bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Activity of Extracts from the Humiria balsamifera (Aubl)
Authors: Dias Edelson de J. S. , Cantanhede Filho Antônio J. , Carneiro Fernando J. C. , da Rocha Cláudia Q. , da Silva Luís Cláudio N. , Santos Joice C. B. , Barros Thayná F. , Santos Deivid M.
Published: July 2021

Humiria balsamifera (Aubl), commonly known as “mirim”, is a plant of the Humiriaceae family, which consists of 39 species divided between eight genera: Duckesia, Endopleura, Humiria, Humiriastrum, Hylocara, Sacoglottis, Schistostemon, and Vantenea. This study aimed to characterize H. balsamifera extracts by LC-MS/MS and evaluate their antimicrobial potential through in vitro and in vivo assays. The leaves and stem bark of H. balsamifera were collected and dried at room temperature and then ground in a knife mill. The extracts were prepared with organic solvents in order to increase the polarity index (hexane, ethyl acetate, and methanol). The antimicrobial effects of these extracts were evaluated against the following bacterial strains: Escherichia coli ATCC 25922, Listeria monocytogenes ATCC 15313, Salmonella enterica Typhimurium ATCC 14028, and Staphylococcus aureus ATCC 6538. The best activity was observed in the ethyl acetate (EALE = 780 µg/mL), methanol (MLE = 780 µg/mL), and hexane (HLE = 1560 µg/mL) leaf extracts against S. aureus. Considering the results for both antimicrobial and antibiofilm activities, the EALE extract was chosen to proceed to the infection assays, which used Tenebrio molitor larvae. The EALE treatment was able to extend the average lifespan of the larvae (6.5 days) in comparison to S. aureus-infected larvae (1 day). Next, the samples were characterized by High-Performance Liquid Chromatography coupled to a mass spectrometer, allowing the identification of 11 substances, including seven flavonoids, substances whose antimicrobial activity is already well-reported in the literature. The number of bioactive compounds found in the chemical composition of H. balsamifera emphasizes its significance in both traditional medicine and scientific research that studies new treatments based on substances from the Brazilian flora.
Prediction of the Miscibility of PBAT/PLA Blends
Authors: Su Shen
Published: July 2021

Designing polymer structures and polymer blends opens opportunities to improve the performance of plastics. Blending poly(butylene adipate-co-terephthalate) (PBAT) and polylactide (PLA) is a cost-effective approach to achieve a new sustainable material with complementary properties. This study aimed to predict the theoretical miscibility of PBAT/PLA blends at the molecular level. First, the basic properties and the structure of PBAT and PLA are introduced, respectively. Second, using the group contribution methods of van Krevelen and Hoy, the Hansen and Hildebrand solubility parameters of PBAT and PLA were calculated, and the effect of the molar ratio of the monomers in PBAT on the miscibility with PLA was predicted. Third, the dependence of the molecular weight on the blend miscibility was simulated using the solubility parameters and Flory–Huggins theory. Next, the glass transition temperature of miscible PBAT/PLA blends, estimated using the Fox equation, is shown graphically. According to the prediction and simulation, the blends with a number-average molecular weight of 30 kg/mol for each component were thermodynamically miscible at 296 K and 463 K with the possibility of spinodal decomposition at 296 K and 30% volume fraction of PBAT. This study contributes to the strategic synthesis of PBAT and the development of miscible PBAT/PLA blends.
Role of the Anilinium Ion on the Selective Polymerization of Anilinium 2-Acrylamide-2-methyl-1-propanesulfonate
Authors: Conejo-Dávila Alain Salvador , Moya-Quevedo Marco Armando , Chávez-Flores David , Vega-Rios Alejandro , Zaragoza-Contreras Erasto Armando
Published: July 2021

The development of anilinium 2-acrylamide-2-methyl-1-propanesulfonate (Ani-AMPS) monomer, confirmed by 1H NMR, 13C NMR, and FTIR, is systematically studied. Ani-AMPS contains two polymerizable functional groups, so it was submitted to selective polymerization either by free-radical or oxidative polymerization. Therefore, poly(anilinium 2-acrylamide-2-methyl-1-propanesulfonic) [Poly(Ani-AMPS)] and polyaniline doped with 2-acrylamide-2-methyl-1-propanesulfonic acid [PAni-AMPS] can be obtained. First, the acrylamide polymer, poly(Ani-AMPS), favored the π-stacking of the anilinium group produced by the inter- and intra-molecular interactions and was studied utilizing 1H NMR, 13C NMR, FTIR, and UV-Vis-NIR. Furthermore, poly(Ani-AMPS) fluorescence shows quenching in the presence of Fe2+ and Fe3+ in the emission spectrum at 347 nm. In contrast, the typical behavior of polyaniline is observed in the cyclic voltammetry analysis for PAni-AMPS. The optical properties also show a significant change at pH 4.4. The PAni-AMPS structure was corroborated through FTIR, while the thermal properties and morphology were analyzed utilizing TGA, DSC (except PAni-AMPS), and FESEM.
Characterization of Low-Density Polyethylene and LDPE-Based/Ethylene-Vinyl Acetate with Medium Content of Vinyl Acetate
Authors: Pham Nga Thi-Hong
Published: July 2021

Low-density polyethylene (LDPE) and ethylene vinyl acetate copolymer (EVA), which are non-polar and polar polymers, are immiscible and form a polyphase system. In this study, LDPE was mixed with 2.5%, 5%, 7.5%, 10%, 12.5% Ethylene-vinyl acetate (EVA-28) with a medium content of vinyl acetate (28% VA), respectively by injection molding machine and LDPE. Tensile strength and flexural strength were tested according to ASTM D638-02 standard and ISO 178 standard. The results showed that adding EVA-28 increased the elongation at break of the LDPE/2.5% EVA, LDPE/5% EVA and LDPE/10% EVA blend samples. In addition, the tensile and flexural strength of the LDPE/EVA blend decreases gradually as the EVA-28 content in the blend increases. The hardness decreases with the increasing EVA-28 content. EVA-28 spherical particles appeared scattered on the surface of the LDPE matrix, in the highest EVA-28 percent sample (12.5% EVA-28), the number of particles appeared to be quite a lot, and was dispersed quite evenly on the surface. The LDPE/EVA-28 blend achieved a higher elongation at the break than LDPE, in which 10% EVA-28 gives the highest elongation at break.
A Phenomenological Primary–Secondary–Tertiary Creep Model for Polymer-Bonded Composite Materials
Authors: Duan Xiaochang , Yuan Hongwei , Tang Wei , He Jingjing , Guan Xuefei
Published: July 2021

This study develops a unified phenomenological creep model for polymer-bonded composite materials, allowing for predicting the creep behavior in the three creep stages, namely the primary, the secondary, and the tertiary stages under sustained compressive stresses. Creep testing is performed using material specimens under several conditions with a temperature range of 20 °C–50 °C and a compressive stress range of 15 MPa–25 MPa. The testing data reveal that the strain rate–time response exhibits the transient, steady, and unstable stages under each of the testing conditions. A rational function-based creep rate equation is proposed to describe the full creep behavior under each of the testing conditions. By further correlating the resulting model parameters with temperature and stress and developing a Larson–Miller parameter-based rupture time prediction model, a unified phenomenological model is established. An independent validation dataset and third-party testing data are used to verify the effectiveness and accuracy of the proposed model. The performance of the proposed model is compared with that of an existing reference model. The verification and comparison results show that the model can describe all the three stages of the creep process, and the proposed model outperforms the reference model by yielding 28.5% smaller root mean squared errors on average.
Biofabrication of Cell-Laden Gelatin Methacryloyl Hydrogels with Incorporation of Silanized Hydroxyapatite by Visible Light Projection
Authors: Su Jimmy Jiun-Ming , Lin Chih-Hsin , Chen Hsuan , Lee Shyh-Yuan , Lin Yuan-Min
Published: July 2021

Gelatin methacryloyl (GelMA) hydrogel is a photopolymerizable biomaterial widely used for three-dimensional (3D) cell culture due to its high biocompatibility. However, the drawback of GelMA hydrogel is its poor mechanical properties, which may compromise the feasibility of biofabrication techniques. In this study, a cell-laden GelMA composite hydrogel with a combination incorporating silanized hydroxyapatite (Si-HAp) and a simple and harmless visible light crosslinking system for this hydrogel were developed. The incorporation of Si-HAp into the GelMA hydrogel enhanced the mechanical properties of the composite hydrogel. Moreover, the composite hydrogel exhibited low cytotoxicity and promoted the osteogenic gene expression of embedded MG63 cells and Human bone marrow mesenchymal stem cells (hBMSCs). We also established a maskless lithographic method to fabricate a defined 3D structure under visible light by using a digital light processing projector, and the incorporation of Si-HAp increased the resolution of photolithographic hydrogels. The GelMA-Si-HAp composite hydrogel system can serve as an effective biomaterial in bone regeneration.
The Compatibility of Three Silicone Oils with Polydimethylsiloxane and the Microstructure and Properties of Their Composite Coatings
Authors: Jiang Yuguo , Zhang Zhanping , Qi Yuhong
Published: July 2021

The compatibility of three types of silicone oil with polydimethylsiloxane, the phase separation of their mixture and the microstructure and properties of their composite coatings were investigated. The existing form of silicone oil in the coating and the precipitation behavior were also studied. The compatibility observed experimentally of the three silicone oils with PDMS is consistent with the results of the thermodynamic calculation. The silicone oil droplet produced by phase separation in the mixture solution can keep its shape in the cured coating, also affecting the microstructure and mechanical properties of the coating. It was found that methyl silicone oil and methyl fluoro silicone oil do not precipitate on the surface, and they have no effect on the surface properties of the coating. In contrast, phenyl silicone oil has obvious effect on the surface, which makes the water contact angle and diiodomethane contact angle of the coating decrease significantly.
Physicochemical and Biological Properties of Polysaccharides from Dictyophora indusiata Prepared by Different Extraction Techniques
Authors: Wu Ding-Tao , Zhao Yun-Xuan , Guo Huan , Gan Ren-You , Peng Lian-Xin , Zhao Gang , Zou Liang
Published: July 2021

In this study, different extraction techniques, including traditional hot water extraction (HWE), microwave-assisted extraction (MAE), pressurized assisted extraction (PAE), and ultrasonic-assisted extraction (UAE), were used to extract Dictyophora indusiata polysaccharides (DFPs), and their physicochemical and biological properties were compared. Results revealed that extraction yields of D. indusiata polysaccharides prepared by different extraction techniques ranged from 5.62% to 6.48%. D. indusiata polysaccharides prepared by different extraction techniques possessed similar chemical compositions and monosaccharide compositions, while exhibited different molecular weights (Mw), apparent viscosities, and molar ratios of constituent monosaccharides. In particularly, D. indusiata polysaccharides prepared by HWE (DFP-H) had the highest Mw and apparent viscosity among all DFPs, while D. indusiata polysaccharides extracted by UAE (DFP-U) possessed the lowest Mw and apparent viscosity. In addition, the in vitro antioxidant effects of D. indusiata polysaccharides prepared by PAE (DFP-P) and DFP-U were significantly higher than that of others. Indeed, both DFP-P and DFP-H exhibited much higher in vitro binding properties, including fat, cholesterol, and bile acid binding properties, and lipase inhibitory effects than that of D. indusiata polysaccharides prepared by MAE (DFP-M) and DFP-U. These findings suggest that the PAE technique has good potential for the preparation of D. indusiata polysaccharides with desirable bioactivities for the application in the functional food industry.
Properties of Stone Matrix Asphalt Modified with Polyvinyl Chloride and Nano Silica
Authors: Nguyen Hoang Phong , Cheng Peifeng , Nguyen Tat Thang
Published: July 2021

In this study, the effects of polyvinyl chloride (PVC) and nano silica (NS) as modifiers on the properties of stone matrix asphalt (SMA) were studied. The experiment was performed with five modes: 1% NS was mixed into SMA; 5% PVC was mixed into SMA; and the ratio of NS was changed (1, 2, and 3%) with 5% PVC being mixed into SMA. The properties of modified and unmodified SMA materials were determined and compared by performing the penetration test, softening points test, viscosity measurements, dynamic shear rheometry, and multiple stress creep recovery under aging conditions. Moreover, the properties of the modified SMA were also determined in terms of Marshall stability, water stability, and rutting resistance. The obtained results indicate that the physical properties of SMA materials could be significantly improved by using a combination of PVC and NS as a modifier. Moreover, the SMA mixtures modified with PVC and NS exhibited high Marshall stability, good moisture damage resistance, and rutting resistance. Modified SMA mixtures with 5% PVC and 1% NS exhibited the best quality. This research has opened up a new avenue for the development of effective additives for SMA materials.
Simulation and Experiment of Gas-Solid Flow in a Safflower Sorting Device Based on the CFD-DEM Coupling Method
Authors: Hu Zhizheng , Zeng Haifeng , Ge Yun , Wang Wendong , Wang Jiangkun
Published: July 2021

To study the movement characteristics and separation mechanism of safflower petals and their impurities under the action of airflow and lower the impurity rate in the cleaning operation process, integration of computational fluid dynamics (CFD) and discrete element method (DEM) codes was performed to study the motion and sorting behavior of impurity particles and safflower petals under different airflow inclination angles, dust removal angles and inlet airflow velocities by establishing a true particle model. In this model, the discrete particle phase was applied by the DEM software, and the continuum gas phase was described by the ANSYS Fluent software. The Box-Behnken experimental design with three factors and three levels was performed, and parameters such as inlet airflow velocity, airflow inclined angle, and dust remover angle were selected as independent variables that would influence the cleaning impurity rate and the cleaning loss rate. A mathematical model was established, and then the effects of various parameters and their interactions were analyzed. The test results show that the cleaning effect is best when the inlet airflow velocity is 7 m/s, the airflow inclined angle is 0°, and the dust remover angle is 25°. Confirmatory tests showed that the average cleaning impurity rate and cleaning loss rate were 0.69% and 2.75%, respectively, which dropped significantly compared with those from previous optimization. An experimental device was designed and set up; the experimental results were consistent with the simulation results, indicating that studying the physical behavior of safflower petals-impurity separation in the airflow field by using the DEM-CFD coupling method is reliable. This result provides a basis for follow-up studies of separation and cleaning devices for lightweight materials such as safflower petals.
Low Molecular Weight Kappa-Carrageenan Based Microspheres for Enhancing Stability and Bioavailability of Tea Polyphenols
Authors: Feng Tao , Wu Kai , Xu Jianying , Hu Zhongshan , Zhang Xiaolei
Published: July 2021

Tea polyphenols (TP) are a widely acknowledged bioactive natural product, however, low stability and bioavailability have restricted their application in many fields. To enhance the stability and bioavailability of TP under certain moderate conditions, encapsulation technique was applied. Kappa–Carrageenan (KCG) was initially degraded to a lower molecular weight KCG (LKCG) by H2O2, and was selected as wall material to coat TP. The obtained LKCG (Mn = 13,009.5) revealed narrow dispersed fragments (DPI = 1.14). FTIR and NMR results demonstrated that the main chemical structure of KCG remained unchanged after degradation. Subsequently, LK-CG and TP were mixed and homogenized to form LK-CG-TP microspheres. SEM images of the microspheres revealed a regular spherical shape and smooth surface with a mean diameter of 5–10 μM. TG and DSC analysis indicated that LK-CG-TP microspheres exhibited better thermal stability as compared to free TP. The release profile of LK-CG-TP in simulated gastric fluid (SGF) showed a slowly release capacity during the tested 180 min with the final release rate of 88.1% after digestion. Furthermore, in vitro DPPH radical scavenging experiments revealed that LK-CG-TP had an enhanced DPPH scavenging rate as compared to equal concentration of free TP. These results indicated that LK-CG-TP microspheres were feasible for protection and delivery of TP and might have extensive potential applications in other bioactive components.
Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging
Authors: Gomes Véronique , Reis Marco S. , Rovira-Más Francisco , Mendes-Ferreira Ana , Melo-Pinto Pedro
Published: July 2021

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.
Changes in Hydrophobic Interactions among Gluten Proteins during Dough Formation
Authors: Iwaki Sonoo , Hayakawa Katsuyuki , Fu Bin-Xiao , Otobe Chikako
Published: July 2021

In this study, changes in hydrophobic interactions among gluten proteins were analyzed during dough mixing. Size-exclusion high-performance chromatography and two-dimensional fluorescence difference gel electrophoresis were performed on proteins extracted with 1-propanol by weakening the hydrophobic interaction. The amount of proteins extracted with 30% 1-propanol increased from the start of mixing to peak consistency, suggesting that the hydrophobic interactions among the strongly aggregated proteins weakened and resulted in disaggregation. The amount of proteins extracted with 10% 1-propanol decreased during hydration, indicating that these proteins aggregated through relatively weak hydrophobic interactions. The proteins that extractability decreased were mainly low molecular weight glutenin, α-gliadin, and γ-gliadin. The amount of monomeric proteins extracted with 30% 1-propanol decreased after peak consistency. The decreased protein was mainly ω-gliadin, indicating that ω-gliadin aggregated with other proteins through hydrophobic interactions. A front-face fluorescence analysis was performed on the dough with the addition of 8-anilino-1-naphthalenesulfonic acid or thioflavin T. The fluorescence intensity increased as a result of exposure to the hydrophobic groups of the gluten proteins and the formation of protein aggregates during dough mixing. These results indicate the importance of hydrophobic interactions in dough formation.
Preliminary Application of Ground-Penetrating Radar for Reconstruction of Root System Architecture in Moso Bamboo
Authors: Xiao Longdong , Li Chong , Cai Yue , Zhou Mingxing , Zhou Tao , Gao Xueyan , Du Huaqiang , Zhou Yufeng , Zhou Guomo
Published: July 2021

Root system architecture (RSA) refers to the geometric features and topology of the root system. Ground-penetrating radar (GPR) is a possible method of RSA reconstruction. However, because the topology of the root system is not directly accessible by GPR, GPR-based reconstruction must be complemented by manual connection of root points, resulting in limited accuracy. In this study, we used both GPR and direct excavation to obtain 3D coordinates (XYZ coordinates) and diameters of moso bamboo rhizomes on an orthogonal grid. A score function for selecting the best-connected root points was developed using rhizome diameter, depth, extension angle, and measured line spacing, which was then used to recover the topology of discrete root points. Based on the recovered topology, the 3D RSA of the rhizomes was reconstructed using a smoothing function. Based on the excavation data, the reconstructed RSA was generally consistent with the measured RSA, with 78.13% of root points correctly connected. The reconstructed RSA based on GPR data thus provided a rough approximation of the measured RSA, with errors arising due to missing root points and rhizome displacement. The proposed algorithm for reconstructing 3D RSA further enriches the application of ground-penetrating radar to root detection.
Semi-Supervised Deep Learning for Lunar Crater Detection Using CE-2 DOM
Authors: Zang Sudong , Mu Lingli , Xian Lina , Zhang Wei
Published: July 2021

Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection models, and the influence of solar illumination, Crater Detection Algorithms (CDAs) based on Digital Orthophoto Maps (DOMs) have not yet been well-developed. In this paper, a large number of training data are labeled manually in the Highland and Maria regions, using the Chang’E-2 (CE-2) DOM; however, the labeled data cannot cover all kinds of crater types. To solve the problem of small crater detection, a new crater detection model (Crater R-CNN) is proposed, which can effectively extract the spatial and semantic information of craters from DOM data. As incomplete labeled samples are not conducive for model training, the Two-Teachers Self-training with Noise (TTSN) method is used to train the Crater R-CNN model, thus constructing a new model—called Crater R-CNN with TTSN—which can achieve state-of-the-art performance. To evaluate the accuracy of the model, three other detection models (Mask R-CNN, no-Mask R-CNN, and Crater R-CNN) based on semi-supervised deep learning were used to detect craters in the Highland and Maria regions. The results indicate that Crater R-CNN with TTSN achieved the highest precision (of 91.4% and 88.5%, respectively) in the Highland and Maria regions, even obtaining the highest recall and F1 score. Compared with Mask R-CNN, no-Mask R-CNN, and Crater R-CNN, Crater R-CNN with TTSN had strong robustness and better generalization ability for crater detection within 1 km in different terrains, making it possible to detect small craters with high accuracy when using DOM data.
Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling
Authors: Xiang Yiheng , Chen Jie , Li Lu , Peng Tao , Yin Zhiyuan
Published: July 2021

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.
A Novel Sensor Foil to Measure Ski Deflections: Development and Validation of a Curvature Model
Authors: Thorwartl Christoph , Kröll Josef , Tschepp Andreas , Schäffner Philipp , Holzer Helmut , Stöggl Thomas
Published: July 2021

The ski deflection with the associated temporal and segmental curvature variation can be considered as a performance-relevant factor in alpine skiing. Although some work on recording ski deflection is available, the segmental curvature among the ski and temporal aspects have not yet been made an object of observation. Therefore, the goal of this study was to develop a novel ski demonstrator and to conceptualize and validate an empirical curvature model. Twenty-four PyzoFlex® technology-based sensor foils were attached to the upper surface of an alpine ski. A self-developed instrument simultaneously measuring sixteen sensors was used as a data acquisition device. After calibration with a standardized bending test, using an empirical curvature model, the sensors were applied to analyze the segmental curvature characteristic (m−1) of the ski in a quasi-static bending situation at five different load levels between 100 N and 230 N. The derived curvature data were compared with values obtained from a high-precision laser measurement system. For the reliability assessment, successive pairs of trials were evaluated at different load levels by calculating the change in mean (CIM), the coefficient of variation (CV) and the intraclass correlation coefficient (ICC 3.1) with a 95% confidence interval. A high reliability of CIM −1.41–0.50%, max CV 1.45%, and ICC 3.1 > 0.961 was found for the different load levels. Additionally, the criterion validity based on the Pearson correlation coefficient was R2 = 0.993 and the limits of agreement, expressed by the accuracy (systematic bias) and the precision (SD), was between +9.45 × 10−3 m−1 and −6.78 × 10−3 m−1 for all load levels. The new measuring system offers both good accuracy (1.33 × 10−3 m−1) and high precision (4.14 × 10−3 m−1). However, the results are based on quasi-static ski deformations, which means that a transfer into the field is only allowed to a limited extent since the scope of the curvature model has not yet been definitely determined. The high laboratory-related reliability and validity of our novel ski prototype featuring PyzoFlex® technology make it a potential candidate for on-snow application such as smart skiing equipment.
Custom-Technology Single-Photon Avalanche Diode Linear Detector Array for Underwater Depth Imaging
Authors: Maccarone Aurora , Acconcia Giulia , Steinlehner Ulrich , Labanca Ivan , Newborough Darryl , Rech Ivan , Buller Gerald S.
Published: July 2021

We present an optical depth imaging system suitable for highly scattering underwater environments. The system used the time-correlated single-photon counting (TCSPC) technique and the time-of-flight approach to obtain depth profiles. The single-photon detection was provided by a linear array of single-photon avalanche diode (SPAD) detectors fabricated in a customized silicon fabrication technology for optimized efficiency, dark count rate, and jitter performance. The bi-static transceiver comprised a pulsed laser diode source with central wavelength 670 nm, a linear array of 16 × 1 Si-SPAD detectors, with a dedicated TCSPC acquisition module. Cylindrical lenses were used to collect the light scattered by the target and image it onto the sensor. These laboratory-based experiments demonstrated single-photon depth imaging at a range of 1.65 m in highly scattering conditions, equivalent up to 8.3 attenuation lengths between the system and the target, using average optical powers of up to 15 mW. The depth and spatial resolution of this sensor were investigated in different scattering conditions.
Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
Authors: de Alteriis Giorgio , Accardo Domenico , Conte Claudia , Schiano Lo Moriello Rosario
Published: July 2021

The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.
Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
Authors: Le Ha An , Van Chien Trinh , Nguyen Tien Hoa , Choo Hyunseung , Nguyen Van Duc
Published: July 2021

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.
Chipless RFID Label with Identification and Touch-Sensing Capabilities
Authors: Unnikrishnan Rahul , Rance Olivier , Barbot Nicolas , Perret Etienne
Published: July 2021

This article presents a 14-bit chipless RFID label which, in addition to classical identification feature, can be used as decimal numeric keypad, allowing the deployment of secure access control applications. A low-cost single layer label comprising 10 RF loop scatterers is used to code information in the frequency domain. In addition, each resonator is associated to a digit in the decimal number system, and the difference in the spectrum caused by the touch event is exploited for the detection of each key pressing. The shape of the resonators has been carefully selected to be both highly resonant and to show high sensitivity to the presence or absence of the human finger. The concept is validated by measurements in an office environment using an FCC compliant low-cost chipless reader and microstrip vivaldi antennas. Simple detection algorithms are proposed for both identification and touch sensing in real environment.
Influence of Load Plates Diameters, Shapes of Columns and Columns Spacing on Results of Load Plate Tests of Columns Formed by Dynamic Replacement
Authors: Kwiecień Sławomir
Published: July 2021

The dynamic replacement method is used to strengthen the subgrade of objects, usually up to 5 to 6 m thick. After the improvement process, acceptance tests in the form of load testing are carried out. Interpretation of the test results can cause some difficulties. Dynamic replacement results in a situation where columns of different shapes, loaded with plates of diameters usually smaller than the head diameter and in the vicinity of adjacent columns, are subjected to load tests. In order to demonstrate the influence of these factors, a spatial model of soil strengthened by dynamic replacement, comprising four material zones, was calibrated on the basis of load testing. The following models were used in the analysis: linear-elastic, elastic–perfectly plastic (Coulomb–Mohr) and elastic–plastic with isotropic hardening (Modified Cam-Clay). This formed the basis for 105 numerical models, which took into account the actual shapes of the columns made at various spacings, subjected to load tests with plates of various diameters. The analyses of the settlements, calculated moduli and stress distribution in the loaded system showed how the results were significantly influenced by mentioned factors. This implies that the interpretation of the results of load tests should be based on advanced spatial numerical analyses, using appropriate constitutive models and including the considered factors.
A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks
Authors: Xu Biao , Lu Minyan , Zhang Hong , Pan Cong
Published: July 2021

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.
Uncertainty Evaluation in Vision-Based Techniques for the Surface Analysis of Composite Material Components
Authors: D’Emilia Giulio , Gaspari Antonella , Natale Emanuela , Ubaldi Davide
Published: July 2021

In this paper, a methodology is discussed concerning the measurement of yarn’s angle of two different glass-reinforced polypropylene matrix materials, widely used in the production of automotive components. The measurement method is based on a vision system and image processing techniques for edge detection. Measurements of angles enable, if accurate, both useful suggestions for process optimization to be made, and the reliable validation of the simulation results of the thermoplastic process. Therefore, uncertainty evaluation of angle measurement is a mandatory pre-requisite. If the image acquisition and processing is considered, many aspects influence the whole accuracy of the method; the most important have been identified and their effects evaluated with reference to two different materials, which present different optical-type characteristics. The influence of piece geometry has also been taken into account, carrying out measurements on flat sheets and on a semi-spherical object, which is a reference standard shape, to verify the effect of thermoforming and to tune the process parameters. Complete uncertainty in the order of a few degrees has been obtained, which is satisfactory for purposes of simulation validation and consequent process optimization. The uncertainty budget also allowed individuation of the most relevant causes of uncertainty for measurement process improvement.
Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution
Authors: Madokoro Hirokazu , Kiguchi Osamu , Nagayoshi Takeshi , Chiba Takashi , Inoue Makoto , Chiyonobu Shun , Nix Stephanie , Woo Hanwool , Sato Kazuhito
Published: July 2021

This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM2.5 sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM2.5 trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.
Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR
Authors: Guindo Mahamed Lamine , Kabir Muhammad Hilal , Chen Rongqin , Liu Fei
Published: July 2021

Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.
Workplace Mental State Monitoring during VR-Based Training for Offshore Environment
Authors: Abu Hasan Rumaisa , Sulaiman Shahida , Ashykin Nur Nabila , Abdullah Mohd Nasir , Hafeez Yasir , Ali Syed Saad Azhar
Published: July 2021

Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.
Bayesian Learning-Based Clustered-Sparse Channel Estimation for Time-Varying Underwater Acoustic OFDM Communication
Authors: Wang Shuaijun , Liu Mingliu , Li Deshi
Published: July 2021

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in underwater acoustic (UWA) communication due to its good anti-multipath performance and high spectral efficiency. For UWA-OFDM systems, channel state information (CSI) is essential for channel equalization and adaptive transmission, which can significantly affect the reliability and throughput. However, the time-varying UWA channel is difficult to estimate because of excessive delay spread and complex noise distribution. To this end, a novel Bayesian learning-based channel estimation architecture is proposed for UWA-OFDM systems. A clustered-sparse channel distribution model and a noise-resistant channel measurement model are constructed, and the model hyperparameters are iteratively optimized to obtain accurate Bayesian channel estimation. Accordingly, to obtain the clustered-sparse distribution, a partition-based clustered-sparse Bayesian learning (PB-CSBL) algorithm was designed. In order to lessen the effect of strong colored noise, a noise-corrected clustered-sparse channel estimation (NC-CSCE) algorithm was proposed to improve the estimation accuracy. Numerical simulations and lake trials are conducted to verify the effectiveness of the algorithms. Results show that the proposed algorithms achieve higher channel estimation accuracy and lower bit error rate (BER).
A High-Precision Automatic Pointer Meter Reading System in Low-Light Environment
Authors: Wu Xuang , Shi Xiaobo , Jiang Yongchao , Gong Jun
Published: July 2021

At present, pointer meters are still widely used because of their mechanical stability and electromagnetic immunity, and it is the main trend to use a computer vision-based automatic reading system to replace inefficient manual inspection. Many correction and recognition algorithms have been proposed for the problems of skew, distortion, and uneven illumination in the field-collected meter images. However, the current algorithms generally suffer from poor robustness, enormous training cost, inadequate compensation correction, and poor reading accuracy. This paper first designs a meter image skew-correction algorithm based on binary mask and improved Mask-RCNN for different types of pointer meters, which achieves high accuracy ellipse fitting and reduces the training cost by transfer learning. Furthermore, the low-light enhancement fusion algorithm based on improved Retinex and Fast Adaptive Bilateral Filtering (RBF) is proposed. Finally, the improved ResNet101 is proposed to extract needle features and perform directional regression to achieve fast and high-accuracy readings. The experimental results show that the proposed system in this paper has higher efficiency and better robustness in the image correction process in a complex environment and higher accuracy in the meter reading process.
Mask-Type Sensor for Pulse Wave and Respiration Measurements and Eye Blink Detection
Authors: Nguyen Thanh-Vinh , Ichiki Masaaki
Published: July 2021

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor.