bluuebunny/crossref_metadata_2025 huggingface dataset:
Deformation and Failure Mechanism of Particulate Filled and Short Fiber Reinforced Thermoplastics: Detection and Analysis by Acoustic Emission Testing
Authors: Ferdinánd Milán , Várdai Róbert , Móczó János , Pukánszky Béla
Published: November 2021

Acoustic emission, the detection of signals during deformation, is a frequently used method for the study of local deformation processes occurring in heterogeneous polymer systems. Most of these processes result in the evolution of elastic waves which can be detected by appropriate sensors. The analysis of several parameters characterizing the waves offers valuable information about the possible deformation mechanism. The acoustic emission testing of composites may yield very different number of signals from a few hundred to more than 100,000. This latter was proved to be affected mainly by particle size, interfacial adhesion and composition, but other factors, such as matrix modulus and specimen size, also influence it. Local deformation processes are claimed to have a strong effect on macroscopic properties. Indeed, a close correlation was found between the initiation stress of the dominating particle related process derived from acoustic emission testing and the tensile strength in both polypropylene (PP) and poly(lactic acid) (PLA) composites. However, in polyamide (PA)-based heterogeneous polymer systems, deformations related to the matrix dominated composite properties. Besides forecasting failure, the method makes possible the determination of the inherent strength of lignocellulosic fibers being around 40 MPa as well as the quantitative estimation of adhesion strength for composites in which interactions are created by mechanisms other than secondary forces. The proposed approach based on acoustic emission testing proved that in PP/CaCO3 composites, the strength of adhesion can be increased by ten times from about 100 mJ/m2 to almost 1000 mJ/m2 in the presence of a functionalized polymer.
Scratch Properties of Clear Coat for Automotive Coating Comprising Molecular Necklace Crosslinkers with Silane Functional Groups for Various Environmental Factors
Authors: Moon Sung Wook , Seo Jiae , Seo Ji-Hun , Choi Byoung-Ho
Published: November 2021

Automotive coatings, which comprise multiple layers, i.e., primer, base coating, and clear coat layers, are exposed to various environmental conditions that pose various types of damages to them. In particular, the outer layer of the automotive coating, i.e., the clear coat, is affected significantly by such damages. Therefore, a reliable and durable clear coat must be developed to improve the appearance of automobiles. In this study, a new clear coat based on an acrylic-based clear coat modified using polyrotaxane crosslinkers, which are necklace-shaped supramolecules composed of ring-shaped host molecules, is developed and characterized. The effects of polyrotaxane and silane on the scratch properties and mechanisms of the clear coating are analyzed. It is observed that the critical loads of the clear coat from scratch tests can be improved by adding optimal molecular necklace crosslinkers comprising silane functional groups. The improvement in the scratch properties of the modified acrylic-based clear coat may be attributed to the crosslinking characteristics and dynamic molecular movements of the polyrotaxane. In addition, the effects of environmental factors on the scratch characteristics of the modified acrylic-based clear coat are investigated by addressing the scratch durability of the clear coat.
The Influence of Chosen Plant Fillers in PHBV Composites on the Processing Conditions, Mechanical Properties and Quality of Molded Pieces
Authors: Frącz Wiesław , Janowski Grzegorz , Smusz Robert , Szumski Marek
Published: November 2021

This work is inspired by the current European policies that aim to reduce plastic waste. This is especially true of the packaging industry. The biocomposites developed in the work belong to the group of environmentally friendly plastics that can reduce the increasing costs of environmental fees in the future. Three types of short fibers (flax, hemp and wood) with a length of 1 mm each were selected as fillers (30% mass content in PHBV). The biocomposites were extruded and then processed by the injection molding process with the same technical parameters. The samples obtained in this way were tested for mechanical properties and quality of the molded pieces. A significant improvement of some mechanical properties of biocomposites containing hemp and flax fibers and quality of molded pieces was obtained in comparison with pure PHBV. Only in the case of wood–PHBV biocomposites was no significant improvement of properties obtained compared to biocomposites with other fillers used in this research. The use of natural fibers, in particular hemp fibers as a filler in the PHBV matrix, in most cases has a positive effect on improving the mechanical properties and quality of molded pieces. In addition, it should be remembered that the obtained biocomposites are of natural origin and are fully biodegradable, which are interesting and desirable properties that are a part of the current trend regarding the production and commercialization of modern biomaterials.
Roles of Waste Glass and the Effect of Process Parameters on the Properties of Sustainable Cement and Geopolymer Concrete—A State-of-the-Art Review
Authors: Siddika Ayesha , Hajimohammadi Ailar , Ferdous Wahid , Sahajwalla Veena
Published: November 2021

Recent research has revealed the promising potential of using waste glass (WG) as a binder or inert filler in cement and geopolymer concrete to deliver economic and environmental benefits to the construction sector. However, the outcomes obtained by different research groups are scattered and difficult to compare directly because of isolated process parameters. In this study, the roles and impacts of WG and process parameters on the performance of WG-added cement and geopolymer concrete are critically reviewed. This study reveals that the chemical and mineralogical composition, and particle size of WG, mix proportion, activation, and curing condition of concrete are the most important parameters that affect the dissolution behavior of WG and chemical reactivity between WG and other elements in concrete; consequently, these show impacts on properties of concrete and optimum WG level for various applications. These parameters are required to be optimized based on the guidelines for high pozzolanicity and less alkali–silica reactivity of WG in concrete. This review provides a critical discussion and guidelines on these parameters and the chemistry of WG in cement and geopolymer concrete for best practice and highlights the current challenges with future research directions.
The Effect of Stacking Sequence on Fatigue Behaviour of Hybrid Pineapple Leaf Fibre/Carbon-Fibre-Reinforced Epoxy Composites
Authors: Hashim Mohd Khairul Rabani , Majid Mohd Shukry Abdul , Jamir Mohd Ridzuan Mohd , Kasim Farizul Hafiz , Sultan Mohamed Thariq Hameed , Shah Ain Umaira Md , Ahmad Kamarul Arifin , Basri Adi Azriff
Published: November 2021

This study examined the fatigue behaviour of pineapple leaf fibre/carbon hybrid laminate composites under various stacking sequences. The vacuum infusion technique was used to fabricate the symmetric quasi-isotropic oriented laminates, in which the stacking was varied. The laminate was tested under static and fatigue tensile load according to ASTM D3039-76 and ASTM D3479-96, respectively. Maximum tensile strength and modulus of 119.34 MPa and 6.86 GPa, respectively, were recorded for the laminate with external PALF ply and internal carbon ply oriented at [± 45°2, 0°/90°2]s (PCCP_45090). The fatigue tests showed that PCCP_45090 and CPPC_09045 (with internal PALF ply and external carbon ply oriented at [0°/90°2, ± 45°2]s) exhibited a higher useful life, especially at the high-stress level of the ultimate tensile strength. The normalised stress against the number of cycles showed that the stacking sequences of different ply orientations affected the fatigue behaviour more than the stacking sequences of the material. The laminate stacking sequence significantly affected the hysteresis energy and stiffness evolution. The scanning electron microscopy images showed that the fatigue failure modes included fibre pull-out, fibre breakage, matrix cracking, debonding, and delamination. The study concluded that PCCP_45090 exhibited an outstanding fatigue performance.
Photothermal Regenerated Fibers with Enhanced Toughness: Silk Fibroin/MoS2 Nanoparticles
Authors: Guo Jianjun , Yang Bo , Ma Qiang , Fometu Sandra Senyo , Wu Guohua
Published: November 2021

The distinctive mechanical and photothermal properties of Molybdenum sulfide (MoS2) have the potential for improving the functionality and utilization of silk products in various sectors. This paper reports on the preparation of regenerated silk fibroin/molybdenum disulfide (RSF/MoS2) nanoparticles hybrid fiber with different MoS2 nanoparticles contents by wet spinning. The simulated sunlight test indicated that the temperature of 2 wt% RSF/MoS2 nanoparticles hybrid fibers could rise from 20.0 °C to 81.0 °C in 1 min and 98.6 °C in 10 min, exhibiting good thermal stability. It was also demonstrated that fabrics made by manual blending portrayed excellent photothermal properties. The addition of MoS2 nanoparticles could improve the toughness of hybrid fibers, which may be since the mixing of MoS2 nanoparticles hindered the self-assembly of β-sheets in RSF solution in a concentration-dependent manner because RSF/MoS2 nanoparticles hybrid fibers showed a lower β-sheet content, crystallinity, and smaller crystallite size. This study describes a new way of producing high toughness and photothermal properties fibers for multifunctional fibers’ applications.
Roles of Chitosan as Bio-Fillers in Radiation-Vulcanized Natural Rubber Latex and Hybrid Radiation and Peroxide-Vulcanized Natural Rubber Latex: Physical/Mechanical Properties under Thermal Aging and Biodegradability
Authors: Thumwong Arkarapol , Poltabtim Worawat , Kerdsang Patcharaporn , Saenboonruang Kiadtisak
Published: November 2021

Although natural rubber was regarded as biodegradable, the degradation is a time-consuming process that could take weeks or months for any degradation or substantial weight loss to be observable, resulting in the need for novel processes/methods to accelerate the rubber degradation. As a result, this work investigated the potential utilization of chitosan (CS) as a biodegradation enhancer for radiation-vulcanized natural rubber latex (R-VNRL) and hybrid radiation and peroxide-vulcanized natural rubber latex (RP-VNRL) composites, with varying CS contents (0, 2, 4, or 6 phr). The R-VNRL samples were prepared using 15 kGy gamma irradiation, while the RP-VNRL samples were prepared using a combination of 0.1 phr tert-butyl hydroperoxide (t-BHPO) and 10 kGy gamma irradiation. The properties investigated were biodegradability in the soil and the morphological, chemical, mechanical, and physical properties, both before and after undergoing thermal aging. The results indicated that the biodegradability of both the R-VNRL and RP-VNRL composites was enhanced with the addition of CS, as evidenced by increases in the percentage weight loss (% weight loss) after being buried in soil for 8 weeks from 6.5 ± 0.1% and 6.4 ± 0.1% in a pristine R-VNRL and RP-VNRL samples, respectively, to 10.5 ± 0.1% and 10.2 ± 0.1% in 6-pph CS/R-VNRL and 6-pph CS/RP-VNRL composites, respectively, indicating the biodegradation enhancement of approximately 60%. In addition, the results revealed that the addition of CS could increase the value of tensile modulus by 119%, while decrease the values of tensile strength and elongation at break by 50% and 43%, respectively, in the specimens containing 6-phr CS. In terms of the color appearances, the samples were lighter and yellower after the addition of CS, as evidenced by the noticeably increased L* and b* values, based on the CIE L*a*b* color space system. Furthermore, the investigation into the effects of thermal aging showed that the overall tensile properties for both curing systems were reduced, while varying degrees of color change were observed, with the pristine R-VNRL and RP-VNRL samples having more pronounced degradation/changes for both properties. In conclusion, the overall results suggested that CS had great potential to be applied as a bio-filler in R-VNRL and RP-VNRL composites to effectively promote the biodegradability, environmental friendliness, and resistance to thermal degradation of the composites.
Performance Enhancement of Pentacene-Based Organic Thin-Film Transistors Using a High-K PVA/Low-K PVP Bilayer as the Gate Insulator
Authors: Fan Ching-Lin , Tsao Hou-Yen , Shiah Yu-Shien , Yao Che-Wei , Cheng Po-Wei
Published: November 2021

In this study, we proposed using the high-K polyvinyl alcohol (PVA)/low-K poly-4-vinylphenol (PVP) bilayer structure as the gate insulator to improve the performance of a pentacene-based organic thin-film transistor. The dielectric constant of the optimal high-K PVA/low-K PVP bilayer was 5.6, which was higher than that of the single PVP layer. It resulted in an increase in the gate capacitance and an increased drain current. The surface morphology of the bilayer gate dielectric could be suitable for pentacene grain growth because the PVP layer was deposited above the organic PVA surface, thereby replacing the inorganic surface of the ITO gate electrode. The device performances were significantly improved by using the bilayer gate dielectric based upon the high-K characteristics of the PVA layer and the enlargement of the pentacene grain. Notably, the field-effect mobility was increased from 0.16 to 1.12 cm2/(Vs), 7 times higher than that of the control sample.
Integrating Porphyrinic Metal-Organic Frameworks in Nanofibrous Carrier for Photodynamic Antimicrobial Application
Authors: Zhang Huiru , Xu Zhihao , Mao Ying , Zhang Yingjie , Li Yan , Lao Jihong , Wang Lu
Published: November 2021

The rise and spread of antimicrobial resistance is creating an ever greater challenge in wound management. Nanofibrous membranes (NFMs) incorporated with antibiotics have been widely used to remedy bacterial wound infections owing to their versatile features. However, misuse of antibiotics has resulted in drug resistance, and it remains a significant challenge to achieve both high antibacterial efficiency and without causing bacterial resistance. Here, the ‘MOF-first’ strategy was adopted, the porphyrinic metal-organic frameworks nanoparticles (PCN−224 NPs) were pre-synthesized first, and then the composite antibacterial PCN−224 NPs @ poly (ε-caprolactone) (PM) NFMs were fabricated via a facile co-electrospinning technology. This strategy allows large amounts of effective MOFs to be integrated into nanofibers to effectively eliminate bacteria without bacterial resistance and to realize a relatively fast production rate. Upon visible light (630 nm) irradiation for 30 min, the PM−25 NFMs have the best 1O2 generation performance, triggering remarkable photodynamic antibacterial effects against both S. aureus, MRSA, and E. coli bacteria with survival rates of 0.13%, 1.91%, and 2.06% respectively. Considering the photodynamic antibacterial performance of the composite nanofibrous membranes functionalized by porphyrinic MOFs, this simple approach may provide a feasible way to use MOF materials and biological materials to construct wound dressing with the versatility to serve as an antibacterial strategy in order to prevent bacterial resistance.
Use of Ionic Liquid Pretreated and Fermented Sugarcane Bagasse as an Adsorbent for Congo Red Removal
Authors: Ejaz Uroosa , Wasim Agha Arslan , Khan Muhammad Nasiruddin , Alzahrani Othman M. , Mahmoud Samy F. , El-Bahy Zeinhom M. , Sohail Muhammad
Published: November 2021

A large amount of industrial wastewater containing pollutants including toxic dyes needs to be processed prior to its discharge into the environment. Biological materials such as sugarcane bagasse (SB) have been reported for their role as adsorbents to remove the dyes from water. In this study, the residue SB after fermentation was utilized for the dye removal. A combined pretreatment of NaOH and methyltrioctylammonium chloride was given to SB for lignin removal, and the pretreated SB was utilized for cellulase production from Bacillus aestuarii UE25. The strain produced 118 IU mL−1 of endoglucanse and 70 IU mL−1 of β-glucosidase. Scanning electron microscopy and FTIR spectra showed lignin and cellulose removal in fermented SB. This residue was utilized for the adsorption of an azo dye, congo red (CR). The thermodynamic, isotherm and kinetics studies for the adsorption of CR revealed distinct adsorption features of SB. Untreated SB followed Langmuir isotherm, whereas pretreated SB and fermented SB obeyed the Freundlich isotherm model. The pseudo-second-order model fitted well for the studied adsorbents. The results of thermodynamic studies revealed spontaneous adsorption with negative standard free energy values. Untreated SB showed a 90.36% removal tendency at 303.15 K temperature, whereas the adsorbents comprised of pretreated and fermented SB removed about 98.35% and 97.70%, respectively. The study provided a strategy to utilize SB for cellulase production and its use as an adsorbent for toxic dyes removal.
Shades of Grace: The Virgin of the Rocks and Leonardo da Vinci’s Notes on Paragone
Authors: De Mambro Santos Ricardo
Published: November 2021

This essay analyses Leonardo da Vinci’s innovative iconography in the making of the panel known as the Virgin of the Rocks (Paris, Louvre) in relation to his considerations on the important Renaissance debate of Paragone, or comparison among the arts, namely, Painting, Sculpture, Music, and Poetry. It will be argued that this work, as a depiction of a complex religious theme, may be interpreted as a laboratory for Paragone-related demonstrations, especially in regards to five points: the optical tangibility of the image; the temporalities involved in the representation; the variety of subjects; and, finally, its iconic stability and semiotic openness as vehicles to further stimulate the devotional value of the painting.
Ethical Aspects of the Prosperity Gospel in the Light of the Arguments Presented by Antonio Spadaro and Marcelo Figueroa
Authors: Kobyliński Andrzej
Published: November 2021

The main aim of the article is to analyze the Prosperity Gospel as an important element of the contemporary pentecostalization of Christianity. The essence of this global process is the emergence of thousands of new Pentecostal denominations and the transformation of other traditional churches into a single, syncretic variety of charismatic Christianity on a global scale. Pentecostal religiosity is characterized, among other things, by prayer in tongues, miracles, exorcisms, healing services, etc. Another key element of this new syncretic religiosity is the Prosperity Gospel which represents the belief that faith may lead to wealth, health and prosperity, and the lack of it ends in poverty, disease, and misfortune. Critics of this new religiosity point out that God must not be seen merely as a realizer of human dreams of happiness, health and wealth. The first part of the article discusses the specific nature of the global process of the pentecostalization of Christianity. It then goes on to present an interpretation of the most important elements of the Prosperity Gospel. In the next part of the article, various charges against the Prosperity Gospel are analyzed, including arguments presented in the widely commented articles that Antonio Spadaro and Marcelo Figueroa published on this topic in 2017–2018.
Henry Corbin’s Oriental Philosophy and Iranian Nativist Ideologies
Authors: Bostani Ahmad
Published: November 2021

This paper aims to explore the roots of the nativist discourse among Iranian intellectuals in the 20th century prior to the Islamic Revolution, a discourse based on Eastern authenticity and the felt need for a return to Islamic, Persian, or Asian traditions. This general tendency took various forms among anti- and even pro-regime intellectuals, including severe anti-modernist evaluations of Al-e-Ahmad, Hossein Nasr, Ahmad Fardid, and Ehsan Naraqi. This nativist movement, as some scholars have shown, played a significant role in the victory of the Islamic Revolution in 1979. This paper aims to discuss some philosophical origins of these East-based and anti-West ideologies in the specific interpretation of Henry Corbin of the East/West spiritual split. This paper demonstrates that these ideas, to a considerable extent, stemmed from Corbin’s “Eastern scheme,” based on the authenticity of spiritual illumination. This paper explores how this Oriental philosophy, rooted in ancient Persia and medieval Iranian wisdom, has been used for political purposes through the ideologization of tradition in contemporary Iran. Therefore, it discusses Corbin’s theological scheme’s political and social ramifications to demonstrate the traces of his scheme in the works of a few nativist intellectuals in an ideologized form.
The Trinity: Prototype of Real Existence or Danger to Political Wellbeing? Tanner, Volf, and Yannaras in Conversation
Authors: Cole Jonathan
Published: November 2021

Kathryn Tanner maintains that political theologies based on the Trinity are not only unsound, but potentially dangerous. Her primary concern is that the Trinity, by definition, cannot serve as a “model” for human socio-political organization. Miroslav Volf, while sharing Tanner’s sense that Trinitarian political theologies are fraught, nevertheless, maintains that the Trinity can serve as a “vision” for human socio-political relations, albeit not as a “program”. This article brings Tanner and Volf into conversation with Eastern Orthodox philosopher-theologian Christos Yannaras, whose Trinitarian political theology regards the Trinity as the “prototype” or “archetype” of a mode of existence in which humans can participate by transcending their natures, with the aim of realizing truth. This article argues that Yannaras offers a novel way of conceptualizing Trinitarian political theology which escapes Tanner and Volf’s criticisms, on the one hand, and offers Social Trinitarianism a fresh and fertile perspective that could advance its discourse.
Insularity and Religious Life: The Case of Hellenistic Ikaros/Failaka Island
Authors: Stavrou Dorothea
Published: November 2021

This paper explores the notion of insularity and religious life in the sacred landscape of Ikaros/Failaka with a particular focus on the Hellenistic period. The little island of Ikaros/Failaka in the Persian Gulf had a long pre-Hellenistic religious history and was occupied by Alexander, explored by his officials and became part of the Seleucid kingdom. From the mid-20th century, archaeological missions working on the nesiotic space of the Persian Gulf have revealed material evidence that has altered our view of this remote part of the Hellenistic world. Research revealed a flourishing network of cultural communication and contacts between the indigenous population of the East and Greco-Macedonians. These interactions mirror the landscape of the Hellenistic East. Thus Ikaros/Failaka, an island on the periphery of the Seleucid kingdom, situated at a strategic point (near the mouth of the River Euphrates and close to the shores of the Persian Gulf) appears to be part of a chain of locations that possessed political/military, economic, and religious importance for the Seleucids. It became a fruitful landscape, where the Seleucids pursued their political and religious agenda.
Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale
Authors: Chen Xingdi , Kong Peng , Jiang Peng , Wu Yanlan
Published: November 2021

Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.
An Implementation of Open Source-Based Software as a Service (SaaS) to Produce TOA and TOC Reflectance of High-Resolution KOMPSAT-3/3A Satellite Image
Authors: Kim Kwangseob , Lee Kiwon
Published: November 2021

The majority of cloud applications are created or delivered to provide users with access to system resources or prebuilt processing algorithms for efficient data storage, management, and production. The number of cases linking cloud computing to the use of global observation satellite data continues to rise, owing to the benefits of cloud computing. This study aims to develop a cloud software as a service (SaaS) that yields reflectance products in high-resolution Korea Multi-Purpose Satellite (KOMPSAT)-3/3A satellite images. The SaaS model was designed as three subsystems: a Calibration Processing System (CPS), a Request System for CPS supporting RESTful application programming interface (API), and a Web Interface Application System. Open-source components, libraries, and frameworks were used in this study’s SaaS, including an OpenStack for infrastructure as a service. An absolute atmospheric correction scheme based on a Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code with atmospheric variable inputs was used to generate the top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The SaaS implemented in this study provides users with the absolute atmospheric calibration functionality to apply their KOMPSAT-3/3A satellite image set through a web browser and obtain output directly from this service. According to experiments to check the total performance time for images, bundled with four bands of red, green, blue, and near-infrared, it took approximately 4.88 min on average for the execution time to obtain all reflectance results since satellite images were registered into the SaaS. The SaaS model proposed and implemented in this study can be used as a reference model for the production system to generate reflectance products from other optical sensor images. In the future, SaaS, which offers professional analysis functions based on open source, is expected to grow and expand into new application fields for public users and communities.
Evaluation of Seven Near-Real-Time Satellite-Based Precipitation Products for Wet Seasons in the Nierji Basin, China
Authors: Dou Yanhong , Ye Lei , Zhang Jiayan , Zhang Chi , Zhou Huicheng
Published: November 2021

This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle–high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements
Authors: Luo Lili , Chang Qingrui , Wang Qi , Huang Yong
Published: November 2021

Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.
InSAR Monitoring of Arctic Landfast Sea Ice Deformation Using L-Band ALOS-2, C-Band Radarsat-2 and Sentinel-1
Authors: Chen Zhaohua , Montpetit Benoit , Banks Sarah , White Lori , Behnamian Amir , Duffe Jason , Pasher Jon
Published: November 2021

Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. The objective of this study is to determine the spatial-temporal pattern of deformation over landfast ice in the Arctic using time series SAR imagery. We present Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2 in this paper. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes along a line-of-sight direction (LOS) over the winter. It was found that temporal and spatial patterns of deformation observed from different sensors were generally consistent. Horizontal and vertical deformations were also retrieved by a multi-dimensional SBAS technique using both ascending and descending Sentinel-1 observations. Results showed a horizontal deformation in the range of −95–85 cm, and vertical deformation in the range of −41–63 cm in Cambridge Bay, Nunavut, Canada during February-April 2019. High coherence over ice from C-band was maintained over a shorter time interval of acquisitions than L-band due to temporal decorrelation.
Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau
Authors: Zhong Yanmei , Meng Lingkui , Wei Zushuai , Yang Jian , Song Weiwei , Basir Mohammad
Published: November 2021

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
Authors: Zhu Yongchao , Tao Tingye , Li Jiangyang , Yu Kegen , Wang Lei , Qu Xiaochuan , Li Shuiping , Semmling Maximilian , Wickert Jens
Published: November 2021

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.
Landslide Characteristics and Evolution: What We Can Learn from Three Adjacent Landslides
Authors: Yang Dongdong , Qiu Haijun , Zhu Yaru , Liu Zijing , Pei Yanqian , Ma Shuyue , Du Chi , Sun Hesheng , Liu Ya , Cao Mingming
Published: November 2021

Landslide processes are a consequence of the interactions between their triggers and the surrounding environment. Understanding the differences in landslide movement processes and characteristics can provide new insights for landslide prevention and mitigation. Three adjacent landslides characterized by different movement processes were triggered from August to September in 2018 in Hualong County, China. A combination of surface and subsurface characteristics illustrated that Xiongwa (XW) landslides 1 and 2 have deformed several times and exhibit significant heterogeneity, whereas the Xiashitang (XST) landslide is a typical retrogressive landslide, and its material has moved downslope along a shear surface. Time-series Interferometric Synthetic Aperture Radar (InSAR) and Differential InSAR (DInSAR) techniques were used to detect the displacement processes of these three landslides. The pre-failure displacement signals of a slow-moving landslide (the XST landslide) can be clearly revealed by using time-series InSAR. However, these sudden landslides, which are a typical catastrophic natural hazard across the globe, are easily ignored by time-series InSAR. We confirmed that effective antecedent precipitation played an important role in the three landslides’ occurrence. The deformation of an existing landslide itself can also trigger new adjacent landslides in this study. These findings indicate that landslide early warnings are still a challenge since landslide processes and mechanisms are complicated. We need to learn to live with natural disasters, and more relevant detection and field investigations should be conducted for landslide risk mitigation.
Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM
Authors: Ma Zhen , Machado José J. M. , Tavares João Manuel R. S.
Published: November 2021

Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.
Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
Authors: Qian Chen , Leelaprachakul Patraporn , Landers Matthew , Low Carissa , Dey Anind K. , Doryab Afsaneh
Published: November 2021

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients.
Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts
Authors: Wutke Martin , Heinrich Felix , Das Pronaya Prosun , Lange Anita , Gentz Maria , Traulsen Imke , Warns Friederike K. , Schmitt Armin Otto , Gültas Mehmet
Published: November 2021

The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head–head and head–tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems.
Design of an Automated Multiposition Dynamic Wheelchair
Authors: Aguilar-Pérez Luis Antonio , Paredes-Rojas Juan Carlos , Sanchez-Cruz Jose Israel , Leal-Naranjo Jose Alfredo , Oropeza-Osornio Armando , Torres-SanMiguel Christopher Rene
Published: November 2021

This work presents a design for an automatized multiposition dynamic wheelchair used to transport quadriplegic patients by reconfiguring a manual wheelchair structure. An electric actuator is attached to a four-bar mechanism fixed to each side of a wheelchair’s backrest to reach multiposition. The entire device is actuated through a PID controller. An experimental test is carried out in a simplified wheelchair structure. Finally, the structure of the wheelchair is evaluated through the Dynamic analysis and Finite Element Method under the payload computed with the most critical position reached by the mechanism.
A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
Authors: Sighencea Bogdan Ilie , Stanciu Rareș Ion , Căleanu Cătălin Daniel
Published: November 2021

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.
Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study
Authors: Lin Yi-Chun , Liu Jidong , Cheng Yi-Ting , Hasheminasab Seyyed Meghdad , Wells Timothy , Bullock Darcy , Habib Ayman
Published: November 2021

Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.