bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Environmental Conditions on the Quality of UAS Orthophoto-Maps in the Coastal Environment
Authors: Doukari Michaela , Katsanevakis Stelios , Soulakellis Nikolaos , Topouzelis Konstantinos
Published: January 2021

Marine conservation and management require detailed and accurate habitat mapping, which is usually produced by collecting data using remote sensing methods. In recent years, unmanned aerial systems (UAS) are used for marine data acquisition, as they provide detailed and reliable information through very high-resolution orthophoto-maps. However, as for all remotely sensed data, it is important to study and understand the accuracy and reliability of the produced maps. In this study, the effect of different environmental conditions on the quality of UAS orthophoto-maps was examined through a positional and thematic accuracy assessment. Selected objects on the orthophoto-maps were also assessed as to their position, shape, and extent. The accuracy assessment results showed significant errors in the different maps and objects. The accuracy of the classified images varied between 2.1% and 27%. Seagrasses were under-classified, while the mixed substrate class was overclassified when environmental conditions were not optimal. The highest misclassifications were caused due to sunglint presence in combination with a rough sea-surface. A change detection workflow resulted in detecting misclassifications of up to 45%, on orthophoto-maps that had been generated under non-optimal environmental conditions. The results confirmed the importance of optimal conditions for the acquisition of reliable marine information using UAS.
Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds
Authors: Tsai Fuan , Lai Jhe-Syuan , Nguyen Kieu Anh , Chen Walter
Published: January 2021

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%.
L-Arginine Exerts Excellent Anti-Stress Effects on Stress-Induced Shortened Lifespan, Cognitive Decline and Depression
Authors: Pervin Monira , Unno Keiko , Konishi Tomokazu , Nakamura Yoriyuki
Published: January 2021

The anti-stress potential of dietary L-arginine (Arg) was assessed in psychosocially stress-loaded senescence-accelerated (SAMP10) mice. Although this strain of mouse is sensitive to stress, daily administration of Arg at 3 mg/kg significantly suppressed aging-related cognitive decline and behavioral depression at nine months of age and counteracted stress-induced shortened lifespan. To investigate the mechanism of the anti-stress effect of Arg in the brain, early changes in oxidative damage and gene expression levels were measured using SAMP10 mice that were stress-loaded for three days. Increased lipid peroxidation in the brains of stressed mice was significantly lowered by Arg intake. Several genes associated with oxidative stress response and neuronal excitotoxic cell death, including Nr4a1, Arc, and Cyr61, remarkably increased in response to psychosocial stress; however, their expression was significantly suppressed in mice that ingested Arg even under stress conditions. In contrast, the genes that maintain mitochondrial functions and neuronal survival, including Hba-a2 and Hbb-b2, were significantly increased in mice that ingested Arg. These results indicate that Arg reduces oxidative damage and enhances mitochondrial functions in the brain. We suggest that the daily intake of Arg plays important roles in reducing stress-induced brain damage and slowing aging.
N-Glycosylation as a Tool to Study Antithrombin Secretion, Conformation, and Function
Authors: Águila Sonia , Noto Rosina , Luengo-Gil Ginés , Espín Salvador , Bohdan Nataliya , de la Morena-Barrio María Eugenia , Peñas Julia , Rodenas Maria Carmen , Vicente Vicente , Corral Javier , Manno Mauro , Martínez-Martínez Irene
Published: January 2021

N-linked glycosylation is a crucial post-translational modification involved in protein folding, function, and clearance. N-linked glycosylation is also used therapeutically to enhance the half-lives of many proteins. Antithrombin, a serpin with four potential N-glycosylation sites, plays a pivotal role in hemostasis, wherein its deficiency significantly increases thrombotic risk. In this study, we used the introduction of N-glycosylation sites as a tool to explore what effect this glycosylation has on the protein folding, secretion, and function of this key anticoagulant. To accomplish this task, we introduced an additional N-glycosylation sequence in each strand. Interestingly, all regions that likely fold rapidly or were surrounded by lysines were not glycosylated even though an N-glycosylation sequon was present. The new sequon in the strands of the A- and B-sheets reduced secretion, and the B-sheet was more sensitive to these changes. However, the mutations in the strands of the C-sheet allowed correct folding and secretion, which resulted in functional variants. Therefore, our study revealed crucial regions for antithrombin secretion and could potentially apply to all serpins. These results could also help us understand the functional effects of natural variants causing type-I deficiencies.
Identifying the miRNA Signature Association with Aging-Related Senescence in Glioblastoma
Authors: Gnanavel Mutharasu , Murugesan Akshaya , Konda Mani Saravanan , Yli-Harja Olli , Kandhavelu Meenakshisundaram
Published: January 2021

Glioblastoma (GBM) is the most common malignant brain tumor and its malignant phenotypic characteristics are classified as grade IV tumors. Molecular interactions, such as protein–protein, protein–ncRNA, and protein–peptide interactions are crucial to transfer the signaling communications in cellular signaling pathways. Evidences suggest that signaling pathways of stem cells are also activated, which helps the propagation of GBM. Hence, it is important to identify a common signaling pathway that could be visible from multiple GBM gene expression data. microRNA signaling is considered important in GBM signaling, which needs further validation. We performed a high-throughput analysis using micro array expression profiles from 574 samples to explore the role of non-coding RNAs in the disease progression and unique signaling communication in GBM. A series of computational methods involving miRNA expression, gene ontology (GO) based gene enrichment, pathway mapping, and annotation from metabolic pathways databases, and network analysis were used for the analysis. Our study revealed the physiological roles of many known and novel miRNAs in cancer signaling, especially concerning signaling in cancer progression and proliferation. Overall, the results revealed a strong connection with stress induced senescence, significant miRNA targets for cell cycle arrest, and many common signaling pathways to GBM in the network.
Nitric Oxide and S-Nitrosylation in Cardiac Regulation: G Protein-Coupled Receptor Kinase-2 and β-Arrestins as Targets
Authors: Kayki-Mutlu Gizem , Koch Walter J.
Published: January 2021

Cardiac diseases including heart failure (HF), are the leading cause of morbidity and mortality globally. Among the prominent characteristics of HF is the loss of β-adrenoceptor (AR)-mediated inotropic reserve. This is primarily due to the derangements in myocardial regulatory signaling proteins, G protein-coupled receptor (GPCR) kinases (GRKs) and β-arrestins (β-Arr) that modulate β-AR signal termination via receptor desensitization and downregulation. GRK2 and β-Arr2 activities are elevated in the heart after injury/stress and participate in HF through receptor inactivation. These GPCR regulators are modulated profoundly by nitric oxide (NO) produced by NO synthase (NOS) enzymes through S-nitrosylation due to receptor-coupled NO generation. S-nitrosylation, which is NO-mediated modification of protein cysteine residues to generate an S-nitrosothiol (SNO), mediates many effects of NO independently from its canonical guanylyl cyclase/cGMP/protein kinase G signaling. Herein, we review the knowledge on the NO system in the heart and S-nitrosylation-dependent modifications of myocardial GPCR signaling components GRKs and β-Arrs.
Sustainable Investments in Responsible SMEs: That’s What’s Distinguish Government VCs from Private VCs
Authors: Johansson Jeaneth , Malmström Malin , Wincent Joakim
Published: January 2021

Researchers question the impact of governmental venture capitalists (GVC) compared to private venture capitalists (PVC), but we know little about why this difference occurs and if this criticism is justified. We observed a group of GVCs and developed a new model that describes the way that GVCs process signals pre- and post-decisions. Certain macro level factors severely undermine micro level performance, causing GVCs to financially underperform with respect to PVCs. This helped us to understand that GVCs do not make investment decisions in the same way as PVCs, and what undermines the performance of GVCs’ decision-making processes. The main goals of GVCs are to promote investments in responsible SMEs, mobilizing societal impact. We discuss that the criticism of GVC needs to be more nuanced, as they have a different role than PVC in the financial system as providers of sustainable investments in responsible SMEs.
Mechanical Performance and Environmental Assessment of Sustainable Concrete Reinforced with Recycled End-of-Life Tyre Fibres
Authors: Pawelska-Mazur Magdalena , Kaszynska Maria
Published: January 2021

The presented research’s main objective was to develop the solution to the global problem of using steel waste obtained during rubber recovery during the tire recycling. A detailed comparative analysis of mechanical and physical features of the concrete composite with the addition of recycled steel fibres (RSF) in relation to the steel fibre concrete commonly used for industrial floors was conducted. A study was carried out using micro-computed tomography and the scanning electron microscope to determine the fibres’ characteristics, incl. the EDS spectrum. In order to designate the full performance of the physical and mechanical features of the novel composite, a wide range of tests was performed with particular emphasis on the determination of the tensile strength of the composite. This parameter appointed by tensile strength testing for splitting, residual tensile strength test (3-point test), and a wedge splitting test (WST), demonstrated the increase of tensile strength (vs unmodified concrete) by 43%, 30%, and 70% relevantly to the method. The indication of the reinforced composite’s fracture characteristics using the digital image correlation (DIC) method allowed to illustrate the map of deformation of the samples during WST. The novel composite was tested in reference to the circular economy concept and showed 31.3% lower energy consumption and 30.8% lower CO2 emissions than a commonly used fibre concrete.
Pseudococyclic Partial Hadamard Matrices over Latin Rectangles
Authors: Falcón Raúl M. , Álvarez Víctor , Frau María Dolores , Gudiel Félix , Güemes María Belén
Published: January 2021

The classical design of cocyclic Hadamard matrices has recently been generalized by means of both the notions of the cocycle of Hadamard matrices over Latin rectangles and the pseudococycle of Hadamard matrices over quasigroups. This paper delves into this topic by introducing the concept of the pseudococycle of a partial Hadamard matrix over a Latin rectangle, whose fundamentals are comprehensively studied and illustrated.
Applying Heath-Jarrow-Morton Model to Forecasting the US Treasury Daily Yield Curve Rates
Authors: Maltsev Valerii , Pokojovy Michael
Published: January 2021

The Heath-Jarrow-Morton (HJM) model is a powerful instrument for describing the stochastic evolution of interest rate curves under no-arbitrage assumption. An important feature of the HJM approach is the fact that the drifts can be expressed as functions of respective volatilities and the underlying correlation structure. Aimed at researchers and practitioners, the purpose of this article is to present a self-contained, but concise review of the abstract HJM framework founded upon the theory of interest and stochastic partial differential equations in infinite dimensions. To illustrate the predictive power of this theory, we apply it to modeling and forecasting the US Treasury daily yield curve rates. We fit a non-parametric model to real data available from the US Department of the Treasury and illustrate its statistical performance in forecasting future yield curve rates.
Sampling Method Affects HR-MAS NMR Spectra of Healthy Caprine Brain Biopsies
Authors: Häni Annakatrin , Diserens Gaëlle , Oevermann Anna , Vermathen Peter , Precht Christina
Published: January 2021

The metabolic profiling of tissue biopsies using high-resolution–magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy may be influenced by experimental factors such as the sampling method. Therefore, we compared the effects of two different sampling methods on the metabolome of brain tissue obtained from the brainstem and thalamus of healthy goats by 1H HR-MAS NMR spectroscopy—in vivo-harvested biopsy by a minimally invasive stereotactic approach compared with postmortem-harvested sample by dissection with a scalpel. Lactate and creatine were elevated, and choline-containing compounds were altered in the postmortem compared to the in vivo-harvested samples, demonstrating rapid changes most likely due to sample ischemia. In addition, in the brainstem samples acetate and inositols, and in the thalamus samples ƴ-aminobutyric acid, were relatively increased postmortem, demonstrating regional differences in tissue degradation. In conclusion, in vivo-harvested brain biopsies show different metabolic alterations compared to postmortem-harvested samples, reflecting less tissue degradation. Sampling method and brain region should be taken into account in the analysis of metabolic profiles. To be as close as possible to the actual situation in the living individual, it is desirable to use brain samples obtained by stereotactic biopsy whenever possible.
Publisher’s Note: Continued Publication of Microbiology Research by MDPI
Authors: Lin Shu-Kun , Galera Laura , Vazquez Franck
Published: January 2021

Launched in 2010, Microbiology Research has been published over the past 10 years by PAGEPress Publications [...]
Fabrication and Characterization of Double- and Single-Clamped CuO Nanowire Based Nanoelectromechanical Switches
Authors: Jasulaneca Liga , Livshits Alexander I. , Meija Raimonds , Kosmaca Jelena , Sondors Raitis , Ramma Matiss M. , Jevdokimovs Daniels , Prikulis Juris , Erts Donats
Published: January 2021

Electrostatically actuated nanoelectromechanical (NEM) switches hold promise for operation with sharply defined ON/OFF states, high ON/OFF current ratio, low OFF state power consumption, and a compact design. The present challenge for the development of nanoelectromechanical system (NEMS) technology is fabrication of single nanowire based NEM switches. In this work, we demonstrate the first application of CuO nanowires as NEM switch active elements. We develop bottom-up and top-down approaches for NEM switch fabrication, such as CuO nanowire synthesis, lithography, etching, dielectrophoretic alignment of nanowires on electrodes, and nanomanipulations for building devices that are suitable for scalable production. Theoretical modelling finds the device geometry that is necessary for volatile switching. The modelling results are validated by constructing gateless double-clamped and single-clamped devices on-chip that show robust and repeatable switching. The proposed design and fabrication route enable the scalable integration of bottom-up synthesized nanowires in NEMS.
Modeling Root Exudate Accumulation Gradients to Estimate Net Exudation Rates by Peatland Soil Depth
Authors: Proctor Cameron , He Yuhong
Published: January 2021

Root exudates accumulate as a radial gradient around the root, yet little is known about variability at the individual root level. Vertical gradients in soil properties are hypothesized to cause greater accumulation of exudates in deeper soil through hindering diffusion, increasing sorption, and decreasing mineralization. To this end, a single root exudation model coupling concentration specific exudation and depth dependent soil properties was developed. The model was parameterized for a peatland ecosystem to explore deposition to the methanogen community. Numerical experiments indicate that exudates accumulated to a greater extent in deeper soil, albeit the effect was solute specific. Rhizosphere size for glucose doubled between the 10 and 80 cm depths, while the rhizoplane concentration was 1.23 times higher. Root influx of glucose increased from 1.431 to 1.758 nmol cm−1 hr−1, representing a recapture efficiency gain of 15.74% (i.e., 69.06% versus 84.8%). Driven by increased root influx, overall net exudation rates of select sugars and amino acids varied by a factor two. Model sensitivity analysis revealed that soil depth and root influx capability are key determinants of the rhizoplane concentration and subsequently net exudation, which determines whether effluxed compounds escape the root oxic shell and are available to the methanogen community.
Comparative and Phylogenetic Analysis of Complete Chloroplast Genomes in Eragrostideae (Chloridoideae, Poaceae)
Authors: Liu Kuan , Wang Rong , Guo Xiu-Xiu , Zhang Xue-Jie , Qu Xiao-Jian , Fan Shou-Jin
Published: January 2021

Eragrostideae Stapf, the second-largest tribe in Chloridoideae (Poaceae), is a taxonomically complex tribe. In this study, chloroplast genomes of 13 Eragrostideae species were newly sequenced and used to resolve the phylogenetic relationships within Eragrostideae. Including seven reported chloroplast genomes from Eragrostideae, the genome structure, number and type of genes, codon usage, and repeat sequences of 20 Eragrostideae species were analyzed. The length of these chloroplast genomes varied from 130,773 bp to 135,322 bp. These chloroplast genomes showed a typical quadripartite structure, including a large single-copy region (77,993–80,643 bp), a small single-copy region (12,410–12,668 bp), and a pair of inverted repeats region (19,394–21,074 bp). There were, in total, 129–133 genes annotated in the genome, including 83–87 protein-coding genes, eight rRNA genes, and 38 tRNA genes. Forward and palindromic repeats were the most common repeat types. In total, 10 hypervariable regions (rpl22, rpoA, ndhF, matK, trnG–UCC-trnT–GGU, ndhF–rpl32, ycf4–cemA, rpl32–trnL–UAG, trnG–GCC–trnfM–CAU, and ccsA–ndhD) were found, which can be used as candidate molecular markers for Eragrostideae. Phylogenomic studies concluded that Enneapogon diverged first, and Eragrostis including Harpachne is the sister to Uniola. Furthermore, Harpachne harpachnoides is considered as a species of Eragrostis based on morphological and molecular evidence. In addition, the interspecies relationships within Eragrostis are resolved based on complete chloroplast genomes. This study provides useful chloroplast genomic information for further phylogenetic analysis of Eragrostideae.
The Appropriateness of Using Aquatic Snails as Bioindicators of Toxicity for Oil Sands Process-Affected Water
Authors: Chen Zhongzhi , Eaton Brian , Davies Jim
Published: January 2021

Canada’s oil sands mining activity produces large volumes of oil sands process-affected water (OSPW), and there have been increasing concerns regarding the potential environmental impacts associated with this material. Developing an understanding of the toxicity of OSPW is critical to anticipating and mitigating the potential risks and effects of the oil sands industry on surrounding ecosystems. The composition of OSPW is highly variable and is influenced by a range of factors. While numerous research projects have been conducted on the toxicity of OSPW, much remains unknown about its impact on various biota. Freshwater gastropods (snails and slugs) are an ecologically crucial aquatic group, and members of this taxa have been used as bioindicators in a range of ecological settings. The literature suggests freshwater snails could be used as an indicator of toxicity in monitoring programs associated with oil sands development. This mini-review explores the use of snails as bioindicators in aquatic systems affected by oil sands development, focusing on how snails may respond to potential constituents of concern in systems exposed to OSPW.
Alternatives to Pharmacological and Psychotherapeutic Treatments in Psychiatric Disorders
Authors: Vetrano Ignazio Gaspare , Bonomo Giulio , Messina Giuseppe
Published: January 2021

Nowadays, most of the patients affected by psychiatric disorders are successfully treated with psychotherapy and pharmacotherapy. Nevertheless, according to the disease, a variable percentage of patients results resistant to such modalities, and alternative methods can then be considered. The purpose of this review is to summarize the techniques and results of invasive modalities for several treatment-resistant psychiatric diseases. A literature search was performed to provide an up-to-date review of advantages, disadvantages, efficacy, and complications of Deep-Brain Stimulation, Magnetic Resonance-guided Focused-Ultrasound, radiofrequency, and radiotherapy lesioning for depression, obsessive-compulsive disorder, schizophrenia, addiction, anorexia nervosa, and Tourette’s syndrome. The literature search did not strictly follow the criteria for a systematic review: due to the large differences in methodologies and patients’ cohort, we tried to identify the highest quality of available evidence for each technique. We present the data as a comprehensive, narrative review about the role, indication, safety, and results of the contemporary instrumental techniques that opened new therapeutic fields for selected patients unresponsive to psychotherapy and pharmacotherapy.
Supply Chain Risk Management: Literature Review
Authors: Gurtu Amulya , Johny Jestin
Published: January 2021

The risks associated with global supply chain management has created a discourse among practitioners and academics. This is evident by the business uncertainties growing in supply chain management, which pose threats to the entire network flow and economy. This paper aims to review the existing literature on risk factors in supply chain management in an uncertain and competitive business environment. Papers that contained the word “risk” in their titles, keywords, or abstracts were selected for conducting the theoretical analyses. Supply chain risk management is an integral function of the supply network. It faces unpredictable challenges due to nations’ economic policies and globalization, which have raised uncertainty and challenges for supply chain organizations. These significantly affect the financial performance of the organizations and the economy of a nation. Debate on supply chain risk management may promote competitiveness in business. Risk mitigation strategies will reduce the impact caused due to natural and human-made disasters.
Minimal Expected Time in Drawdown through Investment for an Insurance Diffusion Model
Authors: Brinker Leonie Violetta
Published: January 2021

Consider an insurance company whose surplus is modelled by an arithmetic Brownian motion of not necessarily positive drift. Additionally, the insurer has the possibility to invest in a stock modelled by a geometric Brownian motion independent of the surplus. Our key variable is the (absolute) drawdown Δ of the surplus X, defined as the distance to its running maximum X¯. Large, long-lasting drawdowns are unfavourable for the insurance company. We consider the stochastic optimisation problem of minimising the expected time that the drawdown is larger than a positive critical value (weighted by a discounting factor) under investment. A fixed-point argument is used to show that the value function is the unique solution to the Hamilton–Jacobi–Bellman equation related to the problem. It turns out that the optimal investment strategy is given by a piecewise monotone and continuously differentiable function of the current drawdown. Several numerical examples illustrate our findings.
Satellite Based Fraction of Absorbed Photosynthetically Active Radiation Is Congruent with Plant Diversity in India
Authors: Mahanand Swapna , Behera Mukunda Dev , Roy Partha Sarathi , Kumar Priyankar , Barik Saroj Kanta , Srivastava Prashant Kumar
Published: January 2021

A dynamic habitat index (DHI) based on satellite derived biophysical proxy (fraction of absorbed photosynthetically active radiation, FAPAR) was used to evaluate the vegetation greenness pattern across deserts to alpine ecosystems in India that account to different biodiversity. The cumulative (DHI-cum), minimum (DHI-min), and seasonal (DHI-sea) DHI were generated using Moderate Resolution Imaging Spectroradiometer (MODIS)-based FAPAR. The higher DHI-cum and DHI-min represented the biodiversity hotspots of India, whereas the DHI-sea was higher in the semi-arid, the Gangetic plain, and the Deccan peninsula. The arid and the trans-Himalaya are dominated with grassland or barren land exhibit very high DHI-sea. The inter-year correlation demonstrated an increase in vegetation greenness in the semi-arid region, and continuous reduction in greenness in the Northeastern region. The DHI components validated using field-measured plant richness data from four biogeographic regions (semi-arid, eastern Ghats, the Western Ghats, and Northeast) demonstrated good congruence. DHI-cum that represents the annual greenness strongly correlated with the plant richness (R2 = 0.90, p-value < 0.001), thereby emerging as a suitable indicator for assessing plant richness in large-scale biogeographic studies. Overall, the FAPAR-based DHI components across Indian biogeographic regions provided understanding of natural variability of the greenness pattern and its congruence with plant diversity.
AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector
Authors: Yan Jiangqiao , Zhao Liangjin , Diao Wenhui , Wang Hongqi , Sun Xian
Published: January 2021

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.
Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing
Authors: Oh Sangmin , Lee Dongmin , Kim Minju , Kim Taehoon , Cho Hunhee
Published: January 2021

With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.
Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive
Authors: Chen Bin , Li Jing , Jin Yufang
Published: January 2021

Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016–2019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.
Mapping the Coastal Upwelling East of Taiwan Using Geostationary Satellite Data
Authors: Huang Zhi , Hu Jianyu , Shi Weian
Published: January 2021

Coastal upwelling is important for coastal ecosystems and the blue economy because of its large productivity and large potential for catching fish. However, coastal upwelling along the Taiwan east coast has received little attention from the research community. This study used five-year daily Himawari-8 geostationary satellite sea surface temperature data to map the coastal upwelling east of Taiwan during the summer monsoon season. We applied a semi-automatic image process technique based on the topographic position index for the quantitative upwelling mapping. The results show clear evidence of seasonal coastal upwelling along the entire Taiwan east coast, mainly under the influence of upwelling-favorable southwesterly/southerly winds. There are three broad upwelling centers along the Taiwan east coast: north, central, and south. The upwelling around the northern center has the longest upwelling season, lasting from May to September. The upwelling extents are larger between June and August during the height of the summer monsoon.
Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance
Authors: Crusiol  Luis Guilherme Teixeira , Nanni Marcos Rafael , Furlanetto Renato Herrig , Sibaldelli Rubson Natal Ribeiro , Cezar Everson , Sun Liang , Foloni José Salvador Simonetto , Mertz-Henning Liliane Marcia , Nepomuceno Alexandre Lima , Neumaier Norman , Farias José Renato Bouças
Published: January 2021

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.
Analyzing Changes in Frozen Soil in the Source Region of the Yellow River Using the MODIS Land Surface Temperature Products
Authors: Cao Huiyu , Gao Bing , Gong Tingting , Wang Bo
Published: January 2021

The degradation of the frozen soil in the Qinghai–Tibetan Plateau (QTP) caused by climate warming has attracted extensive worldwide attention due to its significant effects on the ecosystem and hydrological processes. In this study, we propose an effective approach to estimate the spatial distribution and changes in the frozen soil using the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products as inputs. A comparison with in-situ observations suggests that this method can accurately estimate the mean daily land surface temperature, the spatial distribution of the permafrost, and the maximum thickness of the seasonally-frozen ground in the source region of the Yellow River, located in the northeastern area of the QTP. The results of The Temperature at the Top of the Permafrost model indicates that the area of permafrost in the source region of the Yellow River decreased by 4.82% in the period from 2003 to 2019, with an increase in the areal mean air temperature of 0.35 °C/10 years. A high spatial heterogeneity in the frozen soil changes was revealed. The basin-averaged active layer thickness of the permafrost increased at a rate of 5.46 cm/10 years, and the basin-averaged maximum thickness of the seasonally-frozen ground decreased at a rate of 3.66 cm/10 years. The uncertainties in calculating the mean daily land surface temperature and the soil’s thermal conductivity were likely to influence the accuracy of the estimation of the spatial distribution of the permafrost and the maximum thickness of the seasonally-frozen ground, which highlight the importance of the better integration of field observations and multi-source remote sensing data in order to improve the modelling of frozen soil in the future. Overall, the approach proposed in this study may contribute to the improvement of the application of the MODIS land surface temperature data in the study of frozen soil changes in large catchments with limited in-situ observations in the QTP.
High-Performance Ultra-Thin Spectrometer Optical Design Based on Coddington’s Equations
Authors: Feng Zhiwei , Xia Guo , Lu Rongsheng , Cai Xiaobo , Cui Hao , Hu Mingyong
Published: January 2021

A unique method to design a high-throughput and high-resolution ultrathin Czerny–Turner (UTCT) spectrometer is proposed. This paper reveals an infrequent design process of spectrometers based on Coddington’s equations, which will lead us to develop a high-performance spectrometer from scratch. The spectrometer is composed of cylindrical elements except a planar grating. In the simulation design, spot radius is sub-pixel size, which means that almost all of the energy is collected by the detector. The spectral resolution is 0.4 nm at central wavelength and 0.75 nm at edge wavelength when the width of slit is chosen to be 25 μm and the groove density is 900 lines/mm.
BLE-Based Indoor Tracking System with Overlapping-Resistant IoT Solution for Tourism Applications
Authors: Belka Radosław , Deniziak Roman Stanisław , Łukawski Grzegorz , Pięta Paweł
Published: January 2021

In this paper, an overlapping-resistant Internet of Things (IoT) solution for a Bluetooth Low Energy (BLE)-based indoor tracking system (BLE-ITS) is presented. The BLE-ITS is a promising, inexpensive alternative to the well-known GPS. It can be used in human traffic analysis, such as indoor tourist facilities. Tourists or other customers are tagged by a unique MAC address assigned to a simple and energy-saving BLE beacon emitter. Their location is determined by a distributed and scalable network of popular Raspberry Pi microcomputers equipped with BLE and WiFi/Ethernet modules. Only simple triggered messages in the form of login records (LRs) are sent to a server, where the so-called path vectors (PVs) and interest profile (IPr) are set. The authors implemented the prototype and demonstrated its usefulness in a controlled environment. As it is shown in the paper, the solution is highly overlap-resistant and mitigates the so-called multilocation problem.
Double-Antigen Lateral Flow Immunoassay for the Detection of Anti-HIV-1 and -2 Antibodies Using Upconverting Nanoparticle Reporters
Authors: Martiskainen Iida , Juntunen Etvi , Salminen Teppo , Vuorenpää Karoliina , Bayoumy Sherif , Vuorinen Tytti , Khanna Navin , Pettersson Kim , Batra Gaurav , Talha Sheikh M.
Published: January 2021

Rapid diagnostic tests (RDTs) are often used for the detection of anti-human immunodeficiency virus (HIV) antibodies in remote locations in low- and middle-income countries (LMIC) with low or limited access to central laboratories. The typical format of an RDT is a lateral flow assay (LFA) with visual interpretation prone to subjectivity. This risk of misinterpretation can be overcome with luminescent upconverting nanoparticle reporters (UCNPs) measured with a miniaturized easy-to-use reader instrument. An LFA with UCNPs for anti-HIV-1/2 antibodies was developed and the assay performance was evaluated extensively with challenging patient sample panels. Sensitivity (n = 145) of the UCNP-LFA was 96.6% (95% CI: 92.1–98.8%) and specificity (n = 309) was 98.7% (95% CI: 96.7–99.7%). Another set of samples (n = 200) was used for a comparison between the UCNP-LFA and a conventional visual RDT. In this comparison, the sensitivities for HIV-1 were 96.4% (95% CI: 89.8–99.3%) and 97.6% (95% CI: 91.6–99.7%), for the UCNP-LFA and conventional RDT, respectively. The specificity was 100% (95% CI: 96.4–100%) for both assays. The developed UCNP-LFA demonstrates the applicability of UCNPs for the detection of anti-HIV antibodies. The signal measurement is done by a reader instrument, which may facilitate automated result interpretation, archiving and transfer of data from de-centralized locations.
Real-Time Coseismic Displacement Retrieval Based on Temporal Point Positioning with IGS RTS Correction Products
Authors: Zhang Yuanfan , Nie Zhixi , Wang Zhenjie , Wu Huisheng , Xu Xiaofei
Published: January 2021

With the rapid development of the global navigation satellite system (GNSS), high-rate GNSS has been widely used for high-precision GNSS coseismic displacement retrieval. In recent decades, relative positioning (RP) and precise point positioning (PPP) are mainly adopted to retrieve coseismic displacements. However, RP can only obtain relative coseismic displacements with respect to a reference station, which might be subject to quaking during a large seismic event. While PPP needs a long (re)convergence period of tens of minutes. There is no convergence time needed in the variometric approach for displacements analysis standalone engine (VADASE) but the derived displacements are accompanied by a drift. Temporal point positioning (TPP) method adopts temporal-differenced ionosphere-free phase measurements between a reference epoch and the current epoch, and there is almost no drift in the displacement derived from TPP method. Nevertheless, the precise orbit and clock products should be applied in the TPP method. The studies in recent years are almost based on the postprocessing precise orbits and clocks or simulated real-time products. Since 2013, international GNSS service (IGS) has been providing an open-access real-time service (RTS), which consists of orbit, clock and other corrections. In this contribution, we evaluated the performance of real-time coseismic displacement retrieval based on TPP method with IGS RTS correction products. At first, the real-time precise orbit and clock offsets are derived from the RTS correction products. Then, the temporal-differenced ionosphere-free (IF) combinations are formed and adopted as the TPP measurements. By applying real-time precise orbit and clock offsets, the coseismic displacement can be real-timely retrieved based on TPP measurements. To evaluate the accuracy, two experiments including a stationary experiment and an application to an earthquake event were carried out. The former gives an accuracy of 1.8 cm in the horizontal direction and 4.1 cm in the vertical direction during the whole period of 15-min. The latter gives an accuracy of 1.2 cm and 2.4 cm in the horizontal and vertical components, respectively.
Noncontact Body Temperature Measurement: Uncertainty Evaluation and Screening Decision Rule to Prevent the Spread of COVID-19
Authors: Dell’Isola Giovanni Battista , Cosentini Elena , Canale Laura , Ficco Giorgio , Dell’Isola Marco
Published: January 2021

The need to measure body temperature contactless and quickly during the COVID-19 pandemic emergency has led to the widespread use of infrared thermometers, thermal imaging cameras and thermal scanners as an alternative to the traditional contact clinical thermometers. However, limits and issues of noncontact temperature measurement devices are not well known and technical–scientific literature itself sometimes provides conflicting reference values on the body and skin temperature of healthy subjects. To limit the risk of contagion, national authorities have set the obligation to measure body temperature of workers at the entrance to the workplace. In this paper, the authors analyze noncontact body temperature measurement issues from both clinical and metrological points of view with the aim to (i) improve body temperature measurements accuracy; (ii) estimate the uncertainty of body temperature measurement on the field; (iii) propose a screening decision rule for the prevention of the spread of COVID-19. The approach adopted in this paper takes into account both the traditional instrumental uncertainty sources and clinical–medical ones related to the subjectivity of the measurand. A proper screening protocol for body temperature measurement considering the role of uncertainty is essential to correctly choose the threshold temperature value and measurement method to access critical places during COVID-19 pandemic emergency.
Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks
Authors: Wang Jinlei , Li Bing , Lei Bingjie , Ma Peiyuan , Lian Sai , Wang Ning , Li Xin , Lei Shaochong
Published: January 2021

Natural gas component analysis is one of the significant technologies in the exploitation and utilization of natural gas. A stable and accurate online natural gas monitoring system is necessary for the gas extracting industry. We have developed an online monitoring system of natural gas with a novel hardware architecture. It improves the dependability and maintainability of the system. A specific instruction set is designed to facilitate the coordination of software and hardware. To reduce the sample noise, the exponentially weighted moving average (EWMA) method is used to preprocess the real-time raw data of the sensor array. A tailored neural network is designed for calibration. And the relationship between the performance and the structure of the gas neural network is demonstrated to find the optimal solution for accuracy and hardware scale. The design not only focuses on the optimization of individual components but also focuses on system-level improvement. The system has been running stably for several months in the gas fields. It meets the requirements of stability, ease of use, maintainability, and online monitoring in industrial applications.