bluuebunny/crossref_metadata_2025 huggingface dataset:
“Smart” Antimicrobial Nanocomplexes with Potential to Decrease Surgical Site Infections (SSI)
Authors: Edis Zehra , Haj Bloukh Samir , Ibrahim May Reda , Abu Sara Hamed
Published: April 2020

The emergence of resistant pathogens is a burden on mankind and threatens the existence of our species. Natural and plant-derived antimicrobial agents need to be developed in the race against antibiotic resistance. Nanotechnology is a promising approach with a variety of products. Biosynthesized silver nanoparticles (AgNP) have good antimicrobial activity. We prepared AgNPs with trans-cinnamic acid (TCA) and povidone–iodine (PI) with increased antimicrobial activity. We synthesized also AgNPs with natural cinnamon bark extract (Cinn) in combination with PI and coated biodegradable Polyglycolic Acid (PGA) sutures with the new materials separately. These compounds (TCA-AgNP, TCA-AgNP-PI, Cinn-AgNP, and Cinn-AgNP-PI) and their dip-coated PGA sutures were tested against 10 reference strains of microorganisms and five antibiotics by zone inhibition with disc- and agar-well-diffusion methods. The new compounds TCA-AgNP-PI and Cinn-AgNP-PI are broad spectrum microbicidal agents and therefore potential coating materials for sutures to prevent Surgical Site Infections (SSI). TCA-AgNP-PI inhibits the studied pathogens stronger than Cinn-AgNP-PI in-vitro and on coated sutures. Dynamic light scattering (DLS), ultraviolet-visible spectroscopy (UV-Vis), Fourier Transform infrared spectroscopy (FT-IR), Raman, X-ray diffraction (XRD), microstructural analysis by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) confirmed the composition of TCA-AgNP-PI and Cinn-AgNP-PI. Smart solutions involving hybrid materials based on synergistic antimicrobial action have promising future perspectives to combat resistant microorganisms.
Multiplicity of Research Programs in the Biological Systematics: A Case for Scientific Pluralism
Authors: Pavlinov Igor Y.
Published: April 2020

Biological diversity (BD) explored by biological systematics is a complex yet organized natural phenomenon and can be partitioned into several aspects, defined naturally with reference to various causal factors structuring biota. These BD aspects are studied by particular research programs based on specific taxonomic theories (TTs). They provide, in total, a framework for comprehending the structure of biological systematics and its multi-aspect relations to other fields of biology. General principles of individualizing BD aspects and construing TTs as quasi-axiomatics are briefly considered. It is stressed that each TT is characterized by a specific combination of interrelated ontological and epistemological premises most adequate to the BD aspect a TT deals with. The following contemporary research programs in systematics are recognized and characterized in brief: phenetic, rational (with several subprograms), numerical, typological (with several subprograms), biosystematic, biomorphic, phylogenetic (with several subprograms), and evo-devo. From a scientific pluralism perspective, all of these research programs, if related to naturally defined particular BD aspects, are of the same biological and scientific significance. They elaborate “locally” natural classifications that can be united by a generalized faceted classification.
Eco-Physiological Screening of Different Tomato Genotypes in Response to High Temperatures: A Combined Field-to-Laboratory Approach
Authors: Arena Carmen , Conti Stefano , Francesca Silvana , Melchionna Giuseppe , Hájek Josef , Barták Miloš , Barone Amalia , Rigano Maria Manuela
Published: April 2020

High temperatures represent a limitation for growth and development of many crop species. Several studies have demonstrated that the yield reduction of tomato under high temperatures and drought is mainly due to a photosynthetic decline. In this paper, a set of 15 tomato genotypes were screened for tolerance to elevated temperatures by cultivating plants under plastic walk-in tunnels. To assess the potential tolerance of tomato genotypes to high temperatures, measurements of chlorophyll fluorescence, pigments content and leaf functional traits have been carried out together with the evaluation of the final yields. Based on the greenhouse trials, a group of eight putative heat-sensitive and heat-tolerant tomato genotypes was selected for laboratory experiments aimed at investigating the effects of short-term high temperatures treatments in controlled conditions. The chlorophyll fluorescence induction kinetics were recorded on detached leaves treated for 60 min at 35 °C or at 45 °C. The last treatment significantly affected the photosystem II (PSII) photochemical efficiency (namely maximum PSII quantum efficiency, Fv/Fm, and quantum yield of PSII electron transport, ΦPSII) and the non-photochemical quenching (NPQ) in the majority of genotypes. The short-term heat shock treatments also led to significant differences in the shape of the slow Kautsky kinetics and its significant time points (chlorophyll fluorescence levels minimum O, peak P, semi-steady state S, maximum M, terminal steady state T) compared to the control, demonstrating heat shock-induced changes in PSII functionality. Genotypes potentially tolerant to high temperatures have been identified. Our findings support the idea that chlorophyll fluorescence parameters (i.e., ΦPSII or NPQ) and some leaf functional traits may be used as a tool to detect high temperatures-tolerant tomato cultivars.
Thrips as the Transmission Bottleneck for Mixed Infection of Two Orthotospoviruses
Authors: Zhao Kaixi , Rosa Cristina
Published: April 2020

Mixed infections provide opportunities for viruses to increase genetic diversity by facilitating genomic reassortment or recombination, and they may lead to the emergence of new virus species. Mixed infections of two economically important orthotospoviruses, Tomato spotted wilt orthotospovirus (TSWV) and Impatiens necrotic spot orthotospovirus (INSV), were found in recent years, but no natural reassortants between INSV and TSWV were ever reported. The goal of this study was to establish how vector preferences and the ability to transmit INSV and TSWV influence transmission and establishment of mixed infections. Our results demonstrate that thrips prefer to oviposit on TSWV and INSV mixed-infected plants over singly infected or healthy plants, providing young nymphs with the opportunity to acquire both viruses. Conversely, we observed that thrips served as a bottleneck during transmission and favored transmission of one of the two viruses over the second one, or over transmission of both viruses simultaneously. This constraint was relaxed in plants, when transmission of TSWV and INSV occurred sequentially, demonstrating that plants serve as orthotospovirus permissive hosts, while thrips serve as a bottleneck. Viral fitness, as measured by virus replication, transmission, and competition with other viral strains, is not well studied in mixed infection. Our study looks at the success of transmission during mixed infection of orthotopoviruses, enhancing the understanding of orthotospovirus epidemiology and evolution.
Plant Polyamines
Authors: Takahashi Taku
Published: April 2020

Polyamines are small organic compounds found in all living organisms. According to the high degree of positive charge at physiological pH, they interact with negatively charged macromolecules, such as DNA, RNA, and proteins, and modulate their activities. In plants, polyamines, some of which are presented as a conjugated form with cinnamic acids and proteins, are involved in a variety of physiological processes. In recent years, the study of plant polyamines, such as their biosynthetic and catabolic pathways and the roles they play in cellular processes, has flourished, becoming an exciting field of research. There is accumulating evidence that polyamine oxidation, the main catabolic pathway of polyamines, may have a potential role as a source of hydrogen peroxide. The papers in this Special Issue highlight new discoveries and research in the field of plant polyamine biology. The information will help to stimulate further research and make readers aware of the link between their own work and topics related to polyamines.
Freezing Tolerance and Expression of β-amylase Gene in Two Actinidia arguta Cultivars with Seasonal Changes
Authors: Sun Shihang , Fang Jinbao , Lin Miaomiao , Qi Xiujuan , Chen Jinyong , Wang Ran , Li Zhi , Li Yukuo , Muhammad Abid
Published: April 2020

Low temperature causes injuries to plants during winter, thereby it affects kiwi fruit quality and yield. However, the changes in metabolites and gene expression during cold acclimation (CA) and deacclimation (DA) in kiwi fruit remain largely unknown. In this study, freezing tolerance, carbohydrate metabolism, and β-amylase gene expression in two Actinidia arguta cv. “CJ-1” and “RB-3” were detected from CA to DA stages. In all acclimation stages, the “CJ-1” was hardier than “RB-3” and possessed lower semi-lethal temperature (LT50). Furthermore, “CJ-1” had a more rapid acclimation speed than “RB-3”. Changes of starch, β-amylase, and soluble sugars were associated with freezing tolerance in both cultivars. Starch contents continued to follow a declining trend, while soluble sugars contents continuously accumulated in both cultivars during CA stages (from October to January). To investigate the possible molecular mechanism underlying cold response in A. arguta, in total, 16 AcBAMs genes for β-amylase were identified in the kiwi fruit genome. We carried out localization of chromosome, gene structure, the conserved motif, and the analysis of events in the duplication of genes from AcBAMs. Finally, a strong candidate gene named AaBAM3 from AcBAMs was cloned in Actinidia arguta (A. arguta), The real-time qPCR showed that AaBAM3 gene expression in seasonal changes was consistent with changes of soluble sugars. These results reveal that AaBAM3 may enhance the freezing tolerance of A. arguta through increasing soluble sugar content.
Novel Anion Exchange Membrane Based on Poly(Pentafluorostyrene) Substituted with Mercaptotetrazole Pendant Groups and Its Blend with Polybenzimidazole for Vanadium Redox Flow Battery Applications
Authors: Cho Hyeongrae , Atanasov Vladimir , Krieg Henning M. , Kerres Jochen A.
Published: April 2020

In order to evaluate the performance of the anion exchange membranes in a vanadium redox flow battery, a novel anion exchange polymer was synthesized via a three step process. Firstly, 1-(2-dimethylaminoethyl)-5-mercaptotetrazole was grafted onto poly(pentafluorostyrene) by nucleophilic F/S exchange. Secondly, the tertiary amino groups were quaternized by using iodomethane to provide anion exchange sites. Finally, the synthesized polymer was blended with polybenzimidazole to be applied in vanadium redox flow battery. The blend membranes exhibited better single cell battery performance in terms of efficiencies, open circuit voltage test and charge-discharge cycling test than that of a Nafion 212 membrane. The battery performance results of synthesized blend membranes suggest that those novel anion exchange membranes are promising candidates for vanadium redox flow batteries.
Rheological and Electrical Study of a Composite Material Based on an Epoxy Polymer Containing Cyclotriphosphazene
Authors: Dagdag O. , El Gouri M. , El Mansouri A. , Outzourhit A. , El Harfi A. , Cherkaoui O. , El Bachiri A. , Hamed O. , Jodeh S. , Hanbali G. , Khalaf B.
Published: April 2020

In this work, we have studied, formulated, prepared, and characterized the rheological and electrical behavior of a composite material based on an epoxy resin Diglycidyl Ether of Bisphenol A (DGEBA) reinforced with hexaglycidyl cyclotriphosphazene (HGCP). The epoxy system was cured with 4,4’-methylene dianiline (MDA). DGEBA-HGCP-MDA epoxy composite materials with reinforced HGCP which varied from 5% to 10% by weight were prepared by mixing in the molten state. The morphology was evaluated by SEM. The rheological behavior was studied using small deformation rheology. The electrical characterization was carried out with a frequency variation range from 1 Hz to 100 KHz at room temperature. These measurements revealed that the rheological and electrical behaviors strongly depend on the quantity of HGCP in the DGEBA matrix. The linear viscoelastic properties study reveals that the modulus of elasticity G’ is dependent on the amount of HGCP present in the epoxy resin DGEBA. The capacitance-frequency measurements suggest a distribution of localized states in the band gap of the blends.
A Novel Porous Ni, Ce-Doped PbO2 Electrode for Efficient Treatment of Chloride Ion in Wastewater
Authors: Liu Sheng , Gui Lin , Peng Ruichao , Yu Ping
Published: April 2020

The porous Ti/Sb-SnO2/Ni-Ce-PbO2 electrode was prepared by using a porous Ti plate as a substrate, an Sb-doped SnO2 as an intermediate, and a PbO2 doped with Ni and Ce as an active layer. The surface morphology and crystal structure of the electrode were characterized by scanning electron microscope(SEM), energy dispersive spectrometer(EDS), and X-Ray diffraction(XRD). The electrochemical performance of the electrodes was tested by linear sweep voltammetry (LSV), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and electrode life test. The results show that the novel porous Ni-Ce-PbO2 electrodes with larger active surface area have better electrochemical activity and longer electrode life than porous undoped PbO2 electrodes and flat Ni-Ce-PbO2 electrodes. In this work, the removal of Cl− in simulated wastewater on three electrodes was also studied. The results show that the removal effect of the porous Ni-Ce-PbO2 electrode is obviously better than the other two electrodes, and the removal rate is 87.4%, while the removal rates of the other two electrodes were 72.90% and 80.20%, respectively. In addition, the mechanism of electrochemical dechlorinating was also studied. With the progress of electrolysis, we find that the increase of OH- inhibits the degradation of Cl−, however, the porous Ni-Ce-PbO2 electrode can effectively improve the removal of Cl−.
Microbial Natural Products in Drug Discovery
Authors: Abdel-Razek Ahmed S. , El-Naggar Mehrez E. , Allam Ahmed , Morsy Osama M. , Othman Sarah I.
Published: April 2020

Over a long period of time, humans have explored many natural resources looking for remedies of various ailments. Traditional medicines have played an intrinsic role in human life for thousands of years, with people depending on medicinal plants and their products as dietary supplements as well as using them therapeutically for treatment of chronic disorders, such as cancer, malaria, diabetes, arthritis, inflammation, and liver and cardiac disorders. However, plant resources are not sufficient for treatment of recently emerging diseases. In addition, the seasonal availability and other political factors put constrains on some rare plant species. The actual breakthrough in drug discovery came concurrently with the discovery of penicillin from Penicillium notatum in 1929. This discovery dramatically changed the research of natural products and positioned microbial natural products as one of the most important clues in drug discovery due to availability, variability, great biodiversity, unique structures, and the bioactivities produced. The number of commercially available therapeutically active compounds from microbial sources to date exceeds those discovered from other sources. In this review, we introduce a short history of microbial drug discovery as well as certain features and recent research approaches, specifying the microbial origin, their featured molecules, and the diversity of the producing species. Moreover, we discuss some bioactivities as well as new approaches and trends in research in this field.
The Vulnerable (Post) Modern Self and the “Greening” of Spiritual Personhood through Life in the Spirit
Authors: Frohlich Mary
Published: April 2020

In the period now being called the Anthropocene, the fatal vulnerabilities of the modern way of constructing selfhood are becoming ever more evident. Joanna Macy, who writes from a Buddhist perspective, has argued for the need to “green” the self by rediscovering its participation in ecological and cosmic networks. From a Christian perspective, I would articulate this in terms of an imperative to rediscover our spiritual personhood as radical communion in both God and cosmos. In this paper, “self” refers to an ever-restless process of construction of identity based in self-awareness and aimed at maintaining one’s integrity, coherence, and social esteem. I use the term “person,” on the other hand, to refer to a relational center that exists to be in communion with other persons. How—within the conditions of the dawning Anthropocene—can the tension between these two essential aspects of human existence be opened up in a way that can more effectively protect human and other life on Earth? This would require, it seems, harnessing both the self-protective and the self-giving potentials of human beings. The proposed path is to give ourselves over into the rhythms of the Spirit, being breathed in to selfless personal communion and out to co-creation of our refreshed selfhood.
Systems Engineering Approach to Slope Stability Monitoring in the Digital Mine
Authors: Elmouttie Marc , Dean Peter
Published: April 2020

Slope stability monitoring in open cut mining is increasingly based on the use of a variety of different sensors and associated analytics, each capable of providing part of the understanding required to manage complex geotechnical environments. Designing an integrated monitoring system that is both attainable and fit for purpose can therefore be particularly challenging. In this paper, a systems engineering approach based on a novel methodology is presented to design the slope monitoring system. The methodology uses the rock engineering systems (RES) approach to system decomposition for geotechnical engineering problems, to determine the critical rock mass behaviours requiring monitoring. It follows this with the application of the system theoretic process analysis (STPA) approach, to design the control system for the monitoring system and identify and mitigate sub-optimal configurations. We demonstrate that the approach is practical to implement and supports transparent and defensible decision making for designing and implementing slope monitor systems. We apply the method to the design of a monitoring system for an Australian coal mine and demonstrate how the approach can facilitate the identification and design of new sensing modalities.
The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales
Authors: Avian Michael , Bauer Christian , Schlögl Matthias , Widhalm Barbara , Gutjahr Karl-Heinz , Paster Michael , Hauer Christoph , Frießenbichler Melina , Neureiter Anton , Weyss Gernot , Flödl Peter , Seier Gernot , Sulzer Wolfgang
Published: April 2020

Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative manner—in recent years. The availability of satellite data with very high revisiting time has gained momentum through the European Space Agency’s Sentinel missions, offering new application potential for Earth observation. This paper reviews the status of recent techniques such as terrestrial laserscanning, remote sensed imagery, and synthetic aperture radar in monitoring high mountain environments with a particular focus on the impact of new platforms such as Sentinel-1 and -2 as well as unmanned aerial vehicles. The study area comprises the high mountain glacial environment at the Pasterze Glacier, Austria. The area is characterized by a highly dynamic geomorphological evolution and by being subject to intensive scientific research as well as long-term monitoring. We primarily evaluate landform classification and process characterization for: (i) the proglacial lake; (ii) icebergs; (iii) the glacier river; (iv) valley-bottom processes; (v) slope processes; and (vi) rock wall processes. We focus on assessing the potential of every single method both in spatial and temporal resolution in characterizing different geomorphic processes. Examples of the individual techniques are evaluated qualitatively and quantitatively in the context of: (i) morphometric analysis; (ii) applicability in high alpine regions; and (iii) comparability of the methods among themselves. The final frame of this article includes considerations on scale dependent process detectability and characterization potentials of these Earth observation methods, along with strengths and limitations in applying these methods in high alpine regions.
Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height
Authors: Schlembach Florian , Passaro Marcello , Quartly Graham D. , Kurekin Andrey , Nencioli Francesco , Dodet Guillaume , Piollé Jean-François , Ardhuin Fabrice , Bidlot Jean , Schwatke Christian , Seitz Florian , Cipollini Paolo , Donlon Craig
Published: April 2020

Radar altimeters have been measuring ocean significant wave height for more than three decades, with their data used to record the severity of storms, the mixing of surface waters and the potential threats to offshore structures and low-lying land, and to improve operational wave forecasting. Understanding climate change and long-term planning for enhanced storm and flooding hazards are imposing more stringent requirements on the robustness, precision, and accuracy of the estimates than have hitherto been needed. Taking advantage of novel retracking algorithms, particularly developed for the coastal zone, the present work aims at establishing an objective baseline processing chain for wave height retrieval that can be adapted to all satellite missions. In order to determine the best performing retracking algorithm for both Low Resolution Mode and Delay-Doppler altimetry, an objective assessment is conducted in the framework of the European Space Agency Sea State Climate Change Initiative project. All algorithms process the same Level-1 input dataset covering a time-period of up to two years. As a reference for validation, an ERA5-based hindcast wave model as well as an in-situ buoy dataset from the Copernicus Marine Environment Monitoring Service In Situ Thematic Centre database are used. Five different metrics are evaluated: percentage and types of outliers, level of measurement noise, wave spectral variability, comparison against wave models, and comparison against in-situ data. The metrics are evaluated as a function of the distance to the nearest coast and the sea state. The results of the assessment show that all novel retracking algorithms perform better in the majority of the metrics than the baseline algorithms currently used for operational generation of the products. Nevertheless, the performance of the retrackers strongly differ depending on the coastal proximity and the sea state. Some retrackers show high correlations with the wave models and in-situ data but significantly under- or overestimate large-scale spectral variability. We propose a weighting scheme to select the most suitable retrackers for the Sea State Climate Change Initiative programme.
Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data
Authors: Böhler Jonas E. , Schaepman Michael E. , Kneubühler Mathias
Published: April 2020

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%).
Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data
Authors: Shirmard Hodjat , Farahbakhsh Ehsan , Beiranvand Pour Amin , Muslim Aidy M , Müller R. Dietmar , Chandra Rohitash
Published: April 2020

There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.
Analysis of the Recent Agricultural Situation of Dakhla Oasis, Egypt, Using Meteorological and Satellite Data
Authors: Kimura Reiji , Iwasaki Erina , Matsuoka Nobuhiro
Published: April 2020

Dakhla Oasis is the most highly populated oasis in Egypt. Although the groundwater resource is very large, there is essentially no rainfall and the aquifer from which the water is drawn is not recharged. Therefore, for the future development and sustainability of Dakhla Oasis, it is important to understand how land and water are used in the oasis and meteorological conditions there. In this study, meteorological and satellite data were used to examine the recent agricultural situation and water use. The results showed that the meteorological conditions are suitable for plant production, and the maximum vegetation index value was comparable to the Nile delta. The cultivated area increased between 2001 and 2019 by 13.8 km2 year−1, with most of the increase occurring after the 2011 revolution (21.2 km2 year−1). People living in Dakhla Oasis derive their income primarily from agricultural activity, which requires abundant water. Thus, the increasing demand for water is likely to put pressure on the groundwater resource and limit its sustainability.
A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection
Authors: Šipoš Danijel , Gleich Dušan
Published: April 2020

This paper presents the development of a lightweight and low-power Ground Penetrating Radar (GPR) to detect buried landmines in harsh terrain, using an Unmanned Aerial Vehicle (UAV). Despite the fact that GPR airborne systems have been already used for a while, there has yet been no focus on the UAV autonomy, which depends on the payload itself. Therefore, the contribution of this work is the introduction of a lightweight and low-power consumption GPR system, which is based on the Stepped Frequency Continuous Wave (SFCW) radar principle. The Radio Frequency (RF) transceiver represents an improved implementation of the super-heterodyne architecture, which currently offers higher sensitivity. This is achieved by combining analog and digital processing techniques. The experimental results showed that the developed system can detect both metallic and plastic buried targets. Target detection with a scanning height up to about 0.5 m shows good applicability in an unstructured, harsh environment, which is typical of mined terrain. The proposed system still needs some improvements for a fully operational system regarding different aspects of scanning speeds and soil properties such as moisture content.
Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing
Authors: des Tombe Bas , Schilperoort Bart , Bakker Mark
Published: April 2020

Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a sub-meter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scattering-loss coefficients for these wavelengths are temperature-dependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multi-variate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the open-source Python package “dtscalibration”.
An End-To-End LwM2M-Based Communication Architecture for Multimodal NB-IoT/BLE Devices
Authors: Basu Subho Shankar , Haxhibeqiri Jetmir , Baert Mathias , Moons Bart , Karaagac Abdulkadir , Crombez Pieter , Camerlynck Pieterjan , Hoebeke Jeroen
Published: April 2020

The wireless Internet of Things (IoT) landscape is quite diverse. For instance, Low-Power Wide-Area Network (LPWAN) technologies offer low data rate communication over long distance, whereas Wireless Personal Area Network (WPAN) technologies can reach higher data rates, but with a reduced range. For simple IoT applications, communication requirements can be fulfilled by a single technology. However, the requirements of more demanding IoT use cases can vary over time and with the type of data being exchanged. This is pushing the design towards multimodal approaches, where different wireless IoT technologies are combined and the most appropriate one is used as per the need. This paper considers the combination of Narrow Band IoT (NB-IoT) and Bluetooth Low Energy (BLE) as communication options for an IoT device that is running a Lightweight Machine to Machine/Constrained Application Protocol (LwM2M/CoAP) protocol stack. It analyses the challenges incurred by different protocol stack options, such as different transfer modes (IP versus non-IP), the use of Static Context Header Compression (SCHC) techniques, and Datagram Transport Layer Security (DTLS) security modes, and discusses the impact of handover between both communication technologies. A suitable end-to-end architecture for the targeted multimodal communication is presented. Using a prototype implementation of this architecture, an in-depth assessment of handover and its resulting latency is performed.
Computationally Efficient 3D Orientation Tracking Using Gyroscope Measurements
Authors: Stančin Sara , Tomažič Sašo
Published: April 2020

Computationally efficient 3D orientation (3DO) tracking using gyroscope angular velocity measurements enables a short execution time and low energy consumption for the computing device. These are essential requirements in today’s wearable device environments, which are characterized by limited resources and demands for high energy autonomy. We show that the computational efficiency of 3DO tracking is significantly improved by correctly interpreting each triplet of gyroscope measurements as simultaneous (using the rotation vector called the Simultaneous Orthogonal Rotation Angle, or SORA) rather than as sequential (using Euler angles) rotation. For an example rotation of 90°, depending on the change in the rotation axis, using Euler angles requires 35 to 78 times more measurement steps for comparable levels of accuracy, implying a higher sampling frequency and computational complexity. In general, the higher the demanded 3DO accuracy, the higher the computational advantage of using the SORA. Furthermore, we demonstrate that 12 to 14 times faster execution is achieved by adapting the SORA-based 3DO tracking to the architecture of the executing low-power ARM Cortex® M0+ microcontroller using only integer arithmetic, lookup tables, and the small-angle approximation. Finally, we show that the computational efficiency is further improved by choosing the appropriate 3DO computational method. Using rotation matrices is 1.85 times faster than using rotation quaternions when 3DO calculations are performed for each measurement step. On the other hand, using rotation quaternions is 1.75 times faster when only the final 3DO result of several consecutive rotations is needed. We conclude that by adopting the presented practices, the clock frequency of a processor computing the 3DO can be significantly reduced. This substantially prolongs the energy autonomy of the device and enhances its usability in day-to-day measurement scenarios.
Impact of Self-Assembled Monolayer Design and Electrochemical Factors on Impedance-Based Biosensing
Authors: Brothers Michael C. , Moore David , St. Lawrence Michael , Harris Jonathan , Joseph Ronald M. , Ratcliff Erin , Ruiz Oscar N. , Glavin Nicholas , Kim Steve S.
Published: April 2020

Real-time sensing of proteins, especially in wearable devices, remains a substantial challenge due to the need to convert a binding event into a measurable signal that is compatible with the chosen analytical instrumentation. Impedance spectroscopy enables real-time detection via either measuring electrostatic interactions or electron transfer reactions while simultaneously being amenable to miniaturization for integration into wearable form-factors. To create a more robust methodology for optimizing impedance-based sensors, additional fundamental studies exploring components influencing the design and implementation of these sensors are needed. This investigation addresses a sub-set of these issues by combining optical and electrochemical characterization to validate impedance-based sensor performance as a function of (1) biorecognition element density, (2) self-assembled monolayer chain length, (3) self-assembled monolayer charge density, (4) the electrochemical sensing mechanism and (5) the redox reporter selection. Using a pre-existing lysozyme aptamer and lysozyme analyte combination, we demonstrate a number of design criteria to advance the state-of-the-art in protein sensing. For this model system we demonstrated the following: First, denser self-assembled monolayers yielded substantially improved sensing results. Second, self-assembled monolayer composition, including both thickness and charge density, changed the observed peak position and peak current. Third, single frequency measurements, while less informative, can be optimized to replace multi-frequency measurements and in some cases (such as that with zwitterionic self-assembled monolayers) are preferred. Finally, various redox reporters traditionally not used in impedance sensing should be further explored. Collectively, these results can help limit bottlenecks associated with device development, enabling realization of next-generation impedance-based biosensing with customize sensor design for the specific application.
NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals
Authors: Dash Debadatta , Ferrari Paul , Dutta Satwik , Wang Jun
Published: April 2020

Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a higher communication rate than the current BCIs. Although recent progress has demonstrated the potential of speech-BCIs from either invasive or non-invasive neural signals, the majority of the systems developed so far still assume knowing the onset and offset of the speech utterances within the continuous neural recordings. This lack of real-time voice/speech activity detection (VAD) is a current obstacle for future applications of neural speech decoding wherein BCI users can have a continuous conversation with other speakers. To address this issue, in this study, we attempted to automatically detect the voice/speech activity directly from the neural signals recorded using magnetoencephalography (MEG). First, we classified the whole segments of pre-speech, speech, and post-speech in the neural signals using a support vector machine (SVM). Second, for continuous prediction, we used a long short-term memory-recurrent neural network (LSTM-RNN) to efficiently decode the voice activity at each time point via its sequential pattern-learning mechanism. Experimental results demonstrated the possibility of real-time VAD directly from the non-invasive neural signals with about 88% accuracy.
A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot
Authors: Liu Jikai , Wang Pengfei , Zha Fusheng , Guo Wei , Jiang Zhenyu , Sun Lining
Published: April 2020

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.
Network Coding for Efficient Video Multicast in Device-to-Device Communications
Authors: Wang Lei , Li Yulong , Pan Bo , Wu Qiuwei , Yin Jun , Xu Lijie
Published: April 2020

Device-to-Device (D2D) communication is one of the critical technologies for the fifth-generation network, which allows devices to communicate directly with each other while increasing transmission rate, but this communication is vulnerable to interference. When video transmission is carried out in an environment with interference, problems such as high packet loss rate, poor quality of the video, and blurred screen may exist. These problems can be effectively solved by redundant coding operations at the source node, but the extra coding operation imposes a heavy computational burden on the source node. In order to alleviate the computational overhead of the source node, reduce transmission delay, and guarantee transmission quality, this paper proposes an efficient video multicast transmission scheme based on Random Linear Network Coding (RLNC) in D2D networks. In the scheme, the receiving devices in the transmission participate in the process of generating repair packets that are used to remedy the loss of encoded packets during transmission. The source node multicasts the encoded video file. The receiving nodes re-encode the received data packets with RLNC and then send them to the network again. The nearby nodes can decode the original data through the encoded or re-encoded data packets. The performance of the proposed scheme is evaluated through both simulation and real experiments. The experimental results show that compared with the traditional RLNC scheme, this scheme could balance the computation overhead of the mobile devices and reduce the encoding and decoding delay by about 8%. When the packet loss rate is high, the proposed scheme can obtain better video quality than the traditional replication-based scheme.
Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations
Authors: Borisov Andrey , Bosov Alexey , Miller Boris , Miller Gregory
Published: April 2020

The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent “prediction–correction” form with some predefined basic prediction and correction terms, and then they are optimally fused. The CMNF estimates have the following advantageous features. First, the obtained estimates are unbiased. Second, the theoretical covariance matrix of CMNF errors meets the real values. Third, the CMNF algorithm gives a possibility to choose the preliminary observation transform, basic prediction, and correction functions in any specific case of the observation system to improve the estimate accuracy significantly. All the features of conditionally-minimax estimates are demonstrated by the regression example of random position estimate given the noisy bearing observations. The contribution of the paper is the numerical study of the CMNF algorithm applied to the underwater target tracking given bearing-only and bearing-Doppler observations.
Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic
Authors: Wang Chang , Sun Qinyu , Li Zhen , Zhang Hongjia
Published: April 2020

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.
Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging
Authors: Zhang Dongyan , Wang Qian , Lin Fenfang , Yin Xun , Gu Chunyan , Qiao Hongbo
Published: April 2020

Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB is a short cycle disease and is highly destructive under conducive environments. To provide technical support for the rapid detection of the FHB disease, we proposed to develop a new Fusarium disease index (FDI) based on the spectral data of 374–1050 nm. This study was conducted through the analysis of reflectance spectral data of healthy and diseased wheat ears at the flowering and filling stages by hyperspectral imaging technology and the random forest method. The characteristic wavelengths selected were 570 nm and 678 nm for the late flowering stage, 565 nm and 661 nm for the early filling stage, 560 nm and 663 nm for the combined stage (combining both flowering and filling stages) by random forest. FDI at each stage was derived from the wavebands of each corresponding stage. Compared with other 16 existing spectral indices, FDI demonstrated a stronger ability to determine the severity of the FHB disease. Its determination coefficients (R2) values exceeded 0.90 and the RMSEs were less than 0.08 in the models for each stage. Furthermore, the model for the combined stage performed better when used at single growth stage, but its effect was weaker than that of the models for the two individual growth stages. Therefore, using FDI can provide a new tool to detect the FHB disease at different growth stages in wheat.