bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Constituents and Antiproliferative Activities of Essential Oils from Four Varieties of Malaysian Zingiber officinale Roscoe against Human Cervical Cancer Cell Line
Authors: Zaid Atiqah , Haw Xue Rou , Alkatib Huda Hisham , Sasidharan Sreenivasan , Marriott Philip J. , Wong Yong Foo
Published: May 2022

This study evaluates the volatile metabolic constituents and anticancer potential of essential oils distilled from the rhizomes of four Malaysian Zingiber officinale Roscoe (Zingiberaceae family) varieties (Bentong (BE), Cameron Highlands (CH), Sabah (SA), and Bara (BA)). The ginger essential oils were analyzed by gas chromatography coupled with quadrupole mass spectrometry (GC qMS). A total of 58 secondary compounds were tentatively identified, representing 82.6–87.4% of the total ion count. These metabolites comprise mainly of monoterpene hydrocarbons (19.7–25.5%), oxygenated monoterpenes (23.6–33.7%), sesquiterpene hydrocarbons (21.3–35.6%), oxygenated sesquiterpenes (1.5–3.9%), and other minor classes of compounds (0.7–2.7%). Principal component analysis (PCA) enabled differentiation of the analyzed ginger essential oils according to their varieties, with respect to their metabolites and relative quantities. The antiproliferative activity against the HeLa cervical cancer cell line was investigated via the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The oils were found to exhibit strong antiproliferative activities with IC50 values of 23.8, 35.3, 41.3, and 42.5 μg/mL for BA, BE, SA, and CH, respectively. These findings suggest that the differences among the secondary metabolites and their abundance in different varieties of Z. officinale essential oils appear to be related to their antiproliferative potential. The strong antiproliferative effects of these oils signified their potential in the prevention and chemotherapy of cervical carcinoma treatment.
The Effects of Hydro-Priming and Colonization with Piriformospora indica and Azotobacter chroococcum on Physio-Biochemical Traits, Flavonolignans and Fatty Acids Composition of Milk Thistle (Silybum marianum) under Saline Conditions
Authors: Yaghoubian Iraj , Antar Mohammed , Ghassemi Saeid , Modarres-Sanavy Seyed Ali Mohammad , Smith Donald L.
Published: May 2022

Salinity is an important challenge around the world, effecting all physiological and biochemical processes of plants. It seems that seed priming can diminish the negative impacts of salinity. To study the effects of hydro-priming and inoculation with Piriformospora indica (Pi) and Azotobacter chroococcum (Az) on physio-biochemical traits, flavonolignans and fatty acids composition of milk thistle under saline conditions, a greenhouse experiment was carried out. Our results indicated that under salinity, seed priming, especially Pi, improved physio-biochemical properties in milk thistle. Under 120 mM NaCl, inoculation with Pi increased membrane stability index (MSI) and relative water content (RWC) (by 21.86 and 33.43%, respectively). However, peroxidase (POX) (5.57- and 5.68-fold in roots and leaves, respectively), superoxide dismutase (SOD) (4.74- and 4.44-fold in roots and leaves, respectively), catalase (CAT) (6.90- and 8.50-fold in roots and leaves, respectively) and ascorbate peroxidase (APX) (5.61- and 5.68-fold in roots and leaves, respectively) activities increased with increasing salinity. Contrary to salinity, hydro-priming with Az and Pi positively altered all these traits. The highest content of the osmolytes, adenosine triphosphate (ATP) content and rubisco activity were recorded in Pi treatments under 120 mM NaCl. Stearic acid (20.24%), oleic acid (21.06%) and palmitic acid (10.48%) increased, but oil content (3.81%), linolenic and linoleic acid content (22.21 and 15.07%, respectively) decreased under saline conditions. Inoculations of Pi positively altered all these traits. The present study indicated that seed priming with Pi under 120 mM NaCl resulted in maximum silychristin, taxidolin, silydianin, isosilybin, silybin and silymarin of milk thistle seeds.
The Conservation Physiology of Bryophytes
Authors: Sabovljević Marko S. , Ćosić Marija V. , Jadranin Bojana Z. , Pantović Jovana P. , Giba Zlatko S. , Vujičić Milorad M. , Sabovljević Aneta D.
Published: May 2022

An introduction to the conservation physiology of bryophytes is given. The insights into the problems, solutions and examples of the physiological approach to conservation within bryophyte representatives are discussed. The significance of experimental treatments of bryophytes is highlighted. The documentation of bryophyte functional traits and eco-physiological mechanisms in the conservation background for protection purposes is highlighted by the selected examples. The introduction of bryophytes into a new scientific field is resumed and some insights from specific case studies are presented.
Mechanical Performance of RPC and Steel–RPC Composite Structure with Different Fiber Parameters: Experimental and Theoretical Research
Authors: Luo Jun , Quan Ziran , Shao Xudong , Li Fangyuan , He Shangwen
Published: May 2022

This paper aims to explore the material properties of RPC and transverse-bending performance, as well as the crack-width-calculation theory of a densely reinforced steel–RPC composite structure with different fiber parameters. Two fiber types (straight fiber, hybrid fiber) and four fiber volume contents (2%, 2.5%, 3%, 3.5%) were selected to explore the mechanical properties of RPC materials, and the influences of fiber parameters on compressive strength, modulus of elasticity, flexural strength and axial tensile property were investigated. Eight steel–RPC composite plates with different design parameters (fiber type and reinforcement ratio) were conducted to study the transverse-bending performance of steel–RPC composite deck structures. The results show that the addition of 3.5% hybrid fibers to the RPC matrix leads to the optimum axial tensile and flexural properties. Furthermore, the failure mode, load–displacement curve, crack occurrence and propagation characteristics of the composite structure are analyzed in detail. Based on the experimental results, the calculation methods of reinforcement stress and crack width of densely reinforced steel–RPC composite structure are proposed. The calculated results of reinforcement stress and maximum crack width are in good agreement with the actual measured values, which can provide a reference for engineering design.
Recovery of Waste Material from Biobags: 3D Printing Process and Thermo-Mechanical Characteristics in Comparison to Virgin and Composite Matrices
Authors: Patti Antonella , Acierno Stefano , Cicala Gianluca , Zarrelli Mauro , Acierno Domenico
Published: May 2022

The purpose of this study is to limit the environmental impact of packaging applications by promoting the recycling of waste products and the use of sustainable materials in additive manufacturing technology. To this end, a commercial polylactide acid (PLA)-based filament derived from waste production of bio-bags is herein considered. For reference, a filament using virgin PLA and one using a wood-based biocomposite were characterized as well. Preliminary testing involved infrared spectroscopy, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). The effect of printing parameters (namely bed temperature, layer thickness, top surface layers, retraction speed, and distance) on the final aesthetics of 3D printed parts was verified. The results allow us to attest that the thermal properties of recycled polymer are comparable to those of virgin PLA and biocomposite. In the case of recycled polymer, after the extrusion temperature, bed temperature, and printing speed are estabilished the lowest allowable layer thickness and an appropriate choice of retraction movements are required in order to realize 3D-printed objects without morphological defects visible to the naked eyes. In the case of wood biocomposite, the printing process was complicated by frequent obstructions, and in none of the operating conditions was it possible to obtain an aesthetically satisfying piece of the chosen geometry (Lego-type bricks) Finally, mechanical testing on the 3D printed parts of each system showed that the recycled PLA behaves similarly to virgin and wood/PLA filaments.
Automation of Modeling and Calibration of Integrated Preparative Protein Chromatography Systems
Authors: Tallvod Simon , Andersson Niklas , Nilsson Bernt
Published: May 2022

With the increasing global demand for precise and efficient pharmaceuticals and the biopharma industry moving towards Industry 4.0, the need for advanced process integration, automation, and modeling has increased as well. In this work, a method for automatic modeling and calibration of an integrated preparative chromatographic system for pharmaceutical development and production is presented. Based on a user-defined system description, a system model was automatically generated and then calibrated using a sequence of experiments. The system description and model was implemented in the Python-based preparative chromatography control software Orbit.
Optimization of Cold Chain Logistics with Fuzzy MCDM Model
Authors: Hien Do Ngoc , Thanh Nguyen Van
Published: May 2022

Vaccines are biological products containing a weakened, inactivated part of bacteria or viruses that are not harmful to the human body. Vaccine manufacturers and distributors should always store vaccines at the right temperature. To do this task, manufacturers and distributors need to manage cold supply chains to the required standards. Cold chain management helps manufacturers control and keep vaccines at the right temperature while ensuring quality and extending their expiration date. That will help businesses in the medical industry reduce economic losses, avoid waste, and bring more significant benefits to patients. The selection and evaluation process for logistics suppliers, especially those who deal with low-temperature storage, considers many factors to reduce the potential waste of products from poor storage strategies. The author introduces an integrated approach to solve such a fuzzy multiple criteria decision-making (MCDM) problem based on the Fuzzy Analytical Hierarchy Process (FAHP) model and an Interactive and Multi-criteria Decision-Making in Portuguese Model (TODIM) model methods under the fuzzy linguistic environment. In this work, the SF-AHP method derives criteria weights in the first stage, and then a TODIM method is presented to identify the ranking of logistics providers. Finally, the authors present a case study on the evaluation and selection of cold chain logistics suppliers to demonstrate the applicability of the proposed fuzzy MCDM model.
Performance Characteristics of In-Line Oil Separator with Various Airfoil Vane Configurations of the Axial-Flow Swirl Generator
Authors: Je Yeong-Wan , Lee Jong-Chul , Kim Youn-Jea
Published: May 2022

Recently, as the industry develops, global energy consumption has been increasing. Power generation using various energy sources is used to meet energy consumption. The demand for renewable energy resources is increasing as well as the demand for fossil fuels. However, fossil fuel reserves offshore are limited, and the continued resource development is causing the depletion of fossil fuels. Accordingly, there is a demand for resource development not only offshore but also in the deep sea. In order to efficiently separate water and oil, it is necessary to study a compact in-line oil separator. In this study, the oil–water separation characteristics according to various airfoil vane configurations of the in-line type oil separator are numerically calculated. The maximum camber and location of the maximum camber of the NACA(National Advisory Committee for Aeronautics) airfoil model were selected as design parameters. As a result, the maximum separation efficiency of 63.9% was predicted when the maximum camber value was 13.51% and the maximum camber position was 50%.
Numerical Simulation Experimental Study of the Deformation and Failure of Granite with Multiaxial Tension
Authors: Zhou Lang , Ma Zhenqian , Xie Hongfei , Yang Wei , Zheng Hanghang
Published: May 2022

A numerical simulation method is proposed to study the deformation and failure rule of granite with multi-directional tensile stress, based on the importance of the rock tension. This investigation took into consideration the fact that the current experimental equipment cannot complete multi-directional tension experiments for rock. The deformation and failure rule of the granite material model with biaxial and triaxial tensile stress are studied using the numerical simulation software CASRock. The results show that in a biaxial tensile stress state, the tensile strength of granite decreases with the increase in the confining pressure, but the influence of the compression confining pressure on the strength reduction is greater than the tensile confining pressure. The number of cracks generated during failure decreases with the increase in the compressive confining pressure, and the inclination angle of the failure surface increases with the increase in the compressive confining pressure. In the three-direction tension stress state, the tensile strength of granite decreases slightly with the increase in the compressive confining pressure. However, when the compressive confining pressure in one direction is close to the uniaxial tensile strength, the tensile strength of granite will decrease quickly, and the failure result is similar to that of the uniaxial tensile failure.
Use of Natural Microtalcs during the Virgin Olive Oil Production Process to Increase Its Content in Antioxidant Compounds
Authors: Sánchez Sebastián , Olivares Inmaculada , Puentes Juan G. , Órpez Rafael , La Rubia M. Dolores , Pacheco Rafael , García Martín Juan F.
Published: May 2022

During the olive oil production process, certain olive varieties, such as ‘Hojiblanca’ and ‘Picual’, create pastes from which it is difficult to separate the oil, resulting in low extraction yields. To improve oil extraction, one alternative is the addition of natural microtalcs (NMT). In the present study, a NMT of great purity (CaCO3 concentration less than 6 wt.%) and small average particle size (ϕ ≤ 2.1 µm) was added in the malaxation stage on an industrial scale at two olive mills. In one of them and using ‘Hojiblanca’ olives, the performance of the high-purity NMT was compared with that of a traditional NMT, while in the other, the effect of its dosage in the quality of ‘Picual’ oils was assessed. The performance of the high-purity NMT was evaluated in terms of industrial oil yield, extractability index, quality parameters and oxidative stability of the resulting oils. The addition of the high-purity NMT not only increased the extraction yields but also improved the quality of the virgin olive oils, especially in relation to antioxidant compounds (tocopherols and phenolic compounds). Increases of 10.4% in phenolic compounds and of 21.5% in the tocopherols were found, thus enhancing the oxidative stability of the oils.
Development of an Online Monitoring Device for the Mixing Ratio of Two-Part Epoxy Adhesives Using an Electrical Impedance Spectroscopy Technique and Machine Learning
Authors: Choi Jeong Hee , An Chae Hun
Published: May 2022

Two-part epoxy adhesives are widely used in a range of industries. Two-part epoxy adhesive is composed of a resin and a hardener. Both materials remain stable in the general environment but curing begins when mixed in the specified mixing ratio. However, it has the disadvantage of requiring a specific mixing device. In addition, if the mixing ratio is different from the specified ratio due to the error of the mixing system, it has a fatal effect on the adhesion performance. The dielectric constant is a characteristic constant of a material. Therefore, it represents the mixing ratio of mixed two-part epoxy adhesives. With the electrical impedance spectroscopy technique, it can be measured indirectly by measuring impedance according to frequency and temperature. In this study, a sensor and embedded device for an online monitoring of its integrity using a regression method among machine learning are developed, which can acquire impedance data with frequency and temperature data according to the change in the mixing ratio of a two-part epoxy adhesive. The experimentally collected data were used as training data for the machine learning algorithm. It was found that the learned machine learning algorithm effectively estimates the mixing ratio of the two-part epoxy with an arbitrary value.
Comprehensive Evaluation of Ecological Environmental Quality in Small-Scale Coal Mining Subsidence Area Based on Hierarchical Structure—A Case Study of Shendong Coalfield in Western China
Authors: Yang Yingming , Liu Shuyu , Zhang Kai , Bai Lu , Wu Fengxiao
Published: May 2022

Aiming at the problem that the current research is not suitable for the evaluation of the surface ecological environmental quality in a small-scale coal mining subsidence area, a model combining the hierarchical method and the weighted method was constructed to realize the comprehensive evaluation of the surface ecological environmental quality in a small-scale subsidence area. The results showed that the change in the water and soil environment caused by coal mining subsidence was the main factor affecting the quality of the ecological environment in the subsidence area; the evaluation results in the control area and uniform subsidence area were of grade III, which was at the “general” level, and the evaluation results in the non-uniform subsidence area were of grade VI, which was at the “poor” level. Coal mining subsidence has a great impact on the quality of ecological environment in a non-uniform subsidence area.
New Technology for Preventing and Controlling Air Leakage in Goaf Based on the Theory of Wind Flow Boundary Layer
Authors: Miao Dejun , Chen Xiujie , Ji Jiaqi , Lv Yueying , Zhang Yiran , Sui Xiuhua
Published: May 2022

Air leakage in the goaf is a fundamental reason for the high-temperature heat damage of the coal mining face and the gas concentration in the upper corner exceeding the limit. Combined with the boundary layer theory, this study analyzes the airflow state of the coal mining face. We propose installing a new air curtain system to prevent air leakage in the goaf. The length of the intake air curtain is determined by solving the theoretical equation. Numerical simulation is used to study different layout schemes of air curtains, and the spatial distribution law of different air volumes inside the working face is analyzed. The simulation results are compared with the field-measured data. The results show that when the length of the air curtain on the air inlet side is 20 m, the wind flow on the working face can be approximated as a state of attached jet, and a diffuse turbulent flow area will be formed outside the air curtain. Gas concentration will increase in this area. The air leakage prevention effect is best when the air curtains with a length of 20 m for the inlet air and 10 m for the return air are arranged at both ends of the working face. This air curtain system can reduce the temperature of the working face and the gas concentration in the upper corner and has certain guiding significance for the air leakage prevention work in the goaf.
Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images
Authors: Gao Min , Yang Fengbao , Wei Hong , Liu Xiaoxia
Published: May 2022

Crop height is an essential parameter used to monitor overall crop growth, forecast crop yield, and estimate crop biomass in precision agriculture. However, individual maize segmentation is the prerequisite for precision field monitoring, which is a challenging task because the maize stalks are usually occluded by leaves between adjacent plants, especially when they grow up. In this study, we proposed a novel method that combined seedling detection and clustering algorithms to segment individual maize plants from UAV-borne LiDAR and RGB images. As seedlings emerged, the images collected by an RGB camera mounted on a UAV platform were processed and used to generate a digital orthophoto map. Based on this orthophoto, the location of each maize seedling was identified by extra-green detection and morphological filtering. A seed point set was then generated and used as input for the clustering algorithm. The fuzzy C-means clustering algorithm was used to segment individual maize plants. We computed the difference between the maximum elevation value of the LiDAR point cloud and the average elevation value of the bare digital terrain model (DTM) at each corresponding area for individual plant height estimation. The results revealed that our height estimation approach test on two cultivars produced the accuracy with R2 greater than 0.95, with the mean square error (RMSE) of 4.55 cm, 3.04 cm, and 3.29 cm, as well as the mean absolute percentage error (MAPE) of 3.75%, 0.91%, and 0.98% at three different growth stages, respectively. Our approach, utilizing UAV-borne LiDAR and RGB cameras, demonstrated promising performance for estimating maize height and its field position.
Multi-Station and Multi-Instrument Observations of F-Region Irregularities in the Taiwan–Philippines Sector
Authors: Tsai Lung-Chih , Su Shin-Yi , Lv Jun-Xian , Bullett Terry , Liu Chao-Han
Published: May 2022

In this study, a multi-station and multi-instrument system, organized and proposed for ionospheric scintillation and equatorial spread-F (ESF) specification and their associated motions in the Taiwan–Philippines sector, is outlined. The issues related to the scintillation and ESF event observed on 26 October 2021, at magnetic quiet conditions are presented and discussed. We first indicate the existence of a plasma bubble in the Taiwan–Philippines sector by using the FormoSat-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (FS7/COSMIC2) GPS/GLONASS radio occultation observations. We verify the latitudinal extent of the tracked plasma bubble using the recorded ionograms from the Vertical Incidence Pulsed Ionospheric Radar located at Hualien, Taiwan. We further discuss the spatial and temporal variabilities of two-dimensional vertical scintillation index VS4 maps based on the simultaneous GPS L1-band signal measurements from 133 ground-based receivers located in Taiwan and the surrounding islands. We also operate two high-sampling, software-defined GPS receivers and characterize the targeted plasma irregularities by carrying out spectrum analyses of the received signal. As a result, the derived plasma irregularities moved eastward and northward. Furthermore, the smaller the irregularity scale, the higher the spectral index and the stronger the scintillation intensity were at lower latitudes on the aimed irregularity feature.
Analysis of Two Convective Storms Using Polarimetric X-Band Radar and Satellite Data
Authors: Bobotová Gabriela , Sokol Zbyněk , Popová Jana , Fišer Ondřej , Zacharov Petr
Published: May 2022

We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements can be used to analyze the internal cloud structure of severe convective storms. The XCLASS classification was developed by major innovation of a previously published algorithm. The XCLASS algorithm identifies seven types of hydrometeors: light rain, rain, wet snow, dry snow, ice, graupel, and hail. It uses measured horizontal and vertical radar reflectivity, specific differential phase, co-polar correlation coefficient, and temperature, and applies fuzzy logic to determine the type of hydrometeor. The new algorithm practically eliminates unrealistic results around and below the melting layer provided by the original algorithm. It identifies wet snow in more cases, and areas with individual hydrometeors have more realistic shapes compared to the original algorithm. The XCLASS algorithm shows reasonable results for the classification of hydrometeors and can be used to study the structure of convective storms.
Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites
Authors: Möller Markus , Zepp Simone , Wiesmeier Martin , Gerighausen Heike , Heiden Uta
Published: May 2022

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99.
Insect Migration Flux Estimation Based on Statistical Hypothesis for Entomological Radar
Authors: Yu Teng , Li Muyang , Li Weidong , Cai Jiong , Wang Rui , Hu Cheng
Published: May 2022

Measuring migration flux with entomological radar is of great importance to assess the biomass of migratory insects and study the influence of insects on the ecosystem. However, the migration flux is measured with a large quantity of errors for the entomological radar without the ability of in-beam angle measurement, because the insect RCS is measured with the assumption that the insect flies over the beam center. When the insect does not pass through the beam center, the measured RCS is less than the true value. To improve the estimation accuracy of migration flux, a new estimation method of migration flux based on statistical hypothesis is proposed for radars working in the fixed-beam vertical-looking mode. This method avoids the RCS measurement error caused by the offset of the insect trajectory to the radar beam center by assuming that the insect flight trajectory is evenly distributed in the beam and calculating the average value of flux. This method is extended to be used in fixed-beam arbitrary pointing mode and a new proposed scanning mode. The effectiveness of the proposed method is verified by simulations and migration insect data measured by a radar.
RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment
Authors: Krapf Sebastian , Bogenrieder Lukas , Netzler Fabian , Balke Georg , Lienkamp Markus
Published: May 2022

Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associated the implications on the training and evaluation of two convolutional neural networks and found that the quality of the prediction behaved similarly to the annotator agreement for most classes. The class photovoltaic module was predicted to be best with a class-specific mean intersection over union of 0.69. By providing the datasets in initial and reviewed versions, we promote a data-centric approach for the semantic segmentation of roof information. Finally, we conducted a photovoltaic potential analysis case study and demonstrated the high impact of roof superstructures as well as the viability of the computer vision approach to increase accuracy. While this paper’s primary use case was roof information extraction for photovoltaic potential analysis, its implications can be transferred to other computer vision applications in remote sensing and beyond.
Enhanced Turbulent Mixing in the Upper Ocean Induced by Super Typhoon Goni (2015)
Authors: Qiao Mengtian , Cao Anzhou , Song Jinbao , Pan Yunhe , He Hailun
Published: May 2022

Based on the satellite-observed sea surface temperature (SST) data, high-resolution Argo observations and hybrid coordinate model (HYCOM) reanalysis results, this study examined the upper ocean response to Super Typhoon Goni in 2015 in the western north Pacific and revealed the significant diapycnal diffusivity enhancement in the upper ocean induced by Goni. Results indicate that the maximum SST cooling caused by Goni was 7.7 °C, which is greater than the SST cooling caused by most typhoons. The severe SST cooling was related to the enhancement of turbulent mixing induced by Goni. To the right of the typhoon track, the diapycnal diffusivity enhancement in the upper ocean caused by Goni could reach three orders of magnitude, from O (10−5 m2/s) to O (10−2 m2/s) and could last at least 9 days after the passage of Goni. In contrast, the diapycnal diffusivity to the left of the typhoon track did not show significant variations. The enhancement of turbulent mixing was found to be consistent with Goni-induced near-inertial kinetic energy calculated from the HYCOM reanalysis results, which suggests that the enhanced turbulent mixing was caused by Goni-induced near-inertial waves.
Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification
Authors: Wang Jiayan , Wang Xueqin , Xing Lei , Liu Bao-Di , Li Zongmin
Published: May 2022

In recent years, few-shot remote sensing scene classification has attracted significant attention, aiming to obtain excellent performance under the condition of insufficient sample numbers. A few-shot remote sensing scene classification framework contains two phases: (i) the pre-training phase seeks to adopt base data to train a feature extractor, and (ii) the meta-testing phase uses the pre-training feature extractor to extract novel data features and design classifiers to complete classification tasks. Because of the difference in the data category, the pre-training feature extractor cannot adapt to the novel data category, named negative transfer problem. We propose a novel method for few-shot remote sensing scene classification based on shared class Sparse Principal Component Analysis (SparsePCA) to solve this problem. First, we propose, using self-supervised learning, to assist-train a feature extractor. We construct a self-supervised assisted classification task to improve the robustness of the feature extractor in the case of fewer training samples and make it more suitable for the downstream classification task. Then, we propose a novel classifier for the few-shot remote sensing scene classification named Class-Shared SparsePCA classifier (CSSPCA). The CSSPCA projects novel data features into subspace to make reconstructed features more discriminative and complete the classification task. We have conducted many experiments on remote sensing datasets, and the results show that the proposed method dramatically improves classification accuracy.
Comparison of Deep Learning Models for the Classification of Noctilucent Cloud Images
Authors: Sapkota Rajendra , Sharma Puneet , Mann Ingrid
Published: May 2022

Optically thin layers of tiny ice particles near the summer mesopause, known as noctilucent clouds, are of significant interest within the aeronomy and climate science communities. Ground-based optical cameras mounted at various locations in the arctic regions collect the dataset during favorable summer times. In this paper, first, we compare the performances of various deep learning-based image classifiers against a baseline machine learning model trained with support vector machine (SVM) algorithm to identify an effective and lightweight model for the classification of noctilucent clouds. The SVM classifier is trained with histogram of oriented gradient (HOG) features, and deep learning models such as SqueezeNet, ShuffleNet, MobileNet, and Resnet are fine-tuned based on the dataset. The dataset includes images observed from different locations in northern Europe with varied weather conditions. Second, we investigate the most informative pixels for the classification decision on test images. The pixel-level attributions calculated using the guide back-propagation algorithm are visualized as saliency maps. Our results indicate that the SqueezeNet model achieves an F1 score of 0.95. In addition, SqueezeNet is the lightest model used in our experiments, and the saliency maps obtained for a set of test images correspond better with relevant regions associated with noctilucent clouds.
Application of Gaofen-6 Images in the Downscaling of Land Surface Temperatures
Authors: Li Xiaoyuan , He Xiufeng , Pan Xin
Published: May 2022

The coarse resolution of land surface temperatures (LSTs) retrieved from thermal-infrared (TIR) satellite images restricts their usage. One way to improve the resolution of such LSTs is downscaling using high-resolution remote sensing images. Herein, Gaofen-6 (GF-6) and Landsat-8 images were used to obtain original and retrieved LSTs (Landsat-8- and GF-6-retrieved-LSTs) to perform LST downscaling in the Ebinur Lake Watershed. Downscaling model was constructed, and the regression kernel was explored. The results of downscaling LST using the GF-6 normalized difference vegetation index with red-edge band 2, ratio built-up index, normalized difference sand index, and normalized difference water index as multi-remote sensing indices with multiple remote sensing indices with random forest regression method provided optimal downscaling results, with R2 of 0.836, 0.918, and 0.941, root mean square difference of 1.04 K, 2.06 K, and 1.80 K, and the number of pixels with LST errors between −1 K and +1 K of 87.2%, 76.4%, and 81.9%, respectively. The expression of spatial distribution of 16 m-LST downscaling results corresponded with that of Landsat-8- and GF-6-retrieved-LST, and provided additional details spatial description of LST variations, which was absent in the Landsat-8- and GF-6-retrieved LSTs. The results of downscaling LST could satisfy the application requirements of LST spatial resolution.
3D Sparse SAR Image Reconstruction Based on Cauchy Penalty and Convex Optimization
Authors: Wang Yangyang , He Zhiming , Yang Fan , Zeng Qiangqiang , Zhan Xu
Published: May 2022

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical L1 regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than L1. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with L1 regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.
The Feasibility of Leaf Reflectance-Based Taxonomic Inventories and Diversity Assessments of Species-Rich Grasslands: A Cross-Seasonal Evaluation Using Waveband Selection
Authors: Thornley Rachael Helen , Verhoef Anne , Gerard France F. , White Kevin
Published: May 2022

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity.
Anomaly Detection for Agricultural Vehicles Using Autoencoders
Authors: Mujkic Esma , Philipsen Mark P. , Moeslund Thomas B. , Christiansen Martin P. , Ravn Ole
Published: May 2022

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.
Research on the Error of Global Positioning System Based on Time Series Analysis
Authors: Song Lijun , Zhou Lei , Xu Peiyu , Zhao Wanliang , Li Shaoliang , Li Zhe
Published: May 2022

Due to the poor dynamic positioning precision of the Global Positioning System (GPS), Time Series Analysis (TSA) and Kalman filter technology are used to construct the positioning error of GPS. According to the statistical characteristics of the autocorrelation function and partial autocorrelation function of sample data, the Autoregressive (AR) model which is based on a Kalman filter is determined, and the error model of GPS is combined with a Kalman filter to eliminate the random error in GPS dynamic positioning data. The least square method is used for model parameter estimation and adaptability tests, and the experimental results show that the absolute value of the maximum error of longitude and latitude, the mean square error of longitude and latitude and average absolute error of longitude and latitude are all reduced, and the dynamic positioning precision after correction has been significantly improved.
SCD: A Stacked Carton Dataset for Detection and Segmentation
Authors: Yang Jinrong , Wu Shengkai , Gou Lijun , Yu Hangcheng , Lin Chenxi , Wang Jiazhuo , Wang Pan , Li Minxuan , Li Xiaoping
Published: May 2022

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons and the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this article, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.1∼4.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.8∼2.2% and 3.4∼4.3%, respectively.
Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier
Authors: Srivastava Rohit , Bhardwaj Ved Prakash , Othman Mohamed Tahar Ben , Pushkarna Mukesh , Anushree  , Mangla Arushi , Bajaj Mohit , Rehman Ateeq Ur , Shafiq Muhammad , Hamam Habib
Published: May 2022

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.
Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence
Authors: Oh Esther , Kearns William , Laine Megan , Demiris George , Thompson Hilaire J.
Published: May 2022

This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users’ actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.