bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemically Functionalized Cellulose Nanocrystals as Reactive Filler in Bio-Based Polyurethane Foams
Authors: Coccia Francesca , Gryshchuk Liudmyla , Moimare Pierluigi , Bossa Ferdinando de Luca , Santillo Chiara , Barak-Kulbak Einav , Verdolotti Letizia , Boggioni Laura , Lama Giuseppe Cesare
Published: August 2021

Cellulose Nanocrystals, CNC, opportunely functionalized are proposed as reactive fillers in bio-based flexible polyurethane foams to improve, mainly, their mechanical properties. To overcome the cellulose hydrophilicity, CNC was functionalized on its surface by linking covalently a suitable bio-based polyol to obtain a grafted-CNC. The polyols grafted with CNC will react with the isocyanate in the preparation of the polyurethane foams. An attractive way to introduce functionalities on cellulose surfaces in aqueous media is silane chemistry by using functional trialkoxy silanes, X-Si (OR)3. Here, we report the synthesis of CNC-grafted-biopolyol to be used as a successful reactive filler in bio-based polyurethane foams, PUFs. The alkyl silanes were used as efficient coupling agents for the grafting of CNC and bio-polyols. Four strategies to obtain CNC-grafted-polyol were fine-tuned to use CNC as an active filler in PUFs. The effective grafting of the bio polyol on CNC was evaluated by FTIR analysis, and the amount of grafted polyol by thermogravimetric analysis. Finally, the morphological, thermal and mechanical properties and hydrophobicity of filled PUFs were thoughtfully assessed as well as the structure of the foams and, in particular, of the edges and walls of the cell foams by means of the Gibson–Ashby model. Improved thermal stability and mechanical properties of PU foams containing CNC-functionalized-polyol are observed. The morphology of the PU foams is also influenced by the functionalization of the CNC.
Novel Aminosilane (APTES)-Grafted Polyaniline@Graphene Oxide (PANI-GO) Nanocomposite for Electrochemical Sensor
Authors: Alruwais Raja Saad , Adeosun Waheed A. , Marwani Hadi M. , Jawaid Mohammad , Asiri Abdullah M. , Khan Anish
Published: August 2021

Lead is a potentially toxic element (PTE) that has several adverse medical effects in humans. Its presence in the environment became prominent due to anthropogenic activities. The current study explores the use of newly developed composite materials (organic–inorganic hybrid) based on PANI-GO-APTES for electrochemical detection of Pb2+ in aqueous solution. The composite material (PANI-GO-APTES) was synthesized by chemical method and was characterized with SEM, XPS, XEDS, XRD, TGA, FTIR, EIS and CV. The result of characterization indicates the successful synthesis of the intended material. The PANI-GO-APTES was successfully applied for electrochemical detection of Pb2+ using cyclic voltammetry and linear sweep voltammetry method. The limit of detection of Pb2+ was 0.0053 µM in the linear range of 0.01 µM to 0.4 µM. The current response produced during the electrochemical reduction of Pb2+ catalyzed by PANI-GO-APTES was also very repeatable, reproducible and rapid. The application of PANI-GO-APTES-modified GCE in real sample analysis was also established. Therefore, PANI-GO-APTES is presented as a potential Pb2+ sensor for environmental and human health safety.
Role of Chemically Functionalization of Bamboo Fibers on Polyethylene-Based Composite Performance: A Solution for Recycling
Authors: Kouhi Meisam , Butan Simona , Li Yang , Shakour Elias , Banu Mihaela
Published: August 2021

Low-density polyethylene is the most common polymer for manufacturing containers, bottles, tubes, plastic bags, computer components and so on. There is an urgent need to find solutions for its recycling and reintegration in high volume production components such as non-structural auto applications. The reinforcement of recycled low-density polyethylene with natural fibers represents a solution for the re-use of the recycled low-density polyethylene. However, there is a lack of understanding of how the natural fibers influence the behavior of the bare low-density polyethylene, and furthermore, how the interface between the fibers and the matrix can be controlled in composite to obtain the designed toughness, strength, stiffness and damping. In this sense, the study presents an in-depth analysis of the behavior of three coupling agents used in the chemically functionalized bamboo fibers interface for reinforcing low-density polyethylene composites. Through mechanical tests, the mechanical properties are determined and compared and finally, a correlation between the viscous behavior of the resulted composites and the toughening mechanism is proposed. The conclusion of the study enables a flexible design of polymer composite components fabricated of recycled and non-recycled low-density polyethylene and natural fibers.
Graphitic Carbon Nitride as a New Sustainable Photocatalyst for Textile Functionalization
Authors: Vasiljević Jelena , Jerman Ivan , Simončič Barbara
Published: August 2021

As a promising organic semiconducting material, polymeric graphitic carbon nitride (g-C3N4) has attracted much attention due to its excellent optical and photoelectrochemical properties, thermal stability, chemical inertness, nontoxicity, abundance, and low cost. Its advantageous visible light-induced photocatalytic activity has already been beneficially used in the fields of environmental remediation, biological applications, healthcare, energy conversion and storage, and fuel production. Despite the recognized potential of g-C3N4, there is still a knowledge gap in the application of g-C3N4 in the field of textiles, with no published reviews on the g-C3N4-functionalization of textile materials. Therefore, this review article aims to provide a critical overview of recent advances in the surface and bulk modification of textile fibres by g-C3N4 and its composites to tailor photocatalytic self-cleaning, antibacterial, and flame retardant properties as well as to create a textile catalytic platform for water disinfection, the removal of various organic pollutants from water, and selective organic transformations. This paper highlights the possibilities of producing g-C3N4-functionalized textile substrates and suggests some future prospects for this research area.
Effect of Straight, Inclined and Curved Fins on Natural Convection and Entropy Generation of a Nanofluid in a Square Cavity Influenced by a Magnetic Field
Authors: Khetib Yacine , Alahmadi Ahmad Aziz , Alzaed Ali , Azimy Hamidreza , Sharifpur Mohsen , Cheraghian Goshtasp
Published: August 2021

In this paper, the free convective heat transfer of nanofluids in a square cavity is simulated using a numerical method. The angle of the cavity could be changed in the horizontal axis from 0 to 90 degrees. The cavity is exposed under a constant magnetic field. Two opposite walls of the cavity are cold and warm, and the rest of the walls are insulated. On the hot wall, there are two fins with the same wall temperature. The equations were discretized by the finite volume method (FVM) and then solved using the SIMPLE algorithm. Three different fin configurations (straight, inclined and curved) were studied in terms of heat transfer rate and generation of entropy. According to the simulation results, the heat transfer rate was improved by tilting the fins toward the top or bottom of the cavity. At Ra = 105 and Ha = 20, the maximum heat transfer rate was achieved at a cavity inclination of 90° and 45°, respectively, for straight and curved fins. In the horizontal cavity, heat transfer rate could be improved up to 6.4% by tilting the fins and up to 4.9% by warping them. Increasing the Hartmann number from 0 to 40 reduced the Nusselt number and entropy generation by 37.9% and 33.8%, respectively.
Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model
Authors: Bingham Frederick M. , Fournier Severine , Brodnitz Susannah , Ulfsax Karly , Zhang Hong
Published: August 2021

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.
Comparison of Gridded DEMs by Buffering
Authors: Ariza-López Francisco Javier , Reinoso-Gordo Juan Francisco
Published: August 2021

Comparing two digital elevation models (DEMs), S1 (reference) and S2 (product), in order to get the S2 quality, has usually been performed on sampled points. However, it seems more natural, as we propose, comparing both DEMs using 2.5D surfaces: applying a buffer to S1 (single buffer method, SBM) or to both S1 and S2 (double buffer method, DBM). The SBM and DBM approaches have been used in lines accuracy assessment and, in this paper, we generalize them to a DEM surface, so that more area of the S2 surface (in the case of the SBM), or the area and volume (in the case of the DBM) that are involved, more similarly are S1 and S2. The results obtained show that across both methods, SBM recognizes the presence of outliers and vertical bias while DBM allows a richer and more complex analysis based on voxel intersection. Both methods facilitate creating observed distribution functions that eliminate the need for the hypothesis of normality on discrepancies and allow the application of quality control techniques based on proportions. We consider that the SBM is more suitable when the S1 accuracy is much greater than that of S2 and DBM is preferred when the accuracy of S1 and S2 are approximately equal.
Moving Target Shadow Analysis and Detection for ViSAR Imagery
Authors: He Zhihua , Chen Xing , Yi Tianzhu , He Feng , Dong Zhen , Zhang Yue
Published: August 2021

The video synthetic aperture radar (ViSAR) is a new application in radar techniques. ViSAR provides high- or moderate-resolution SAR images with a faster frame rate, which permits the detection of the dynamic changes in the interested area. A moving target with moderate velocity can be detected by shadow detection in ViSAR. This paper analyses the frame rate and the shadow feature, discusses the velocity limitation of ViSAR moving target shadow detection and quantitatively gives the expression of velocity limitation. Furthermore, a fast factorized back projection (FFBP) based SAR video formation method and a shadow-based ground moving target detection method are proposed to generate SAR videos and detect the moving target shadow. The experimental results with simulated data prove the validity and feasibility of the proposed quantitative analysis and the proposed methods.
Lightning Activity Observed by the FengYun-4A Lightning Mapping Imager
Authors: Cao Dongjie , Lu Feng , Zhang Xiaohu , Yang Jing
Published: August 2021

The Lightning Mapping Imager (LMI) onboard the geostationary meteorological satelliteFengYun-4A (FY-4A) detects both intra-cloud (IC) and cloud-to-ground (CG) lightning continuously during daytime and nighttime. This study examined, for the first time, the optical characteristics and distribution of the “Event,” “Group,” and “Flash” observed by the LMI in the whole LMI observation domain. The optical properties and spatial distribution of the LMI lightning were compared with those of the Lightning Imaging Sensor on the International Space Station (ISS-LIS) based on the dataset during 2018–2020. Due to the different spatial resolutions and detection efficiencies of these two lightning imagers, the number of ISS-LIS lightning was more than that of LMI lightning. The ISS-LIS Flash duration was also larger than that of the LMI Flash. The duration, radiance, and footprint of LMI lightning in different regions were analyzed in detail based on the LMI lightning dataset in 2019. The duration and radiance of the Flash were generally less than 50–500 ms and 200 Jm−2ster−1μm−1, respectively. The footprint of Flashes was distributed from 200 to 600 km2. The number of Groups per Flash was mostly less than five. Considering the spatial distribution and temporal variations in the LMI lightning compared with the ground-based Lightning Location Network in China (LLNC), it was found that the LMI Group number was close to the LLNC CG (Cloud-to-Ground) Event number. The maximum Flash density was found in the middle and lower south of the Yangtze River and Pearl River Delta region, respectively, while the lower values were in western China, where the mean radiance per Flash was greater. There was more LMI lightning during the nighttime than that during the daytime, indicating the higher detection efficiency of the LMI in the nighttime than in the daytime.
Progress in Grassland Cover Conservation in Southern European Mountains by 2020: A Transboundary Assessment in the Iberian Peninsula with Satellite Observations (2002–2019)
Authors: Monteiro Antonio T. , Carvalho-Santos Cláudia , Lucas Richard , Rocha Jorge , Costa Nuno , Giamberini Mariasilvia , Costa Eduarda Marques da , Fava Francesco
Published: August 2021

Conservation and policy agendas, such as the European Biodiversity strategy, Aichi biodiversity (target 5) and Common Agriculture Policy (CAP), are overlooking the progress made in mountain grassland cover conservation by 2020, which has significant socio-ecological implications to Europe. However, because the existing data near 2020 is scarce, the shifting character of mountain grasslands remains poorly characterized, and even less is known about the conservation outcomes because of different governance regimes and map uncertainty. Our study used Landsat satellite imagery over a transboundary mountain region in the northwestern Iberian Peninsula (Peneda-Gerês) to shed light on these aspects. Supervised classifications with a multiple classifier ensemble approach (MCE) were performed, with post classification comparison of maps established and bias-corrected to identify the trajectory in grassland cover, including protected and unprotected governance regimes. By analysing class-allocation (Shannon entropy), creating 95% confidence intervals for the area estimates, and evaluating the class-allocation thematic accuracy relationship, we characterized uncertainty in the findings. The bias-corrected estimates suggest that the positive progress claimed internationally by 2020 was not achieved. Our null hypothesis to declare a positive progress (at least equality in the proportion of grassland cover of 2019 and 2002) was rejected (X2 = 1972.1, df = 1, p < 0.001). The majority of grassland cover remained stable (67.1 ± 10.1 relative to 2002), but loss (−32.8 ± 7.1% relative to 2002 grasslands cover) overcame gain areas (+11.4 ± 6.6%), indicating net loss as the prevailing pattern over the transboundary study area (−21.4%). This feature prevailed at all extents of analysis (lowlands, −22.9%; mountains, −17.9%; mountains protected, −14.4%; mountains unprotected, −19.7%). The results also evidenced that mountain protected governance regimes experienced a lower decline in grassland extent compared to unprotected. Shannon entropy values were also significantly lower in correctly classified validation sites (z = −5.69, p = 0.0001, n = 708) suggesting a relationship between the quality of pixel assignment and thematic accuracy. We therefore encourage a post-2020 conservation and policy action to safeguard mountain grasslands by enhancing the role of protected governance regimes. To reduce uncertainty, grassland gain mapping requires additional remote sensing research to find the most adequate spatial and temporal data resolution to retrieve this process.
Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations
Authors: Cheng Yueming , Dai Tie , Goto Daisuke , Murakami Hiroshi , Yoshida Mayumi , Shi Guangyu , Nakajima Teruyuki
Published: August 2021

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.
A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition
Authors: Yu Jimin , Zhou Guangyu , Zhou Shangbo , Yin Jiajun
Published: August 2021

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and noise corruption. To improve the recognition performance, methods based on convolutional neural networks (CNN) have been introduced to solve such problems and have shown outstanding performance. However, most of these methods rely on continuously increasing the width and depth of networks. This adds a large number of parameters and computational overhead, which is not conducive to deployment on edge devices. To solve these problems, a novel lightweight fully convolutional neural network based on Channel-Attention mechanism, Channel-Shuffle mechanism, and Inverted-Residual block, namely the ASIR-Net, is proposed in this paper. Specifically, we deploy Inverted-Residual blocks to extract features in high-dimensional space with fewer parameters and design a Channel-Attention mechanism to distribute different weights to different channels. Then, in order to increase the exchange of information between channels, we introduce the Channel-Shuffle mechanism into the Inverted-Residual block. Finally, to alleviate the matter of the scarcity of SAR images and strengthen the generalization performance of the network, four approaches of data augmentation are proposed. The effect and generalization performance of the proposed ASIR-Net have been proved by a lot of experiments under both SOC and EOCs on the MSTAR dataset. The experimental results indicate that ASIR-Net achieves higher recognition accuracy rates under both SOC and EOCs, which is better than the existing excellent ATR methods.
Coastal Wetland Shoreline Change Monitoring: A Comparison of Shorelines from High-Resolution WorldView Satellite Imagery, Aerial Imagery, and Field Surveys
Authors: Smith Kathryn E. L. , Terrano Joseph F. , Pitchford Jonathan L. , Archer Michael J.
Published: August 2021

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr−1). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices.
Family-Centered Design: Interactive Performance Testing and User Interface Evaluation of the Slovenian eDavki Public Tax Portal
Authors: Trilar Jure , Sobočan Tjaša , Stojmenova Duh Emilija
Published: August 2021

This is the latest article in a series of research on the family-centered design concept. The theoretical context was revisited and expounded to support its usefulness in conjunction with a user-centered design approach within distinct application domains. A very important contribution is applied through the development of the instruments—website capture, a public testing platform, results processing and the Web Content Accessibility Guide 2.1 recommendation tool—to conduct unmoderated remote testing of user interfaces that corresponds to the requirements of general digitalization efforts as well as the response to current and future health risks. With this set of instruments, an experiment was conducted to address the differences in usage, and performance-wise and user-based evaluation methods, of the eDavki public tax portal, among two generations, adults and elderly citizens, and between an original and an adapted user interface that respects accessibility and other recommendations. The differences found are further discussed and are congruent to particularities that have been modified within interfaces.
Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review
Authors: Cano Sandra , González Carina S. , Gil-Iranzo Rosa María , Albiol-Pérez Sergio
Published: August 2021

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitive-affective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.
Wireless Geophone Networks for Land Seismic Data Acquisition: A Survey, Tutorial and Performance Evaluation
Authors: Makama Aliyu , Kuladinithi Koojana , Timm-Giel Andreas
Published: August 2021

Seismic data acquisition in oil and gas exploration employs a large-scale network of geophone sensors deployed in thousands across a survey field. A central control unit acquires and processes measured data from geophones to come up with an image of the earth’s subterranean structure to locate oil and gas traps. Conventional seismic acquisition systems rely on cables to connect each sensor. Although cable-based systems are reliable, the sheer amount of cable required is tremendous, causing complications in survey logistics as well as survey downtime. The need for a cable-free seismic data acquisition system has attracted much attention from contractors, exploration companies, and researchers to lay out the enabling wireless technology and architecture in seismic explorations. This paper gives a general overview of land seismic data acquisition and also presents a current and retrospective review of the state-of-the-art wireless seismic data acquisition systems. Furthermore, a simulation-based performance evaluation of real-time, small-scale wireless geophone subnetwork is carried out using the IEEE 802.11 g technology based on the concept of seismic data acquisition during the geophone listen or recording period. In addition, we investigate an optimal number of seismic samples that could be sent by each geophone during this period.
FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation
Authors: Dong Yuying , Wang Liejun , Cheng Shuli , Li Yongming
Published: August 2021

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.
Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems
Authors: Hassani Hossein , Razavi-Far Roozbeh , Saif Mehrdad , Palade Vasile
Published: August 2021

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.
Investigation on the Printed CNT-Film-Based Electrochemical Sensor for Detection of Liquid Chemicals
Authors: Noh Jaeha , An Sangsu , Lee Changhan , Chang Jiho , Lee Snagtae , Lee Moonjin , Seo Dongmin
Published: August 2021

We studied electrochemical sensors using printed carbon nanotubes (CNT) film on a polyethylene telephtalate (PET) substrate. The mechanical stability of the printed CNT film (PCF) was confirmed by using bending and Scotch tape tests. In order to determine the optimum sensor structure, a resistance-type PCF sensor (R-type PCF sensor) and a comb-type PCF sensor (C-type PCF sensor) were fabricated and compared using a diluted NH3 droplet with various concentrations. The magnitude of response, response time, sensitivity, linearity, and limit of detection (LOD) were compared, and it was concluded that C-type PCF sensor has superior performance. In addition, the feasibility of PCF electrochemical sensor was investigated using 12 kinds of hazardous and noxious substances (HNS). The detection mechanism and selectivity of the PCF sensor are discussed.
A Practice of BLE RSSI Measurement for Indoor Positioning
Authors: Ramirez Ramiro , Huang Chien-Yi , Liao Che-An , Lin Po-Ting , Lin Hsin-Wei , Liang Shu-Hao
Published: August 2021

Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.
High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging
Authors: Seiffert Alexander P. , Gómez-Grande Adolfo , Villarejo-Galende Alberto , González-Sánchez Marta , Bueno Héctor , Gómez Enrique J. , Sánchez-González Patricia
Published: August 2021

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson’s correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.
An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios
Authors: Motroni Andrea , Buffi Alice , Nepa Paolo , Pesi Mario , Congi Antonio
Published: August 2021

The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in an indoor scenario. A key component to accomplish this goal is the UHF-RFID Smart Gate, which consists of a checkpoint infrastructure based on RFID technology to identify forklifts and their direction of transit. This paper presents the implementation of a UHF-RFID Smart Gate with a single reader antenna with asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The action classification method exploits the signal phase backscattered by RFID tags placed on the forklifts. The performance and the method capabilities are demonstrated through an on-site demonstrator in a real warehouse.
Ten Years of Lateral Flow Immunoassay Technique Applications: Trends, Challenges and Future Perspectives
Authors: Di Nardo Fabio , Chiarello Matteo , Cavalera Simone , Baggiani Claudio , Anfossi Laura
Published: August 2021

The Lateral Flow Immunoassay (LFIA) is by far one of the most successful analytical platforms to perform the on-site detection of target substances. LFIA can be considered as a sort of lab-in-a-hand and, together with other point-of-need tests, has represented a paradigm shift from sample-to-lab to lab-to-sample aiming to improve decision making and turnaround time. The features of LFIAs made them a very attractive tool in clinical diagnostic where they can improve patient care by enabling more prompt diagnosis and treatment decisions. The rapidity, simplicity, relative cost-effectiveness, and the possibility to be used by nonskilled personnel contributed to the wide acceptance of LFIAs. As a consequence, from the detection of molecules, organisms, and (bio)markers for clinical purposes, the LFIA application has been rapidly extended to other fields, including food and feed safety, veterinary medicine, environmental control, and many others. This review aims to provide readers with a 10-years overview of applications, outlining the trends for the main application fields and the relative compounded annual growth rates. Moreover, future perspectives and challenges are discussed.
Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals
Authors: Martinek Radek , Ladrova Martina , Sidikova Michaela , Jaros Rene , Behbehani Khosrow , Kahankova Radana , Kawala-Sterniuk Aleksandra
Published: August 2021

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network
Authors: Hasegawa Mitsugu , Kurihara Daiki , Egami Yasuhiro , Sakaue Hirotaka , Jemcov Aleksandar
Published: August 2021

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.
Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area
Authors: Li Chang , Yi Bangjin , Gao Peng , Li Hui , Sun Jixing , Chen Xueye , Zhong Cheng
Published: August 2021

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.
Centered Multi-Task Generative Adversarial Network for Small Object Detection
Authors: Wang Hongfeng , Wang Jianzhong , Bai Kemeng , Sun Yong
Published: August 2021

Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.
Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data
Authors: Rojas Fernando , Leiva Víctor , Huerta Mauricio , Martin-Barreiro Carlos
Published: August 2021

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.
Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
Authors: Atutxa Asier , Franco David , Sasiain Jorge , Astorga Jasone , Jacob Eduardo
Published: August 2021

Industrial networks are introducing Internet of Things (IoT) technologies in their manufacturing processes in order to enhance existing methods and obtain smarter, greener and more effective processes. Global predictions forecast a massive widespread of IoT technology in industrial sectors in the near future. However, these innovations face several challenges, such as achieving short response times in case of time-critical applications. Concepts like in-network computing or edge computing can provide adequate communication quality for these industrial environments, and data plane programming has been proved as a useful mechanism for their implementation. Specifically, P4 language is used for the definition of the behavior of programmable switches and network elements. This paper presents a solution for industrial IoT (IIoT) network communications to reduce response times using in-network computing through data plane programming and P4. Our solution processes Message Queuing Telemetry Transport (MQTT) packets sent by a sensor in the data plane and generates an alarm in case of exceeding a threshold in the measured value. The implementation has been tested in an experimental facility, using a Netronome SmartNIC as a P4 programmable network device. Response times are reduced by 74% while processing, and delay introduced by the P4 network processing is insignificant.
Nanozymes—Hitting the Biosensing “Target”
Authors: Wu Yingfen , Darland Diane C. , Zhao Julia Xiaojun
Published: August 2021

Nanozymes are a class of artificial enzymes that have dimensions in the nanometer range and can be composed of simple metal and metal oxide nanoparticles, metal nanoclusters, dots (both quantum and carbon), nanotubes, nanowires, or multiple metal-organic frameworks (MOFs). They exhibit excellent catalytic activities with low cost, high operational robustness, and a stable shelf-life. More importantly, they are amenable to modifications that can change their surface structures and increase the range of their applications. There are three main classes of nanozymes including the peroxidase-like, the oxidase-like, and the antioxidant nanozymes. Each of these classes catalyzes a specific group of reactions. With the development of nanoscience and nanotechnology, the variety of applications for nanozymes in diverse fields has expanded dramatically, with the most popular applications in biosensing. Nanozyme-based novel biosensors have been designed to detect ions, small molecules, nucleic acids, proteins, and cancer cells. The current review focuses on the catalytic mechanism of nanozymes, their application in biosensing, and the identification of future directions for the field.
Time-Resolved FDTD and Experimental FTIR Study of Gold Micropatch Arrays for Wavelength-Selective Mid-Infrared Optical Coupling
Authors: Fu Ying , Yager Tom , Chikvaidze George , Iyer Srinivasan , Wang Qin
Published: August 2021

Infrared radiation reflection and transmission of a single layer of gold micropatch two-dimensional arrays, of patch length ∼1.0 μm and width ∼0.2 μm, have been carefully studied by a finite-difference time-domain (FDTD) method, and Fourier-transform infrared spectroscopy (FTIR). Through precision design of the micropatch array structure geometry, we achieve a significantly enhanced reflectance (85%), a substantial diffraction (10%), and a much reduced transmittance (5%) for an array of only 15% surface metal coverage. This results in an efficient far-field optical coupling with promising practical implications for efficient mid-infrared photodetectors. Most importantly we find that the propagating electromagnetic fields are transiently concentrated around the gold micropatch array in a time duration of tens of ns, providing us with a novel efficient near-field optical coupling.