bluuebunny/crossref_metadata_2025 huggingface dataset:
Nanomaterial-Based Dual-Emission Ratiometric Fluorescent Sensors for Biosensing and Cell Imaging
Authors: Zhang Yanan , Hou Dajun , Wang Zelong , Cai Ning , Au Chaktong
Published: August 2021

Owing to the unique optophysical properties of nanomaterials and their self-calibration characteristics, nanomaterial-based (e.g., polymer dots (Pdots) quantum dots (QDs), silicon nanorods (SiNRs), and gold nanoparticle (AuNPs), etc.) ratiometric fluorescent sensors play an essential role in numerous biosensing and cell imaging applications. The dual-emission ratiometric fluorescence technique has the function of effective internal referencing, thereby avoiding the influence of various analyte-independent confounding factors. The sensitivity and precision of the detection can therefore be greatly improved. In this review, the recent progress in nanomaterial-based dual-emission ratiometric fluorescent biosensors is systematically summarized. First, we introduce two general design approaches for dual-emission ratiometric fluorescent sensors, involving ratiometric fluorescence with changes of one response signal and two reversible signals. Then, some recent typical examples of nanomaterial-based dual-emission ratiometric fluorescent biosensors are illustrated in detail. Finally, probable challenges and future outlooks for dual-emission ratiometric fluorescent nanosensors for biosensing and cell imaging are rationally discussed.
Hybrid Copolymerization of Ethylene Oxide and tert-Butyl Methacrylate with Organocatalyst
Authors: Xiao Wenhao , Xu Liguo , Liu Pan , Chen Yang , Zhang Jie , Xu Jinbao
Published: August 2021

Hybrid copolymerization of structurally different, reactivity and mechanism distinct monomers (e.g., cyclic and vinyl type monomers) is of great interest and challenge for both academic research and practical application. Herein, ethylene oxide-co-tert-butyl methacrylate-co-poly(ethylene glycol) benzyl methacrylate (EO-co-BMA-co-bPEO), a statistical copolymer was synthesized via hybrid copolymerization of EO and BMA using an uncharged, non-nucleophilic organobase t-BuP4 as the catalyst. Detailed characterizations indicate that hybrid copolymerization of ethylene oxide and vinyl monomer forms a statistical copolymer concurrently with the transesterification of tert-butyl group and oligomer PEO anions. The application of the copolymer as all solid lithium-ion battery polymer electrolyte was investigated by detecting the ionic conductivity (σ) with electrical impedance spectrum measurement.
Effects of Raster Angle and Material Components on Mechanical Properties of Polyether-Ether-Ketone/Calcium Silicate Scaffolds
Authors: Zheng Jibao , Dong Enchun , Kang Jianfeng , Sun Changning , Liu Chaozong , Wang Ling , Li Dichen
Published: August 2021

Polyetheretherketone (PEEK) was widely used in the fabrication of bone substitutes for its excellent chemical resistance, thermal stability and mechanical properties that were similar to those of natural bone tissue. However, the biological inertness restricted the osseointegration with surrounding bone tissue. In this study, calcium silicate (CS) was introduced to improve the bioactivity of PEEK. The PEEK/CS composites scaffolds with CS contents in gradient were fabricated with different raster angles via fused filament fabrication (FFF). With the CS content ranging from 0 to 40% wt, the crystallinity degree (from 16% to 30%) and surface roughness (from 0.13 ± 0.04 to 0.48 ± 0.062 μm) of PEEK/CS scaffolds was enhanced. Mechanical testing showed that the compressive modulus of the PEEK/CS scaffolds could be tuned in the range of 23.3–541.5 MPa. Under the same printing raster angle, the compressive strength reached the maximum with CS content of 20% wt. The deformation process and failure modes could be adjusted by changing the raster angle. Furthermore, the mapping relationships among the modulus, strength, raster angle and CS content were derived, providing guidance for the selection of printing parameters and the control of mechanical properties.
The Adaptive Power of Ammophila arenaria: Biomimetic Study, Systematic Observation, Parametric Design and Experimental Tests with Bimetal
Authors: Andrade Tarciana , Beirão José , Arruda Amilton , Cruz Cristina
Published: August 2021

The aim of our study was to apply a biomimetic approach, inspired by the Ammophila arenaria. This organism possesses a reversible leaf opening and closing mechanism that responds to water and salt stress (hydronastic movement). We adopted a problem-based biomimetic methodology in three stages: (i) two observation studies; (ii) how to abstract and develop a parametric model to simulate the leaf movement; and (iii) experiments with bimetal, a smart material that curls up when heated. We added creases to the bimetal active layer in analogy to the position of bulliform cells. These cells determine the leaf-closing pattern. The experiments demonstrated that creases influence and can change the direction of the bimetal natural movement. Thus, it is possible to replicate the Ammophila arenaria leaf-rolling mechanism in response to temperature variation and solar radiation in the bimetal. In future works, we will be able to propose responsive facade solutions based on these results.
Understanding Wood Surface Chemistry and Approaches to Modification: A Review
Authors: Rowell Roger M.
Published: August 2021

Wood was designed, after millions of years of evolution, to perform in a wet environment. Nature is programmed to recycle it, in a timely way, back to the basic building blocks of carbon dioxide and water. All recycling chemistries start with an invasion of the wood surface. The surface of wood is porous, hygroscopic, viscoelastic, and anisotropic that is better defined in interface/interphase zones. This surface is dynamic and in constant change with changing humidity, temperature, oxygen levels, ultraviolet energy, microorganisms and stress. This chapter is a review of the chemical properties of a wood surface and performance issues associated with it.
Biodegradable Packaging Materials from Animal Processing Co-Products and Wastes: An Overview
Authors: Khodaei Diako , Álvarez Carlos , Mullen Anne Maria
Published: August 2021

Biodegradable polymers are non-toxic, environmentally friendly biopolymers with considerable mechanical and barrier properties that can be degraded in industrial or home composting conditions. These biopolymers can be generated from sustainable natural sources or from the agricultural and animal processing co-products and wastes. Animals processing co-products are low value, underutilized, non-meat components that are generally generated from meat processing or slaughterhouse such as hide, blood, some offal etc. These are often converted into low-value products such as animal feed or in some cases disposed of as waste. Collagen, gelatin, keratin, myofibrillar proteins, and chitosan are the major value-added biopolymers obtained from the processing of animal’s products. While these have many applications in food and pharmaceutical industries, a significant amount is underutilized and therefore hold potential for use in the generation of bioplastics. This review summarizes the research progress on the utilization of meat processing co-products to fabricate biodegradable polymers with the main focus on food industry applications. In addition, the factors affecting the application of biodegradable polymers in the packaging sector, their current industrial status, and regulations are also discussed.
Main Morphological Characteristics of Tubular Polymeric Scaffolds to Promote Peripheral Nerve Regeneration—A Scoping Review
Authors: Apablaza Josefa Alarcón , Lezcano María Florencia , Lopez Marquez Alex , Godoy Sánchez Karina , Oporto Gonzalo H. , Dias Fernando José
Published: August 2021

The “nerve guide conduits” (NGC) used in nerve regeneration must mimic the natural environment for proper cell behavior. Objective: To describe the main morphological characteristics of polymeric NGC to promote nerve regeneration. Methods: A scoping review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) criteria in the PubMed, Web of Science, Science Direct, and Scientific Electronic Library Online (SciELO) databases. Primary studies that considered/evaluated morphological characteristics of NGC to promote nerve regeneration were included. Result: A total of 704 studies were found, of which 52 were selected. The NGC main morphological characteristics found in the literature were: (I) NGC diameter affects the mechanical properties of the scaffold. (II) Wall thickness of NGC determines the exchange of nutrients, molecules, and neurotrophins between the internal and external environment; and influences the mechanical properties and biodegradation, similarly to NGC (III) porosity, (IV) pore size, and (V) pore distribution. The (VI) alignment of the NGC fibers influences the phenotype of cells involved in nerve regeneration. In addition, the (VII) thickness of the polymeric fiber influences neurite extension and orientation. Conclusions: An NGC should have its diameter adjusted to the nerve with wall thickness, porosity, pore size, and distribution of pores, to favor vascularization, permeability, and exchange of nutrients, and retention of neurotrophic factors, also favoring its mechanical properties and biodegradability.
Phase and Structure Behavior vs. Electromechanical Performance of Electrostrictive P(VDF-HFP)/ZnO Composite Nanofibers
Authors: Tohluebaji Nikruesong , Thainiramit Panu , Putson Chatchai , Muensit Nantakan
Published: August 2021

In this work, we improved the electromechanical properties, electrostrictive behavior and energy-harvesting performance of poly(vinylidenefluoridene-hexafluoropropylene) P(VDF-HFP)/zinc oxide (ZnO) composite nanofibers. The main factor in increasing their electromechanical performance and harvesting power based on electrostrictive behavior is an improved coefficient with a modified crystallinity phase and tuning the polarizability of material. These blends were fabricated by using a simple electrospinning method with varied ZnO contents (0, 5, 10, 15 and 20 wt%). The effects of the ZnO nanoparticle size and content on the phase transformation, dielectric permittivity, strain response and vibration energy harvesting were investigated. The characteristics of these structures were evaluated utilizing SEM, EDX, XRD, FT-IR and DMA. The electrical properties of the fabrication samples were examined by LCR meter as a function of the concentration of the ZnO and frequency. The strain response from the electric field was observed by the photonic displacement apparatus and lock-in amplifier along the thickness direction at a low frequency of 1 Hz. Moreover, the energy conversion behavior was determined by an energy-harvesting setup measuring the current induced in the composite nanofibers. The results showed that the ZnO nanoparticles’ component effectively achieves a strain response and the energy-harvesting capabilities of these P(VDF-HFP)/ZnO composites nanofibers. The electrostriction coefficient tended to increase with a higher ZnO content and an increasing dielectric constant. The generated current increased with the ZnO content when the external electric field was applied at a vibration of 20 Hz. Consequently, the ZnO nanoparticles dispersed into electrostrictive P(VDF-HFP) nanofibers, which offer a large power density and excellent efficiency of energy harvesting.
Influence of Graphene Nanoplatelet Lateral Size on the Electrical Conductivity and Electromagnetic Interference Shielding Performance of Polyester Nanocomposites
Authors: Madinehei Milad , Kuester Scheyla , Kaydanova Tatiana , Moghimian Nima , David Éric
Published: August 2021

Polyester nanocomposites reinforced with graphene nanoplatelets (GnPs) with two different lateral sizes are prepared by high shear mixing, followed by compression molding. The effects of the size and concentration of GnP, as well as of the processing method, on the electrical conductivity and electromagnetic interference (EMI) shielding behavior of these nanocomposites are experimentally investigated. The in-plane electrical conductivity of the nanocomposites with larger-size GnPs is approximately one order of magnitude higher than the cross-plane volume conductivity. According to the SEM images, the compression-induced alignments of GnPs is found to be responsible for this anisotropic behavior. The orientation of the small size GnPs in the composite is not influenced by the compression process as strongly, and consequently, the electrical conductivity of these nanocomposites exhibits only a slight anisotropy. The maximum EMI shielding effectiveness (SE) of 27 dB (reduction of 99.8% of the incident radiation) is achieved at 25 wt.% of the smaller-size GnP loading. Experimental results show that the EMI shielding mechanism of these composites has a strong dependency on the lateral dimension of GnPs. The non-aligned smaller-size GnPs are leveraged to obtain a relatively high absorption coefficient (≈40%). This absorption coefficient is superior to the existing single-filler bulk polymer composite with a similar thickness.
Densely Packed Tethered Polymer Nanoislands: A Simulation Study
Authors: Chen Nicolas , Davydovich Oleg , McConnell Caitlyn , Sidorenko Alexander , Moore Preston B.
Published: August 2021

COordinated Responsive Arrays of Surface-Linked polymer islands (CORALS) allow for the creation of molecular surfaces with novel and switchable properties. Critical components of CORALs are the uniformly distributed islands of densely grafted polymer chains (nanoislands) separated by regions of bare surface. The grafting footprint and separation distances of nanoislands are comparable to that of the constituent polymer chains themselves. Herein, we characterize the structural features of the nanoislands and semiflexible polymers within to better understand this critical constituent of CORALs. We observe different characteristics of grafted semiflexible polymers depending on the polymer island’s size and distance from the center of the island. Specifically, the characteristics of the chains at the island periphery are similar to isolated tethered polymer chains (full flexible chains), while chains in the center of the island experience the neighbor effect such as chains in the classic polymer brush. Chains close to the edge of the islands exhibit unique structural features between these two regimes. These results can be used in the rational design of CORALs with specific interfacial characteristics and predictable responses to external stimuli. It is hoped that this the discussion of the different morphologies of the polymers as a function of distance from the edge of the polymer will find applications in a wide variety of systems.
Application of Micromechanical Modelling for the Evaluation of Elastic Moduli of Hybrid Woven Jute–Ramie Reinforced Unsaturated Polyester Composites
Authors: Hadi Agung Efriyo , Hamdan Mohammad Hazim Mohamad , Siregar Januar Parlaungan , Junid Ramli , Tezara Cionita , Irawan Agustinus Purna , Fitriyana Deni Fajar , Rihayat Teuku
Published: August 2021

Woven laminated composite has gained researchers’ and industry’s interest over time due to its impressive mechanical performance compared to unidirectional composites. Nevertheless, the mechanical properties of the woven laminated composite are hard to predict. There are many micromechanical models based on unidirectional composite but limited to the woven laminated composite. The current research work was conducted to evaluate elastic moduli of hybrid jute–ramie woven reinforced unsaturated polyester composites using micromechanical effectiveness unidirectional models, such as ROM, IROM, Halpin–Tsai, and Hirsch, which are based on stiffness. The hybrid jute–ramie laminated composite was fabricated with different layering sizes, and the stacking sequence was completed via hand lay-up with the compression machine. Tensile modulus values for hybrid composites are between those for single jute and single ramie. Obtained p-values less than 0.05 prove the relationship between layering size and tensile modulus. This study showed that several micromechanical models, such as Halpin–Tsai’s predicted value of homogenized mechanical properties, were in good agreement with the experimental result. In the case of the hybrid composite, the micromechanical model deviates from the experimental result. Several modifications are required to improve the current existing model. A correlation function was calculated based on the differences between the elastic modulus values determined experimentally and those derived from each micromechanical model calculation.
Enhancement of Light Amplification of CsPbBr3 Perovskite Quantum Dot Films via Surface Encapsulation by PMMA Polymer
Authors: Qaid Saif M. H. , Ghaithan Hamid M. , AlHarbi Khulod K. , Al-Asbahi Bandar Ali , Aldwayyan Abdullah S.
Published: August 2021

Photonic devices based on perovskite materials are considered promising alternatives for a wide range of these devices in the future because of their broad bandgaps and ability to contribute to light amplification. The current study investigates the possibility of improving the light amplification characteristics of CsPbBr3 perovskite quantum dot (PQD) films using the surface encapsulation technique. To further amplify emission within a perovskite layer, CsPbBr3 PQD films were sandwiched between two transparent layers of poly(methyl methacrylate) (PMMA) to create a highly flexible PMMA/PQD/PMMA waveguide film configuration. The prepared perovskite film, primed with a polymer layer coating, shows a marked improvement in both emission efficiency and amplified spontaneous emission (ASE)/laser threshold compared with bare perovskite films on glass substrates. Additionally, significantly improved photoluminescence (PL) and long decay lifetime were observed. Consequently, under pulse pumping in a picosecond duration, ASE with a reduction in ASE threshold of ~1.2 and 1.4 times the optical pumping threshold was observed for PQDs of films whose upper face was encapsulated and embedded within a cavity comprising two PMMA reflectors, respectively. Moreover, the exposure stability under laser pumping was greatly improved after adding the polymer coating to the top face of the perovskite film. Finally, this process improved the emission and PL in addition to enhancements in exposure stability. These results were ascribed in part to the passivation of defects in the perovskite top surface, accounting for the higher PL intensity, the slower PL relaxation, and for about 14 % of the ASE threshold decrease.
Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets
Authors: Zhao Zheng , Tian Weiming , Deng Yunkai , Hu Cheng , Zeng Tao
Published: August 2021

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.
Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data
Authors: Zitzlsberger Georg , Podhorányi Michal , Svatoň Václav , Lazecký Milan , Martinovič Jan
Published: August 2021

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.
European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability
Authors: Niro Fabrizio , Goryl Philippe , Dransfeld Steffen , Boccia Valentina , Gascon Ferran , Adams Jennifer , Themann Britta , Scifoni Silvia , Doxani Georgia
Published: August 2021

Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems.
A Comprehensive Evaluation of Key Tropospheric Parameters from ERA5 and MERRA-2 Reanalysis Products Using Radiosonde Data and GNSS Measurements
Authors: Guo Lijie , Huang Liangke , Li Junyu , Liu Lilong , Huang Ling , Fu Bolin , Xie Shaofeng , He Hongchang , Ren Chao
Published: August 2021

Tropospheric delay is a major error source in the Global Navigation Satellite System (GNSS), and the weighted mean temperature (Tm) is a key parameter in precipitable water vapor (PWV) retrieval. Although reanalysis products like the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim) data have been used to calculate and model the tropospheric delay, Tm, and PWV, the limitations of the temporal and spatial resolutions of the reanalysis data have affected their performance. The release of the fifth-generation accurate global atmospheric reanalysis (ERA5) and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) provide the opportunity to overcome these limitations. The performances of the zenith tropospheric delay (ZTD), zenith wet delay (ZWD), Tm, and zenith hydrostatic delay (ZHD) of ERA5 and MERRA-2 data from 2016 to 2017 were evaluated in this work using GNSS ZTD and radiosonde data over the globe. Taking GNSS ZTD as a reference, the ZTD calculated from MERRA-2 and ERA5 pressure-level data were evaluated in temporal and spatial scales, with an annual mean bias and root mean square (RMS) of 2.3 and 10.9 mm for ERA5 and 4.5 and 13.1 mm for MERRA-2, respectively. Compared to radiosonde data, the ZHD, ZWD, and Tm derived from ERA5 and MERRA-2 data were also evaluated on temporal and spatial scales, with annual mean bias values of 1.1, 1.7 mm, and 0.14 K for ERA5 and 0.5, 4.8 mm, and –0.08 K for MERRA-2, respectively. Meanwhile, the annual mean RMS was 4.5, 10.5 mm, and 1.03 K for ERA5 and 4.4, 13.6 mm, and 1.17 K for MERRA-2, respectively. Tropospheric parameters derived from MERRA-2 and ERA5, with improved temporal and spatial resolutions, can provide a reference for GNSS positioning and PWV retrieval.
Signatures of Wetland Impact: Spatial Distribution of Forest Aboveground Biomass in Tumen River Basin
Authors: Lv Guanting , Cui Guishan , Wang Xiaoyi , Yu Hangnan , Huang Xiao , Zhu Weihong , Lin Zhehao
Published: August 2021

The Tumen River Basin, located in the cross-border region of China, North Korea, and Russia, constitutes an important ecological barrier in China. Forest here is mainly distributed around wetland, with the distribution of wetland having the potential to regulate regional forest carbon storage. However, the spatially explicit map of forest aboveground biomass (AGB) and potential impact of drivers, i.e., wetland distribution and climate, is still lacking. We thus use a deep neural network and multi-source remote sensing data to quantify forest AGB in the Tumen River Basin. Our results show the mean forest AGB is 103.43 Mg ha−1, with divergent spatial variation along its distance to wetland. The results of correlation analysis showed that with sufficient soil moisture supply, temperature dominant spatial variation of forest aboveground biomass. Noted that using the space for time substitution, we find when wetland decreased by less than 11.1%, the forest AGB decreased by more than 8%. Our result highlight the signatures of wetland impact on its nearby forest carbon storage, and urge the wetland protection, especially under the warming and drying future.
Evaluation of Extreme Precipitation Based on Three Long-Term Gridded Products over the Qinghai-Tibet Plateau
Authors: He Qingshan , Yang Jianping , Chen Hongju , Liu Jun , Ji Qin , Wang Yanxia , Tang Fan
Published: August 2021

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling–Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products’ to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.
Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight
Authors: Ma Huiqin , Huang Wenjiang , Dong Yingying , Liu Linyi , Guo Anting
Published: August 2021

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.
Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe
Authors: Ibrahim Saleem , Landa Martin , Pešek Ondřej , Pavelka Karel , Halounova Lena
Published: August 2021

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.
A Second-Order Time-Difference Position Constrained Reduced-Dynamic Technique for the Precise Orbit Determination of LEOs Using GPS
Authors: Wei Hui , Li Jiancheng , Xu Xinyu , Zhang Shoujian , Kuang Kaifa
Published: August 2021

In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show that the accuracies of integrating the STPs of LEOs at 30 s intervals are better than 0.01 m when the forces (<10−5 ms−2) acting on the LEOs are ignored. Therefore, only using the Earth’s gravity model is good enough for the proposed RD_STP method. All unmodeled dynamic models (e.g., luni-solar gravitation, tide forces) are treated as the error sources of the STP pseudo-observation. In addition, there are no pseudo-stochastic orbit parameters to be estimated in the RD_STP method. Finally, we use the RD_STP method to process 15 days of GPS data from the GOCE mission. The results show that the accuracy of the RD_STP solution is more accurate and smoother than the kinematic solution in nearly polar and equatorial regions, and consistent with the RD solution. The 3D RMS of the differences between the RD_STP and RD solutions is 1.93 cm for 1 s sampling. This indicates that the proposed method has a performance comparable to the RD method, and could be an alternative for the POD of LEOs.
FEA and Machine Learning Techniques for Hidden Structure Analysis
Authors: Shi Xijin , Hsieh Sheng-Jen , Romero Roseli Aparecida Francelin
Published: August 2021

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass. A finite element analysis (FEA) model was built to simulate the infrared imaging process to realize the detection and prediction given the amount of heat flux required to obtain thermal images and data. For the root system, based on a tree structure thermal profile over time derived from the FEA model, a line scan method was developed to predict root structure. The main root branches can be viewed from the detection results. Polynomial regression, support vector machine (SVM), and artificial neural network (ANN) models were designed to predict root depth. For bubble defects, three ANN models were developed to predict bubble size using temperature data generated by the FEA model. Results indicated that these models provide valid predictions. Statistical tests were applied to evaluate and compare the above predictive models. Results suggest that infrared imaging and machine learning models can be used to provide information on both hidden structures.
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification
Authors: Costa Joana , Silva Catarina , Santos Miguel , Fernandes Telmo , Faria Sérgio
Published: August 2021

Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged F1 of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.
Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets
Authors: Wang He , Rajotte Kiriaki J. , Wang Haopeng , Dai Chenyun , Zhu Ziling , Huang Xinming , Clancy Edward A.
Published: August 2021

To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.
Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot
Authors: Pathmakumar Thejus , Sivanantham Vinu , Anantha Padmanabha Saurav Ghante , Elara Mohan Rajesh , Tun Thein Than
Published: August 2021

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.
Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation
Authors: Piorecky Marek , Koudelka Vlastimil , Piorecka Vaclava , Strobl Jan , Dudysova Daniela , Koprivova Jana
Published: August 2021

Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature.
Protection and Installation of FBG Strain Sensor in Deep Boreholes for Subsurface Faults Behavior Monitoring
Authors: Choi Sang-Jin , Park Kwon Gyu , Park Chan , Lee Changhyun
Published: August 2021

Fiber optic sensors are gradually replacing electrical sensors in geotechnical applications owing to their immunity to electrical interference, durability, and cost-effectiveness. However, additional protective measures are required to prevent loss of functionality due to damage to the sensors, cables, or connection parts (splices and/or connectors) during installation and completion processes in borehole applications. We introduce two cases of installing fiber Bragg grating (FBG) strain sensors in 1 km boreholes to monitor the behavior of deep subsurface faults. We present our fiber-reinforced plastic (FRP) forming schemes to protect sensors and splices. We also present uniaxial load test and post-completion monitoring results for assessing the effects and performance of the protective measures. The uniaxial load test and post-completion monitoring show that FBG sensors are well protected by FRP forming without significant impact on sensor performance itself and that they are successfully installed in deep boreholes. In addition to summarizing our learning from experiences, we also suggest several points for consideration to improve the applicability of FBG sensors in borehole environment of the geotechnical field.
Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects
Authors: Shinba Toshikazu , Murotsu Keizo , Usui Yosuke , Andow Yoshinori , Terada Hiroshi , Kariya Nobutoshi , Tatebayashi Yoshitaka , Matsuda Yoshiki , Mugishima Go , Shinba Yujiro , Sun Guanghao , Matsui Takemi
Published: August 2021

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients’ HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement.