bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study of Durability Behaviors of Thermoplastic Polypropylene and Thermosetting Epoxy Exposed to Elevated Temperature, Water Immersion and Sustained Bending Loading
Authors: Zhou Ping , Tian Jingwei , Li Chenggao , Tang Zhecheng
Published: July 2022

The long-term degradation of epoxy as the matrix and adhesive serviced in harsh environments plays a key role in engineering applications. Understanding how to improve the toughness and durability of epoxy through reasonable material replacement and design is significant to prolong the service life of engineering structures. In the present paper, thermoplastic polypropylene and thermosetting epoxy were exposed in a coupling environment of elevated temperature, water immersion and sustained bending loading. The evolutions of mechanical and thermal properties were further analyzed and compared. Long-term life prediction was conducted to evaluate the corrosive resistances of polypropylene and epoxy. It can be found that polypropylene has better hydrophobic behavior compared to epoxy. At 80 °C, the ratios of the diffusion coefficient and saturated water uptake between the two matrices were 114.4 and 2.94. At the longest immersion time of 90 days, the degradation percentages of tensile strength were 4.7% (40 °C), 7.5% (60 °C) and 8.8% (80 °C), respectively, which had the higher strength retention (>90%). The maximum strength increase in the multiples of polypropylene/epoxy and polypropylene/polyurethane was 1.95 and 1.75, respectively. The bending loading led to a maximum increase in tensile strength (~1.47%) owing to the oxygen isolation effect. The degradation mechanism was attributed to the active functional groups from the production process reacting with oxygen, resulting in the fracture of the local chain segment. By comparison, water molecules reacted with the hydroxyl groups or interrupted the intermolecular Van der Waals force/hydrogen bond of the epoxy, resulting in irreversible hydrolysis and property degradation. Through the comparison, it can be found that polypropylene and its composites have outstanding properties compared to epoxy, which can make them achieve great application prospects in engineering applications when considering a complex service environment.
Development of a Custom-Made 3D Printing Protocol with Commercial Resins for Manufacturing Microfluidic Devices
Authors: Subirada Francesc , Paoli Roberto , Sierra-Agudelo Jessica , Lagunas Anna , Rodriguez-Trujillo Romen , Samitier Josep
Published: July 2022

The combination of microfluidics and photo-polymerization techniques such as stereolithography (SLA) has emerged as a new field which has a lot of potential to influence in such important areas as biological analysis, and chemical detection among others. However, the integration between them is still at an early stage of development. In this article, after analyzing the resolution of a custom SLA 3D printer with commercial resins, microfluidic devices were manufactured using three different approaches. First, printing a mold with the objective of creating a Polydimethylsiloxane (PDMS) replica with the microfluidic channels; secondly, open channels have been printed and then assembled with a flat cover of the same resin material. Finally, a closed microfluidic device has also been produced in a single process of printing. Important results for 3D printing with commercial resins have been achieved by only printing one layer on top of the channel. All microfluidic devices have been tested successfully for pressure-driven fluid flow.
Mechanical and Thermophysical Properties of Carbon Fiber-Reinforced Polyethersulfone
Authors: Torokhov Valerii G. , Chukov Dilyus I. , Tcherdyntsev Victor V. , Sherif Galal , Zadorozhnyy Mikhail Y. , Stepashkin Andrey A. , Larin Ilya I. , Medvedeva Elena V.
Published: July 2022

In this study, the mechanical and thermophysical properties of carbon fiber-reinforced polyethersulfone are investigated. To enhance the interfacial interaction between carbon fibers and the polymer matrix, the surface modification of carbon fibers by thermal oxidation is conducted. By means of AFM and X-ray spectroscopy, it is determined that surface modification changes the morphology and chemical composition of carbon fibers. It is shown that surface modification dramatically increases the mechanical properties of the composites. Thus, flexural strength and the E-modulus of the composites reinforced with modified fibers reached approximately 962 MPa and 60 GPa, respectively, compared with approximately 600 MPa and 50 GPa for the composites reinforced with the initial ones. The heat deflection temperatures of the composites reinforced with the initial and modified fibers were measured. It is shown that composites reinforced with modified fibers lose their stability at temperatures of about 211 °C, which correlates with the glass transition temperature of the PES matrix. The thermal conductivity of the composites with different fiber content is investigated in two directions: in-plane and transverse to layers of carbon fibers. The obtained composites had a relatively high realization of the thermal conductive properties of carbon fibers, up to 55–60%.
Effect of Thermal Exposure on Residual Properties of Wet Layup Carbon Fiber Reinforced Epoxy Composites
Authors: Hong SoonKook , Karbhari Vistasp M.
Published: July 2022

Ambient cured wet layup carbon fiber reinforced epoxy composites used extensively in the rehabilitation of infrastructure and in structural components can be exposed to elevated temperature regimes for extended periods of time of hours to a few days due to thermal excursions. These may be severe enough to cause a significant temperature rise without deep charring as through fires at a small distance and even high-temperature industrial processes. In such cases, it is critical to have information related to the post-event residual mechanical properties and damage states. In this paper, composites are subjected to a range of elevated temperatures up to 260 °C over periods of time up to 72 h. Exposure to elevated temperature regimes is noted to result in a competition between the mechanisms of post-cure that can increase the levels of mechanical characteristics, and the deterioration of the resin and the bond between the fibers and resin that can reduce them. Mechanical tests indicate that tensile and short beam shear properties are not affected negatively until the highest temperatures of exposure considered in this investigation. In contrast, all elevated temperature conditions cause deterioration in resin-dominated characteristics such as shear and flexure, emphasizing the weakness of this mode in layered composites formed from unidirectional fabric architectures due to resin deterioration. Transitions in failure modes are correlated through microscopy to damage progression both at the level of fiber-matrix interface integrity and through the bulk resin, especially at the inter-layer level. The changes in glass transition temperature determined through differential scanning calorimetry can be related to thresholds that indicate changes in the mechanisms of damage.
New Opportunities for Organic Semiconducting Polymers in Biomedical Applications
Authors: Kim Kyunghun , Yoo Hocheon , Lee Eun Kwang
Published: July 2022

The life expectancy of humans has been significantly elevated due to advancements in medical knowledge and skills over the past few decades. Although a lot of knowledge and skills are disseminated to the general public, electronic devices that quantitatively diagnose one’s own body condition still require specialized semiconductor devices which are huge and not portable. In this regard, semiconductor materials that are lightweight and have low power consumption and high performance should be developed with low cost for mass production. Organic semiconductors are one of the promising materials in biomedical applications due to their functionalities, solution-processability and excellent mechanical properties in terms of flexibility. In this review, we discuss organic semiconductor materials that are widely utilized in biomedical devices. Some advantageous and unique properties of organic semiconductors compared to inorganic semiconductors are reviewed. By critically assessing the fabrication process and device structures in organic-based biomedical devices, the potential merits and future aspects of the organic biomedical devices are pinpointed compared to inorganic devices.
UV-Femtosecond-Laser Structuring of Cyclic Olefin Copolymer
Authors: Bischoff Kay , Mücke Dominik , Roth Gian-Luca , Esen Cemal , Hellmann Ralf
Published: July 2022

We report on the laser ablation of cyclic olefin copolymer using an amplified ultrashort pulsed laser in the ultraviolet spectral range. In addition to a high ablation depth per laser-structured layer up to 74 μm at a fluence of 22 J cm−2, an excellent mean roughness Ra of laser-patterned surfaces down to 0.5 μm is demonstrated. Furthermore, with increasing fluence, increasing ablation efficiencies up to 2.5 mm3 W−1 min−1 are determined. Regarding the quality of the ablation, we observed steep ablation flanks and low debris formation, though for fluences above 10.5 J cm−2 the formation of troughs was observed, being attributed to multiple reflections on the ablation flanks. For comparison, laser ablation was performed under identical conditions with an infrared laser wavelength. The results highlight that UV ablation exhibits significant advantages in terms of ablation efficiency, surface roughness and quality. Moreover, our results show that a larger UV focus spot accelerates the ablation process with comparable quality, paving the way for high-power UV ultrashort pulsed lasers towards an efficient and qualitative tool for the laser machining of cyclic olefin copolymer. The production of complex microfluidics further underlines the suitability of this type of laser.
Locally Applied Repositioned Hormones for Oral Bone and Periodontal Tissue Engineering: A Narrative Review
Authors: Abdel Nasser Atia Gamal , Shalaby Hany K. , Zehravi Mehrukh , Ghobashy Mohamed Mohamady , Ahmad Zubair , Khan Farhat S. , Dey Abhijit , Rahman Md. Habibur , Joo Sang Woo , Barai Hasi Rani , Cavalu Simona
Published: July 2022

Bone and periodontium are tissues that have a unique capacity to repair from harm. However, replacing or regrowing missing tissues is not always effective, and it becomes more difficult as the defect grows larger. Because of aging and the increased prevalence of debilitating disorders such as diabetes, there is a considerable increase in demand for orthopedic and periodontal surgical operations, and successful techniques for tissue regeneration are still required. Even with significant limitations, such as quantity and the need for a donor area, autogenous bone grafts remain the best solution. Topical administration methods integrate osteoconductive biomaterial and osteoinductive chemicals as hormones as alternative options. This is a promising method for removing the need for autogenous bone transplantation. Furthermore, despite enormous investigation, there is currently no single approach that can reproduce all the physiologic activities of autogenous bone transplants. The localized bioengineering technique uses biomaterials to administer different hormones to capitalize on the host’s regeneration capacity and capability, as well as resemble intrinsic therapy. The current study adds to the comprehension of the principle of hormone redirection and its local administration in both bone and periodontal tissue engineering.
Human Enamel Fluorination Enhancement by Photodynamic Laser Treatment
Authors: Tisler Corina Elena , Moldovan Marioara , Petean Ioan , Buduru Smaranda Dana , Prodan Doina , Sarosi Codruta , Leucuţa Daniel-Corneliu , Chifor Radu , Badea Mîndra Eugenia , Ene Razvan
Published: July 2022

Poor oral hygiene leads to serious damages of theteeth’s surface enamel such as micro-abrasions and acid erosion. These alterations combined with bacterial plaque result in cavity appearance. Prophylactic measures include various techniques for enamel surface restoration. Fluorination is one of the most important treatments for this purpose. Therefore, in the present research, we investigated the classical fluorination treatment compared with laser photodynamic fluorination performed on human enamel samples with poor surface quality. Three sample groups were investigated: veneer (F), inlay (I), and crowns (C). The general morphologic aspect was investigated by scanning electron microscopy (SEM), and the specific details such as the fine microstructure and nanostructure were investigated by atomic force microscopy (AFM) of the surface roughness. The samples were also investigated by Fourier transformed infrared attenuated total reflectance (FTIR-ATR) to evidence the fluorination effect on the enamel surface. Results showed that all initial samples had an altered state with micro-abrasions and erosion with mineral loss, which increase the surface roughness. The F group was the most damaged, having a higher roughness, and the I group was less damaged. Classic fluorination treatment partially restored the enamel by local re-mineralization, but did not obtain the parameters of healthy enamel. However, a significant decrease of the roughness was observed (statistical relevance p = 0.001 with the Breusch–Pagan Test). This fact was supported by the presence of newly formed fluorides in the FTIR-ATR spectra. The photodynamic laser fluorination restores the enamel in an enhanced manner by a strong re-mineralization, which implies a significant roughness value decrease comparable to healthy enamel. The Breusch–Pagan Test confirmed the relevance with p = 0.001. This is due to an extended re-mineralization abundant in fluoride crystals as observed by AFM and FTIR. Statistical p-values regarding laser application were in the range of 0.02–0.06, supporting its relevance in the fluorination effect. The final conclusion is that the photodynamic effect is able to favor the newly formed fluoride deposition onto the affected sites of the enamel surface.
Factor Model for Online Education during the COVID-19 Pandemic Using the IoT
Authors: Khan Faheem , Tarimer Ilhan , Taekeun Whangbo
Published: July 2022

Coronavirus disease (COVID-19) has spread quickly around the globe. COVID-19 has affected the education sector due to partial or complete lockdowns that were implemented throughout the world between 2019 and 2022. This pandemic severely affected the education sectors in developing countries such as Pakistan. All the educational institutions in Pakistan turned to online education. However, the education sector lacked the teaching experts, digital experts, the Internet of Things (IoT), and resources needed for online education. The shift from traditional to online education has created many challenges for developing countries during a pandemic such as COVID-19, for example, access to the IoT. This paper aims to introduce the factor model (F model), which will provide guidelines for the government and universities for minimizing the deficiencies related to online education. The F-model will identify all the factors that affect the performance and guide the user about their importance. This will allow the user to resolve that issue and improve the performance of their department or institution. Thus, the F model will benefit the education sector by mitigating the challenges related to online education. The F model is not only confined to online education but can be operated in the fields of science and industry for data extraction and the calculation of results. First, the data is collected physically and online through a student survey related to the challenges of online education during a pandemic. The data extraction and the calculation of the results are carried out using the F model. The results of the survey are alarming and the government has a lot of work to do to improve online education using the IoT. According to the F model, the government should take serious action to improve the performance of students, teachers, and all education sectors not only during the COVID-19 pandemic but also for possible future pandemics.
Influence of Varying Compression Ratio of a Compression Ignition Engine Fueled with B20 Blends of Sea Mango Biodiesel
Authors: Renish R Rohith , Selvam Amala Justus , Čep Robert , Elangovan Muniyandy
Published: July 2022

The ever-worsening environmental situation brought on by the huge use of fossil fuels has ramped up biodiesel production. Several studies have shown that a 20% biodiesel-diesel blend (B20) could be the best for utility in a compression ignition (CI) engine. The present study focuses on the characteristics of a variable compression ratio (VCR) engine running with a B20 blend of sea mango biodiesel at compression ratios of 16:1, 17:1 and 18:1. VCR is a technology which permits the engine to modify its compression ratio to improve the fuel economy under varying loads. The experimental results reveal an improvement of 5.27% and 6.25% in the BTE as well as SFC with B20 mix, respectively, at compression ratio (CR) 18:1 against diesel at standard CR, which is 17:1. At CR 18:1, the CO, HC and smoke emissions of B20 fuel at full load were 26.78%, 37.76% and 23.44%, correspondingly lower than those of diesel at standard CR. However, the blend was found to have higher NOx emissions at all the CRs. The least NOx emissions of the blend were noted to be at CR 16:1, although it was 0.77% higher than diesel at standard CR. The combustion characteristics also improved at higher CRs. The findings of this study indicate that the B20 blend of sea mango biodiesel could be utilized at CR 18:1 to replace diesel without any engine modifications.
A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network
Authors: Wang Baiyang , Feng Guifang , Huo Dongyue , Kang Yuyun
Published: July 2022

With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time classification of multichannel, multidimensional sensor signals is increasing. In view of the need for intelligent methods for fault diagnosis methods of mechanical equipment, the generalization ability of fault diagnosis models also needs to be further strengthened. In this context, in order to make fault diagnosis intelligent and efficient, a bearing fault diagnosis method based on spectrum map information fusion and convolutional neural network (CNN) is proposed. First, short-time Fourier transform (STFT) is used to analyze the multichannel vibration signal of the rolling bearing and obtain the frequency domain information of the signal over a period of time. Second, the information fusion is converted into two-dimensional (2D) images, which are input into CNN for training, and the bearing fault identification model is obtained. Next, the frequency domain information of each signal is converted into a 2D spectrum map, which is used as a CNN training dataset to train a bearing fault identification model. Finally, the diagnostic model is validated using the existing datasets. The results show that the accuracy of fault diagnosis using the proposed bearing is greater than 99.4% and can even reach 100%. The proposed method considerably reduces the workload of the diagnosis process, with strong robustness and generalization ability.
An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces
Authors: Rasheed Jawad , Waziry Sadaf , Alsubai Shtwai , Abu-Mahfouz Adnan M.
Published: July 2022

In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of soft biometrics, which provides extra information about a person’s identification, thus, identifying a gender based on a veiled face is among the urgent challenges that must be advocated for in the next decade. Therefore, this study exploited various pre-trained deep learning networks (DenseNet121, DenseNet169, ResNet50, ResNet101, Xception, InceptionV3, MobileNetV2, EfficientNetB0, and VGG16) to analyze the effect of the mask while identifying the gender using facial images of human beings. The study comprises two strategies. First, the experimental part involves the training of models using facial images with and without masks, while the second strategy considers images with masks only, to train the pre-trained models. Experimental results reveal that DenseNet121 and Xception networks performed well for both strategies. Besides this, the Inception network outperformed all others by attaining 98.75% accuracy for the first strategy, whereas EfficientNetB0 performed well for the second strategy by securing 97.27%. Moreover, results suggest that facemasks evidently impact the performance of state-of-the-art pre-trained networks for gender classification.
The Young Goddess Who Dances through the Ordinariness of Life―A Study on the Tantric Traditions of Kerala
Authors: Karasinski-Sroka Maciej , Sharman G. Sudev Krishna
Published: July 2022

Drawing on both ethnographic and literary sources, this paper indicates that initiations into the mantra of Bālā are essential rites of passage for various Tantric communities. We focus on two previously unstudied texts: Bālāviṃśati stotra (“Twenty Verses on the Bālā Goddess”), a popular eulogy sung on festive occasions in Keralan temples, and Bālādīkṣāpaddhati (“A Treatise on Initiation into the Bālā Mantra”), a short treatise explaining the rules of initiation into the Bālā cult of Kerala. The article contextualizes the texts by providing commentaries of practitioners and interpretations of Keralan gurus who initiate their adepts into Śrīvidyā.
Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification
Authors: Wang Wuli , Jiang Yumeng , Wang Ge , Guo Fangming , Li Zhongwei , Liu Baodi
Published: July 2022

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification.
Spatio-Temporal Heterogeneous Impacts of the Drivers of NO2 Pollution in Chinese Cities: Based on Satellite Observation Data
Authors: Cui Yuanzheng , Zha Hui , Dang Yunxiao , Qiu Lefeng , He Qingqing , Jiang Lei
Published: July 2022

Rapid urbanization in China has led to an increasing problem of atmospheric nitrogen dioxide (NO2) pollution, which negatively impacts urban ecology and public health. Nitrogen dioxide is an important atmospheric pollutant, and quantitative spatio-temporal analysis and influencing factor analysis of Chinese cities can help improve urban air pollution. In this study, the spatio-temporal analysis methods were used to explore the variations of NO2 pollution in Chinese cities from 2005 to 2020. The findings are as follows. In more than half of Chinese cities, NO2 levels remarkably decreased between 2005 and 2020. The effective NO2 reduction strategies contributed to the significant NO2 reduction during the 13th Five-Year Plan (2016–2020). Moreover, we found that the pandemic of COVID-19 alleviated NO2 pollution in China since it reduced the traffic, industrial, and living activities. The NO2 pollution in Chinese cities was found highly spatially clustered. The geographically and temporally weighted regression model was used to analyze the spatio-temporal heterogeneity of NO2 pollution influencing factors in Chinese cities, including natural meteorological and socio-economic factors. The results showed that the GDPPC, population densities, and ambient air pressure were positively correlated with NO2 pollution. In contrast, the ratio of the tertiary to the secondary industry, temperature, wind speed, and relative humidity negatively impacted the NO2 pollution level. The findings of this research contribute to the improvement of urban air quality, stimulating the achievements of the sustainable development goals of Chinese cities.
A European-Chinese Exploration: Part 2—Urban Ecosystem Service Patterns, Processes, and Contributions to Environmental Equity under Different Scenarios
Authors: Wu Wanben , Luo Xiangyu , Knopp Julius , Jones Laurence , Banzhaf Ellen
Published: July 2022

Urban expansion and ecological restoration policies can simultaneously affect land-cover changes and further affect ecosystem services (ES). However, it is unclear whether and to what extent the distribution and equity of urban ES are influenced by the stage of urban development and government policies. This study aims to assess the quantity and equity of ES under different scenarios in cites of China and Europe. Firstly, we used the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model to simulate future land cover under three scenarios: business-as-usual (BAU), a market-liberal scenario (MLS), and an ecological protection scenario (EPS). Then using ecosystem service model approaches and the landscape analysis, the dynamics of green infrastructure (GI) fraction and connectivity, carbon sequestration, and PM2.5 removal were further evaluated. The results show that: (1) over the past 20 years, Chinese cities have experienced dramatic changes in land cover and ES relative to European cities. (2) Two metropolises in China, Shanghai and Beijing have experienced an increase in the fraction and connectivity of GI and ES in the long-term built-up areas between 2010 and 2020. (3) EPS scenarios are not only effective in increasing the quantity of ES but also in improving the equity of ES distribution. The proposed framework as well as the results may provide important guidance for future urban planning and sustainable city development.
Quantifying Vegetation Vulnerability to Climate Variability in China
Authors: Jiang Liangliang , Liu Bing , Yuan Ye
Published: July 2022

Climate variability has profound effects on vegetation. Spatial distributions of vegetation vulnerability that comprehensively consider vegetation sensitivity and resilience are not well understood in China. Furthermore, the combination of cumulative climate effects and a one-month-lagged autoregressive model represents an advance in the technical approach for calculating vegetation sensitivity. In this study, the spatiotemporal characteristics of vegetation sensitivity to climate variability and vegetation resilience were investigated at seasonal scales. Further analysis explored the spatial distributions of vegetation vulnerability for different regions. The results showed that the spatial distribution pattern of vegetation vulnerability exhibited spatial heterogeneity in China. In spring, vegetation vulnerability values of approximately 0.9 were mainly distributed in northern Xinjiang and northern Inner Mongolia, while low values were scattered in Yunnan Province and the central region of East China. The highest proportion of severe vegetation vulnerability to climate variability was observed in the subhumid zone (28.94%), followed by the arid zone (26.27%). In summer and autumn, the proportions of severe vegetation vulnerability in the arid and humid zones were higher than those in the other climate zones. Regarding different vegetation types, the highest proportions of severe vegetation vulnerability were found in sparse vegetation in different seasons, while the highest proportions of slight vegetation vulnerability were found in croplands in different seasons. In addition, vegetation with high vulnerability is prone to change in Northeast and Southwest China. Although ecological restoration projects have been implemented to increase vegetation cover in northern China, low vegetation resilience and high vulnerability were observed in this region. Most grasslands, which were mainly concentrated on the Qinghai–Tibet Plateau, had high vulnerability. Vegetation areas with low resilience were likely to be degraded in this region. The areas with highly vulnerable vegetation on the Qinghai–Tibet Plateau could function as warning signals of vegetation degradation. Knowledge of spatial patterns of vegetation resilience and vegetation vulnerability will help provide scientific guidance for regional environmental protection.
Spatial–Temporal Variability of Global GNSS-Derived Precipitable Water Vapor (1994–2020) and Climate Implications
Authors: Ding Junsheng , Chen Junping , Tang Wenjie , Song Ziyuan
Published: July 2022

Precipitable water vapor (PWV) is an important component in the climate system and plays a pivotal role in the global water and energy cycles. Over the years, many approaches have been devised to accurately estimate the PWV. Among them, global navigation satellite systems (GNSS) have become one of the most promising and fastest-growing PWV acquisition methods because of its high accuracy, high temporal and spatial resolution, and ability to acquire PWV in all weather and in near real time. We compared GNSS-derived PWV with a 5 min resolution globally distributed over 14,000 stations from the Nevada Geodetic Laboratory (NGL) from 1994 to 2020 with global radiosonde (RS) data, temperature anomalies, and sea height variations. Then, we examined the temporal and spatial variability of the global PWV and analyzed its climate implications. On a global scale, the average bias and root mean square error (RMSE) between GNSS PWV and RS PWV were ~0.72 ± 1.29 mm and ~2.56 ± 1.13 mm, respectively. PWV decreased with increasing latitude, and the rate of this decrease slowed down at latitudes greater than 35°, with standard deviation (STD) values reaching a maximum at latitudes less than 35°. The global average linear trend was ~0.64 ± 0.81 mm/decade and strongly correlated with temperature and sea height variations. For each 1 °C and 1 mm change, PWV increased by ~2.075 ± 0.765 mm and ~0.015 ± 0.005 mm, respectively. For the time scale, the PWV content peaked ~40 days after the maximum solar radiation of the year (the summer solstice), and the delay was ~40 days relative to the summer solstice.
Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method
Authors: Dong Lin , Qi Jifeng , Yin Baoshu , Zhi Hai , Li Delei , Yang Shuguo , Wang Wenwu , Cai Hong , Xie Bowen
Published: July 2022

Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.
Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador
Authors: Bravo-López Esteban , Fernández Del Castillo Tomás , Sellers Chester , Delgado-García Jorge
Published: July 2022

Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area.
Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia
Authors: Xia Qian-Qian , Chen Ya-Ning , Zhang Xue-Qi , Ding Jian-Li
Published: July 2022

Maintaining the ecological security of arid Central Asia (CA) is essential for the sustainable development of arid CA. Based on the moderate-resolution imaging spectroradiometer (MODIS) data stored on the Google Earth Engine (GEE), this paper investigated the spatiotemporal changes and factors related to ecological environment quality (EEQ) in CA from 2000 to 2020 using the remote sensing ecological index (RSEI). The RSEI values in CA during 2000, 2005, 2010, 2015, and 2020 were 0.379, 0.376, 0.349, 0.360, and 0.327, respectively; the unchanged/improved/deteriorated areas during 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were about 83.21/7.66%/9.13%, 77.28/6.68%/16.04%, 79.03/11.99%/8.98%, and 81.29/2.16%/16.55%, respectively, which indicated that the EEQ of CA was poor and presented a trend of gradual deterioration. Consistent with the RSEI trend, Moran’s I index values in 2000, 2005, 2010, 2015, and 2020 were 0.905, 0.893, 0.901, 0.898, and 0.884, respectively, revealing that the spatial distribution of the EEQ was clustered rather than random. The high–high (H-H) areas were mainly located in mountainous areas, and the low–low (L-L) areas were mainly distributed in deserts. Significant regions were mainly located in H-H and L-L, and most reached the significance level of 0.01, indicating that EEQ exhibited strong correlation. The EEQ in CA is affected by both natural and human factors. Among the natural factors, greenness and wetness promoted the EEQ, while heat and dryness reduced the EEQ, and heat had greater effects than the other three indexes. Human factors such as population growth, overgrazing, and hydropower development are important factors affecting the EEQ. This study provides important data for environmental protection and regional planning in arid and semi-arid regions.
Spatial Resolution and Data Integrity Enhancement of Microwave Radiometer Measurements Using Total Variation Deconvolution and Bilateral Fusion Technique
Authors: Hu Weidong , Yao Zhiyu , Chen Shi , Xu Zhihao , Liu Yang , Feng Zhiyan , Ligthart Leo
Published: July 2022

Passive multi-frequency microwave sensors are indispensable instruments for worldwide environmental monitoring. However, they often suffer from the issues of poor spatial resolution and the original land–sea transition zone data are contaminated severely. Conventional analytical deconvolution methods enhance the spatial resolution at the expense of noise amplification and Gibbs fluctuations in the land–sea transition zone. In order to enhance the spatial resolution as well as simultaneously enhance the integrity of the Microwave Radiometer data, a method based on Total Variation deconvolution, Bilateral Filter, and data fusion (TVBF+) is proposed. Our method substantially improves data integrity and obtains similar enhanced resolution compared to existing methods. Experiments performed using both simulated and actual microwave radiation Imager (MWRI) data demonstrate the method’s robustness and effectiveness.
A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef
Authors: Patricio-Valerio Larissa , Schroeder Thomas , Devlin Michelle J. , Qin Yi , Smithers Scott
Published: July 2022

Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km2 spatial resolution. Coupled ocean–atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L−1 within the validation range of 0.14 to 24 mg L−1, with a detection limit of 0.25 mg L−1. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR.
Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning
Authors: Fan Kai , Li Fenling , Chen Xiaokai , Li Zhenfa , Mulla David
Published: July 2022

Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat.
Comparative Study on Compressive and Flexural Properties of Concrete-Filled Steel Tubular Arch Joints
Authors: Sun Huibin , Lu Wei , Wang Jiancai , Ren Quangang , Xu Xin , Han Debin , Li Xu , Yang Huixiang , Wei Lianbang , Liu Yan
Published: July 2022

Studying the bearing mechanism of concrete-filled steel tubular (CFST) arch components and constructing the quantitative design method of the CFST arch is an important subject in underground support. In order to clarify the bending and compression properties of CFST arch joints, considering different structural parameters of the joint, bending and compression tests of square CFST components without joints, with tubular joints and with flange joints were carried out. The mechanical properties and failure modes of the bending and compression combinations of each component were analyzed, and the influence of structural parameters of joints on their bearing capacity was clarified. The results show that (1) the failure mode of the component without a joint and the component with a tubular joint present uniform curve deformation, and the flange joint presents typical brittle failure and broken line failure; (2) compared to the specimens without a joint and with a flange joint, the tubular joint has higher yielding strength and ultimate strength due to the strengthening effect of the tubular joint, while the bending bearing capacity is 623.639 KN; (3) the tubular length and flange thickness are the key structural parameters of the two types of joints, which have a significant influence on the bending capacity of the specimens; (4) the tubular joint has a simple structure and high bearing capacity, so it should be used as the preferred joint connection form of the concrete-filled steel tubular support arch in deep mine roadways with complex conditions.
Study on the Impact of Internet Use on Farmers’ Straw Returning to the Field: A Micro Survey Data from China
Authors: Chen Fan , Zhang Can , Wang Wenna
Published: July 2022

Promoting the use of straw returning technology in rural areas is of great value to improve the use of agricultural technology and to reduce agricultural carbon emissions. However, the low proportion of straw recycling has also attracted more attention all over the world. An effective measure to solve this problem is to increase farmers’ internet use to encourage them to participate in straw recycling. Based on micro survey data from 2126 households obtained from China, this paper uses an ordered probability model (OPM) and seeks to measure the impact of internet use on farmers’ active use of straw returning and its heterogeneity. It aims to answer two questions: what the impact of internet use on farmers’ willingness to participate in straw recycling is, and whether there is heterogeneity. The study found that the use of the internet can significantly drive farmers to use straw to return to the field. Specifically, farmers using the internet are less likely to become non-adoption (−2.95%) or non-voluntary (3.09%), but have a greater probability of becoming voluntary (6.04%). Moreover, the heterogeneity analysis showed that older groups and lower income groups could improve their willingness to actively adopt straw returning technology after using the internet. According to this research, it is suggested to improve local network resources, guide farmers to effectively entertain, study, live, and better enjoy the dividends brought by the internet. In terms of environmental publicity and education, the government should make policy adjustments for older groups and low-income groups to promote the use of technology.