bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemicals and Enzyme Inhibitory Capacities of the Methanolic Extracts from the Italian Apple Cultivar Mela Rosa dei Monti Sibillini
Authors: López Víctor , Les Francisco , Mevi Serena , Nkuimi Wandjou Joice Guileine , Cásedas Guillermo , Caprioli Giovanni , Maggi Filippo
Published: June 2020

The phytochemical profile of the methanolic extracts (pulp and peel) obtained from two dehydration methods (drying and freeze-lyophilization) of the traditional Italian apple Mela Rosa dei Monti Sibillini, as well as their inhibitory properties against some biological enzymes (α-glucosidase, lipase, monoamine oxidase A, tyrosinase and acetylcholinesterase) were assessed in this study. HPLC-DAD-MS technique was used for the determination of polyphenolic and triterpenic compounds. The determination of the enzymes inhibitory effect was made through spectrophotometric techniques. The peel extracts were richer in bioactive compounds than the pulp. In this regard, the extracts from freeze-lyophilization displayed higher levels of flavan-3-ols, flavonol glycosides and dihydrochalcones. However, the extracts obtained from dried material displayed a stronger enzyme inhibition. Notably, the peel extracts showed a higher activity than the pulp ones, especially in terms of α-glucosidase whereby some samples exerted a similar enzymatic inhibition than acarbose (100% inhibition) at high concentrations (1 mg/mL). These results encourage thus further studies on this traditional Italian apple as a potential source of nutraceuticals helpful to prevent the insurgence of some pathologies.
Introduction to the Special Issue: Religion, Power, and Resistance: New Ideas for a Divided World
Authors: Halafoff Anna , Han Sam , Starkey Caroline , Spickard James V.
Published: June 2020

The world is currently gripped by pressing environmental, social, and economic challenges [...]
Correction: Smol, M., et al. Importance of Sustainable Mineral Resource Management in Implementing the Circular Economy (CE) Model and the European Green Deal Strategy. Resource 2020, 9, 55
Authors: Smol Marzena , Marcinek Paulina , Duda Joanna , Szołdrowska Dominika
Published: June 2020

In the published article [...]
Multi-Robot Coverage and Persistent Monitoring in Sensing-Constrained Environments
Authors: Alam Tauhidul , Bobadilla Leonardo
Published: June 2020

This article examines the problems of multi-robot coverage and persistent monitoring of regions of interest with limited sensing robots. A group of robots, each equipped with only contact sensors and a clock, execute a simple trajectory by repeatedly moving straight and then bouncing at perimeter boundaries by rotating in place. We introduce an approach by finding a joint trajectory for multiple robots to cover a given environment and generating cycles for the robots to persistently monitor the target regions in the environment. From a given initial configuration, our approach iteratively finds the joint trajectory of all the robots that covers the entire environment. Our approach also computes periodic trajectories of all the robots for monitoring of some regions, where trajectories overlap but do not involve robot-robot collisions. We present experimental results from multiple simulations and physical experiments demonstrating the practical utility of our approach.
Exploring the Consistency of Data Collected in Archaeological Geophysics: A Case Study from the Iron Age Hillfort of Villasviejas del Tamuja (Extremadura, Spain)
Authors: Pro Carmen , Caldeira Bento , de Tena Maria Teresa , Charro Cristina , Oliveira Rui Jorge , Borges José Fernando , Mayoral Victorino
Published: June 2020

Different geophysical methods applied at the settlement of Villasviejas del Tamuja (Botija, Spain) have identified robust anomalies located at the same position, but some anomalies are reflected by only one method. Furthermore, analysing the spatial correlation of these anomalies is of fundamental importance for obtaining a correct archaeological interpretation. In this work, we analysed the main results of electrical resistivity tomography (ERT), ground-penetrating radar (GPR) and magnetic gradiometry methods in a particular area of the archaeological site. In this analysis, we performed graphical and numerical spatial correlation analyses of the anomalies and observed strong agreement among the results provided by each method. Certain anomalies were reflected only in the magnetic and ERT studies. The results highlight the importance of applying several geophysical methods and performing spatial correlational analyses. Furthermore, the methodology that we have applied to evaluate the spatial correlation offers interesting results.
Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction
Authors: Wagner Matthias P. , Oppelt Natascha
Published: June 2020

Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.
High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data
Authors: Huang Chenhui , Shibuya Akinobu
Published: June 2020

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.
Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation
Authors: Jiao Libin , Huo Lianzhi , Hu Changmiao , Tang Ping
Published: June 2020

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data.
An Estimation of Top-Down NOx Emissions from OMI Sensor Over East Asia
Authors: Han Kyung , Kim Hyun , Song Chul
Published: June 2020

This study focuses on the estimation of top-down NOx emissions over East Asia, integrating information on the levels of NO2 and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations. An algorithm was developed based on mass conservation to estimate the 30 km × 30 km resolved top-down NOx emissions over East Asia. In particular, the algorithm developed in this study considered two main atmospheric factors—(i) NOx transport from/to adjacent cells and (ii) calculations of the lifetimes of column NOx (τ). In the sensitivity test, the analysis showed the improvements in the top-down NOx estimation via filtering the data (τ ≤ 2 h). The best top-down NOx emissions were inferred after the sixth iterations. Those emissions were 11.76 Tg N yr−1 over China, 0.13 Tg N yr−1 over North Korea, 0.46 Tg N yr−1 over South Korea, and 0.68 Tg N yr−1 over Japan. These values are 34%, 62%, 60%, and 47% larger than the current bottom-up NOx emissions over these countries, respectively. A comparison between the CMAQ-estimated and OMI-retrieved NO2 columns was made to confirm the accuracy of the newly estimated NOx emission. The comparison confirmed that the estimated top-down NOx emissions showed better agreements with observations (R2 = 0.88 for January and 0.81 for July).
Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem
Authors: Vasilakos Christos , Kavroudakis Dimitris , Georganta Aikaterini
Published: June 2020

Land cover type classification still remains an active research topic while new sensors and methods become available. Applications such as environmental monitoring, natural resource management, and change detection require more accurate, detailed, and constantly updated land-cover type mapping. These needs are fulfilled by newer sensors with high spatial and spectral resolution along with modern data processing algorithms. Sentinel-2 sensor provides data with high spatial, spectral, and temporal resolution for the in classification of highly fragmented landscape. This study applies six traditional data classifiers and nine ensemble methods on multitemporal Sentinel-2 image datasets for identifying land cover types in the heterogeneous Mediterranean landscape of Lesvos Island, Greece. Support vector machine, random forest, artificial neural network, decision tree, linear discriminant analysis, and k-nearest neighbor classifiers are applied and compared with nine ensemble classifiers on the basis of different voting methods. kappa statistic, F1-score, and Matthews correlation coefficient metrics were used in the assembly of the voting methods. Support vector machine outperformed the base classifiers with kappa of 0.91. Support vector machine also outperformed the ensemble classifiers in an unseen dataset. Five voting methods performed better than the rest of the classifiers. A diversity study based on four different metrics revealed that an ensemble can be avoided if a base classifier shows an identifiable superiority. Therefore, ensemble approaches should include a careful selection of base-classifiers based on a diversity analysis.
A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands
Authors: Issa Salem , Dahy Basam , Ksiksi Taoufik , Saleous Nazmi
Published: June 2020

Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ecosystems is constrained by unique challenges specific to their environmental conditions, leading to high inaccuracies when applying biomass estimation techniques developed for other ecosystems. This study reviews and highlights advantages and limitations of the various techniques and sensors, including optical, RADAR and LiDAR, that have been extensively used to estimate AGB and assess CS with RS data. Other new methods are introduced and discussed as well. Finally, the study highpoints the need for further work to fill the gaps and overcome limitations in using these emerging techniques for precision carbon management. Geo-spatial technologies are shown to be a valuable tool for estimating carbon sequestered especially in difficult and remote areas such as arid land.
A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets
Authors: Seydi Seyd Teymoor , Hasanlou Mahdi , Amani Meisam
Published: June 2020

The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.
Geographic Object-Based Image Analysis: A Primer and Future Directions
Authors: Kucharczyk Maja , Hay Geoffrey J. , Ghaffarian Salar , Hugenholtz Chris H.
Published: June 2020

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.
Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction
Authors: Kim Yongchae , Kudo Hiroyuki
Published: June 2020

We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty’s framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes.
Synchronous Sampling-Based Direct Current Estimation Method for Self-Sensing Active Magnetic Bearings
Authors: Hu Xiongxin , Xu Fang , Wang Ronghui , Tan Dapeng
Published: June 2020

Active magnetic bearings (AMBs) commonly use pulse-width modulation to reduce analogous hardware and manufacturing costs, but they experience sensing process, sensing accuracy and stability problems. To address these issues, a synchronous sampling-based direct current estimation (SS-DCE) method is proposed herein with a bistate switching power amplifier. First—considering the reluctance evolution mechanism of AMBs—a coupling relation mathematical model between rotor displacement and voltage/current is presented to acquire the rotor position from the working coil current alone. Then—assuming that the switching current was an approximately triangular signal—a DCE for the rotor position was established based on the estimation inductance of the charging/discharging phase. Finally—to decrease the phase shift caused by the self-sensing filters and position estimation algorithms—the SS-DCE method was introduced to conduct precise position detection for rotors with high velocities. The simulation and experimental results indicated that the proposed method could improve the sensing accuracy and stability. Compared to other AMB position estimation methods, the simple linearity of the SS-DCE method was greatly improved and could be controlled below 4%. Evaluation using frequency response analysis showed that the SS-DCE method had excellent dynamic accuracy and could perform at a higher phase margin, especially for the uprising/landing transient state. Moreover, there was a phase margin of 158° at the natural frequency of 19.26 HZ, and the peak sensitivity in the 50–250 μm range reached 10.7 dB.
Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar
Authors: Wu Qisong , Gao Teng , Lai Zhichao , Li Dianze
Published: June 2020

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.
Enzyme-Based Biosensors: Tackling Electron Transfer Issues
Authors: Bollella Paolo , Katz Evgeny
Published: June 2020

This review summarizes the fundamentals of the phenomenon of electron transfer (ET) reactions occurring in redox enzymes that were widely employed for the development of electroanalytical devices, like biosensors, and enzymatic fuel cells (EFCs). A brief introduction on the ET observed in proteins/enzymes and its paradigms (e.g., classification of ET mechanisms, maximal distance at which is observed direct electron transfer, etc.) are given. Moreover, the theoretical aspects related to direct electron transfer (DET) are resumed as a guideline for newcomers to the field. Snapshots on the ET theory formulated by Rudolph A. Marcus and on the mathematical model used to calculate the ET rate constant formulated by Laviron are provided. Particular attention is devoted to the case of glucose oxidase (GOx) that has been erroneously classified as an enzyme able to transfer electrons directly. Thereafter, all tools available to investigate ET issues are reported addressing the discussions toward the development of new methodology to tackle ET issues. In conclusion, the trends toward upcoming practical applications are suggested as well as some directions in fundamental studies of bioelectrochemistry.
Highly Reproducible Surface-Enhanced Raman Scattering Detection of Alternariol Using Silver-Embedded Silica Nanoparticles
Authors: Hahm Eunil , Kim Yoon-Hee , Pham Xuan-Hung , Jun Bong-Hyun
Published: June 2020

Alternariol (AOH) is a mycotoxin from fungi that has been found in processed foods due to its high thermal stability. To address the complexity and costs of conventional AOH detection methods, we propose an alternative based on surface-enhanced Raman scattering (SERS) and specially designed nanoparticle substrate. Herein, silver-embedded silica (SiO2@Ag) nanoparticles with a highly reproducible SERS signal were successfully developed for detecting AOH. Silica nanoparticles (~145 nm) were used as a template to deposit silver nanoparticles (~17 nm), thereby generating SiO2@Ag. The SiO2@Ag nanoparticles showed a good linearity between SERS signal intensity and AOH concentrations from 16 to 1000 nM with a limit of detection of 4.83 nM. Additionally, the SERS signal of the SiO2@Ag nanoparticles was highly reproducible, with relative standard deviations of 2.33–5.95% in the AOH concentration range from 10 to 10,000 nM, demonstrating the reliability of the proposed SERS method.
Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video
Authors: Martinez Bridget , Green Andre , Silva Moises Felipe , Yang Yongchao , Mascareñas David
Published: June 2020

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.
Testing System for the Mechanical Properties of Small-Scale Specimens Based on 3D Microscopic Digital Image Correlation
Authors: Liu Xu , Lu Rongsheng
Published: June 2020

The testing of the mechanical properties of materials on a small scale is difficult because of the small specimen size and the difficulty of measuring the full-field strain. To tackle this problem, a testing system for investigating the mechanical properties of small-scale specimens based on the three-dimensional (3D) microscopic digital image correlation (DIC) combined with a micro tensile machine is proposed. Firstly, the testing system is described in detail, including the design of the micro tensile machine and the 3D microscopic DIC method. Then, the effects of different shape functions on the matching accuracy obtained by the inverse compositional Gauss–Newton (IC-GN) algorithm are investigated and the numerical experiment results verify that the error due to under matched shape functions is far larger than that of overmatched shape functions. The reprojection error is shown to be smaller than before when employing the modified iteratively weighted radial alignment constraint method. Both displacement and uniaxial measurements were performed to demonstrate the 3D microscopic DIC method and the testing system built. The experimental results confirm that the testing system built can accurately measure the full-field strain and mechanical properties of small-scale specimens.
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies
Authors: Lykou Georgia , Moustakas Dimitrios , Gritzalis Dimitris
Published: June 2020

As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.
Differential Sensing of Saccharides Based on an Array of Fluorinated Benzosiloxaborole Receptors
Authors: Ćwik Paweł , Ciosek-Skibińska Patrycja , Zabadaj Marcin , Luliński Sergiusz , Durka Krzysztof , Wróblewski Wojciech
Published: June 2020

Fluorinated benzosiloxaboroles–silicon congeners of benzoxaboroles, were synthesized and tested as molecular receptors for mono- and disaccharides. The receptors differed in the Lewis acidity of the boron center as well as in the number of potential binding sites. The calculated stability constants indicated different binding affinity of benzosiloxaborole derivatives towards selected saccharides, enabling their classification using a receptor array-based sensing. Unique fluorescence fingerprints were created on the basis of competitive interactions of the studied receptors with both Alizarin Red S (ARS) and tested saccharide molecules. Detailed chemometric analysis of the obtained fluorescence data (based on partial least squares-discriminant analysis and hierarchical clustering analysis) provided the differential sensing of common saccharides, in particular the differentiation between glucose and fructose. In addition, DFT calculations were carried out to shed light on the binding mechanism under different pH conditions.
Boxer Underwear Incorporating Textile Moisture Sensor to Prevent Nocturnal Enuresis
Authors: Gaubert Valentin , Gidik Hayriye , Koncar Vladan
Published: June 2020

Around 15% of children still wet their bed after five years old. Although bedwetting alarms have proven to be effective to achieve nighttime dryness, they are cumbersome so children could be reluctant to use them. Therefore, the moisture sensor and wire were made unobtrusive by seamlessly integrated them into fully textile underwear by using conductive yarns. Consequently, the alarm acceptability should be enhanced by improving children’s comfort. Three conductive textile metallic yarns, made of silver or stainless steel, were considered to fabricate the urine leakage sensor. Silver-plated-nylon yarn, which showed the highest electrical conductivity, outperformed the stainless-steel yarns regarding its ability to detect urine leakage as well as its detection speed. Furthermore, it was proven to withstand multiple urine soakings and the following machine-washings, even at high temperature (60 °C). However, the electrical current, necessary to detect the leakage, tends to corrode the silver. Therefore, the detection circuit was adapted. Eventually, the designed leakage sensor was seamlessly integrated into a child’s trunk underwear, into which a miniaturized alarm can be plugged. The resulting textile underwear aims at replacing the rigid alarm system currently available, hence improving the quality of life of enuretic children and help them achieving nighttime dryness.
Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning
Authors: Carotenuto Riccardo , Merenda Massimo , Iero Demetrio , G. Della Corte Francesco
Published: June 2020

Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points—namely, the linear chirp—due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used.
Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing
Authors: Vinagre Díaz Juan José , Fernández Pozo Rubén , Rodríguez González Ana Belén , Wilby Mark R. , Sánchez Ávila Carmen
Published: June 2020

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.
Multisensor Systems and Arrays for Medical Applications Employing Naturally-Occurring Compounds and Materials
Authors: Pauliukaite Rasa , Voitechovič Edita
Published: June 2020

The significant improvement of quality of life achieved over the last decades has stimulated the development of new approaches in medicine to take into account the personal needs of each patient. Precision medicine, providing healthcare customization, opens new horizons in the diagnosis, treatment and prevention of numerous diseases. As a consequence, there is a growing demand for novel analytical devices and methods capable of addressing the challenges of precision medicine. For example, various types of sensors or their arrays are highly suitable for simultaneous monitoring of multiple analytes in complex biological media in order to obtain more information about the health status of a patient or to follow the treatment process. Besides, the development of sustainable sensors based on natural chemicals allows reducing their environmental impact. This review is concerned with the application of such analytical platforms in various areas of medicine: analysis of body fluids, wearable sensors, drug manufacturing and screening. The importance and role of naturally-occurring compounds in the development of electrochemical multisensor systems and arrays are discussed.
A Closed-Form Method of Acoustic Emission Source Location for Velocity-Free System Using Complete TDOA Measurements
Authors: Zhou Zilong , Rui Yichao , Cai Xin , Lan Riyan , Cheng Ruishan
Published: June 2020

A closed-form method of acoustic emission (AE) source location for a velocity-free system using complete time difference of arrival (TDOA) measurements is proposed in this paper. First, this method established the governing equation of unknown acoustic velocity for each sensor; then, the governing equations of each of the three sensors were transformed into a linear equation, which can form a system of linear equations with the complete TDOA measurements. Third, the least squares solutions of the AE source coordinate and acoustic velocity were separately solved by an orthogonal projection operator. The proposed method was verified by the pencil-lead break experiment, and the results showed that the location accuracy and stability of the proposed method were better than those of traditional methods. Moreover, a simulation test was carried out to investigate the influence of noise scales on the location accuracy, and the results further prove that the proposed method holds higher noise immunity than the traditional methods.
Direction-of-Arrival Estimation Methods in Interferometric Echo Sounding
Authors: Grall Piotr , Kochanska Iwona , Marszal Jacek
Published: June 2020

Nowadays, there are two leading sea sounding technologies: the multibeam echo sounder and the multiphase echo sounder (also known as phase-difference side scan sonar or bathymetric side scan sonar). Both solutions have their advantages and disadvantages, and they can be perceived as complementary to each other. The article reviews the development of interferometric echo sounding array configurations and the various methods applied to determine the direction-of-arrival. “Interferometric echo sounder” is a broad term, applied to various devices that primarily utilize phase difference measurements to estimate the direction-of-arrival. The article focuses on modifications to the interferometric sonar array that have led to the state-of-the-art multiphase echo sounder. The main algorithms for classical and modern interferometric echo sounder direction-of-arrival estimation are also outlined. The accuracy of direction-of-arrival estimation methods is dependent on the configuration of the array and external and internal noise sources. The main sources of errors, which influence the accuracy of the phase difference measurements, are also briefly characterized. The article ends with a review of the current research into improvements in the accuracy of interferometric echo sounding and the application of the principle of interferometric in other devices.
How Does the Arctic Council Support Conservation of Arctic Biodiversity?
Authors: Barry Tom , Daviðsdóttir Brynhildur , Einarsson Níels , Young Oran R.
Published: June 2020

The Arctic Council is an intergovernmental forum promoting cooperation, coordination and interaction among Arctic states, indigenous communities, and peoples on issues of common importance. The rising geo-political importance of the Arctic and the onset of climate change has resulted in the Council becoming a focus of increasing interest from both inside and beyond the Arctic. This has resulted in new demands placed on the Council, attracting an increasing number of participants, and instigating a period of transformation as Arctic states work to find a way to balance conflicting demands to improve the Council’s effectiveness and take care of national interests. This paper considers whether, during this time of change, the Council is having an impact on the issues it was formed to address, i.e., environmental protection and sustainable development. To provide answers, it looks at how the Council reports on and evaluates progress towards the implementation of recommendations it makes regarding biodiversity, how it identifies where activities have had impacts and uncovers the mechanisms through which they were successful, to provide an insight into how the Arctic Council can be an agent of change.
Effects of a One-day Environmental Education Program on Sixth-Graders’ Environmental Literacy at a Nature Center in Eastern Taiwan
Authors: Pan Chung-Ting , Hsu Shih-Jang
Published: June 2020

This study assessed the effects of a one-day environmental education program on sixth-graders’ environmental literacy at a nature center in eastern Taiwan. In this program, the use of role play and games for teaching about Formosan black bear and forest conservation was adopted. A nonequivalent control group design was used to collect data. The experimental group (N = 100) received the one-day environmental education program and the control group (N = 73) did not receive any environmental instruction. The ANCOVA results indicated that students’ environmental knowledge and locus of control were effectively improved by the end of the program. Besides, students in the experimental group showed a lower utilization preference than students in the control group. One month after the end of the program, the present study discovered retained effects on students’ environmental knowledge, environmental responsibility, locus of control, and environmental action. Moreover, students in the experimental group showed a higher preservation and a lower utilization preference than those in the control group in the follow-up test. Based on this study, implications for program development and instructional practice were presented.
Foreign-Funded Enterprises and Pollution Halo Hypothesis: A Spatial Econometric Analysis of Thirty Chinese Regions
Authors: Repkine Alexandre , Min Dongki
Published: June 2020

China is one of the world’s major environmental polluters, therefore, Chinese environmental efficiency is an issue of global importance. In this study, we aim to identify economic factors affecting environmental efficiency scores in different regions of China from a spatial econometric perspective. We measure environmental efficiency scores, relative to the theoretically consistent production possibilities frontier estimated, according to a novel iterative methodology. As expected, we find that environmental efficiency scores are negatively affected by the prevalence of heavy industry sector in the economy, with a higher share of coal as a source of energy exacerbating the problem. We also find evidence that strongly support the pollution halo hypothesis, which credits foreign-funded enterprises with producing in a more environmentally-friendly way. Surprisingly, we find a negative association between the share of tertiary sectors in a regional economy and environmental efficiency—emphasizing the need to address the indirect effects produced on the environment by the seemingly innocuous sectors, such as the hotel sector. We encourage the creation of foreign-funded enterprises, and support formulating environmental protection policies that consider the indirect effects various economic sectors have on the environment.
Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage
Authors: Szwedziak Katarzyna , Polańczyk Ewa , Grzywacz Żaneta , Niedbała Gniewko , Wojtkiewicz Wiktoria
Published: June 2020

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.