bluuebunny/crossref_metadata_2025 huggingface dataset:
Anxiety Effect on Communication Skills in Nursing Supervisors: An Observational Study
Authors: Colomer-Sánchez Ana , Ayuso-Murillo Diego , Lendínez-Mesa Alejandro , Ruiz-Nuñez Carlos , Fontán-Vinagre Guadalupe , Herrera-Peco Iván
Published: April 2021

Communication represents an essential skill in nurse managers’ performance of everyday activities to ensure a good coordination of the team, since it focuses on the transmission of information in an understandable way. At the same time, anxiety is an emotion that can be caused by demanding and stressful work environments, such as those of nurse managers. The aim of the present study was to analyze the impact of anxiety management on nurse managers’ communication skills. The sample comprised 90 nursing supervisors from hospitals in Madrid, Spain; 77.8% were women, and 22.2% were men, with an average of 10.9 years of experience as nursing supervisors. The instruments used for analysis were the Sixteen Personality Factor Questionnaire: version five (16PF5) and State Trait Anxiety Inventory (STAI) questionnaires, validated for the Spanish population. The results showed that emotional stability was negatively affected by anxiety (r = −0.43; p = 0.001), while apprehension was positively affected (r = 0.382; p = 0.000). Nursing supervisors, as managers, were found to possess a series of personality factors and skills to manage stress and communication situations that prevent them from being influenced by social pressure and the opinion of others.
The Ambivalent Role of miRNAs in Carcinogenesis: Involvement in Renal Cell Carcinoma and Their Clinical Applications
Authors: Spadaccino Federica , Gigante Margherita , Netti Giuseppe Stefano , Rocchetti Maria Teresa , Franzin Rossana , Gesualdo Loreto , Castellano Giuseppe , Stallone Giovanni , Ranieri Elena
Published: April 2021

The analysis of microRNA (miRNAs), small, non-coding endogenous RNA, plays a crucial role in oncology. These short regulatory sequences, acting on thousands of messenger RNAs (mRNAs), modulate gene expression at the transcriptional and post-transcriptional level leading to translational repression or degradation of target molecules. Although their function is required for several physiological processes, such as proliferation, apoptosis and cell differentiation, miRNAs are also responsible for development and/or progression of several cancers, since they may interact with classical tumor pathways. In this review, we highlight recent advances in deregulated miRNAs in cancer focusing on renal cell carcinoma (RCC) and provide an overview of the potential use of miRNA in their clinical settings, such as diagnostic and prognostic markers.
Testing a Benchtop Wet-Milling Method for Preparing Nanoparticles and Suspensions as Hospital Formulations
Authors: Kawano Yayoi , Shimizu Yuichiro , Hanawa Takehisa
Published: April 2021

In clinical practice, for elderly or pediatric patients who have difficulty swallowing, solid dosage forms such as tablets or capsules are crushed or unsealed, prepared as powder forms, and often administered as suspensions. However, because their dispersibility is poor, aggregation or precipitation occurs readily. Once precipitation and deposition happen, redispersion is difficult, which can limit patient and caretaker drug adherence. In this study, we attempted to prepare nanoparticles as a hospital formulation by a benchtop wet-milling method to obtain a suspension with high dispersibility. This is the first study to apply the wet-milling method to prepare the hospital formulation. We chose cefditoren pivoxil (CDTR-PI) as an experimental active pharmaceutical ingredient. CDTR-PI crystals were physically mixed with various water-soluble polymers such as polyvinylpyrrolidone, polyethylene oxide, hydroxypropyl cellulose, or hypromellose and wet-milled with a surface-active agent (sodium lauryl sulfate) under different conditions. The mean particle diameter of most of the samples was less than 200 nm. In FTIR spectra of ground samples, peak shifts suggesting inter- or intramolecular interactions between CDTR-PI and the other additive agents were not observed. Besides, the nanoparticle suspension had favorable dispersibility, as determined using a dispersion stability analyzer. Providing a suspension with high dispersibility makes dispense with the resuspension, the patient’s medication adherence would improve. These results show that suspended liquid formulations of active pharmaceutical ingredients could be obtained by the simple wet-milling method as hospital formulations.
A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework
Authors: Pérez-Moraga Raul , Forés-Martos Jaume , Suay-García Beatriz , Duval Jean-Louis , Falcó Antonio , Climent Joan
Published: April 2021

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.
Tackling Immune Pathogenesis of COVID-19 through Molecular Pharmaceutics
Authors: Puccetti Matteo , Costantini Claudio , Ricci Maurizio , Giovagnoli Stefano
Published: April 2021

An increasing number of clinical studies worldwide are investigating the repurposing of antiviral, immune-modulatory, and anti-inflammatory agents to face the coronavirus disease-19 (COVID-19) pandemic. Nevertheless, few effective therapies exist to prevent or treat COVID-19, which demands increased drug discovery and repurposing efforts. In fact, many currently tested drugs show unknown efficacy and unpredictable drug interactions, such that interventions are needed to guarantee access to effective and safe medicines. Anti-inflammatory therapy has proven to be effective in preventing further injury in COVID-19 patients, but the benefit comes at a cost, as targeting inflammatory pathways can imply an increased risk of infection. Thus, optimization of the risk/benefit ratio is required in the anti-inflammatory strategy against COVID-19, which accounts for drug formulations and delivery towards regionalization and personalization of treatment approaches. In this perspective, we discuss how better knowledge of endogenous immunomodulatory pathways may optimize the clinical use of novel and repurposed drugs against COVID-19 in inpatient, outpatient, and home settings through innovative drug discovery, appropriate drug delivery systems and dedicated molecular pharmaceutics.
Curators Serving the Public Good
Authors: Martinon Jean-Paul
Published: April 2021

This article investigates a principle inscribed at the top of most codes of ethics for curators: they should always “serve the public good.” No self-respecting curator would ever admit to serve “the private good,” that is, the good of the few, whether that of an elite in power or of a circle of friends or allies. The principle of “serving the public good” is inalienable and unquestionable even in situations where it is most open to doubt. However, what exactly is the meaning of this seemingly “true” and on all accounts “universal” principle: “to serve the public good”? To address this question, I look at this principle for the way it is perceived as being both majestic in its impressive widespread acceptance and cloaked in ridicule for being so often disregarded. I will argue—with an example taken from the history of curating—that it is not the meaning attached to the principle that counts, but the respect that it enjoins. I conclude by drawing a few remarks on how the value of the “good” remains, after the principle has been cast aside and the priority of respect is acknowledged, a ghost on the horizon of all curators’ work.
Subcellular Localization and Vesicular Structures of Anthocyanin Pigmentation by Fluorescence Imaging of Black Rice (Oryza sativa L.) Stigma Protoplast
Authors: Mackon Enerand , Ma Yafei , Jeazet Dongho Epse Mackon Guibeline Charlie , Li Qiufeng , Zhou Qiong , Liu Piqing
Published: April 2021

Anthocyanins belong to the group of flavonoid compounds broadly distributed in plant species responsible for attractive colors. In black rice (Oryza sativa L.), they are present in the stems, leaves, stigmas, and caryopsis. However, there is still no scientific evidence supporting the existence of compartmentalization and trafficking of anthocyanin inside the cells. In the current study, we took advantage of autofluorescence with anthocyanin’s unique excitation/emission properties to elucidate the subcellular localization of anthocyanin and report on the in planta characterization of anthocyanin prevacuolar vesicles (APV) and anthocyanic vacuolar inclusion (AVI) structure. Protoplasts were isolated from the stigma of black and brown rice and imaging using a confocal microscope. Our result showed the fluorescence displaying magenta color in purple stigma and no fluorescence in white stigma when excitation was provided by a helium–neon 552 nm and emission long pass 610–670 nm laser. The fluorescence was distributed throughout the cell, mainly in the central vacuole. Fluorescent images revealed two pools of anthocyanin inside the cells. The diffuse pools were largely found inside the vacuole lumen, while the body structures could be observed mostly inside the cytoplasm (APV) and slightly inside the vacuole (AVI) with different shapes, sizes, and color intensity. Based on their sizes, AVI could be grouped into small (Ф < 0.5 um), middle (Ф between 0.5 and 1 um), and large size (Ф > 1 um). Together, these results provided evidence about the sequestration and trafficking of anthocyanin from the cytoplasm to the central vacuole and the existence of different transport mechanisms of anthocyanin. Our results suggest that stigma cells are an excellent system for in vivo studying of anthocyanin in rice and provide a good foundation for understanding anthocyanin metabolism in plants, sequestration, and trafficking in black rice.
Non-Thermal Plasma Treatment Influences Shoot Biomass, Flower Production and Nutrition of Gerbera Plants Depending on Substrate Composition and Fertigation Level
Authors: Cannazzaro Samantha , Traversari Silvia , Cacini Sonia , Di Lonardo Sara , Pane Catello , Burchi Gianluca , Massa Daniele
Published: April 2021

Non-thermal plasma (NTP) appears a promising strategy for supporting crop protection, increasing yield and quality, and promoting environmental safety through a decrease in chemical use. However, very few NTP applications on containerized crops are reported under operational growing conditions and in combination with eco-friendly growing media and fertigation management. In this work, NTP technology is applied to the nutrient solution used for the production of gerbera plants grown in peat or green compost, as an alternative substrate to peat, and with standard or low fertilization. NTP treatment promotes fresh leaf and flower biomass production in plants grown in peat and nutrient adsorption in those grown in both substrates, except for Fe, while decreasing dry plant matter. However, it causes a decrease in the leaf and flower biomasses of plants grown in compost, showing a substrate-dependent effect under a low fertilization regime. In general, the limitation in compost was probably caused by the high-substrate alkalinization that commonly interferes with gerbera growth. Under low fertilization, a reduction in the photosynthetic capacity further penalizes plant growth in compost. A lower level of fertilization also decreases gerbera quality, highlighting that Ca, Mg, Mn, and Fe could be reduced with respect to standard fertilization.
Effect of Surfactant Molecular Structure on Emulsion Stability Investigated by Interfacial Dilatational Rheology
Authors: Jin Yuejie , Liu Dingrong , Hu Jinhua
Published: April 2021

Polyglycerol polyricinolate (PGPR) and polyglycerol-2 dioleate were selected as model surfactants to construct water-in-oil (W/O) emulsions, and the effect of interfacial rheological properties of surfactant film on the stability of emulsions were investigated based on the interfacial dilatational rheological method. The hydrophobicity chain of PGPR is polyricinic acid condensed from ricinic acid, and that of polyglycerol-2 dioleate is oleic acid. Their dynamic interfacial tensions in 15 cycles of interfacial compression-expansion were determined. The interfacial dilatational viscoelasticity was analyzed by amplitude scanning in the range of 1–28% amplitude and frequency sweep in the range of 5–45 mHz under 2% amplitude. It was found that PGPR could quickly reach adsorption equilibrium and form interfacial film with higher interfacial dilatational viscoelastic modulus to resist the deformation of interfacial film caused by emulsion coalescence, due to its branched chain structure and longer hydrophobic chain, and the emulsion thus presented good stability. However, polyglycerol-2 dioleate with a straight chain structure had lower interfacial tension, and it failed to resist the interfacial disturbance caused by coalescence because of its lower interfacial dilatational viscoelastic modulus, and thus the emulsion was unstable. This study reveals profound understanding of the influence of branched structure of PGPR hydrophobic chain on the interfacial film properties and the emulsion stability, providing experimental reference and theoretical guidance for future design or improvement of surfactant.
Tuning Thermal and Mechanical Properties of Polydimethylsiloxane with Carbon Fibers
Authors: Gupta Nevin Stephen , Lee Kwan-Soo , Labouriau Andrea
Published: April 2021

In order to meet the needs of constantly advancing technologies, fabricating materials with improved properties and predictable behavior has become vital. To that end, we have prepared polydimethylsiloxane (PDMS) polymer samples filled with carbon nanofibers (CFs) at 0, 0.5, 1.0, 2.0, and 4.0 CF loadings (w/w) to investigate and optimize the amount of filler needed for fabrication with improved mechanical properties. Samples were prepared using easy, cost-efficient mechanical mixing to combine the PDMS and CF filler and were then characterized by chemical (FTIR), mechanical (hardness and tension), and physical (swelling, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and coefficient of thermal expansion) analyses to determine the material properties. We found that hardness and thermal stability increased predictably, while the ultimate strength and toughness both decreased. Repeated tension caused the CF-filled PDMS samples to lose significant toughness with increasing CF loadings. The hardness and thermal degradation temperature with 4 wt.% CF loading in PDMS increased more than 40% and 25 °C, respectively, compared with the pristine PDMS sample. Additionally, dilatometer measurements showed a 20% decrease in the coefficient of thermal expansion (CTE) with a small amount of CF filler in PDMS. In this study, we were able to show the mechanical and thermal properties of PDMS can be tuned with good confidence using CFs.
Characteristic Evaluation of Gel Formulation Containing Niosomes of Melatonin or Its Derivative and Mucoadhesive Properties Using ATR-FTIR Spectroscopy
Authors: Uthaiwat Prangtip , Priprem Aroonsri , Puthongking Ploenthip , Daduang Jureerut , Nukulkit Chatchanok , Chio-Srichan Sirinart , Boonsiri Patcharee , Thapphasaraphong Suthasinee
Published: April 2021

Chitosan or polyvinyl pyrrolidone (PVP) were used in combination with hydroxypropyl methylcellulose (HPMC) and poloxamer 407 (P407) as gelling agents for oral drug delivery. The performance interaction with mucin of chitosan-composed gel (F1) and PVP-composed gel (F2) was compared using attenuated total reflectance–Fourier-transform infrared (ATR-FTIR) spectroscopy at controlled temperatures of 25 and 37 °C for 1 and 5 min. F1 containing niosome-entrapped melatonin or its derivatives was investigated for mucoadhesive interaction on mucosa by ATR-FTIR spectroscopy under the same conditions. The results showed that F1-treated mucin gave a significantly lower amide I/amide II ratio than untreated mucin and F2-treated mucin did within 1 min, suggesting improved rapid affinity between mucin and chitosan. The spectra of mucosa treated with F1 incorporating niosomes of melatonin or its derivatives showed peak shifts at C=O (amide I), N-H (amide II), and carbohydrate regions and an associated decrease in the amide I/amide II ratio and increase in the carbohydrate/amide II ratio. These results indicated electrostatic interaction and hydrogen bonding between chitosan and mucin on the mucosa. In conclusion, the molecular interaction between gels and mucin/mucosa detected at amide I and amide II of proteins and the carbohydrate region could lead to an improved mucoadhesive property of the gel on the mucosa.
The Role of Orientation and Temperature on the Mechanical Properties of a 20 Years Old Wind Turbine Blade GFR Composite
Authors: Ahmed Mohamed M. Z. , Alzahrani Bandar , Jouini Nabil , Hessien Mahmoud M. , Ataya Sabbah
Published: April 2021

This work evaluates the mechanical properties of the glass fiber reinforced polymer (GFRP) material taken from an out of service 100 KW power wind turbine blade which has been in service life of 20 years old. Investigated samples were taken from two positions of undamaged regions at 1.6 m and 5.4 m from the rotor hub, respectively. Microstructure investigation and lay-up analysis were carried out. Fiber weight fraction of the investigated samples was ranging between 0.55–0.60. Tensile and compression tests were carried out at the temperature range from −10 °C to +50 °C on specimens which were machined so as to be loaded in the blade length direction LD, transverse to the blade length TD and off axis; 45° to the blade length. Tensile elastic modulus of the investigated GFRP was determined in the three direction tested. The number of fiber fabric layers found to be decreasing along the blade length away from the root and the density of the fibers along the length is the highest (858 gm/mm2) and in the transverse direction is the lowest (83 gm/mm2). The microstructure of the GFRP composite showed good wetting for the fiber by the polymer with some features of lack of penetration at the high density fiber bundles and some production porosity in the matrix. The tensile Properties at room temperature (RT) and high temperature are almost similar with the highest properties for the samples aligned with the blade length. The compressive strength is highest at the transverse direction samples and lowest at the blade length direction and decreasing with the increase of the test temperature. The bending properties are significantly affected by the fiber orientation with the highest properties for samples aligned with the blade length and the lowest for the samples with the transverse direction.
Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations
Authors: Baek Sujeong , Kim Dong Oh
Published: April 2021

In manufacturing systems, pick-up operations by vacuum grippers may fail owing to manufacturing errors in an object’s surface that are within the allowable tolerance limits. In such situations, manual interference is required to resume system operation, which results in considerable loss of time as well as economic losses. Although vacuum grippers have many advantages and are widely used in the industry, it is highly difficult to directly monitor the current machine status and provide appropriate recovery feedback for stable operation. Therefore, this paper proposes a method to detect the success or failure of a suction operation in advance by analyzing the amount of outlet air pressure in the Venturi line. This was achieved by installing an air pressure sensor on the Venturi line to predict whether the current suction action will be successful. Through empirical experiments, it was found that downward movements in the z-axis of the vacuum gripper can easily rectify a faulty gripper suction operation. Real-time monitoring results verified that predictive process adjustment of the pick-up operation can be performed by modifying the z-position of the vacuum gripper.
A UAV Open Dataset of Rice Paddies for Deep Learning Practice
Authors: Yang Ming-Der , Tseng Hsin-Hung , Hsu Yu-Chun , Yang Chin-Ying , Lai Ming-Hsin , Wu Dong-Hong
Published: April 2021

Recently, unmanned aerial vehicles (UAVs) have been broadly applied to the remote sensing field. For a great number of UAV images, deep learning has been reinvigorated and performed many results in agricultural applications. The popular image datasets for deep learning model training are generated for general purpose use, in which the objects, views, and applications are for ordinary scenarios. However, UAV images possess different patterns of images mostly from a look-down perspective. This paper provides a verified annotated dataset of UAV images that are described in data acquisition, data preprocessing, and a showcase of a CNN classification. The dataset collection consists of one multi-rotor UAV platform by flying a planned scouting routine over rice paddies. This paper introduces a semi-auto annotation method with an ExGR index to generate the training data of rice seedlings. For demonstration, this study modified a classical CNN architecture, VGG-16, to run a patch-based rice seedling detection. The k-fold cross-validation was employed to obtain an 80/20 dividing ratio of training/test data. The accuracy of the network increases with the increase of epoch, and all the divisions of the cross-validation dataset achieve a 0.99 accuracy. The rice seedling dataset provides the training-validation dataset, patch-based detection samples, and the ortho-mosaic image of the field.
Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics
Authors: Zhu A-Xing , Zhao Fang-He , Pan Hao-Bo , Liu Jun-Zhi
Published: April 2021

Two main approaches are used in mapping rice paddy distribution from remote sensing images: phenological methods or machine learning methods. The phenological methods can map rice paddy distribution in a simple way but with limited accuracy. Machine learning, particularly deep learning, methods that learn the spectral signatures can achieve higher accuracy yet require a large number of field samples. This paper proposed a pheno-deep method to couple the simplicity of the phenological methods and the learning ability of the deep learning methods for mapping rice paddy at high accuracy without the need of field samples. The phenological method was first used to initially delineate the rice paddy for the purpose of creating training samples. These samples were then used to train the deep learning model. The trained deep learning model was applied to map the spatial distribution of rice paddy. The effectiveness of the pheno-deep method was evaluated in Jin’an District, Lu’an City, Anhui Province, China. Results show that the pheno-deep method achieved a high performance with the overall accuracy, the precision, the recall, and AUC (area under curve) being 88.8%, 87.2%, 91.1%, and 94.4%, respectively. The pheno-deep method achieved a much better performance than the phenological alone method and can overcome the noises in the training samples from the phenological method. The overall accuracy of the pheno-deep method is only 2.4% lower than that of the deep learning alone method trained with field samples and this difference is not statistically significant. In addition, the pheno-deep method requires no field sampling, which would be a noteworthy advantage for situations when large training samples are difficult to obtain. This study shows that by combining knowledge-based methods with data-driven methods, it is possible to achieve high mapping accuracy of geographic variables using remote sensing even with little field sampling efforts.
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Authors: Xu Jinhuan , Xiao Liang , Yang Jingxiang
Published: April 2021

Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is unstable as the k-means method is sensitive to the initialization of the cluster centers. In order to address these issues, we propose a novel unified low-rank subspace clustering method with dynamic hypergraph for hyperspectral images (HSIs). In our method, the hypergraph is adaptively learned from the low-rank subspace feature, which can capture a more complex manifold structure effectively. In addition, we introduce a rotation matrix to simultaneously learn continuous and discrete clustering labels without any relaxing information loss. The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught property. The experimental results on real HSIs show that the proposed methods can achieve better performance compared to eight state-of-the-art clustering methods.
Mapping Surficial Soil Particle Size Fractions in Alpine Permafrost Regions of the Qinghai–Tibet Plateau
Authors: Wang Chong , Zhao Lin , Fang Hongbing , Wang Lingxiao , Xing Zanpin , Zou Defu , Hu Guojie , Wu Xiaodong , Zhao Yonghua , Sheng Yu , Pang Qiangqiang , Du Erji , Liu Guangyue , Yun Hanbo
Published: April 2021

Spatial information of particle size fractions (PSFs) is primary for understanding the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) in response to climate change. However, the limitation of field observations and the tremendous spatial heterogeneity hamper the digital mapping of PSF. This study integrated log-ratio transformation approaches, variable searching methods, and machine learning techniques to map the surficial soil PSF distribution of two typical permafrost regions. Results showed that the Boruta technique identified different covariates but retained those covariates of vegetation and land surface temperature in both regions. Variable selection techniques effectively decreased the data redundancy and improved model performance. In addition, the spatial distribution of soil PSFs generated by four log-ratio models presented similar patterns. Isometric log-ratio random forest (ILR-RF) outperformed the other models in both regions (i.e., R2 ranged between 0.36 to 0.56, RMSE ranged between 0.02 and 0.10). Compared with three legacy datasets, our prediction better captured the spatial pattern of PSFs with higher accuracy. Although this study largely improved the accuracy of spatial distribution of soil PSFs, further endeavors should also be made to improve model accuracy and interpretability for a better understanding of the interaction and processes between environmental predictors and soil PSFs at permafrost regions.
Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors
Authors: Ayari Emna , Kassouk Zeineb , Lili-Chabaane Zohra , Baghdadi Nicolas , Bousbih Safa , Zribi Mehrez
Published: April 2021

This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.
Shoreline Changes along Northern Ibaraki Coast after the Great East Japan Earthquake of 2011
Authors: Nguyen Hao Quang , Takewaka Satoshi
Published: April 2021

In this study, we analyze the influence of the Great East Japan Earthquake, which occurred on 11 March 2011, on the shoreline of the northern Ibaraki Coast. After the earthquake, the area experienced subsidence of approximately 0.4 m. Shoreline changes at eight sandy beaches along the coast are estimated using various satellite images, including the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), ALOS AVNIR-2 (Advanced Land Observing Satellite, Advanced Visible and Near-infrared Radiometer type 2), and Sentinel-2 (a multispectral sensor). Before the earthquake (for the period March 2001–January 2011), even though fluctuations in the shoreline position were observed, shorelines were quite stable, with the averaged change rates in the range of ±1.5 m/year. The shoreline suddenly retreated due to the earthquake by 20–40 m. Generally, the amount of retreat shows a strong correlation with the amount of land subsidence caused by the earthquake, and a moderate correlation with tsunami run-up height. The ground started to uplift gradually after the sudden subsidence, and shoreline positions advanced accordingly. The recovery speed of the beaches varied from +2.6 m/year to +6.6 m/year, depending on the beach conditions.
A Remote Sensor System Based on TDLAS Technique for Ammonia Leakage Monitoring
Authors: Lu Hongbin , Zheng Chuantao , Zhang Lei , Liu Zhiwei , Song Fang , Li Xiuying , Zhang Yu , Wang Yiding
Published: April 2021

The development of an efficient, portable, real-time, and high-precision ammonia (NH3) remote sensor system is of great significance for environmental protection and citizens’ health. We developed a NH3 remote sensor system based on tunable diode laser absorption spectroscopy (TDLAS) technique to measure the NH3 leakage. In order to eliminate the interference of water vapor on NH3 detection, the wavelength-locked wavelength modulation spectroscopy technique was adopted to stabilize the output wavelength of the laser at 6612.7 cm−1, which significantly increased the sampling frequency of the sensor system. To solve the problem in that the light intensity received by the detector keeps changing, the 2f/1f signal processing technique was adopted. The practical application results proved that the 2f/1f signal processing technique had a satisfactory suppression effect on the signal fluctuation caused by distance changing. Using Allan deviation analysis, we determined the stability and limit of detection (LoD). The system could reach a LoD of 16.6 ppm·m at an average time of 2.8 s, and a LoD of 0.5 ppm·m at an optimum averaging time of 778.4 s. Finally, the measurement result of simulated ammonia leakage verified that the ammonia remote sensor system could meet the need for ammonia leakage detection in the industrial production process.
An Online Calibration Method Based on n-Tuple and Opportunistic Communication for Mine Mass Portable Gas Sensors
Authors: Wang Gang , Zhao Yang , Ding Zeheng , Zhao Xiaohu
Published: April 2021

Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., ‘single-sensor, multi-position’ and ‘multi-sensor, single-position’) of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved.
Magnetic Internal Corrosion Detection Sensor for Exposed Oil Storage Tanks
Authors: Aljarah Ahmad , Vahdati Nader , Butt Haider
Published: April 2021

Corrosion in the oil and gas industry represents one of the major problems that affect oil production and transportation processes. Several corrosion-inspection technologies are in the market to detect internal and external corrosion of oil storage tanks, but inspection of storage tanks occurs every 3 to 7 years. In between inspection interval, aggressive corrosion can potentially occur, which makes the oil and gas industry vulnerable to accidents. This study proposes a new internal corrosion detection sensor based on the magnetic interaction between a rare-earth permanent magnet and the ferromagnetic nature of steel, used to manufacture oil storage tanks. Finite element analysis (FEA) software was used to analyze the effect of various sensor parameters on the attractive force between the magnet and the steel. The corrosion detection sensor is designed based on the FEA results. The experimental testing of the sensor shows that it is capable of detecting internal metal loss due to corrosion in oil storage tanks within approximately 8 mm of the internal surface thickness. The sensor showed more than two-fold improvement in the detection range compared to previous sensor proposed by the authors. Furthermore, the sensor of this paper provides a monitoring rather than occasional inspection solution.
Target Identification with Improved 2D-VMD for Carrier-Free UWB Radar
Authors: Zhu Yuying , Zhang Shuning , Zhao Huichang , Chen Si
Published: April 2021

In recent years, the interest in radar automatic target recognition (RATR) based on the carrier-free ultra-wideband (UWB) radar has been increasing. Compared with narrow-band and other bandwidth radars, the echo signal of the carrier-free UWB radar includes more comprehensive and detailed information with respect to the targeted object. In this paper, we first utilized 3ds Max to acquire accurate geometric models and applied a time-domain integral equation (TDIE) for echo signal acquisition under the condition that the transmitted signals had an extremely short duration period. By comparing the simulated waveform with the actual one, the accuracy of the electromagnetic modeling is verified. Furthermore, given that the actual environment is full of noise and clutter, we propose an improved two-dimensional variational mode decomposition (2D-IVMD), and an algorithm is proposed to eliminate noise and extract edge features preliminarily, which lays a foundation for further in-depth feature extraction. Then, the deep conventional neural network (DCNN) is introduced for the final recognition. The results show that the proposed methods achieve promising classification performance under the condition of low signal-to-noise ratio (SNR) values.
Comparison of Imaging Models for Spectral Unmixing in Oil Painting
Authors: Grillini Federico , Thomas Jean-Baptiste , George Sony
Published: April 2021

The radiation captured in spectral imaging depends on both the complex light–matter interaction and the integration of the radiant light by the imaging system. In order to obtain material-specific information, it is important to define and invert an imaging process that takes into account both aspects. In this article, we investigate the use of several mixing models and evaluate their performances in the study of oil paintings. We propose an evaluation protocol, based on different features, i.e., spectral reconstruction, pigment mapping, and concentration estimation, which allows investigating the different properties of those mixing models in the context of spectral imaging. We conduct our experiment on oil-painted mockup samples of mixtures and show that models based on subtractive mixing perform the best for those materials.
A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data
Authors: Sun Pengchang , Lu Wenbo , Hu Haoran , Zhang Yuzhu , Chen Ming , Yan Peng
Published: April 2021

The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results.
Application of Magnetic Sensor for Magnetic Profile (1D) and Surface (2D) Measurement of Automotive Wheels
Authors: Brol Sebastian
Published: April 2021

This paper shows a report of over three years of intensive work on application of a 3-axis anisotropic magnetoresistive sensor with I2C interface for measurement of magnetic flux density distribution of automotive wheels. The work was undertaken to answer the question of whether is a possibility to effectively apply low-cost magnetic sensors with serial interface to measure the magnetic field surrounding the automotive wheel or tire. Two measurement techniques were discussed: Magnetic profile (1D) and magnetic surface measurement (2D) over tread, and also gear associated with the sensor, as well as its design, layout, operation, and control technique during (1D) and (2D) measurements. Three experiments were performed to asses accuracy and repeatability concerning component and resultant magnetic circumferential profiles and also magnetic surface. Differences between measurement outcomes in experiment were assessed. The results show that accuracy and repeatability lays below maximum admissible uncertainty declared by the producer. This proves directly that there is no measurable influence of motors, gear, operation, or measurement procedure on results obtained by magnetic sensors, and indirectly, that the assumed requirements regarding gear design and parameters are correct, and measurement of magnetic flux density distribution of automotive wheels and tires using (1D) and (2D) techniques is possible using a 3-axis anisotropic magnetoresistive sensor with I2C interface.
Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study
Authors: Roossien Charlotte Christina , Baten Christian Theodoor Maria , van der Waard Mitchel Willem Pieter , Reneman Michiel Felix , Verkerke Gijsbertus Jacob
Published: April 2021

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.
Real-Time HD Map Change Detection for Crowdsourcing Update Based on Mid-to-High-End Sensors
Authors: Zhang Pan , Zhang Mingming , Liu Jingnan
Published: April 2021

Continuous maintenance and real-time update of high-definition (HD) maps is a big challenge. With the development of autonomous driving, more and more vehicles are equipped with a variety of advanced sensors and a powerful computing platform. Based on mid-to-high-end sensors including an industry camera, a high-end Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU), and an onboard computing platform, a real-time HD map change detection method for crowdsourcing update is proposed in this paper. First, a mature commercial integrated navigation product is directly used to achieve a self-positioning accuracy of 20 cm on average. Second, an improved network based on BiSeNet is utilized for real-time semantic segmentation. It achieves the result of 83.9% IOU (Intersection over Union) on Nvidia Pegasus at 31 FPS. Third, a visual Simultaneous Localization and Mapping (SLAM) associated with pixel type information is performed to obtain the semantic point cloud data of features such as lane dividers, road markings, and other static objects. Finally, the semantic point cloud data is vectorized after denoising and clustering, and the results are matched with a pre-constructed HD map to confirm map elements that have not changed and generate new elements when appearing. The experiment conducted in Beijing shows that the method proposed is effective for crowdsourcing update of HD maps.
An Embedded-Sensor Approach for Concrete Resistivity Measurement in On-Site Corrosion Monitoring: Cell Constants Determination
Authors: Ramón Jose Enrique , Martínez Isabel , Gandía-Romero José Manuel , Soto Juan
Published: April 2021

The concrete electrical resistivity is a prominent parameter in structural health monitoring, since, along with corrosion potential, it provides relevant qualitative diagnosis of the reinforcement corrosion. This study proposes a simple expression to reliable determine resistivity from the concrete electrical resistance (RE) provided by the corrosion sensor of the Integrated Network of Sensors for Smart Corrosion Monitoring (INESSCOM) we have developed. The novelty here is that distinct from common resistivity sensors, the cell constants obtained by the proposed expression are intended to be valid for any sensor implementation scenario. This was ensured by studying most significant geometrical features of the sensor in a wide set of calibration solutions. This embedded-sensor approach is intended to be applicable for RE measurements obtained both using potential step voltammetry (PSV, used in the INESSCOM sensor for corrosion rate measurement) and alternating current methods. In this regard, we present a simple protocol to reliably determine RE, and therefore resistivity, from PSV measurements. It consists in adding a very short potentiostatic pulse to the original technique. In this way, we are able to easy monitor resistivity along with corrosion rate through a single sensor, an advantage which is not usual in structural health monitoring.
Integrated, Speckle-Based Displacement Measurement for Lateral Scanning White Light Interferometry
Authors: Behrends Gert , Stöbener Dirk , Fischer Andreas
Published: April 2021

Lateral scanning white light interferometry (LSWLI) is a promising technique for high-resolution topography measurements on moving surfaces. To achieve resolutions typically associated with white light interferometry, accurate information on the lateral displacement of the measured surface is essential. Since the uncertainty requirement for a respective displacement measurement is currently not known, Monte Carlo simulations of LSWLI measurements are carried out at first to assess the impact of the displacement uncertainty on the topography measurement. The simulation shows that the uncertainty of the displacement measurement has a larger influence on the total height uncertainty than the uncertainty of the displacing motion itself. Secondly, a sufficiently precise displacement measurement by means of digital speckle correlation (DSC) is proposed that is fully integrated into the field of view of the interferometer. In contrast to externally applied displacement measurement systems, the integrated combination of DSC with LSWLI needs no synchronization and calibration, and it is applicable for translatory as well as rotatory scans. To demonstrate the findings, an LSWLI setup with integrated DSC measurements is realized and tested on a rotating cylindrical object with a surface made of a linear encoder strip.