bluuebunny/crossref_metadata_2025 huggingface dataset:
Quercus suber Roots Activate Antioxidant and Membrane Protective Processes in Response to High Salinity
Authors: Dias Maria Celeste , Santos Conceição , Araújo Márcia , Barros Pedro M. , Oliveira Margarida , de Oliveira José Miguel P. Ferreira
Published: February 2022

Cork oak (Quercus suber) is a species native to Mediterranean areas and its adaptation to the increasingly prevalent abiotic stresses, such as soil salinization, remain unknown. In sequence with recent studies on salt stress response in the leaf, it is fundamental to uncover the plasticity of roots directly exposed to high salinity to better understand how Q. suber copes with salt stress. In the present study we aimed to unveil the antioxidants and key-genes involved in the stress-responses (early vs. later responses) of Q. suber roots exposed to high salinity. Two-month-old Q. suber plants were watered with 300 mM NaCl solution and enzymatic and non-enzymatic antioxidants, lipid peroxidation and the relative expression of genes related to stress response were analysed 8 h and 6 days after salt treatment. After an 8 h of exposure, roots activated the expression of QsLTI30 and QsFAD7 genes involved in stress membrane protection, and QsRAV1 and QsCZF1 genes involved in tolerance and adaptation. As a result of the continued salinity stress (6 days), lipid peroxidation increased, which was associated with an upregulation of QsLTI30 gene. Moreover, other protective mechanisms were activated, such as the upregulation of genes related to antioxidant status, QsCSD1 and QsAPX2, and the increase of the antioxidant enzyme activities of superoxide dismutase, catalase, and ascorbate peroxidase, concomitantly with total antioxidant activity and phenols. These data suggest a response dependent on the time of salinity exposure, leading Q. suber roots to adopt protective complementary strategies to deal with salt stress.
Chemical and Biological Characterization of Melaleuca alternifolia Essential Oil
Authors: Borotová Petra , Galovičová Lucia , Vukovic Nenad L. , Vukic Milena , Tvrdá Eva , Kačániová Miroslava
Published: February 2022

The essential oil of Melaleuca alternifolia, commonly known as tea tree oil, has many beneficial properties due to its bioactive compounds. The aim of this research was to characterize the tea tree essential oil (TTEO) from Slovakia and its biological properties, which are specific to the chemical composition of essential oil. Gas chromatography/mass spectroscopy revealed that terpinen-4-ol was dominant with a content of 40.3%. γ-Terpinene, 1,8-cineole, and p-cymene were identified in contents of 11.7%, 7.0%, and 6.2%, respectively. Antioxidant activity was determined at 41.6% radical inhibition, which was equivalent to 447 μg Trolox to 1 mL sample. Antimicrobial activity was observed by the disk diffusion method against Gram-positive (G+), Gram-negative (G−) bacteria and against yeasts, where the best antimicrobial activity was against Enterococcus faecalis and Candida albicans with an inhibition zone of 10.67 mm. The minimum inhibitory concentration showed better susceptibility by G+ and G− planktonic cells, while yeast species and biofilm-forming bacteria strains were more resistant. Antibiofilm activity was observed against Pseudomonas fluorescens and Salmonella enterica by MALDI-TOF, where degradation of the protein spectra after the addition of essential oil was obtained. Good biological properties of tea tree essential oil allow its use in the food industry or in medicine as an antioxidant and antimicrobial agent.
Surface Relief Grating on Chitosan-N,N-dimethyl-4-(2-pyridylazo)aniline Thin Film
Authors: Atmah Nadiyah Rashed Al , Caliman Willian R. , Pawlicka Agnieszka , Sabat Ribal Georges , Nunzi Jean-Michel
Published: February 2022

We deposited homogeneous, thin, yellow-colored films of chitosan (Chi)-N,N-dimethyl-4-(2-pyridylazo)aniline (PADA) dye from an acid Chi–PADA solution by spin-coating on glass substrates. We characterized Chi, PADA, and Chi–PADA films by ATR–FTIR spectroscopy, which revealed a slight shift of 3170 and 3268 cm−1 bands, indicating H-bonding between the chitosan hydroxyl (OH) group and the amine (N) of the PADA pyridine ring. Based on these analyses, it was possible to determine the efficiency of the hydrogen bonds to form a Surface Relief Grating (SRG) on azo-polymer thin film. Moreover, we performed UV–VIS spectroscopy analysis of this film, which showed a broad band extending from 400 to 700 nm, with the maximum occurring at 428 nm. Therefore, we selected, within the absorption band, the 532 nm green laser wavelength to irradiate the azo-polymer films at room temperature. For the first time, natural polymer derivative and dye sample Chi–PADA thin films showed unique photoresponsive behavior under irradiation with two interfering laser beams. This permitted us to generate surface inscription patterning known as an SRG, which we confirmed by atomic force microscopy (AFM) and for which we determined a grating depth up to 50 nm. The present study opens the new possibility of using natural polymer-dye thin films.
Polyurea Thickened Lubricating Grease—The Effect of Degree of Polymerization on Rheological and Tribological Properties
Authors: Jopen Max , Degen Patrick , Henzler Stephan , Grabe Bastian , Hiller Wolf , Weberskirch Ralf
Published: February 2022

Lubricating greases based on urea thickeners are frequently used in high-performance applications since their invention in 1954. One property that has so far been neglected in the further development of these systems due to their low solubility and the resulting difficulty of analysis, is to better understand how the degree of polymerization affect such polyurea lubricating systems. In this work, we prepared three different oligo- or polyurea systemswith different degrees of polymerization (DP) and investigated the influence of DP on rheological and tribological properties. The results showed that the DP has an influence on the flow limit in rheology as well as on the extreme pressure (EP) and anti-wear (AW) properties as examined by tribology measurements. By optimizing the DP for a thickener system, comparable EP and AW properties can be achieved through the use of additives. The DP showed an increasing influence on the flow limit. This could reduce damage to rolling bearings due to lateral loading at rest. Therefore, modifying the DP of the polyurea systems shows similar effects as the addition of external additives. Overall, this would reduce the use of additives in industrial applications.
Pavement Properties and Predictive Durability Analysis of Asphalt Mixtures
Authors: Zhang Guohong , Wu Haonan , Li Ping , Qiu Jianhui , Nian Tengfei
Published: February 2022

The actual lifetimes of many highways are lower than that expected based on the initial pavement design, which brings increasingly prohibitive costs of pavement maintenance and repair. Although many works have been done, the real service lifetimes are still disappointing, and the researchers are also trying their best to increase the projects’ life span. In this study, to comprehensively predict the durability and lifetime of newly designed asphalt mixture structures, an asphalt pavement project consisting of three hot mix asphalt (HMA) mixtures were evaluated. The mixtures were constructed in the pavement project of the Weiwu expressway in Gansu Province. Pavement properties of the asphalt mixtures, rutting and temperature fatigue factors of the dynamic modulus are discussed. The fatigue resistance is supposed to improve on increasing the vehicles’ speed below the freezing point, which may be more suitable for applications in expressways. Meanwhile, the lifetime is measured according to the number of fatigue axle loads calculated, which were corrected between the specimens in the lab and the field core samples. Durability analysis prediction can be obtained based on the fatigue lifetime predictive model accordingly, which can provide more information about the fatigue lifetime and the rehabilitation planning of existing pavements in the future accordingly.
Unique Periodic Rings Composed of Fractal-Growth Dendritic Branching in Poly(p-dioxanone)
Authors: Huang Kuan-Ying , Woo Eamor M. , Nagarajan Selvaraj
Published: February 2022

Amorphous poly(p-vinyl phenol) (PVPh) was added into semicrystalline poly(p-dioxanone) (PPDO) to induce a uniquely novel dendritic/ringed morphology. Polarized-light optical, atomic-force and scanning electron microscopy (POM, AFM, and SEM) techniques were used to observe the crystal arrangement of a uniquely peculiar cactus-like dendritic PPDO spherulite, with periodic ring bands not continuingly circular such as those conventional types reported in the literature, but discrete and detached to self-assemble on each of the branches of the lobs. Correlations and responsible mechanisms for the formation of this peculiar banded-dendritic structure were analyzed. The periodic bands on the top surface and interior of each of the cactus-like lobs were discussed. The banded pattern was composed of feather-like lamellae in random fractals alternately varying their orientations from the radial direction to the tangential one. The tail ends of lamellae at the growth front spawned nucleation cites for new branches; in cycles, the feather-like lamellae self-divided into multiple branches following the Fibonacci sequence to fill the ever-expanding space with the increase of the radius. The branching fractals in the sequence and the periodic ring-banded assembly on each of the segregated lobs of cactus-like dendrites were the key characteristics leading to the formation of this unique dendritic/ringed PPDO spherulite.
Abrasion-Induced Acceleration of Melt Crystallisation of Wet Comminuted Polybutylene Terephthalate (PBT)
Authors: Tischer Florentin , Düsenberg Björn , Gräser Timo , Kaschta Joachim , Schmidt Jochen , Peukert Wolfgang
Published: February 2022

Within this contribution, the effect of grinding media wear on the melt crystallisation of polybutylene terephthalate (PBT) is addressed. PBT was wet ground in a stirred media mill in ethanol using different grinding media beads (silica, chrome steel, cerium-stabilised and yttrium-stabilised zirconia) at comparable stress energies with the intention to use the obtained particles as feed materials for the production of feedstocks for laser powder bed fusion additive manufacturing (PBF-AM). In PBF‑AM, the feedstock’s optical, rheological and especially thermal properties—including melt crystallisation kinetics—strongly influence the processability and properties of the manufactured parts. The influence of process parameters and used grinding media during wet comminution on the optical properties, crystal structure, molar mass distribution, inorganic content (wear) and thermal properties of the obtained powders is discussed. A grinding media-dependent acceleration of the melt crystallisation could be attributed to wear particles serving as nuclei for heterogeneous crystallisation. Yttrium-stabilised zirconia grinding beads proved to be the most suitable for the production of polymer powders for the PBF process in terms of (fast) comminution kinetics, unchanged optical properties and the least accelerated crystallisation kinetics.
Numerical Investigation of the Dynamic Responses of Fibre-Reinforced Polymer Composite Bridge Beam Subjected to Moving Vehicle
Authors: Kormanikova Eva , Kotrasova Kamila , Melcer Jozef , Valaskova Veronika
Published: February 2022

In modern industry, heavy traditional materials are being substituted with light and strong fibre-reinforced polymer composite materials. Bridges and railroads made of composite laminates are considerably affected by traffic loads. Therefore, it is very important to analyse this effect which would find practical applications in engineering designs. This paper explains the theoretical formulation that governs the dynamic response of a composite beam subjected to a moving load. The governing equations for the dynamic effect on the laminated composite bridge beam are explained here. The main theories in the micro–macro modelling of composite laminates are also described in the paper. Within the macro modelling, the Classical Laminate and Shear Deformation Laminate Theory of beams are presented. The symmetric cross-ply laminated bridge, made of boron/epoxy is under consideration. The computational two-dimensional model of the vehicle is adopted. The governing equations for the dynamic effect on the laminated composite bridge beam are explained. The calculation of the time response of the bridge for the characteristic speeds of the vehicle is performed in the environment of the MATLAB software. The maximum dynamic magnification factor for the dynamic analysis of a composite beam is found.
Current Advances of Polysaccharide-Based Nanogels and Microgels in Food and Biomedical Sciences
Authors: Papagiannopoulos Aristeidis , Sotiropoulos Konstantinos
Published: February 2022

Polysaccharides are natural polymers with hydrophilic, biocompatible and biodegradable characteristics and have many opportunities in the food and pharmaceutical sectors. This review focuses on the field of nano and microstructures whose internal structure is based on networked polysaccharide chains in 3D i.e., polysaccharide nanogels (NGs) and microgels (MGs). As it is observed the number of articles on NGs and MGs in peer reviewed scientific journals has been increasing over the last two decades. At the same time, the relative contribution of polysaccharides in this field is gaining place. This review focuses on the different applied methods for the fabrication of a variety of polysaccharide-based NGs and MGs and aims to highlight the recent advances on the subject and present their potentials and properties with regards to their integration in aspects of medicinal and food sciences. The presentation of the recent advances in the application of polysaccharide NGs and MGs is divided in materials with potential as emulsion stabilizers and materials with potential as carriers of bioactives. For applications in the medical sector the division is based on the fabrication processes and includes self-assembled, electrostatically complexed/ionically crosslinked and chemically crosslinked NGs and MGs. It is concluded that many advances are expected in the application of these polysaccharide-based materials and in particular as nutrient-loaded emulsion stabilizers, viscosity modifiers and co-assembled structures in combination with proteins.
Study of Lignin Extracted from Rubberwood Using Microwave Assisted Technology for Fuel Additive
Authors: Yimtrakarn Trakarn , Kaveevivitchai Watchareeya , Lee Wen-Chien , Lerkkasemsan Nuttapol
Published: February 2022

Lignin is the most abundant natural aromatic polymer, especially in plant biomass. Lignin-derived phenolic compounds can be processed into high-value liquid fuel. This study aimed to determine the yield of lignin by the microwave-assisted solvent extraction method and to characterize some essential properties of the extracted lignin. Rubberwood sawdust (Hevea brasiliensis) was extracted for lignin with an organic-based solvent, either ethanol or isopropanol, in a microwave oven operating at 2450 MHz. Two levels of power of microwave, 100 W and 200 W, were tested as well as five extraction times (5, 10, 15, 20, 25, and 30 min). The extracted lignin was characterized by Klason lignin, Fourier transform infrared spectroscopy (FT-IR), 2D HSQC NMR, Ultraviolet-visible spectrophotometry (UV-vis), and Bomb calorimeter. The results showed that the yield of extracted lignin increased with the extraction time and power of the microwave. In addition, the extraction yield with ethanol was higher than the yield with isopropanol. The highest yield was 6.26 wt.%, with ethanol, 30 min extraction time, and 200 W microwave power.
Discussion on the Purchase Factors and the User Demands of Electric Scooters from the Perspective of Consumers’ Life Style—A Case Study on Gogoro
Authors: Tu Jui-Che , Jia Xi-Hui , Yang Ting-Jen
Published: February 2022

With the development and popularization of the concept of a sustainable environment, the green energy industry represented by electric scooters has entered a vigorous development period. However, consumers are reluctant to buy electric scooters, resulting in low sales, which is limiting the development of the electric scooter industry. Therefore, from the perspective of different consumer life styles, this study explored the impact on the purchasing decisions and user demands, so as to inform design strategy for the development of a sustainable electric scooter industry. This study adopted the methods of expert interview and questionnaire survey, used the self-made consumer life style and electric scooter product attribute scale as the research tool, and carried out quantitative analysis with the help of statistical software. It showed that compared with consumers’ awareness of environmental protection, product performance is the key factor that attracts consumers to buy electric scooters. More than consumers’ awareness of environmental protection, our study showed that product performance is the primary determinate of a consumers’s decision to purchase electric scooters. The consumer of the electric scooter can be categorized into three life style groups: “brand-conscious group”, “environmental and cost saving group” and “trendy adventurers group”. Outside of the “environmental and cost savings” group, the least important attribute that drive purchasing decisions was environmental sustainability. This can also inform more effective governmental policies and education to promote greater environmental protection. Each life style consumer group prioritizes different product attributes; therefore, electric scooter manufacturers should view each group as distinct market segments and draw up different product design and marketing strategies.
The Influence of a Pumping Chamber on Hydraulic Losses in a Mixed-Flow Pump
Authors: Zhang Huiyan , Meng Fan , Cao Lei , Li Yanjun , Wang Xinkun
Published: February 2022

In this study, entropy generation theory based on computational fluid dynamics (CFD) is used to study the influence of a pumping chamber type (guide vane and volute scheme) on the spatial distribution of hydraulic loss in a mixed-flow pump. The CFD data of the mixed-flow pump with a volute is validated by external characteristic test data under Q = 561.4–1598.6 m3/h. The results show that the efficiency and the head of the guide vanes scheme are lower under Q = 800–1200 m3/h, which resulted from a higher total entropy production (TEP) in the pumping chamber and outlet pipe. The high total entropy production rate (TEPR) inside the guide vanes can be found near the leading edge of the hub side and trailing edge of the rim side due to flow separation, which reduces the recovery efficiency of kinetic energy of the guide vanes. The high TEPR inside outlet pipe can be seen near the inlet, caused by back flow. However, the efficiency and head of the volute scheme are lower, under Q = 1200–1600 m3/h, owing to the fact that the volute cannot effectively convert kinetic energy into pressure energy and thus the high TEPR can be found near outlet of volute and inlet of outlet pipe. These results can provide useful suggestions to the matching optimization of the impeller and pumping chamber in a mixed-flow pump.
Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks
Authors: Ziouzios Dimitris , Baras Nikolaos , Balafas Vasileios , Dasygenis Minas , Stimoniaris Adam
Published: February 2022

In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated; large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the proposed system can outperform existing algorithms found in the literature in real-world conditions, with 92.43% accuracy.
Bank Stock Return Reactions to the COVID-19 Pandemic: The Role of Investor Sentiment in MENA Countries
Authors: Albaity Mohamed , Saadaoui Mallek Ray , Mustafa Hasan
Published: February 2022

In this study, we investigated the impact of COVID-19 investor sentiment (CS), number of cases (CC), and deaths (CD) on bank stock returns in 16 MENA countries. In addition, we examined the interaction effects of CS with CC and CD on bank stock returns. Lastly, we looked at whether Islamic banks outperformed conventional banks during the pandemic. Based on monthly data from the Middle East and North Africa (MENA) countries from February 2020 to July 2021, we used the clustered standard error fixed effect estimation on Islamic and conventional bank stock market returns. The results suggest that CC and CD have negative impacts on bank stock market returns while CS has no effect, except for the lagged value. The interaction effect of CS with CC and CD on stock returns proved to strengthen the link in the current month and weaken the link in the previous month.
Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing
Authors: Melki Paul , Bombrun Lionel , Millet Estelle , Diallo Boubacar , ElChaoui ElGhor Hakim , Da Costa Jean-Pierre
Published: February 2022

A considerable number of metrics can be used to evaluate the performance of machine learning algorithms. While much work is dedicated to the study and improvement of data quality and models’ performance, much less research is focused on the study of these evaluation metrics, their intrinsic relationship, the interplay of the influence among the metrics, the models, the data, and the environments and conditions in which they are to be applied. While some works have been conducted on general machine learning tasks such as classification, fewer efforts have been dedicated to more complex problems such as object detection and image segmentation, in which the evaluation of performance can vary drastically depending on the objectives and domains of application. Working in an agricultural context, specifically on the problem of the automatic detection of plants in proximal sensing images, we studied twelve evaluation metrics that we used to evaluate three image segmentation models recently presented in the literature. After a unified presentation of these metrics, we carried out an exploratory analysis of their relationships using a correlation analysis, a clustering of variables, and two factorial analyses (namely principal component analysis and multiple factorial analysis). We distinguished three groups of highly linked metrics and, through visual inspection of the representative images of each group, identified the aspects of segmentation that each group evaluates. The aim of this exploratory analysis was to provide some clues to practitioners for understanding and choosing the metrics that are most relevant to their agricultural task.
Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus
Authors: Andronis Vassilis , Karathanassi Vassilia , Tsalapati Victoria , Kolokoussis Polychronis , Miltiadou Milto , Danezis Chistos
Published: February 2022

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.
Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data
Authors: Guan Yunyi , Grote Katherine , Schott Joel , Leverett Kelsi
Published: February 2022

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and temporally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agriculture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and alfalfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was performed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil.
Evaluation of the Emissions State of a Satellite Laser Altimeter Based on Laser Footprint Imaging
Authors: Yao Jiaqi , Zhai Haoran , Wu Shuqi , Wen Zhen , Tang Xinming
Published: February 2022

The GaoFen-7(GF-7) satellite is equipped with China’s first laser altimeter for Earth observation; it has the capability of full waveform recording, which can obtain global high-precision three-dimensional coordinates over a wide range. The laser is inevitably affected by platform tremors, random errors in the laser pointing angle, laser state, and other factors, which further affect the measurement accuracy of the laser footprint. Therefore, evaluation of the satellite laser launch state is an important process. This study contributes to laser emission state evaluations based on the laser footprint image in terms of two main two aspects: (1) Monitoring changes in the laser pointing angle—laser pointing is closely related to positioning accuracy, which mainly results from monitoring the change in the laser spot centroid. We propose a threshold constraint algorithm that extracts the centroid of an ellipse-fitting spot. (2) Analysis of the energy distribution state—directly obtaining the parameters used in the traditional evaluation method is a challenge for the satellite. Therefore, an index suitable for evaluating the laser emissions state of the GF-7 satellite was constructed according to the data characteristics. Based on these methods, long time-series data were evaluated and analyzed. The experimental results show that the proposed method can effectively evaluate the emissions state of the laser altimeter, during which the laser pointing angle changes monthly by 0.434″. During each continuous operation of the laser, the energy state decreased gradually, with a small variation range; however, both were generally in a stable state.
Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases
Authors: Liu Zijing , Qiu Haijun , Zhu Yaru , Liu Ya , Yang Dongdong , Ma Shuyue , Zhang Juanjuan , Wang Yuyao , Wang Luyao , Tang Bingzhe
Published: February 2022

Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.
Fusion of Wearable Kinetic and Kinematic Sensors to Estimate Triceps Surae Work during Outdoor Locomotion on Slopes
Authors: Harper Sara E. , Schmitz Dylan G. , Adamczyk Peter G. , Thelen Darryl G.
Published: February 2022

Muscle–tendon power output is commonly assessed in the laboratory through the work loop, a paired analysis of muscle force and length during a cyclic task. Work-loop analysis of muscle–tendon function in out-of-lab conditions has been elusive due to methodological limitations. In this work, we combined kinetic and kinematic measures from shear wave tensiometry and inertial measurement units, respectively, to establish a wearable system for estimating work and power output from the soleus and gastrocnemius muscles during outdoor locomotion. Across 11 healthy young adults, we amassed 4777 strides of walking on slopes from −10° to +10°. Results showed that soleus work scales with incline, while gastrocnemius work is relatively insensitive to incline. These findings agree with previous results from laboratory-based studies while expanding technological capabilities by enabling wearable analysis of muscle–tendon kinetics. Applying this system in additional settings and activities could improve biomechanical knowledge and evaluation of protocols in scenarios such as rehabilitation, device design, athletics, and military training.
A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation
Authors: Nieves Avendano Diego , Vandermoortele Nathan , Soete Colin , Moens Pieter , Ompusunggu Agusmian Partogi , Deschrijver Dirk , Van Hoecke Sofie
Published: February 2022

Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding unexpected breakdowns. This paper presents a methodology for creating health index models with monotonicity in a semi-supervised approach. The health indexes are then used for enhancing remaining useful life estimation models. The methodology is evaluated on two bearing datasets. Results demonstrate the advantage of using the monotonic health index for obtaining insights into the bearing degradation and for remaining useful life estimation.
Near-Infrared Time-Resolved Spectroscopy Shows Anterior Prefrontal Blood Volume Reduction in Schizophrenia but Not in Major Depressive Disorder
Authors: Shinba Toshikazu , Kariya Nobutoshi , Matsuda Saori , Arai Makoto , Itokawa Masanari , Hoshi Yoko
Published: February 2022

Previous studies using various brain imaging methods have reported prefrontal blood flow disturbances in psychiatric disorders, including schizophrenia and major depressive disorder. In both disorders, alterations of the resting blood flow, in addition to that of the activation in response to task load, have been shown, but the results are not consistent. The present study aimed to examine the anterior prefrontal hemoglobin concentration at the resting state in schizophrenia and depression using near-infrared time-resolved spectroscopy (NIR-TRS), which estimates the optical absorption coefficients and calculates the absolute concentrations of oxygenated (oxy-Hb), deoxygenated (deoxy-Hb), and total (total-Hb; sum of oxy-Hb and deoxy-Hb) hemoglobin. Their ratios to systemic blood hemoglobin concentration (blood-Hb) were also assessed. In agreement with our previous data, total-Hb and total-Hb/blood-Hb in schizophrenia were significantly lower. The present study further revealed that both oxy-Hb/blood-Hb and deoxy-Hb/blood-Hb in schizophrenia were reduced. In depression, total-Hb, total-Hb/blood-Hb, oxy-Hb, and oxy-Hb/blood-Hb were higher than in schizophrenia and were not different from the control. The oxygen saturation (oxy-Hb/total-Hb), in addition to the optical pathlengths, did not show group differences. Lowered oxy-Hb/blood-Hb and deoxy-Hb/blood-Hb together with unchanged oxygen saturation may indicate that the prefrontal blood volume is reduced in schizophrenia. The present findings suggest that NIR-TRS is useful in analyzing the hemodynamic aspects of prefrontal dysfunction in schizophrenia and differentiating schizophrenia from depression.
An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput
Authors: Farhan Naureen , Rizvi Safdar
Published: February 2022

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference.
Online Trajectory Estimation Based on a Network-Wide Cellular Fingerprint Map
Authors: Chen Langqiao , Lu Yuhuan , He Zhaocheng , Chen Yixian
Published: February 2022

Cellular signaling data is widely available in mobile communications and contains abundant movement sensing information of individual travelers. Using cellular signaling data to estimate the trajectories of mobile users can benefit many location-based applications, including infectious disease tracing and screening, network flow sensing, traffic scheduling, etc. However, conventional methods rely too much on heuristic hypotheses or hardware-dependent network fingerprinting approaches. To address the above issues, NF-Track (Network-wide Fingerprinting based Tracking) is proposed to realize accurate online map-matching of cellular location sequences. In particular, neither prior assumptions such as arterial preference and less-turn preference or extra hardware-relevant parameters such as RSS and SNR are required for the proposed framework. Therefore, it has a strong generalization ability to be flexibly deployed in the cloud computing environment of telecom operators. In this architecture, a novel segment-granularity fingerprint map is put forward to provide sufficient prior knowledge. Then, a real-time trajectory estimation process is developed for precise positioning and tracking. In our experiments implemented on the urban road network, NF-Track can achieve a recall rate of 91.68% and a precision rate of 90.35% in sophisticated traffic scenes, which are superior to the state-of-the-art model-based unsupervised learning approaches.
Re-Orienting Smartphone-Collected Car Motion Data Using Least-Squares Estimation and Machine Learning
Authors: Bassetti Enrico , Luciani Alessio , Panizzi Emanuele
Published: February 2022

Smartphone sensors can collect data in many different contexts. They make it feasible to obtain large amounts of data at little or no cost because most people own mobile phones. In this work, we focus on collecting motion data in the car using a smartphone. Motion sensors, such as accelerometers and gyroscopes, can help obtain information about the vehicle’s dynamics. However, the different positioning of the smartphone in the car leads to difficulty interpreting the sensed data due to an unknown orientation, making the collection useless. Thus, we propose an approach to automatically re-orient smartphone data collected in the car to a standardized orientation (i.e., with zero yaw, roll, and pitch angles with respect to the vehicle). We use a combination of a least-square plane approximation and a Machine Learning model to infer the relative orientation angles. Then we populate rotation matrices and perform the data rotation. We trained the model by collecting data using a vehicle physics simulator.
Effect of Hydroxybenzoic Acids on Caffeine Detection Using Taste Sensor with Lipid/Polymer Membranes
Authors: Zhao Zeyu , Ishida Misaki , Onodera Takeshi , Toko Kiyoshi
Published: February 2022

A taste sensor with lipid/polymer membranes can objectively evaluate taste. As previously reported, caffeine can be detected electrically using lipid/polymer membranes modified with hydroxybenzoic acids (HBAs). However, a systematic understanding of how HBAs contribute to caffeine detection is still lacking. In this study, we used various HBAs such as 2,6–dihydroxybenzoic acid (2,6–DHBA) to modify lipid/polymer membranes, and we detected caffeine using a taste sensor with the modified membranes. The effect of the concentrations of the HBAs on caffeine detection was also discussed. The results of the caffeine detection indicated that the response to caffeine and the reference potential measured in a reference solution were affected by the log P and pKa of HBAs. Furthermore, the taste sensor displayed high sensitivity to caffeine when the reference potential was adjusted to an appropriate range by modification with 2,6–DHBA, where the slope of the change in reference potential with increasing 2,6–DHBA concentration was steep. This is helpful in order to improve the sensitivity of taste sensors to other taste substances, such as theophylline and theobromine, in the future.
Method for Data Quality Assessment of Synthetic Industrial Data
Authors: Iantovics László Barna , Enăchescu Călin
Published: February 2022

Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.
Joint Communications and Sensing Employing Multi- or Single-Carrier OFDM Communication Signals: A Tutorial on Sensing Methods, Recent Progress and a Novel Design
Authors: Wu Kai , Zhang Jian Andrew , Huang Xiaojing , Guo Yingjie Jay
Published: February 2022

Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.
A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions
Authors: Zhang Ruixin , Gu Yu
Published: February 2022

Accurate and fast rolling bearing fault diagnosis is required for the normal operation of rotating machinery and equipment. Although deep learning methods have achieved excellent results for rolling bearing fault diagnosis, the performance of most methods declines sharply when the working conditions change. To address this issue, we propose a one-dimensional lightweight deep subdomain adaptation network (1D-LDSAN) for faster and more accurate rolling bearing fault diagnosis. The framework uses a one-dimensional lightweight convolutional neural network backbone for the rapid extraction of advanced features from raw vibration signals. The local maximum mean discrepancy (LMMD) is employed to match the probability distribution between the source domain and the target domain data, and a fully connected neural network is used to identify the fault classes. Bearing data from the Case Western Reserve University (CWRU) datasets were used to validate the performance of the proposed framework under different working conditions. The experimental results show that the classification accuracy for 12 tasks was higher for the 1D-LDSAN than for mainstream transfer learning methods. Moreover, the proposed framework provides satisfactory results when a small proportion of the unlabeled target domain data is used for training.