-like synthetic approach provided several advantages including a fast reaction rate, high conversion, and the use of water as a solvent. Among these polysaccharide conjugates, the ones bearing quaternary ammonium salts exhibited competitive antimicrobial performance, as supported by a minimum inhibitory concentration (MIC) study and tracked by SEM characterization. Overall, this methodology provides a versatile route to polysaccharide conjugates with diverse functionalities, enabling applications such as antimicrobial activity, gene or drug delivery, and biomimicry.
Non-Newtonian Droplet Generation in a Cross-Junction Microfluidic Channel
Authors: Fatehifar Maryam , Revell Alistair , Jabbari Masoud
Published: June 2021

A two-dimensional CFD model based on volume-of-fluid (VOF) is introduced to examine droplet generation in a cross-junction microfluidic using an open-source software, OpenFOAM together with an interFoam solver. Non-Newtonian power-law droplets in Newtonian liquid is numerically studied and its effect on droplet size and detachment time in three different regimes, i.e., squeezing, dripping and jetting, are investigated. To understand the droplet formation mechanism, the shear-thinning behaviour was enhanced by increasing the polymer concentrations in the dispersed phase. It is observed that by choosing a shear-dependent fluid, droplet size decreases compared to Newtonian fluids while detachment time increases due to higher apparent viscosity. Moreover, the rheological parameters—n and K in the power-law model—impose a considerable effect on the droplet size and detachment time, especially in the dripping and jetting regimes. Those parameters also have the potential to change the formation regime if the capillary number (Ca) is high enough. This work extends the understanding of non-Newtonian droplet formation in microfluidics to control the droplet characteristics in applications involving shear-thinning polymeric solutions.
The Influence of Reaction Time on Non-Covalent Functionalisation of P3HT/MWCNT Nanocomposites
Authors: Nurazzi N.M. , Abdullah N. , Demon S.Z.N. , Halim N.A. , Mohamad I.S.
Published: June 2021

Non-covalent functionalisation of the carbon nanotube (CNT) sidewall through polymer wrapping is the key strategy for improving well-dispersed CNTs without persistent alteration of their electronic properties. In this work, the effect of reaction time on regioregular poly (3-hexylthiophene-2,5-diyl) (P3HT)-wrapped hydroxylated multi-walled CNT (MWCNT-OH) nanocomposites was investigated. Five different reaction times (24, 48, 72, 96, and 120 h) were conducted at room temperature in order to clearly determine the factors that influenced the quality of wrapped MWCNT-OH. Morphological analysis using Field Emission Scanning Electron Microscopic (FESEM) and High-Resolution Transmission Electron Microscope (HRTEM) analysis showed that P3HT successfully wrapped the MWCNT-OH sidewall, evidenced by the changes in the mean diameter size of the nanocomposites. Results obtained from Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS) as well as Thermogravimetric Analysis (TGA) showed a significant effect of the wrapped polymer on the CNT sidewall as the reaction time increased. Overall, the method used during the preparation of P3HT-wrapped MWCNT-OH and the presented results significantly provided a bottom-up approach to determine the effect of different reaction times on polymer wrapping to further expand this material for novel applications, especially chemical sensors.
Effect of Solvent Extraction and Blanching Pre-Treatment on Phytochemical, Antioxidant Properties, Enzyme Inactivation and Antibacterial Activities of ‘Wonderful’ Pomegranate Peel Extracts
Authors: Magangana Tandokazi Pamela , Makunga Nokwanda P. , Amos Fawole Olaniyi , Opara Umezuruike Linus
Published: June 2021

‘Wonderful’ pomegranate (Punica granatum L.) peel is rich in phytochemicals which are responsible for its strong antioxidant and antimicrobial activities, but it has low economic value as it is mainly discarded, causing an environmental waste management problem. To examine the best processing regime for pomegranate peel wastes, different solvents (ethanol, methanol and acetone) at various concentrations (50%, 70% and 100%) and blanching at 60, 80 and 100 °C for 1, 3 and 5 min, for each temperature, were tested. Ethanol at 70% (v/v) provided the highest extract yield, total phenolic and total tannin content at 29.46%, 10.61 ± 0.15, and 0.76 ± 0.02 mg GAE/g DM, respectively. Antioxidant activity using the 2,2 diphenyl-1-picryl hydrazyl assay (DPPH), ferric-reducing antioxidant power assay (FRAP) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid assay (ABTS) were reported at 243.97 ± 2.43, 478.04 ± 73.98 and 718.79 ± 2.42 µmol Trolox/g DM, respectively. A blanching temperature of 80 °C for 3 min led to the highest extract that had a total phenolic content of 12.22 ± 0.08 mg GAE/g DM and total tannin content of 1.06 ± 0.06 mg GAE/g DM. This extract also exhibited the best antioxidant activity for the DPPH, FRAP and ABTS assays. Two blanching temperatures, 80 or 100 °C, significantly reduced polyphenol oxidase and peroxidase activities (p < 0.05). Although blanched peel extracts showed a broad-spectrum activity against test bacteria, blanching at 80 °C for 3 or 5 min was most effective. Hot water blanching is thus a suitable environmentally friendly post-harvesting processing method for pomegranate peels that are intended for use as extracts in value-added products with good antioxidant and antibacterial effects.
Removal of Micropollutants by Ozone-Based Processes
Authors: Derco Ján , Gotvajn Andreja Žgajnar , Čižmárová Oľga , Dudáš Jozef , Sumegová Lenka , Šimovičová Katarína
Published: June 2021

Micropollutants and emerging substances pose a serious problem to environmental sustainability and remediation, due to their widespread use and applications in everyday life. This group of chemicals is diverse but with common toxic and harmful properties. Their concentration in the environment is often very low; however, due to their recalcitrant nature, they are persistent in air, water, and soil. From an engineering point of view, the challenge is not straightforward. It is difficult to remove these contaminants from complex mixtures of substances by conventional methods used in wastewater and drinking water treatment. Ozonation and ozone-based AOPs are accepted processes of degradation of resistant substances or at least enhancement of their biodegradability. The aim of this review paper is to present research trends aimed at solving problems in the research and application of ozone-based processes in the removal of micropollutants from wastewater, thus preventing leakage of harmful substances into surface water, soil, and groundwater and facilitating the reuse of wastewater. Priority substances, micropollutants and emerging pollutants, as well as processes and technologies for their transformation and elimination, are briefly specified. Results obtained by the authors in solving research projects that were aimed at eliminating selected micropollutants by ozonation and ozone-based AOPs are also presented. This review focuses on selected alkylphenols, petroleum substances, and organochlorine pesticides.
Advanced Fiber Type-Specific Protein Profiles Derived from Adult Murine Skeletal Muscle
Authors: Eggers Britta , Schork Karin , Turewicz Michael , Barkovits Katalin , Eisenacher Martin , Schröder Rolf , Clemen Christoph S. , Marcus Katrin
Published: June 2021

Skeletal muscle is a heterogeneous tissue consisting of blood vessels, connective tissue, and muscle fibers. The last are highly adaptive and can change their molecular composition depending on external and internal factors, such as exercise, age, and disease. Thus, examination of the skeletal muscles at the fiber type level is essential to detect potential alterations. Therefore, we established a protocol in which myosin heavy chain isoform immunolabeled muscle fibers were laser microdissected and separately investigated by mass spectrometry to develop advanced proteomic profiles of all murine skeletal muscle fiber types. All data are available via ProteomeXchange with the identifier PXD025359. Our in-depth mass spectrometric analysis revealed unique fiber type protein profiles, confirming fiber type-specific metabolic properties and revealing a more versatile function of type IIx fibers. Furthermore, we found that multiple myopathy-associated proteins were enriched in type I and IIa fibers. To further optimize the assignment of fiber types based on the protein profile, we developed a hypothesis-free machine-learning approach, identified a discriminative peptide panel, and confirmed our panel using a public data set.
Þingeyrar Abbey in Northern Iceland: A Benedictine Powerhouse of Cultural Heritage
Authors: Jensson Gottskálk
Published: June 2021

Þingeyrar Abbey was founded in 1133 and dissolved in the wake of the Lutheran Reformation (1550), to virtually disappear with time from the face of the earth. Although highly promising archeological excavations are under way, our material points of access to this important monastic foundation are still only a handful of medieval artifacts. However, throughout its medieval existence Þingeyrar Abbey was an inordinately large producer of Latin and Icelandic literature. We have the names of monastic authors, poets, translators, compilators, and scribes, who engaged creatively with such diverse subjects as Christian hagiography, contemporary history, and Norse mythology, skillfully amalgamating all of this into a coherent, imaginative whole. Thus, Þingeyrar Abbey has a prominent place in the creation and preservation of the Icelandic Eddas and Sagas that have shaped the Northern European cultural memory. Despite the dissolution of monastic libraries and wholesale destruction of Icelandic-Latin manuscripts through a mixture of Protestant zealotry and parchment reuse, philologists have been able to trace a number of surviving codices and fragments back to Þingeyrar Abbey. Ultimately, however, our primary points of access to the fascinating world of this remote Benedictine community remain immaterial, a vast corpus of medieval texts edited on the basis of manuscript copies at unknown degrees of separation from the lost originals.
The Psycho-Didactic Approach in Religious and Moral Education. Towards Personal Growth and Positive Mental Health of Students
Authors: Steć Małgorzata , Kulik Małgorzata Maria
Published: June 2021

The psycho-didactic approach in education is becoming more and more popular. Its supporters emphasize the great values that are associated with the implementation of this approach to various fields of science and school subjects. The greatest value of the psycho-didactic approach is supporting the personal growth of the learner by respecting the developmental factors involved in each subject’s learning processes. Psycho-didactics could also be used in the design of core curricula and their implementation in the field of religious, ethical, and moral education. By supporting personal development through moral and religious education, it is possible to simultaneously influence the agencyof each learner. Personal agency in self-development, as well as in religious and moral development, is one of the greatest challenges for modern education and one of the most significant issues of positive mental health advancement in the field of education. Within psycho-didactics, it becomes possible to foster every student in an individual way, therefore all methods and techniques used in the psycho-didactic approach are focused on the student’s needs.
On the Identification, Evaluation and Treatment of Risks in Smart Homes: A Systematic Literature Review
Authors: Iten Raphael , Wagner Joël , Zeier Röschmann Angela
Published: June 2021

The emergence of smart technologies in homes comes with various services and functions for everyday life. While a smart home (SH) is associated with great potential in terms of comfort and risk treatment, it also introduces new and alters existing risks. Despite a growing number of academic studies on SH risks, research is fragmented with regard to its focus on certain disciplines and is still rather technology-focused. In this paper, we fill this gap by providing a comprehensive understanding of relevant risks through a systematic literature review. Following the guidelines of the PRISMA reporting protocol, we search 1196 academic and practitioners’ publications related to household risks or risk perceptions of SH users. A final set of 59 records results in three main themes. They include (1) a synthesis of pre-existing and emerging risks sketching the new risk landscape of SH households, (2) a discussion of the prevailing risk evaluation methods, and (3) a presentation of SH-related risk treatment options with a particular emphasis on insurance. We specify the influence of SH on risks and risk perception, and highlight the relevance of analyzing the interconnection of risks in complex systems, such as SH. Our review lays the basis for assessing SH risks and for enabling more comprehensive and effective risk management optimization.
An Interval Temporal Logic for Time Series Specification and Data Integration
Authors: Kosiuczenko Piotr
Published: June 2021

The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.
Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations
Authors: Zhang Huixian , Hagan Daniel Fiifi Tawia , Dalagnol Ricardo , Liu Yi
Published: June 2021

Canopy dynamics associated with fires in tropical forests play a critical role in the terrestrial carbon cycle and climate feedbacks. The aim of this study was to characterize forest canopy dynamics in the southern Amazon during the 2019 fire season (July–October) using passive microwave-based vegetation optical depth (VOD) and three optical-based indices. First, we found that precipitation during July–October 2019 was close to the climatic means, suggesting that there were no extreme hydrometeorological events in 2019 and that fire was the dominant factor causing forest canopy anomalies. Second, based on the active fire product (MCD14ML), the total number of active fires over each grid cell was calculated for each month. The number of active fires during the fire season in 2019 was above average, particularly in August and September. Third, we compared the anomalies of VOD and optical-based indices (the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burn ratio (NBR)) against the spatiotemporal distribution of fires during July–October 2019. Spatially, the location with a concentrated distribution of significant negative VOD anomalies was matched with the grid cells with fire activities, whereas the concentrated distribution of strong negative anomalies in optical-based indices were found in both burned and unburned grid cells. When we focused on the temporal pattern over the grid cells with fire activity, the VOD and the optical-based indices behaved similarly from July to October 2019, i.e., the magnitude of negative anomalies became stronger with increased fire occurrences and reached the peak of negative anomalies in September before decreasing in October. A discrepancy was observed in the magnitude of negative anomalies of the optical-based indices and the VOD; the magnitude of optical-based indices was larger than the VOD in August–September and recovered much faster than the VOD over the grid cells with relatively low fire activity in October. The most likely reason for their different responses is that the VOD represents the dynamics of both photosynthetic (leaf) and nonphotosynthetic (branches) biomass, whereas optical-based indices are only sensitive to photosynthetic (leaf) active biomass, which recovers faster. Our results demonstrate that VOD can detect the spatiotemporal of canopy dynamics caused by fire and postfire canopy biomass recovery over high-biomass rainforest, which enables more comprehensive assessments, together with classic optical remote sensing approaches.
The Use of InSAR Phase Coherence Analyses for the Monitoring of Aeolian Erosion
Authors: Kim Jung-Rack , Lin Cheng-Wei , Lin Shih-Yuan
Published: June 2021

Aeolian erosion occurring in sand deserts causes significant socio-economical threats over extensive areas through mineral dust storm generation and soil degradation. To monitor a sequence of aeolian erosion in a sand desert area, we developed an approach fusing a set of remote sensing data. Vegetation index and Interferometric Synthetic Aperture Radar (InSAR) phase coherence derived from space-borne optical/SAR remote sensing data were used. This scheme was applied to Kubuqi Desert in Inner Mongolia where the effects of activity to combat desertification could be used to verify the outcome of the approach. We first established time series phase coherence and conducted a functional operation based on principal component analysis (PCA) to remove uncorrelated noise. Then, through decomposition of vegetation effect, where a regression model together with the Enhanced Vegetation Index (EVI) was employed, we estimated surface migration caused by aeolian interaction, that is, the aeolian erosion rate (AER). AER metrics were normalized and validated by additional satellite and ground data. As a result, the spatiotemporal migration of the target environment, which certainly induced dust storm generation, was traced and analyzed based on the correlations among surface characteristics. It was revealed that the derived AER successfully monitored the surface changes that occurred before and after the activities to combat desertification in the target area. Employing the established observation scheme, we expect a better understanding of the aeolian process in sand deserts with enhanced spatio-temporal resolution. In addition, the scheme will be beneficial for the evaluation of combating desertification activities and early warning of dust storm generations.
Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes
Authors: Liu Jianzhao , Zuo Yunjiang , Wang Nannan , Yuan Fenghui , Zhu Xinhao , Zhang Lihua , Zhang Jingwei , Sun Ying , Guo Ziyu , Guo Yuedong , Song Xia , Song Changchun , Xu Xiaofeng
Published: June 2021

The net ecosystem CO2 exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R2 = 0.66~0.71, normal: R2 = 0.78~0.81; extreme dryness: R2 = 0.14~0.30, normal: R2 = 0.54~0.55), but the impact on forest is less (extreme heat: R2 = 0.50~0.78, normal: R2 = 0.59~0.87; extreme dryness: R2 = 0.86~0.90, normal: R2 = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R2 changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R2 decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R2 decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of >2.5 years to reach a good model performance and >5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.
Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data
Authors: Hennessy Andrew , Clarke Kenneth , Lewis Megan
Published: June 2021

New, accurate and generalizable methods are required to transform the ever-increasing amount of raw hyperspectral data into actionable knowledge for applications such as environmental monitoring and precision agriculture. Here, we apply advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation data, whilst maintaining class relationships. Specifically, a Generative Adversarial Network (GAN) is trained using the Cramér distance on two vegetation hyperspectral datasets, demonstrating the ability to approximate the distribution of the training samples. Evaluation of the synthetic spectra shows that they respect many of the statistical properties of the real spectra, conforming well to the sampled distributions of all real classes. Creation of an augmented dataset consisting of synthetic and original samples was used to train multiple classifiers, with increases in classification accuracy seen under almost all circumstances. Both datasets showed improvements in classification accuracy ranging from a modest 0.16% for the Indian Pines set and a substantial increase of 7.0% for the New Zealand vegetation. Selection of synthetic samples from sparse or outlying regions of the feature space of real spectral classes demonstrated increased discriminatory power over those from more central portions of the distributions.
Recommendations for HCHO and SO2 Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions
Authors: Javed Zeeshan , Tanvir Aimon , Bilal Muhammad , Su Wenjing , Xia Congzi , Rehman Abdul , Zhang Yuanyuan , Sandhu Osama , Xing Chengzhi , Ji Xiangguang , Xie Mingjie , Liu Cheng , Wang Yuhang
Published: June 2021

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO2) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO2, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO2 vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO2 in fog droplets.
Climate Variability May Delay Post-Fire Recovery of Boreal Forest in Southern Siberia, Russia
Authors: Sun Qiaoqi , Burrell Arden , Barrett Kirsten , Kukavskaya Elena , Buryak Ludmila , Kaduk Jörg , Baxter Robert
Published: June 2021

Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive shifts in post-fire plant successional trajectories. The response of post-fire vegetation communities to climate variability is under-studied, despite being a critical phase determining the ultimate successional conclusion. This study investigated the responses of post-fire recruited species to climate change and inter-annual variability at 16 study sites that experienced high-severity fire events, mostly in early 2000, within the Scots pine forest-steppe zone of southeastern Siberia, Russia. These sites were originally dominated by Scots pine, and by 2018, they were recruited by different successional species. Additionally, three mature Scots pine stands were included for comparison. A Bayesian Additive Regression Trees (BART) approach was used to model the relationship between Landsat-derived Normalized Difference Vegetation Index (NDVI) time series, temperature and precipitation in the 15 years after a stand-replacing fire. Using the resulting BART models, together with six projected climate scenarios with increased temperature and enhanced inner-annual precipitation variability, we simulated NDVI at 5-year intervals for 15 years post-fire. Our results show that the BART models performed well, with in-sample Pseudo-R2 varying from 0.49 to 0.95 for fire-disturbed sites. Increased temperature enhanced greenness across all sites and across all three time periods since fires, exhibiting a positive feedback in a warming environment. Repeatedly dry spring periods reduced NDVI at all the sites and wetter summer periods following such dry springs could not compensate for this, indicating that a prolonged dry spring has a strong impact consistently over the entire early developmental stages from the initial 5 years to 15 years post-fire. Further, young forests showed higher climate sensitivity compared to the mature forest, irrespective of species and projected climatic conditions. Our findings suggest that a dry spring not only increases fire risk, but also delays recovery of boreal forests in southern Siberia. It also highlights the importance of changing rainfall seasonality as well as total rainfall in a changing climate for post-fire recovery of forest.
Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi
Authors: Shammi Sadia Alam , Meng Qingmin
Published: June 2021

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.
Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network
Authors: Han Yanling , Shi Xi , Yang Shuhu , Zhang Yun , Hong Zhonghua , Zhou Ruyan
Published: June 2021

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.
Development of a Shoreline Detection Method Using an Artificial Neural Network Based on Satellite SAR Imagery
Authors: Tajima Yoshimitsu , Wu Lianhui , Watanabe Kunihiro
Published: June 2021

Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles.
Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data
Authors: Mamun Md , Ferdous Jannatul , An Kwang-Guk
Published: June 2021

The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (r) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (r = −0.79) and CHL-a (r = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (r = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (r = −0.76). Considering the r values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir.