bluuebunny/crossref_metadata_2025 huggingface dataset:
Suppression of Oxygen Vacancy Defects in sALD-ZnO Films Annealed in Different Conditions
Authors: Zhao Ming-Jie , Sun Zhi-Tao , Zhang Zhi-Xuan , Geng Xin-Peng , Wu Wan-Yu , Lien Shui-Yang , Zhu Wen-Zhang
Published: September 2020

Zinc oxide (ZnO) has drawn much attention due to its excellent optical and electrical properties. In this study, ZnO film was prepared by a high-deposition-rate spatial atomic layer deposition (ALD) and subjected to a post-annealing process to suppress the intrinsic defects and improve the crystallinity and film properties. The results show that the film thickness increases with annealing temperature owing to the increment of oxide layer caused by the suppression of oxygen vacancy defects as indicated by the X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) spectra. The film transmittance is seldom influenced by annealing. The refractive index increases with annealing temperature at 300–700 °C, possibly due to higher density and crystallinity of the film. The band gap decreases after annealing, which should be ascribed to the decrease in carrier concentration according to Burstein–Moss model. The carrier concentration decreases with increasing annealing temperature at 300–700 °C since the oxygen vacancy defects are suppressed, then it increases at 800 °C possibly due to the out-diffusion of oxygen atoms from the film. Meanwhile, the carrier mobility increases with temperature due to higher crystallinity and larger crystallite size. The film resistivity increases at 300–700 °C then decreases at 800 °C, which should be ascribed primarily to the variation of carrier concentration.
Formation of a Bacteriostatic Surface on ZrNb Alloy via Anodization in a Solution Containing Cu Nanoparticles
Authors: Korniienko Viktoriia , Oleshko Oleksandr , Husak Yevheniia , Deineka Volodymyr , Holubnycha Viktoriia , Mishchenko Oleg , Kazek-Kęsik Alicja , Jakóbik-Kolon Agata , Pshenychnyi Roman , Leśniak-Ziółkowska Katarzyna , Kalinkevich Oksana , Kalinkevich Aleksei , Pisarek Marcin , Simka Wojciech , Pogorielov Maksym
Published: September 2020

High strength, excellent corrosion resistance, high biocompatibility, osseointegration ability, and low bacteria adhesion are critical properties of metal implants. Additionally, the implant surface plays a critical role as the cell and bacteria host, and the development of a simultaneously antibacterial and biocompatible implant is still a crucial challenge. Copper nanoparticles (CuNPs) could be a promising alternative to silver in antibacterial surface engineering due to low cell toxicity. In our study, we assessed the biocompatibility and antibacterial properties of a PEO (plasma electrolytic oxidation) coating incorporated with CuNPs (Cu nanoparticles). The structural and chemical parameters of the CuNP and PEO coating were studied with TEM/SEM (Transmission Electron Microscopy/Scanning Electron Microscopy), EDX (Energy-Dispersive X-ray Dpectroscopy), and XRD (X-ray Diffraction) methods. Cell toxicity and bacteria adhesion tests were used to prove the surface safety and antibacterial properties. We can conclude that PEO on a ZrNb alloy in Ca–P solution with CuNPs formed a stable ceramic layer incorporated with Cu nanoparticles. The new surface provided better osteoblast adhesion in all time-points compared with the nontreated metal and showed medium grade antibacterial activities. PEO at 450 V provided better antibacterial properties that are recommended for further investigation.
Laser Powder-Bed Fusion as an Alloy Development Tool: Parameter Selection for In-Situ Alloying Using Elemental Powders
Authors: Shoji Aota Leonardo , Bajaj Priyanshu , Zschommler Sandim Hugo Ricardo , Aimé Jägle Eric
Published: September 2020

The design of advanced alloys specifically tailored to additive manufacturing processes is a research field that is attracting ever-increasing attention. Laser powder-bed fusion (LPBF) commonly uses pre-alloyed, fine powders (diameter usually 15–45 µm) to produce fully dense metallic parts. The availability of such fine, pre-alloyed powders reduces the iteration speed of alloy development for LPBF and renders it quite costly. Here, we overcome these drawbacks by performing in-situ alloying in LPBF starting with pure elemental powder mixtures avoiding the use of costly pre-alloyed powders. Pure iron, chromium, and nickel powder mixtures were used to perform in-situ alloying to manufacture 304 L stainless steel cube-shaped samples. Process parameters including scanning speed, laser power, beam diameter, and layer thickness were varied aiming at obtaining a chemically homogeneous alloy. The scientific questions focused on in this work are: which process parameters are required for producing such samples (in part already known in the state of the art), and why are these parameters conducive to homogeneity? Analytical modelling of the melt pool geometry and temperature field suggests that the residence time in the liquid state is the most important parameter controlling the chemical homogeneity of the parts. Results show that in-situ alloying can be successfully employed to enable faster and cost-efficient rapid alloy development.
Natural Products Repertoire of the Red Sea
Authors: El-Hossary Ebaa M. , Abdel-Halim Mohammad , Ibrahim Eslam S. , Pimentel-Elardo Sheila Marie , Nodwell Justin R. , Handoussa Heba , Abdelwahab Miada F. , Holzgrabe Ulrike , Abdelmohsen Usama Ramadan
Published: September 2020

Marine natural products have achieved great success as an important source of new lead compounds for drug discovery. The Red Sea provides enormous diversity on the biological scale in all domains of life including micro- and macro-organisms. In this review, which covers the literature to the end of 2019, we summarize the diversity of bioactive secondary metabolites derived from Red Sea micro- and macro-organisms, and discuss their biological potential whenever applicable. Moreover, the diversity of the Red Sea organisms is highlighted as well as their genomic potential. This review is a comprehensive study that compares the natural products recovered from the Red Sea in terms of ecological role and pharmacological activities.
The Comparative Immunotropic Activity of Carrageenan, Chitosan and Their Complexes
Authors: Davydova Viktoriya N. , Sorokina Irina V. , Volod’ko Aleksandra V. , Sokolova Ekaterina V. , Borisova Marina S. , Yermak Irina M.
Published: September 2020

The immunotropic activity of polyelectrolyte complexes (PEC) of κ-carrageenan (κ-CGN) and chitosan (CH) of various compositions was assessed in comparison with the initial polysaccharides in comparable doses. For this, two soluble forms of PEC, with an excess of CH (CH:CGN mass ratios of 10:1) and with an excess of CGN (CH: CGN mass ratios of 1:10) were prepared. The ability of PEC to scavenge NO depended on the content of the κ-CGN in the PEC. The ability of the PEC to induce the synthesis of pro-inflammatory (tumor necrosis factor-α (TNF-α)) and anti-inflammatory (interleukine-10 (IL-10)) cytokines in peripheral blood mononuclear cell was determined by the activity of the initial κ-CGN, regardless of their composition. The anti-inflammatory activity of PEC and the initial compounds was studied using test of histamine-, concanavalin A-, and sheep erythrocyte immunization-induced inflammation in mice. The highest activity of PEC, as well as the initial polysaccharides κ-CGN and CH, was observed in a histamine-induced exudative inflammation, directly related to the activation of phagocytic cells, i.e., macrophages and neutrophils.
Removal of Bacteria and Organic Carbon by an Integrated Ultrafiltration—Nanofiltration Desalination Pilot Plant
Authors: Rehman Zahid Ur , Khojah Bayan , Leiknes TorOve , Alsogair Safiya , Alsomali Mona
Published: September 2020

Fouling caused by organic matter and bacteria remains a significant challenge for the membrane-based desalination industry. Fouling decreases the permeate quality and membrane performance and also increases energy demands. Here, we quantified the amount of organic matter and bacteria at several stages along the water-treatment train of an integrated ultrafiltration–nanofiltration seawater treatment pilot plant. We quantified the organic matter, in terms of Total Organic Carbon (TOC) and Assimilable Organic Carbon (AOC), and evaluated its composition using Liquid Chromatography for Organic Carbon Detection (LC-OCD). The bacterial cells were counted using Bactiquant. We found that ultrafiltration (UF) was effective at removing bacterial cells (99.7%) but not TOC. By contrast, nanofiltration (NF) successfully removed both TOC (95%) and bacterial cells. However, the NF permeate showed higher amounts of AOC than seawater. LC-OCD analysis suggested that the AOC was mostly composed of low molecular weight neutral substances. Furthermore, we found that the cleaning of the UF membrane using chemically enhanced backwash reduced the amount of AOC released into the UF permeate. By implementing the cleaning-in-place of the NF membrane, the pressure drop was restored to the normal level. Our results show that the UF and NF membrane cleaning regimes investigated in this study improved membrane performance. However, AOC remained the hardest-to-treat fraction of organic carbon. AOC should, therefore, be monitored closely and regularly to mitigate biofouling in downstream processes.
Improving the Flocculation Performance of Clay-Based Tailings in Seawater: A Population Balance Modelling Approach
Authors: Quezada Gonzalo R. , Jeldres Matías , Robles Pedro , Toro Norman , Torres David , Jeldres Ricardo I.
Published: September 2020

A population balance model described the flocculation of clay-based mining tailings in treated seawater with reduced magnesium content. For the treatment, 0.06 M of lime was added to the liquor, generating solid magnesium complexes that were subsequently removed by vacuum filtration. Magnesium content varied between 10–1440 ppm when mixing raw seawater with treated seawater. The aggregate size was analysed by the Focused Beam Reflectance Measurement (FBRM) technology. The model follows the dynamics of the aggregation-rupture and it provides a good approximation to the temporal evolution. A decrease in collision efficiency was implemented as an indicator of the polymer depletion, describing the size reduction. Lower magnesium content makes larger aggregates with a higher fractal dimension, but an increase in the concentration of clays reduces both the size of aggregates and the fractal dimension, indicating more open and porous structures, with higher permeability to the passage of fluid. The model efficiently illustrates the experimental data, with R-square (R2) greater than 0.9 and Goodness of Fit (GoF) greater than 95% in most cases, wherein the fitting parameters allowed for analysing the impact of magnesium and clays on the collision efficiency, collision frequency, and fragmentation rate. The model is predictive with few parameters, and it is potentially a powerful tool for water management optimisation.
Vitamin B6, Inflammation, and Cardiovascular Outcome in a Population-Based Cohort: The Prevention of Renal and Vascular End-Stage Disease (PREVEND) Study
Authors: Minović Isidor , Kieneker Lyanne M. , Gansevoort Ron T. , Eggersdorfer Manfred , Touw Daan J. , Voerman Albert-Jan , Connelly Margery A. , Boer Rudolf A. de , Hak Eelko , Bos Jens , Dullaart Robin P. F. , Kema Ido P. , Bakker Stephan J. L.
Published: September 2020

Background: a large number of studies have linked vitamin B6 to inflammation and cardiovascular disease in the general population. However, it remains uncertain whether vitamin B6 is associated with cardiovascular outcome independent of inflammation. Methods: we measured plasma pyridoxal 5’-phosphate (PLP), as an indicator of vitamin B6 status, at baseline in a population-based prospective cohort of 6249 participants of the Prevention of Renal and Vascular End-stage Disease (PREVEND) study who were free of cardiovascular disease. As indicators of low-grade systemic inflammation, we measured high-sensitivity C-reactive protein and GlycA; Results: median plasma PLP was 37.2 (interquartile range, 25.1–57.0) nmol/L. During median follow-up for 8.3 (interquartile range, 7.8–8.9) years, 409 non-fatal and fatal cardiovascular events (composite outcome) occurred. In the overall cohort, log transformed plasma PLP was associated with the composite outcome, independent of adjustment for age, sex, smoking, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), total cholesterol:high-density lipoprotein (HDL)-cholesterol ratio, and blood pressure (adjusted hazard ratio per increment of log plasma PLP, 0.66; 95% confidence interval (CI), 0.47–0.93). However, adjustment for high-sensitivity C-reactive protein and GlycA increased the hazard ratio by 9% and 12% respectively, to non-significant hazard ratios of 0.72 (95% confidence interval, 0.51–1.01) and 0.74 (95% confidence interval, 0.53–1.05). The association of plasma PLP with cardiovascular risk was modified by gender (adjusted Pinteraction = 0.04). When stratified according to gender, in women the prospective association with cardiovascular outcome was independent of age, smoking, alcohol consumption, high-sensitivity C-reactive protein, and GlycA (adjusted hazard ratio, 0.50, 95% confidence interval, 0.27–0.94), while it was not in men (adjusted hazard, 0.99, 95% confidence interval, 0.65–1.51). Conclusions: in this population-based cohort, plasma PLP was associated with cardiovascular outcome, but this association was confounded by traditional risk factors and parameters of inflammation. Notably, the association of low plasma PLP with high risk of adverse cardiovascular outcome was modified by gender, with a stronger and independent association in women.
pH-Responsive Polyketone/5,10,15,20-Tetrakis-(Sulfonatophenyl)Porphyrin Supramolecular Submicron Colloidal Structures
Authors: Araya-Hermosilla Esteban , Moreno-Villoslada Ignacio , Araya-Hermosilla Rodrigo , Flores Mario E. , Raffa Patrizio , Biver Tarita , Pucci Andrea , Picchioni Francesco , Mattoli Virgilio
Published: September 2020

In this work, we prepared color-changing colloids by using the electrostatic self-assembly approach. The supramolecular structures are composed of a pH-responsive polymeric surfactant and the water-soluble porphyrin 5,10,15,20-tetrakis-(sulfonatophenyl)porphyrin (TPPS). The pH-responsive surfactant polymer was achieved by the chemical modification of an alternating aliphatic polyketone (PK) via the Paal–Knorr reaction with N-(2-hydroxyethyl)ethylenediamine (HEDA). The resulting polymer/dye supramolecular systems form colloids at the submicron level displaying negative zeta potential at neutral and basic pH, and, at acidic pH, flocculation is observed. Remarkably, the colloids showed a gradual color change from green to pinky-red due to the protonation/deprotonation process of TPPS from pH 2 to pH 12, revealing different aggregation behavior.
Design and Parameter Research of Time-Harmonic Magnetic Field Sensor Based on PDMS in Microfluidic Technology
Authors: Bai Chenzhao , Zhang Hongpeng , Wang Chengjie , Ilerioluwa Joseph Lebile , Wang Qiang , Xie Yucai , Li Guobin
Published: September 2020

In order to improve the throughput and sensitivity of the inductive metal micro-abrasive particle detection sensor, this paper uses microfluidic detection technology to design a high-throughput abrasive particle detection sensor based on PDMS (Polydimethylsiloxane). Theoretical modeling analyzes the magnetization of metal abrasive particles in the coil’s time-harmonic magnetic field, and uses COMSOL simulation to calculate the best performance parameters of the sensor. Through the experiment of the control variable method, the corresponding signal value is obtained and the signal-to-noise ratio (SNR) is calculated. The SNR value and error value are calculated, and the SNR is corrected. The detection limit of the sensor is determined to be 10 μm iron particles and 60 μm copper particles. The optimal design parameters of the 3-D solenoid coil and the frequency characteristics of the sensor are obtained. Finally, through high-throughput experiments and analysis, it was found that there was a reasonable error between the actual throughput and the theoretical throughput. The design ideas suggested in this article can not only improve the sample throughput, but also ensure the detection accuracy. This provides a new idea for the development of an inductive on-line detection method of abrasive particle technology.
“I Love It When You Play that Holy Ghost Chord”: Sounding Sacramentality in the Black Gospel Tradition
Authors: Shelley Braxton D.
Published: September 2020

This essay argues that the distinctive aesthetic practices of many African American Christian congregations, indexed by the phrase “the Black gospel tradition”, are shaped by a sacramentality of sound. I contend that the role music routinely plays in the experience of the holy uncovers sanctity in the sound itself, enabling it to function as a medium of interworldly exchange. As divine power takes an audible form, the faith that “comes by hearing” is confirmed by religious feeling—both individual and collective. This sacramentality of sound is buttressed by beliefs about the enduring efficacy of divine speech, convictions that motivate the intensive character of gospel’s songs, sermons, and shouts. The essay begins with a worship service from Chicago, Illinois’ Greater Harvest Missionary Baptist Church, an occasion in which the musical accompaniment for holy dancing brought sound’s sacramental function into particularly clear relief. In the essay’s second section, I turn to the live recording of Richard Smallwood’s “Hebrews 11”, a recording that accents the creative power of both divine speech and faithful utterances, showing how reverence for “the word of God” inspires the veneration of musical sound. In the article’s final move, I show how both of the aforementioned performances articulate a sacramental theology of sound—the conviction that sound’s invisible force brings spiritual power to bear on the material world.
Interdisciplinary Teaching Using Satellite Images as a Way to Introduce Remote Sensing in Secondary School
Authors: Dziob Daniel , Krupiński Michał , Woźniak Edyta , Gabryszewski Ryszard
Published: September 2020

This article aims to meet two needs: (i) the need for skilled workers and students in the area of remote sensing and (ii) the need to make school science interesting for students. This article addresses both needs by proposing a project for high school students entitled “the Colors of Earth”. The main aim for students was to distinguish between different types of land cover via the creation of various false color band compositions from the satellite Sentinel-2. Achieving this goal requires knowledge from various subjects and enables their practical application via work performed using real data. The project was presented to 39 high-school teachers and 184 high-school students (K-9 and K-10) in the summer semester of the 2019/2020 school year, and their opinions about the project were collected. Overall, both students and teachers judged the project to be interesting, worth introducing to the school, and capable of influencing student opinions of science. In addition, introducing remote sensing elements during pre-university education can help meet the demands for students and workers to study Earth observation.
Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products
Authors: Fu Yuyun , Li Rui , Wang Xuewen , Bergeron Yves , Valeria Osvaldo , Chavardès Raphaël D. , Wang Yipu , Hu Jiheng
Published: September 2020

Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.
Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification
Authors: Łysiak Adam , Froń Anna , Bączkowicz Dawid , Szmajda Mirosław
Published: September 2020

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.
Sit-To-Stand Movement Evaluated Using an Inertial Measurement Unit Embedded in Smart Glasses—A Validation Study
Authors: Hellec Justine , Chorin Frédéric , Castagnetti Andrea , Colson Serge S.
Published: September 2020

Wearable sensors have recently been used to evaluate biomechanical parameters of everyday movements, but few have been located at the head level. This study investigated the relative and absolute reliability (intra- and inter-session) and concurrent validity of an inertial measurement unit (IMU) embedded in smart eyeglasses during sit-to-stand (STS) movements for the measurement of maximal acceleration of the head. Reliability and concurrent validity were investigated in nineteen young and healthy participants by comparing the acceleration values of the glasses’ IMU to an optoelectronic system. Sit-to-stand movements were performed in laboratory conditions using standardized tests. Participants wore the smart glasses and completed two testing sessions with STS movements performed at two speeds (slow and comfortable) under two different conditions (with and without a cervical collar). Both the vertical and anteroposterior acceleration values were collected and analyzed. The use of the cervical collar did not significantly influence the results obtained. The relative reliability intra- and inter-session was good to excellent (i.e., intraclass correlation coefficients were between 0.78 and 0.91) and excellent absolute reliability (i.e., standard error of the measurement lower than 10% of the average test or retest value) was observed for the glasses, especially for the vertical axis. Whatever the testing sessions in all conditions, significant correlations (p < 0.001) were found for the acceleration values recorded either in the vertical axis and in the anteroposterior axis between the glasses and the optoelectronic system. Concurrent validity between the glasses and the optoelectronic system was observed. Our observations indicate that the IMU embedded in smart glasses is accurate to measure vertical acceleration during STS movements. Further studies should investigate the use of these smart glasses to assess the STS movement in unstandardized settings (i.e., clinical and/or home) and to report vertical acceleration values in an elderly population of fallers and non-fallers.
Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery
Authors: Yang Baohua , Gao Yuan , Yan Qian , Qi Lin , Zhu Yue , Wang Bing
Published: September 2020

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.
Real-Time Sensor Networks and Systems for the Industrial IoT: What Next?
Authors: Koulamas Christos , Lazarescu Mihai T.
Published: September 2020

The Industrial Internet of Things (Industrial IoT—IIoT) is the emerging core backbone construct for the various cyber-physical systems constituting one of the principal dimensions of the 4th Industrial Revolution [...]
Experimental Investigation on Choosing a Proper Sensor System for Guided Waves to Check the Integrity of Seven-Wire Steel Strands
Authors: Hou Edison Z.Y. , Rostami Javad , Ng Kim Ming , Tse Peter W.
Published: September 2020

Multiple wire twisted steel strands are commonly used to hoist elevators, concrete structures, etc. Due to frequent and long-time usage, the steel strands are subjected to corrosion, overloads, and aging, making strands may fail unexpectedly. Hence, the health monitoring of steel strands becomes more important to avoid the sudden collapse of hoisting structures. Guided waves (GW) inspection methods have become favorable in recent years due to its long-distance transmission and stability of evaluation in the area of structural health monitoring (SHM) and Non-Destructive Testing (NDT). Many researchers have reported different GW methods to detect different types of defects that occurred in steel strands. However, researchers rarely carry out comparative studies to investigate the effectiveness of each method or system in monitoring the health state of steel strands. This article reports some vital observations revealed from conducting experiments by using contact and noncontact methods, which include three different popular types of GW sensors and methods during their applications in surface-type defect detection. The proper selection of sensors systems has been identified through the present study. The result of the present study is believed to be useful guidance for selecting appropriate GW methods and sensor systems to monitor the integrity of the steel strand and thereby ensure the safety of the hoisted structures.
Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm
Authors: Luo Rubin , Zheng Hongxing , Guo Jifeng
Published: September 2020

The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.
Steel Corrosion Evaluation of Basalt Fiber RPC Affected by Crack and Steel-Concrete Interface Damage Using Electrochemical Methods
Authors: Liu Hanbing , Lyu Xiang , Zhang Yuwei , Luo Guobao , Li Wenjun
Published: September 2020

Basalt fiber (BF) is a new anti-corrosion and environmentally friendly material, which is expected to delay the corrosion process of steel bars and improve the durability of reinforced reactive powder concrete (RPC). The electrochemical method is a nondestructive testing and real-time monitoring technique used to characterize the corrosion behaviors of steel bars embedded in concrete structures. In this paper, the electrochemical technique was employed to evaluate the corrosion of steel bars embedded in basalt fiber modified reactive powder concrete (BFRPC). Besides, crack and steel-concrete interface damage (SCID) were considered as typical factors that affect steel corrosion in concrete. Thus, both reinforced fiber-free RPC and BFRPC specimens with crack and SCID were prepared for evaluating the steel corrosion behaviors by electrochemical methods. The results revealed that both crack and SCID would aggravate the steel corrosion, and the crack was the major factor that affects the corrosion process. Moreover, the excellent compactness of BFRPC and the bridging action of BF could effectively prevent the concrete cracking and steel corrosion process of concrete. Using reinforced BFRPC instead of ordinary concrete in practical projects could greatly extend the service life of steel bars.
Macro-, Micro- and Nano-Roughness of Carbon-Based Interface with the Living Cells: Towards a Versatile Bio-Sensing Platform
Authors: Golubewa Lena , Rehman Hamza , Kulahava Tatsiana , Karpicz Renata , Baah Marian , Kaplas Tommy , Shah Ali , Malykhin Sergei , Obraztsov Alexander , Rutkauskas Danielis , Jankunec Marija , Matulaitienė Ieva , Selskis Algirdas , Denisov Andrei , Svirko Yuri , Kuzhir Polina
Published: September 2020

Integration of living cells with nonbiological surfaces (substrates) of sensors, scaffolds, and implants implies severe restrictions on the interface quality and properties, which broadly cover all elements of the interaction between the living and artificial systems (materials, surface modifications, drug-eluting coatings, etc.). Substrate materials must support cellular viability, preserve sterility, and at the same time allow real-time analysis and control of cellular activity. We have compared new substrates based on graphene and pyrolytic carbon (PyC) for the cultivation of living cells. These are PyC films of nanometer thickness deposited on SiO2 and black silicon and graphene nanowall films composed of graphene flakes oriented perpendicular to the Si substrate. The structure, morphology, and interface properties of these substrates are analyzed in terms of their biocompatibility. The PyC demonstrates interface biocompatibility, promising for controlling cell proliferation and directional intercellular contact formation while as-grown graphene walls possess high hydrophobicity and poor biocompatibility. By performing experiments with C6 glioma cells we discovered that PyC is a cell-friendly coating that can be used without poly-l-lysine or other biopolymers for controlling cell adhesion. Thus, the opportunity to easily control the physical/chemical properties and nanotopography makes the PyC films a perfect candidate for the development of biosensors and 3D bioscaffolds.
Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting
Authors: Chen Jingdao , Yi John Seon Keun , Kahoush Mark , Cho Erin S. , Cho Yong K.
Published: September 2020

Collecting 3D point cloud data of buildings is important for many applications such as urban mapping, renovation, preservation, and energy simulation. However, laser-scanned point clouds are often difficult to analyze, visualize, and interpret due to incompletely scanned building facades caused by numerous sources of defects such as noise, occlusions, and moving objects. Several point cloud scene completion algorithms have been proposed in the literature, but they have been mostly applied to individual objects or small-scale indoor environments and not on large-scale scans of building facades. This paper introduces a method of performing point cloud scene completion of building facades using orthographic projection and generative adversarial inpainting methods. The point cloud is first converted into the 2D structured representation of depth and color images using an orthographic projection approach. Then, a data-driven 2D inpainting approach is used to predict the complete version of the scene, given the incomplete scene in the image domain. The 2D inpainting process is fully automated and uses a customized generative-adversarial network based on Pix2Pix that is trainable end-to-end. The inpainted 2D image is finally converted back into a 3D point cloud using depth remapping. The proposed method is compared against several baseline methods, including geometric methods such as Poisson reconstruction and hole-filling, as well as learning-based methods such as the point completion network (PCN) and TopNet. Performance evaluation is carried out based on the task of reconstructing real-world building facades from partial laser-scanned point clouds. Experimental results using the performance metrics of voxel precision, voxel recall, position error, and color error showed that the proposed method has the best performance overall.
Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data
Authors: Ullah Shan , Kim Deok-Hwan
Published: September 2020

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.
Integration and Testing of a Three-Axis Accelerometer in a Woven E-Textile Sleeve for Wearable Movement Monitoring
Authors: Li Menglong , Torah Russel , Nunes-Matos Helga , Wei Yang , Beeby Steve , Tudor John , Yang Kai
Published: September 2020

This paper presents a method to integrate and package an accelerometer within a textile to create an electronic textile (e-textile). The smallest commercially available accelerometer sensor (2 mm × 2 mm × 0.95 mm) is used in the e-textile and is fully integrated within the weave structure of the fabric itself, rendering it invisible to the wearer. The e-textile forms the basis of a wearable woven sleeve which is applied to arm and knee joint bending angle measurement. The integrated e-textile based accelerometer sensor system is used to identify activity type, such as walking or running, and count the total number of steps taken. Performance was verified by comparing measurements of specific elbow joint angles over the range of 0° to 180° with those obtained from a commercial bending sensor from Bend Labs and from a custom-built goniometer. The joint bending angles, measured by all three sensors, show good agreement with an error of less than ~1% of reading which provides a high degree of confidence in the e-textile sensor system. Subsequently, knee joint angles were measured experimentally on three subjects with each being tested three times on each of three activities (walking, running and climbing stairs). This allowed the minimum and maximum knee joint angles for each activity to be determined. This data is then used to identify activity type and perform step counting.
Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System
Authors: Lee Yung-Lung
Published: September 2020

For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters
Optimization and Communication in UAV Networks
Authors: Caillouet Christelle , Mitton Nathalie
Published: September 2020

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Current trends focus on deployment of a single UAV or a swarm of it to generally map an area, perform surveillance, monitoring or rescue operations, collect data from ground sensors or various communicating devices, provide additional computing services close to data producers, etc. Applications are very diverse and call for different features or requirements. But UAV remain low-power battery powered devices that in addition to their mission, must fly and communicate. Thanks to wireless communications, they participate to mobile dynamic networks composed of UAV and ground sensors and thus many challenges have to be addressed to make UAV very efficient. And behind any UAV application, hides an optimization problem. There is still a criterion or multiple ones to optimize such as flying time, energy consumption, number of UAV, quantity of data to send/receive, etc.
Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
Authors: ElMoaqet Hisham , Eid Mohammad , Glos Martin , Ryalat Mutaz , Penzel Thomas
Published: September 2020

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.
A Recursive Algorithm for Indoor Positioning Using Pulse-Echo Ultrasonic Signals
Authors: Pullano Salvatore A. , Bianco Maria Giovanna , Critello Davide C. , Menniti Michele , La Gatta Antonio , Fiorillo Antonino S.
Published: September 2020

Low frequency ultrasounds in air are widely used for real-time applications in short-range communication systems and environmental monitoring, in both structured and unstructured environments. One of the parameters widely evaluated in pulse-echo ultrasonic measurements is the time of flight (TOF), which can be evaluated with an increased accuracy and complexity by using different techniques. Hereafter, a nonstandard cross-correlation method is investigated for TOF estimations. The procedure, based on the use of template signals, was implemented to improve the accuracy of recursive TOF evaluations. Tests have been carried out through a couple of 60 kHz custom-designed polyvinylidene fluoride (PVDF) hemicylindrical ultrasonic transducers. The experimental results were then compared with the standard threshold and cross-correlation techniques for method validation and characterization. An average improvement of 30% and 19%, in terms of standard error (SE), was observed. Moreover, the experimental results evidenced an enhancement in repeatability of about 10% in the use of a recursive positioning system.
Exploring the Efficiency of UHPLC-Orbitrap MS for the Determination of 20 Pharmaceuticals and Acesulfame K in Hospital and Urban Wastewaters with the Aid of FPSE
Authors: Kalaboka Maria , Chrimatopoulos Christoforos , Jiménez-Holgado Cristina , Boti Vasiliki , Sakkas Vasilios , Albanis Triantafyllos
Published: September 2020

Aside from the classical residues of persistent organic pollutants (POPs), the occurrence of emerging contaminants (ECs) in the environment has become a subject of increasing concern due to their harmful impact on the aquatic environment. Wastewater treatment plant (WWTP) effluents are major sources of environmental pollution. Therefore, data concerning their existence is required. In this study, twenty compounds representative of different drug groups considered ECs and belonging to antibiotics, antipsychotics, anti-inflammatory drugs plus acesulfame K were selected to be accurately detected and quantified with UHPLC–LTQ-Orbitrap MS in hospital and urban WWTP effluents. Chromatographic parameters (column efficiency, mobile phase, etc.), as well as mass spectrometry conditions concerning ionization mode and Orbitrap analysis (ESI options, mass resolving power, AGC target, tube lens, injection time), were evaluated. Moreover, a novel fabric phase sorptive extraction (FPSE) method based on fiber glass coated with PEG300 was employed as sample preparation process. Experimental parameters affecting extraction and desorption steps such as sample pH, extraction time, ionic strength, elution time and solvent have been optimized. The optimized methodology was validated providing excellent linearity (R2 > 0.99), and low detection and quantification limits up to 3.1 and 9.3 ng/L, for carbamazepine, respectively. Relative recoveries ranged from 81.1% to 114.0%, while a medium matrix effect for most of the target compounds occurred. Applying the above analytical method in effluents of WWTPs from NW Greece, nine compounds were quantified with concentrations that varied from 55.4 to 728.4 ng/L.
Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
Authors: Shao Quan , Zhou Yan , Zhu Pei , Ma Yan , Shao Mengxue
Published: September 2020

Although the factors influencing bird strikes have been studied extensively, few works focused on the spatial variations in bird strikes affected by factors due to the difference in the geographical environment around the airport. In this paper, the bird strike density distribution of different seasons affected by factors in a rectangular region of 800 square kilometers centered on the Xi’an Airport runway was investigated based on collected bird strike data. The ordinary least square (OLS) model was used to analyze the global effects of different factors, and the Geographically Weighted Regression (GWR) model was used to analyze the spatial variations in the factors of bird strike density. The results showed that key factors on the kernel density of bird strikes showed evident spatial heterogeneity and the seasonal difference in the different habitats. Based on the results of the study, airport managers are provided with some specific defense measures to reduce the number of bird strikes from the two aspects of expelling birds on the airfield area and reducing the attractiveness of habitats outside the airport to birds, so that achieve the sustainable and safe development of civil aviation and the ecological environment.