bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Water Regime, Genotype, and Formative Stages on the Agro-Physiological Response of Sugarcane (Saccharum officinarum L.) to Drought
Authors: Reyes Jose Arnel O. , Carpentero Arvin S. , Santos Primitivo Jose A. , Delfin Evelyn F.
Published: May 2020

Drought during the formative stages of a plant’s growth triggers a sequence of responses to maintain optimal growing conditions, but often at the expense of crop productivity. Two field experiments were conducted to determine the effect of drought on 10 high-yielding sugarcane genotypes at two formative stages (the tillering stage (TS) and stalk elongation (SS)), within 30 days after treatment imposition. The experiments followed a split-plot in a randomized complete block design with three replicates per genotype. Agro-physiological responses to drought were observed to compare the differences in the response of sugarcane during the two formative stages. Drought significantly reduced total chlorophyll content (Chl) and stomatal conductance (Gs) for both formative stages, while significantly increasing total scavenging activity (AOA) and electrolyte leakage (EC). A higher level of Chl was observed in the stalk elongation stage compared to the tillering stage; however, lower AOA coupled with higher EC in the stalk elongation stage suggests higher drought susceptibility. Pearson's correlation analysis revealed a stronger correlation between plant height, internode length, Chl, AOA, EC, and Gs at the tillering stage relative to the stalk elongation stage. Moreover, results from the multivariate analysis indicate the different contribution values of each parameter, supplementing the hypothesized difference in response between the two formative stages. Multivariate analysis clustered the 10 genotypes into groups based on the traits evaluated, suggesting the ability of these traits to detect differences in a sample population. The observed relationship among traits during the two formative stages of sugarcane will be significant in screening and identifying drought-susceptible and drought-tolerant genotypes for variety development studies.
Development of Poly(l-Lactic Acid)-Based Bending Actuators
Authors: M. Correia Daniela , C. Fernandes Liliana , Cruz Bárbara , Botelho Gabriela , de Zea Bermudez Verónica , Lanceros-Méndez Senentxu
Published: May 2020

This work reports on the development of bending actuators based on poly(l-lactic acid) (PLLA)/ionic liquid (IL) blends, through the incorporation of 40% wt. of the 1-ethyl-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Emim][TFSI]) IL. The films, obtained by solvent casting at room temperature and 50 °C, were subjected to several post-thermal treatments at 70, 90, 120 and 140 °C, in order to modify the crystallinity of the films. The influence of the drying temperature and of [Emim][TFSI] blending on the morphological, structural, mechanical and electrical properties of the composite materials were studied. The IL induced the formation of a porous surface independently of the processing conditions. Moreover, the [Emim][TFSI] dopant and the post-thermal treatments at 70 °C promoted an increase of the degree of crystallinity of the samples. No significant changes were observed in the degree of crystallinity and Young Modulus for samples with thermal treatment between 70 and 140 °C. The viability of the developed high ionic conductive blends for applications as soft actuators was evaluated. A maximum displacement of 1.7 mm was achieved with the PLLA/[Emim][TFSI] composite prepared at 50 °C and thermally treated at 140 °C, for an applied voltage of 10 Vpp, at a frequency of 100 mHz. This work highlights interesting avenues for the use of PLLA in the field of actuators.
Curing Behaviors of Alkynyl-Terminated Copolyether with Glycidyl Azide Polymer in Energetic Plasticizers
Authors: Hu Jinghui , Liu Yina , Cong Kun , He Jiyu , Yang Rongjie
Published: May 2020

Alkynyl-terminated polyethylene oxide−tetrahydrofuran (ATPET) and glycidyl azide polymer (GAP) could be linked through click-chemistry between the alkynyl and azide, and the product may serve a binder for solid propellants. The effects of the energetic plasticizers A3 [1:1 mixture of bis-(2,2-dinitropropy) acetal (BDNPA) and bis-(2,2-dinitropropyl) formal(BDNPN)] and Bu-NENA [N-butyl-N-(2nitroxyethyl) nitramine] on the curing reaction between ATPET and GAP have been studied. A diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY-NMR) approach has been used to monitor the change in the diffusion coefficient of cross-linked polytriazole polyethylene oxide−tetrahydrofuran (PTPET). The change in the diffusion coefficient of PTPET with A3 plasticizer is significantly higher than that of PTPET with Bu-NENA. Viscosity analysis further highlighted the difference between A3 and Bu-NENA in the curing process—the curing curve of PTPET (A3) with time can be divided into two stages, with an inflection point being observed on the fourth day. For PTPET (Bu-NENA), in contrast, only one stage is seen. The above methods, together with gel permeation chromatography (GPC) analysis, revealed distinct effects of A3 and Bu-NENA on the curing process of PTPET. X-ray Photoelectron Spectroscopy (XPS) analysis showed that Bu-NENA has little effect on the valence oxidation of copper in the catalyst. Thermogravimetric (TG) analysis indicated that Bu-NENA helps to improve the thermal stability of the catalyst. After analysis of several possible factors by means of XPS, modeling with Material Studio and TG, the formation of molecular cages between Bu-NENA and copper is considered to be the reason for the above differences. In this article, GAP (Mn = 4000 g/mol) was used to replace GAP (Mn = 427 g/mol) to successfully synthesize the PTPET elastomer with Bu-NENA plasticizer. Mechanical data measured for the PTPET (Bu-NENA) sample included ε = 34.26 ± 2.98%, and σ = 0.198 ± 0.015 MPa.
A Flame-Retardant Phytic-Acid-Based LbL-Coating for Cotton Using Polyvinylamine
Authors: Zilke Olga , Plohl Dennis , Opwis Klaus , Mayer-Gall Thomas , Gutmann Jochen Stefan
Published: May 2020

Phytic acid (PA), as a natural source of phosphorus, was immobilized on cotton (CO) in a layer-by-layer (LbL) approach with polyvinylamine (PVAm) as the oppositely charged electrolyte to create a partly bio-based flame-retardant finish. PVAm was employed as a synthetic nitrogen source with the highest density of amine groups of all polymers. Vertical flame tests revealed a flame-retardant behavior with no afterflame and afterglow time for a coating of 15 bilayers (BL) containing 2% phosphorus and 1.4% nitrogen. The coating achieved a molar P:N ratio of 3:5. Microscale combustion calorimetry (MCC) analyses affirmed the flame test findings by a decrease in peak heat release rate (pkHRR) by more than 60% relative to unfinished CO. Thermogravimetric analyses (TGA) and MCC measurements exhibited a shifted CO peak to lower temperatures indicating proceeding reactions to form an isolating char on the surface. Fourier transform infrared spectroscopy (FTIR) coupled online with a TGA system, allowed the identification of a decreased amount of acrolein, methanol, carbon monoxide and formaldehyde during sample pyrolysis and a higher amount of released water. Thereby the toxicity of released volatiles was reduced. Our results prove that PA enables a different reaction by catalyzing cellulosic dehydration, which results in the formation of a protective char on the surface of the burned fabric.
Organoboron Ionic Liquids as Extractants for Distillation Process of Binary Ethanol + Water Mixtures
Authors: Davletbaeva Ilsiya M. , Klinov Alexander V. , Khairullina Alina R. , Malygin Alexander V. , Dulmaev Sergey E. , Davletbaeva Alisa R. , Mukhametzyanov Timur A.
Published: May 2020

Aminoethers of boric acid, which are organoboron ionic liquids, were synthesized by using boric acid, triethanolamine, and triethylene glycol/diethylene glycol. Due to the formation of intermolecular complexes of borates, the structure of aminoethers of boric acid contains ion pairs separated in space, giving these compounds the properties inherent to ionic liquids. It is established that the thermal stability of aminoethers under normal atmospheric conditions increases with an increase in the size of the glycol. According to measurements of fast scanning calorimetry, density, dynamic viscosity, and electrical conductivity, water is involved in the structural organization of aminoethers of boric acid. The impact of the most thermostable organoboron ionic liquids on the phase equilibrium conditions of the vapor–liquid azeotropic ethanol–water mixture is studied. It is shown that the presence of these substances leads to increase in the relative volatility of ethanol. In general, the magnitude of this effect is at the level shown by imidazole ionic liquids, which provide high selectivity in the separation of aqueous alcohol solutions. A large separation factor, high resistance to thermal oxidative degradation processes, accompanied by low cost start reagents, make aminoethers of boric acid on the basis of triethylene glycol a potentially effective extractant for the extractive distillation of water–alcohol mixtures.
Industrial Processes Management for a Sustainable Society: Global Research Analysis
Authors: Abad-Segura Emilio , Morales Manuel E. , Cortés-García Francisco Joaquín , Belmonte-Ureña Luis Jesús
Published: May 2020

Few decades ago, the development of the industrial sector was disconnected from society’s protection. Negative effects awareness emerges from the current industrial processes through the Sustainable Development Goals (SDGs), considering the causal implications to build up a more sustainable society. The aim of this study is to analyze the state of the art in industrial processes management to obtain positive and sustainable effects on society. Thus, a bibliometric analysis of 1911 articles was set up during the 1988–2019 period, bringing up the authors’ productivity indicators in the scientific field, that is, journals, authors, research institutions, and countries. We have identified environmental management; the impact assessments of industrial processes on the environment and its relation with a more sustainable society; as well as the study of the sustainable management of water resources as the related axes in the study of environmental protection with political, economic, and educational approaches. The growing trend of world scientific publications let us observe the relevance of industrial processes management in the implementation of efficient models to achieve sustainable societies. This research contributes to the academic, scientific, and social debate on decision-making both in public and private institutions, and in multidisciplinary groups.
Graphene-Based Hydrogen Gas Sensors: A Review
Authors: Ilnicka Anna , Lukaszewicz Jerzy P.
Published: May 2020

Graphene is a material gaining attention as a candidate for new application fields such as chemical sensing. In this review, we discuss recent advancements in the field of hydrogen gas sensors based on graphene. Accordingly, the main part of the paper focuses on hydrogen gas sensors and examines the influence of different manufacturing scenarios on the applicability of graphene and its derivatives as key components of sensing layers. An overview of pristine graphene customization methods is presented such as heteroatom doping, insertion of metal/metal oxide nanosized domains, as well as creation of graphene-polymer blends. Volumetric structuring of graphene sheets (single layered and stacked forms) is also considered as an important modifier of its effective use. Finally, a discussion of the possible advantages and weaknesses of graphene as sensing material for hydrogen detection is provided.
Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins
Authors: Poverennaya Ekaterina , Kiseleva Olga , Ilgisonis Ekaterina , Novikova Svetlana , Kopylov Arthur , Ivanov Yuri , Kononikhin Alexei , Gorshkov Mikhail , Kushlinskii Nikolay , Archakov Alexander , Ponomarenko Elena
Published: May 2020

Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).
Validation of the Chinese Version of the Centrality of Religiosity Scale (CRS): Teacher Perspectives
Authors: Lee John Chi-Kin , Kuang Xiaoxue
Published: May 2020

This study applied the Centrality of Religiosity Scale (CRS) to the context of Hong Kong as a part of China with the focus on a specific target group of teachers in primary and secondary schools. For the validation of the scale in the Hong Kong context, the version of CRSi-20 was tested with data collected from local teachers (N = 671). For the validation of the scale, six versions were tested (CRSi-20, CRS-15, CRSi-14, CRS-10, CRSi-7, and CRS-5). Confirmatory Factor Analysis demonstrated that the single-factor solution of five items (CRS-5) had better fit indices than the seven-item version (CRSi-7), which, in turn, was better than CRS-15 with a five-factor solution (Intellect, Ideology, Private Practice, Public Practice, and Religious Experience). The other three versions encountered a problem with high correlations between factors. Multiple-indicators multiple-causes (MIMIC) analysis was used to test the effect of covariates on the established factor structure for CRS-5, CRSi-7, and CRS-15. The results indicated that gender and religious belief are significant predictors of the centrality of religiosity scores for CRS-5, CRSi-7, and CRS-15. In addition, age was a positive predictor for public practice, and teachers’ education level was positively related to private practice for CRS-15. Implications regarding understanding for the existing literature are discussed.
Strategies for the Valorisation of Chestnut Resources in Italian Mountainous Areas from a Sustainable Development Perspective
Authors: Bruzzese Stefano , Blanc Simone , Brun Filippo
Published: May 2020

The chestnut tree has characterised the social history of the Italian hills and mountains. However, in recent years, this resource has been abandoned because of the change in socio-economic needs. The aim of this study is twofold: (i) To give an overview of this resource, assessing the strengths-weaknesses-opportunities-threats of the chestnut tree through the use of the SWOT methodology; (ii) to provide action strategies for resource valorisation, in accordance with the SDGs set out in the UN 2030 Agenda, by using the TOWS methodology. Our results suggest four strategies, specifically; ‘resource-based’, ‘network-based’, ‘policy-based’ and ‘market-based’, capable of valorising the resource from environmental, economic and societal points of view. The active management of chestnut forests, together with the development of a value chain can guarantee sustainable production, technological and process innovation and could empower companies to face global markets in a more competitive way and lead to the creation of high value-added products, generating a strong economic and social engine for the revival of the sector. Moreover, the commitment of the private sector to the reorganisation of the supply chain must also be accompanied by coherent public policy action. This is essential for the adoption of plans and programmes consistent with sustainable development objectives, ensuring their prioritisation and implementation.
Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks
Authors: Pradhan Biswajeet , Al-Najjar Husam A. H. , Sameen Maher Ibrahim , Tsang Ivor , Alamri Abdullah M.
Published: May 2020

Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two, 0.924 top-three, 0.729 F1-score, 0.676 recall and 0.790 precision for the second test dataset. The results demonstrated that the proposed ZSL is a promising tool for land cover mapping based on high-resolution photos.
An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds
Authors: Dai Chenguang , Zhang Zhenchao , Lin Dong
Published: May 2020

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.
Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas
Authors: Bai Xiwei , Wen Weisong , Hsu Li-Ta
Published: May 2020

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.
Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods
Authors: Razavi-Termeh Seyed Vahid , Sadeghi-Niaraki Abolghasem , Choi Soo-Mi
Published: May 2020

This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables.
Exploring the Spatial Characteristics of Typhoon-Induced Vegetation Damages in the Southeast Coastal Area of China from 2000 to 2018
Authors: Lu Lizhen , Wu Chuyi , Di Liping
Published: May 2020

The southeast coastal area of China (SCAC), a typhoon-prone area with a long coastline, suffers severe damage from typhoons almost every year. Exploring the spatial characteristics of historical typhoon-induced vegetation damage (VD) is crucial to predicting VD after severe typhoon landfalls and improving strategies for vegetation protection and restoration. Remote sensing is an efficient and feasible approach for measuring large-scale VD caused by natural disasters. This paper, by exploring the spatial distribution of VD of every severe landfalling typhoon with Google Earth Engine (GEE), aims to reveal the spatial characteristics of typhoon-induced VD in SCAC. Firstly, the values of disaster vegetation damage index (DVDI), difference in enhanced vegetation index (DEVI), and normalized difference vegetation index (DNDVI) for the 28 selected landing typhoons in SCAC were calculated and compared by using moderate resolution imaging spectroradiometer (MODIS) data in GEE. Secondly, every DVDI image was overlaid with land cover, elevation, relative aspect and typhoon path layers in ArcGIS. Thirdly, spatial characteristics of VD were revealed with the aid of spatial statistical analysis. The study found that: (1) DVDI is a more effective index for evaluating VD caused by typhoons. (2) The Pearl River Delta is the most severe VD region. The severe VD regions for four typhoon groups have significantly spatial correlation with typhoon-landing locations. (3) Forests are ranked the first in terms of damaged areas by typhoon in every year, followed by sparse forests. (4) Topography has no influence on VD by a single typhoon event, and relative aspect has no correlation with VD caused by typhoons in SCAC.
Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging
Authors: Bendel Nele , Kicherer Anna , Backhaus Andreas , Köckerling Janine , Maixner Michael , Bleser Elvira , Klück Hans-Christian , Seiffert Udo , Voegele Ralf T. , Töpfer Reinhard
Published: May 2020

Grapevine leafroll disease (GLD) is considered one of the most widespread grapevine virus diseases, causing severe economic losses worldwide. To date, six grapevine leafroll-associated viruses (GLRaVs) are known as causal agents of the disease, of which GLRaV-1 and -3 induce the strongest symptoms. Due to the lack of efficient curative treatments in the vineyard, identification of infected plants and subsequent uprooting is crucial to reduce the spread of this disease. Ground-based hyperspectral imaging (400–2500 nm) was used in this study in order to identify white and red grapevine plants infected with GLRaV-1 or -3. Disease detection models have been successfully developed for greenhouse plants discriminating symptomatic, asymptomatic, and healthy plants. Furthermore, field tests conducted over three consecutive years showed high detection rates for symptomatic white and red cultivars, respectively. The most important detection wavelengths were used to simulate a multispectral system that achieved classification accuracies comparable to the hyperspectral approach. Although differentiation of asymptomatic and healthy field-grown grapevines showed promising results further investigations are needed to improve classification accuracy. Symptoms caused by GLRaV-1 and -3 could be differentiated.
Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing
Authors: Baptista Ríos Marcos , López-Sastre Roberto Javier , Acevedo-Rodríguez Francisco Javier , Martín-Martín Pilar , Maldonado-Bascón Saturnino
Published: May 2020

In this work, we introduce an intelligent video sensor for the problem of Action Proposals (AP). AP consists of localizing temporal segments in untrimmed videos that are likely to contain actions. Solving this problem can accelerate several video action understanding tasks, such as detection, retrieval, or indexing. All previous AP approaches are supervised and offline, i.e., they need both the temporal annotations of the datasets during training and access to the whole video to effectively cast the proposals. We propose here a new approach which, unlike the rest of the state-of-the-art models, is unsupervised. This implies that we do not allow it to see any labeled data during learning nor to work with any pre-trained feature on the used dataset. Moreover, our approach also operates in an online manner, which can be beneficial for many real-world applications where the video has to be processed as soon as it arrives at the sensor, e.g., robotics or video monitoring. The core of our method is based on a Support Vector Classifier (SVC) module which produces candidate segments for AP by distinguishing between sets of contiguous video frames. We further propose a mechanism to refine and filter those candidate segments. This filter optimizes a learning-to-rank formulation over the dynamics of the segments. An extensive experimental evaluation is conducted on Thumos’14 and ActivityNet datasets, and, to the best of our knowledge, this work supposes the first unsupervised approach on these main AP benchmarks. Finally, we also provide a thorough comparison to the current state-of-the-art supervised AP approaches. We achieve 41% and 59% of the performance of the best-supervised model on ActivityNet and Thumos’14, respectively, confirming our unsupervised solution as a correct option to tackle the AP problem. The code to reproduce all our results will be publicly released upon acceptance of the paper.
Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation
Authors: Dul Franciszek , Lichota Piotr , Rusowicz Artur
Published: May 2020

In this paper, the full tracking problem in aircraft system identification and control is presented. Time domain output error method with maximum likelihood principle was used to perform system identification. The linear quadratic regulator (LQR)-based approach has been used for solving aviation full tracking problems in aviation. It has been shown that the generalized nonlinear LQR control is able to handle such problems even in case of inaccurate measurements and in the presence of moderate disturbances provided that the model of an aircraft is properly identified.
Joint Acquisition Estimator of Modern GNSS Tiered Signals Using Block Pre-Correlation Processing of Secondary Code
Authors: Svatoň Jiří , Vejražka František
Published: May 2020

Objective is a joint primary and secondary code (SC) acquisition estimator of tiered Global Navigation Satellite Systems (GNSS) signals. The estimator is based on the Parallel Code Search algorithm (PCS) combined with the Single-Block-Zero-Padding (SBZP) and the Pre-correlation Coherent Accumulation (PCA). The PCA realizes the extension of the coherent integration time in front of the PCS. However, the PCS with the SBZP and the PCA is affected by a navigation/SC bit transition problem due to its cyclic property of a computed Cross-Ambiguity Function (CAF). This CAF is degraded by diverse parasitic fragments and is not directly applicable for an acquisition. A novel analysis of this mechanism and its impact is presented. Then, the proposed modified SBZP (mSBZP) modified PCA (mPCA) PCS estimator is constructed, which does not degrade the CAF. The mSBZP allows the use of the PCS algorithm in the presence of SC bit transition, while the mPCA decreases the number of PCS algorithm calculations by a factor of SC chip count due to SC pre-correlation processing. The algorithm has the same detection performance in comparison with conventional Double-Block-Zero-Padding (DBZP). However, it allows using the PCS of half-length with longer latency up to a factor of SC chip count.
Development of an In Situ Analyzer Based on Sequential Injection Analysis and Liquid Waveguide Capillary Flow Cell for the Determination of Dissolved Reactive Phosphorus in Natural Waters
Authors: Yang Zeming , Li Cai , Zhang Zhenzhao , Lu Guixin , Cai Zifeng , Cao Wenxi
Published: May 2020

This study presents an innovative technique for the in situ analysis of aquatic biochemical elements detected through wet chemical processes. A new compact in situ phosphate analyzer based on sequential injection analysis, liquid waveguide capillary flow cell and spectrophotometry was developed, and a safe and modular electronics-chemical separation mechanical structure was designed. The sequential injection system of this analyzer was optimized, and the major functions of this analyzer were studied and estimated. With a 10 cm liquid waveguide capillary flow cell and a 6.3 min time cost of detection, the analyzer reaches a detection limit of 1.4 μg·L−1 (≈14.7 nM, [PO43−]) and a consumption of 23 μL at most for each reagent. This analyzer was operated in situ and online during two scientific research cruises in the Pearl River Estuary and northern South China Sea. The advantages of this analyzer include its simple versatile manifold, full automation, low chemical consumption and electronics-chemical separate safe structure. Long-term in situ performance of this analyzer will be validated in the future.
Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net
Authors: Gargiulo Massimiliano , Dell’Aglio Domenico A. G. , Iodice Antonio , Riccio Daniele , Ruello Giuseppe
Published: May 2020

In this paper, we present a new approach to the fusion of Sentinel 1 (S1) and Sentinel 2 (S2) data for land cover mapping. The proposed solution aims at improving methods based on Sentinel 2 data, that are unusable in case of cloud cover. This goal is achieved by using S1 data to generate S2-like segmentation maps to be used to integrate S2 acquisitions forbidden by cloud cover. In particular, we propose for the first time in remote sensing a multi-temporal W-Net approach for the segmentation of Interferometric Wide swath mode (IW) Sentinel-1 data collected along ascending/descending orbit to discriminate rice, water, and bare soil. The quantitative assessment of segmentation accuracy shows an improvement of 0.18 and 0.25 in terms of accuracy and F1-score by applying the proposed multi-temporal procedure with respect to the previous single-date approach. Advantages and disadvantages of the proposed W-Net based solution have been tested in the National Park of Albufera, Valencia, and we show a performance gain in terms of the classical metrics used in segmentation tasks and the computational time.
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network
Authors: Park Yunjin , Lee Sukho , Jeong Byeongseon , Yoon Jungho
Published: May 2020

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.
Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model
Authors: Gao Qinghua , Jiang Shuo , Shull Peter B.
Published: May 2020

Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.
Detection of Abnormal Respiration from Multiple-Input Respiratory Signals
Authors: Kim Ju O , Lee Deokwoo
Published: May 2020

In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine respiratory status, one of the most important indicators of human health. In this paper, respiratory status was categorized into two classes, normal and abnormal. Abnormal respiration (apnea in this paper) was emulated by interrupting breathing activity because it is difficult to acquire real apnea from patients in hospital wards. This paper considered two cases, single and multiple respiration. In the first case, a single normal- or abnormal-respiration signal was used as input, and output was the classified status of respiration. In the second case, multiple respiration signals were simultaneously used as inputs, and we focused on determining the existence of abnormal signals in multiple respiration signals. In the case of multiple inputs, filters with varying cut-off frequency were applied to input signals followed by the analysis of output signals in response to the filters. To substantiate the proposed method, experiment results are provided. In this paper, classification results showed 93 % of the successful rate in the case of multiple inputs, and results are promising for applications to monitoring systems of human respiration.
Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach
Authors: de Luna Daniel R. , Palitó T.T.C. , Assagra Y.A.O. , Altafim R.A.P. , Carmo J.P. , Altafim R.A.C. , Carneiro A.A.O. , de Sousa Vicente A.
Published: May 2020

This work focuses on acoustic analysis as a way of discriminating mineral oil, providing a robust technique, immune to electromagnetic noise, and in some cases, depending on the applied sensor, a low-cost technique. Thus, we propose a new method for the diagnosis of the quality of mineral oil used in electrical transformers, integrating a ferroelectric-based hydrophone and an acoustic transducer. Our classification solution is based on a supervised machine learning technique applied to the signals generated by an in-home built hydrophone. A total of three statistical datasets entries were collected during the acoustic experiments on four types of oils. The first, the second, and third datasets contain 180, 240, and 420 entries, respectively. Eighty-four features were considered from each dataset to apply to two classification approaches. The first classification approach is able to distinguish the oils from the four possible classes with a classification error less than 2%, while the second approach is able to successfully classify the oils without errors (e.g., with a score of 100%).
Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb
Authors: Sapone Marie , Martin Pauline , Ben Mansour Khalil , Château Henry , Marin Frédéric
Published: May 2020

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.
Building Dynamic Communities of Interest for Internet of Things in Smart Cities
Authors: Aldelaimi Monira N. , Hossain M. Anwar , Alhamid Mohammed F.
Published: May 2020

The Internet of things (IoT) is a growing area of research in the context of smart cities. It links a city’s physical objects that are equipped with embedded sensing, communicating, and computing technology. These objects possess the capability to connect and share data with minimal human intervention, which creates the potential to establish social relationships among them. However, it is challenging for an object to discover, communicate, and collaborate dynamically with other objects, such as social entities, and provide services to humans. This is due to the increase in the number of objects and the complexity in defining social-like relationships among them. The current research aims to address this by introducing an object architecture and defining a Dynamic Community of Interest Model (DCIM) for IoT objects. The proposed model will help IoT objects to socialize and build communities amongst themselves based on different criteria. In this approach, objects belonging to a community will collaborate with each other to collect, manipulate, and share interesting content and provide services to enhance the quality of human interactions in smart cities.
IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning
Authors: Khan Prince Waqas , Byun Yung-Cheol , Park Namje
Published: May 2020

Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT–blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.
Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009–2016)
Authors: Chatzievangelou Damianos , Aguzzi Jacopo , Scherwath Martin , Thomsen Laurenz
Published: May 2020

Deep-sea environmental datasets are ever-increasing in size and diversity, as technological advances lead monitoring studies towards long-term, high-frequency data acquisition protocols. This study presents examples of pre-analysis data treatment steps applied to the environmental time series collected by the Internet Operated Deep-sea Crawler “Wally” during a 7-year deployment (2009–2016) in the Barkley Canyon methane hydrates site, off Vancouver Island (BC, Canada). Pressure, temperature, electrical conductivity, flow, turbidity, and chlorophyll data were subjected to different standardizing, normalizing, and de-trending methods on a case-by-case basis, depending on the nature of the treated variable and the range and scale of the values provided by each of the different sensors. The final pressure, temperature, and electrical conductivity (transformed to practical salinity) datasets are ready for use. On the other hand, in the cases of flow, turbidity, and chlorophyll, further in-depth processing, in tandem with data describing the movement and position of the crawler, will be needed in order to filter out all possible effects of the latter. Our work evidences challenges and solutions in multiparametric data acquisition and quality control and ensures that a big step is taken so that the available environmental data meet high quality standards and facilitate the production of reliable scientific results.
Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X)
Authors: Gupta Takshi , Arena Fabio , You Ilsun
Published: May 2020

Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable impact on their functionality. Recently, many researchers have suggested using layer-based UAV deployment, which allows better communications between the entities. Regardless of these solutions, there are a limited number of studies which focus on connecting layered-UAVs to everything (U2X). In particular, none of them have actually addressed the aspect of resource allocation. This paper considers the issue of resource allocation and helps decide the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach relies on mutual-agreement based reward theory, which considers Minkowski distance as a decisive metric and helps attain efficient resource allocation for backhaul-aware U2X. The effectiveness of the proposed solution is demonstrated using Monte-Carlo simulations.