bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Structural Variants in Two Novel Genomes of Maize Inbred Lines Possibly Related to Glyphosate Tolerance
Authors: Mahmoud Medhat , Gracz-Bernaciak Joanna , Żywicki Marek , Karłowski Wojciech , Twardowski Tomasz , Tyczewska Agata
Published: April 2020

To study genetic variations between genomes of plants that are naturally tolerant and sensitive to glyphosate, we used two Zea mays L. lines traditionally bred in Poland. To overcome the complexity of the maize genome, two sequencing technologies were employed: Illumina and Single Molecule Real-Time (SMRT) PacBio. Eleven thousand structural variants, 4 million SNPs and approximately 800 thousand indels differentiating the two genomes were identified. Detailed analyses allowed to identify 20 variations within the EPSPS gene, but all of them were predicted to have moderate or unknown effects on gene expression. Other genes of the shikimate pathway encoding bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase and chorismate synthase were altered by variants predicted to have a high impact on gene expression. Additionally, high-impact variants located within the genes involved in the active transport of glyphosate through the cell membrane encoding phosphate transporters as well as multidrug and toxic compound extrusion have been identified.
Selenium Interactions with Algae: Chemical Processes at Biological Uptake Sites, Bioaccumulation, and Intracellular Metabolism
Authors: Ponton Dominic E. , Graves Stephanie D. , Fortin Claude , Janz David , Amyot Marc , Schiavon Michela
Published: April 2020

Selenium (Se) uptake by primary producers is the most variable and important step in determining Se concentrations at higher trophic levels in aquatic food webs. We gathered data available about the Se bioaccumulation at the base of aquatic food webs and analyzed its relationship with Se concentrations in water. This important dataset was separated into lotic and lentic systems to provide a reliable model to estimate Se in primary producers from aqueous exposure. We observed that lentic systems had higher organic selenium and selenite concentrations than in lotic systems and selenate concentrations were higher in lotic environments. Selenium uptake by algae is mostly driven by Se concentrations, speciation and competition with other anions, and is as well influenced by pH. Based on Se species uptake by algae in the laboratory, we proposed an accurate mechanistic model of competition between sulfate and inorganic Se species at algal uptake sites. Intracellular Se transformations and incorporation into selenoproteins as well as the mechanisms through which Se can induce toxicity in algae has also been reviewed. We provided a new tool for risk assessment strategies to better predict accumulation in primary consumers and consequently to higher trophic levels, and we identified some research needs that could fill knowledge gaps.
Agrobacterium-Mediated Transformation of Chrysanthemum with Artemisinin Biosynthesis Pathway Genes
Authors: Firsov Aleksey , Mitiouchkina Tatiana , Shaloiko Lyubov , Pushin Alexander , Vainstein Alexander , Dolgov Sergey
Published: April 2020

Artemisinin-based drugs are the most effective medicine for the malaria treatment. To date, the main method of artemisinin production is its extraction from wormwood plants Artemisia annua L. Due to the limitation of this source, considerable efforts are now directed to the development of methods for artemisinin production using heterologous expression systems. Artemisinin is a sesquiterpene lactone, synthesized through the cyclization of farnesyl diphosphate involved in other sesquiterpene biosynthetic systems. Chrysanthemum species as well as A. annua, belong to Asteraceae family, and had been characterized by containing highly content of sesquiterpenes and their precursors. This makes chrysanthemum a promising target for the production of artemisinin in heterologous host plants. Chrysanthemum (C. morifolium Ramat.) was transformed by Agrobacterium tumefaciens carrying with the binary vectors p1240 and p1250, bearing artemisinin biosynthesis genes coding: amorpha-4,11-diene synthase, artemisinic aldehyde Δ11(13) reductase, amorpha-4,11-diene monooxygenase (p1240 was targeted to the mitochondria and p1250 was targeted to the cytosol), cytochrome P450 reductase from A. annua, as well as yeast truncated 3-hydroxy-3-methylglutarylcoenzyme A reductase. This study obtained 8 kanamycin-resistant lines after transformation with the p1240 and 2 lines from p1250. All target genes were detected in 2 and 1 transgenic lines of the 2 vectors. The transformation frequency of all target genes were 0.33% and 0.17% for p1240 and p1250, relative to the total transformed explant numbers. RT-PCR analysis revealed the transcription of all transferred genes in two lines obtained after transformation with the p1240 vector, confirming the possibility of transferring genetic modules encoding entire biochemical pathways into the chrysanthemum genome. This holds promise for the development of a chrysanthemum-based expression system to produce non-protein substances, such as artemisinin.
Catalytic Oxidation of Methylene Blue by Use of Natural Zeolite-Based Silver and Magnetite Nanocomposites
Authors: Kuntubek Aldiyar , Kinayat Nurassyl , Meiramkulova Kulyash , Poulopoulos Stavros G. , Bear Joseph C. , Inglezakis Vassilis J.
Published: April 2020

This work reports the synthesis of natural zeolite-based silver and magnetite nanocomposites and their application for the catalytic oxidation of methylene blue in water. The zeolite was impregnated with 5.5 wt.% Fe in the form of magnetite nanoparticles with size of 32 nm, and with 6.4 wt.% Ag in the form of silver oxide and metallic silver nanoparticles with sizes of 42 and 20 nm, respectively. The results showed that physical adsorption contributed to the removal of methylene blue by 25–36% and that Fe3O4@NZU is superior to Ag2O@NZU and Ag0@NZU, leading to 55% removal without oxidant and 97% in the presence of H2O2. However, there is no evidence of significant mineralization of methylene blue. The application of reaction rate models showed that the reaction order changes from zero to first and second order depending on the H2O2 concentration.
Polyphenol Profile and Antimicrobial and Cytotoxic Activities of Natural Mentha × piperita and Mentha longifolia Populations in Northern Saudi Arabia
Authors: Elansary Hosam O. , Szopa Agnieszka , Kubica Paweł , Ekiert Halina , Klimek-Szczykutowicz Marta , El-Ansary Diaa O. , Mahmoud Eman A.
Published: April 2020

New sources of polyphenols with anticancer, antioxidant, and antimicrobial properties in arid environments are critical for the development of alternative medicines and natural remedies. This study explored the polyphenol profiles and biological activities of methanolic leaf extracts from natural Mentha × piperita and Mentha longifolia populations in northern Saudi Arabia. Chromatographic analyses identified several polyphenols in M. × piperita including phenolic acids: rosmarinic acid (1547.6 mg/100 g DW (dry weight)), cryptochlorogenic acid (91.7 mg/100 g DW), and chlorogenic acid (69.4 mg/100 g DW), as well as flavonoids: naringin (328.8 mg/100 g DW) and cynaroside (162.8 mg/100 g DW). The major polyphenols in M. longifolia were: rosmarinic acid (781.6 mg/100 g DW), cryptochlorogenic acid (191.1 mg/100 g DW), p-coumaric acid (113.0 mg/100 g DW), m-coumaric acid (112.2 mg/100 g DW), and chlorogenic acid (63.8 mg/100 g DW). M. × piperita and M. longifolia leaf extracts had high antioxidant activities due to the major polyphenols (cynaroside, rosmarinic and cryptochlorogenic acids). M. × piperita had higher activities against different cancer cells than M. longifolia. Naringin, cryptochlorogenic acid, and rosmarinic acid had the highest activities against cancer cells. The leaf extracts had antibacterial effects against most bacteria species (Pseudomonas aeruginosa was most sensitive), which was attributed to the polyphenols. Antifungal activities were similarly broad (Aspergillus flavus was most sensitive) and attributed to naringin, cryptochlorogenic acid, and caffeic acid. Populations of M. × piperita and M. longifolia in Northern Riyadh may be a valuable source of natural biologically active compounds.
Bridge2Hyku: Meeting Practitioners’ Needs in Digital Collection Migration to Open Source Samvera Repository
Authors: Wu Annie , Thompson Santi , Washington Anne , Watkins Sean , Weidner Andrew , Seeman Dean , Woodward Nicholas
Published: April 2020

The University of Houston Libraries, in partnership and consultation with numerous institutions, was awarded an Institute of Museum and Library Services (IMLS) National Leadership/Project Grant to create the Bridge2Hyku (B2H) Toolkit. Content migration from proprietary systems to open source repositories remains a barrier for many institutions due to lack of tools, tutorials, and documentation. The B2H Toolkit, which includes migration strategies, migration tools, as well as system requirements for transitioning from CONTENTdm to Hyku, acts as a comprehensive resource to facilitate repository migration. Through a phased toolkit development process, the project team solicited inputs and feedback from peer migration practitioners via survey and pilot testing. The analysis of the feedback data was built into use cases which informed the development and enhancement of the migration strategies and tools. Working across institutions with migration practitioners’ needs in mind, the project team was able to successfully release a Toolkit that mitigates migration barriers and fills gaps in the migration process. Providing a path to a community-supported open source digital solution, the Bridge2Hyku Toolkits ensures access and expanded use of digital content and collections of libraries and cultural heritage institutions.
Wireless Systems and Networks in the IoT
Authors: Gavalas Damianos , Stavrakis Modestos , Chatzimisios Periklis , Cao Zhichao , Zheng Xiaolong
Published: April 2020

This Special Issue is focused on breakthrough developments in the field of Wireless Systems and Networks in the IoT. The selected contributions report current scientific progress in a wide range of topics covering clock error compensation in sensor networks, backscatter communication networks, Radio-Frequency Identification (RFID)-based inventory management, resource allocation in Long-Term Evolution (LTE)/LTE-A, (Long Range Wide-Area Network (LoRaWAN) modeling and key generation for the IoT.
Analysis of Wave Patterns Under the Region of Macro-Fiber Composite Transducer to Improve the Analytical Modelling for Directivity Calculation in Isotropic Medium
Authors: Tiwari Kumar Anubhav , Raisutis Renaldas , Mazeika Liudas
Published: April 2020

Analytical modelling is an efficient approach to estimate the directivity of a transducer generating guided waves in the research field of ultrasonic non-destructive testing of the large and complex structures due to its short processing time as compared to the numerical modelling and experimental techniques. The wave patterns or the amplitude variations along the region of ultrasonic transducer itself depend on its behavior, excitation frequency, and the type of propagating wave mode. Depending on the wave-pattern of a propagating wave mode, the appropriate value of the amplitude correction factor must be multiplied to the amplitudes of the excitation signal for the accurate evaluation of directivity pattern of the ultrasonic transducers generating guided waves in analytical modelling. The objective of this work is to analyse the wave patterns under the region of macro-fiber composite (MFC) transducer to improve the accuracy of a previously developed analytical model for the prediction of directivity patterns. Firstly, the amplitude correction factor based on the wave patterns under the region of P1-type MFC (MFC-2814) transducer at two different frequencies (80 kHz, 3 periods and 220 kHz, 3 period) glued on 2 mm Al alloy plate has been estimated analytically in the case of an asymmetric (A0) guided Lamb wave. The validation of analytically estimated amplitude correction factor is performed by a proposed experimental method that allows analyzing the behaviour of MFC transducer under its region by gluing MFC on bottom surface and scanning the receiver on the top surface of the sample. Later on, the estimated amplitude correction factor is included in the previously developed 2D analytical model for the improvement in the directivity patterns of the A0 mode. The modified analytical model shows a significant improvement in the directivity pattern of the A0 wave mode in comparison to the results obtained by the previous model without considering the proper wave patterns. The results reveal that errors between the directivity estimated by the present modified 2D analytical model and experimental investigation are reduced by more than 58% in comparison to the previously developed analytical model.
Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN)
Authors: Shaukat Haafizah Rameeza , Hashim Fazirulhisyam , Shaukat Muhammad Arslan , Ali Alezabi Kamal
Published: April 2020

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.
Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation
Authors: Fariña Bibiana , Toledo Jonay , Estevez Jose Ignacio , Acosta Leopoldo
Published: April 2020

This paper describes a localization module for an autonomous wheelchair. This module includes a combination of various sensors such as odometers, laser scanners, IMU and Doppler speed sensors. Every sensor used in the module features variable covariance estimation in order to yield a final accurate localization. The main problem of a localization module composed of different sensors is the accuracy estimation of each sensor. Average static values are normally used, but these can lead to failure in some situations. In this paper, all the sensors have a variable covariance estimation that depends on the data quality. A Doppler speed sensor is used to estimate the covariance of the encoder odometric localization. Lidar is also used as a scan matching localization algorithm, comparing the difference between two consecutive scans to obtain the change in position. Matching quality gives the accuracy of the scan matcher localization. This structure yields a better position than a traditional odometric static covariance method. This is tested in a real prototype and compared to a standard fusion technique.
Research on Remote GPS Common-View Precise Time Transfer Based on Different Ionosphere Disturbances
Authors: Zhang Jingkui , Gao Jingxiang , Yu Baoguo , Sheng Chuanzhen , Gan Xingli
Published: April 2020

Propagation path delays are a major error for the remote precise time transfer of common view; these path delays contain the ionosphere and troposphere impact, while the contributions of the ionosphere and the troposphere from common-view satellites to receivers on the ground tend to become uncorrelated when the distance between these receivers increases. In order to select the appropriate ionospheric correction method for common view under different distances between receivers, a detailed test using multi-source data under different ionosphere disturbances are carried out in this paper. Here, we choose three different ionosphere disturbance methods and analyze the advantages and disadvantages of these methods for common-view time transfer and time comparison. At last, we put forward a suitable ionospheric correction method for different distances common view. The RMS shows that the method proposed for 3000 km remote common view can achieve 2.5 ns.
Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review
Authors: Nie Luwei , Berckmans Daniel , Wang Chaoyuan , Li Baoming
Published: April 2020

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs.
A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number
Authors: Yao Yuanyuan , Lei Hong , He Wenjing
Published: April 2020

Estimating directions of arrival (DOA) without knowledge of the source number is regarded as a challenging task, particularly when coherence among sources exists. Researchers have trained deep learning (DL)-based models to attack the problem of DOA estimation. However, existing DL-based methods for coherent sources do not adapt to variable source numbers or require signal independence. Herein, we put forward a new framework combining parallel DOA estimators with Toeplitz matrix reconstruction to address the problem. Each estimator is constructed by connecting a multi-label classifier to a spatial filter, which is based on convolutional-recurrent neural networks. Spatial filters divide the angle domain into several sectors, so that the following classifiers can extract the arrival directions. Assisted with Toeplitz-based method for source-number determination, pseudo or missed angles classified by the estimators will be reduced. Then, the spatial spectrum can be more accurately recovered. In addition, the proposed method is data-driven, so it is naturally immune to signal coherence. Simulation results demonstrate the predominance of the proposed method and show that the trained model is robust to imperfect circumstances such as limited snapshots, colored Gaussian noise, and array imperfections.
Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis
Authors: Li Wudong , Jiang Weiping , Li Zhao , Chen Hua , Chen Qusen , Wang Jian , Zhu Guangbin
Published: April 2020

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.
A New Construction of High Performance LDPC Matrices for Mobile Networks
Authors: Sarvaghad-Moghaddam Moein , Ullah Waheed , Jayakody Dushantha Nalin K. , Affes Sofiène
Published: April 2020

Secure and reliable information flow is one of the main challenges in social IoT and mobile networks. Information flow and data integrity is still an open research problem. In this paper, we develop new methods of constructing systematic and regular Low-Density Parity-Check Matrices (LDPCM), inspired by the structure of the Sarrus method and geometric designs. Furthermore, these codes have cyclic structure and therefore, are less complex in computation and also require less memory in hardware implementation. Besides, an optimal method of post-processing for deleting girths four is presented. Numerical results show that the codes constructed by these methods perform well over the additive white Gaussian noise (AWGN) channel when decoded with the sum-product LDPC iterative algorithms. The proposed methods can be very efficient in terms of reducing memory consumption and improving the convergence speed of the decoder particularly in IoT and mobile networks.
An Adaptive Autogram Approach Based on a CFAR Detector for Incipient Cavitation Detection
Authors: Chu Ning , Wang Linlin , Yu Liang , He Changbo , Cao Linlin , Huang Bin , Wu Dazhuan
Published: April 2020

Cavitation failure often occurs in centrifugal pumps, resulting in severe harm to their performance and life-span. Nowadays, it has become crucial to detect incipient cavitation ahead of cavitation failure. However, most envelope demodulation methods suffer from strong noise and repetitive impacts. This paper proposes an adaptive Autogram approach based on the Constant False Alarm Rate (CFAR). A cyclic amplitude model (CAM) is presented to reveal the cyclostationarity and autocorrelation-periodicity of pump cavitation-caused signals. The Autogram method is improved for envelope demodulation and cyclic feature extraction by introducing the character to noise ratio (CNR) and CFAR threshold. To achieve a high detection rate, CNR parameters are introduced to represent the cavitation intensity in the combined square-envelope spectrum. To maintain a low false alarm, the CFAR detector is combined with the CNR parameter to obtain adaptive thresholds for different data along with sensor positions. By carrying out various experiments of a centrifugal water pump from Status 1 to 10 at different flow rates, the proposed approach is capable of cavitation feature extraction with respect to the CAM model, and can achieve more than a 90% detection rate of incipient cavitation and maintain a 5% false alarm rate. This paper offers an alternative solution for the predictive maintenance of pump cavitation.
Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing
Authors: Wang Zhongliang , Xiao Hua
Published: April 2020

The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.
An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks
Authors: Adil Muhammad , Almaiah Mohammed Amin , Omar Alsayed Alhuseen , Almomani Omar
Published: April 2020

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.
A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks
Authors: Yuste-Delgado Antonio-Jesus , Cuevas-Martinez Juan-Carlos , Triviño-Cabrera Alicia
Published: April 2020

Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the s k i p value of the network. The s k i p , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.
A New Thresholding Method for IR-UWB Radar-Based Detection Applications
Authors: Quan Xuanjun , Choi Jeong Woo , Cho Sung Ho
Published: April 2020

In this paper, we proposed a new thresholding method for impulse radio ultra-wideband (IR-UWB) radar-based detection applications by taking both the false alarm and miss-detection rates into consideration. The thresholding algorithm is the key point of the detection application, and there have been numerous studies on these developments. Most of these studies were related to the occurrence of false alarms, such as the constant false alarm rate algorithm (CFAR). However, very few studies have considered miss-detection, which is another crucial issue in detection applications. To mitigate this issue, our proposed algorithm considered miss-detection as well as the false alarms occurring during thresholding. In the proposed algorithm, a threshold is determined by combining a noise signal-based threshold, in which the focus point is the false alarm, with a target signal-based threshold, in which the focus point is a miss-detection, at a designed ratio. Therefore, a threshold can be determined based on the focus point by adjusting the designed ratio. In addition, the proposed algorithm can estimate the false alarm and miss-detection rates for the determined threshold, and thus, the threshold can be objectively set. Moreover, the proposed algorithm is better in terms of understanding the target signal for a given environment. A target signal can be affected by the clutter, installation height, and the angle of the radar, which are factors that noise-oriented algorithms do not consider. As the proposed algorithm analyzed the target signal, these factors were all considered. We analyzed the false alarm and miss-detection rates for the thresholds, which were determined by different combination ratios at various distances, and we experimentally verified the validity of the proposed algorithm.
A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar
Authors: Yu Myoungseok , Kim Narae , Jung Yunho , Lee Seongjoo
Published: April 2020

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.
Comparison of Multi-Frequency and Multi-Coil Electromagnetic Induction (EMI) for Mapping Properties in Shallow Podsolic Soils
Authors: Altdorff Daniel , Sadatcharam Kamaleswaran , Unc Adrian , Krishnapillai Manokarajah , Galagedara Lakshman
Published: April 2020

Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (ECa) of soil as a proxy for its physicochemical properties. Multi-frequency (MF) and multi-coil (MC) are the two types of commercially available EMI sensors. Although the working principles are similar, their theoretical and effective depth of investigation and their resolution capacity can vary. Given the recent emphasis on non-invasive mapping of soil properties, the selection of the most appropriate instrument is critical to support robust relationships between ECa and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between ECa (i.e., MF–ECa and MC–ECa) and shallow soil properties. Field experiments were conducted under wet and dry conditions on a silage-corn field in western Newfoundland, Canada. Relationships between temporally stable properties, such as texture and bulk density, and temporally variable properties, such as soil water content (SWC), cation exchange capacity (CEC) and pore water electrical conductivity (ECw) were investigated. Results revealed significant (p < 0.05) positive correlations of ECa to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–ECa. Under wet conditions, correlation of MF–ECa to temporally variable properties decreased, particularly to SWC, while the correlations to sand and silt increased. We concluded that the MF sensor is more sensitive to changes in SWC which influenced its ability to map temporally variable properties. The performance of the MC sensor was less affected by variable weather conditions, providing overall stronger correlations to both, temporally stable or variable soil properties for the tested Podzol and hence the more suitable sensor toward various precision agricultural practices.
Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss
Authors: Yin Aijun , Yan Yinghua , Zhang Zhiyu , Li Chuan , Sánchez René-Vinicio
Published: April 2020

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.
Spectrometer-Free Graphene Plasmonics Based Refractive Index Sensor
Authors: Zhang Li , Farhat Mohamed , Salama Khaled Nabil
Published: April 2020

We propose a spectrometer-free refractive index sensor based on a graphene plasmonic structure. The spectrometer-free feature of the device is realized thanks to the dynamic tunability of graphene’s chemical potential, through electrostatic biasing. The proposed sensor exhibits a 1566 nm/RIU sensitivity, a 250.6 RIU−1 figure of merit in the optical mode of operation and a 713.2 meV/RIU sensitivity, a 246.8 RIU−1 figure of merit in the electrical mode of operation. This performance outlines the optimized operation of this spectrometer-free sensor that simplifies its design and can bring terahertz sensing one step closer to its practical realization, with promising applications in biosensing and/or gas sensing.
A Simple, Reliable, and Inexpensive Solution for Contact Color Measurement in Small Plant Samples
Authors: Sanmartín Patricia , Gambino Michela , Fuentes Elsa , Serrano Miguel
Published: April 2020

Correct color measurement by contact-type color measuring devices requires that the sample surface fully covers the head of the device, so their use on small samples remains a challenge. Here, we propose to use cardboard adaptors on the two aperture masks (3 and 8 mm diameter measuring area) of a broadly used portable spectrophotometer. Adaptors in black and white to reduce the measuring area by 50% and 70% were applied in this study. Representatives of the family Campanulaceae have been used to test the methodology, given the occurrence of small leaves. Our results show that, following colorimetric criteria, the only setting providing indistinguishable colors according to the perception of the human eye is the use of a 50%-reducing adaptor on the 3-mm aperture. In addition, statistical analysis suggests the use of the white adaptor. Our contribution offers a sound measurement technique to gather ecological information from the color of leaves, petals, and other small samples.
Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders
Authors: De Feis Italia , Masiello Guido , Cersosimo Angela
Published: April 2020

Thermal infrared remote sensing measurements have greatly improved in terms of spectral, spatial, and temporal resolution. These improvements are producing a clearer picture of the land surface and Earth atmospheric composition than ever before. Nevertheless, the analysis of this big quantity of data presents important challenges due to incomplete temporal and spatial recorded information. The aim of the present paper is to discuss a methodology to retrieve missing values of some interesting geophysical variables on a spatial field retrieved from spatially scattered infrared satellite observations in order to yield level 3, regularly gridded, data. The technique is based on a 2-Dimensional (2D) Optimal Interpolation (OI) scheme and is derived from the broad class of Kalman filter or Bayesian estimation theory. The goodness of the approach has been tested on 15-min temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and surface temperature (ST) products over South Italy (land and sea), on Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia ( N H 3 ) concentration over North Italy and carbon monoxide ( C O ), sulfur dioxide ( S O 2 ) and N H 3 concentrations over China. All these gases affect air quality. Moreover, sea surface temperature (SST) retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. For gases concentration we have considered data from 3 different emission inventories, that is, Emissions Database for Global Atmospheric Research v3.4.2 (EDGARv3.4.2), the Regional Emission inventory in ASiav3.1 (REASv3.1) and MarcoPolov0.1, plus an independent study. The results show the efficacy of the proposed strategy to better capture the daily cycle for surface parameters and to detect hotspots of severe emissions from gas sources affecting air quality such as C O and N H 3 and, therefore, to yield valuable information on the variability of gas concentration to complete ground stations measurements.
Spatiotemporal Pattern of Wind Erosion on Unprotected Topsoil Replacement Sites in Mainland China
Authors: Zhu Chuxin , Fan Xiang , Bai Zhongke
Published: April 2020

Topsoil is required to be stripped and reused to maintain land productivity in mining and construction activities. However, as a great threat to unprotected soil, wind erosion on topsoil replacement sites has not received enough research attention, which hinders the efficient implementation of wind erosion control measures in the right time and place on a national scale. This study aims to evaluate wind erosion on unprotected topsoil replacement sites (WEUTRS) in mainland China, examining its spatiotemporal pattern and demonstrating its significance for the relevant research and industry. The WEUTRS was calculated by the Revised Wind Erosion Equation with meteorological data (1988–2017) and raster data of soil properties. The results showed a strong spatiotemporal heterogeneity of WEUTRS. The highest (>300 kg m−2) and the lowest (<0.5 kg m−2) WEUTRS appeared in Northwest and Central Southern China, respectively. The most drastic temporal change through the year was in Northwest China (as high as 335.4 kg m−2 on the example site), followed by Qinghai–Tibet Plateau and Shandong Province. By contrast, almost no temporal changes happened in Central Southern China. The ratio of monthly WEUTRS to respread the topsoil mass (Rw) in Northwest China and Mongolia Plateau reached 10% or more in specific months, and less than 0.1% in most of Southern China. The WEUTRS quantification could be applied to the wind erosion control on topsoil replacement sites on both a national scale and a regional scale. The spatiotemporal pattern of WEUTRS may be a scientific basis for a nationwide or regionwide differentiated policy on the wind erosion control on topsoil replacement sites for policy makers, as well as the reference to the proper working schedule and the control measures for local mining and construction projects for management authorities and practitioners.
The Impact of Internet Use on Income: The Case of Rural Ghana
Authors: Siaw Anthony , Jiang Yuansheng , Twumasi Martinson Ankrah , Agbenyo Wonder
Published: April 2020

This study analyzed the effects of internet use on farm income and household income using survey data from 478 rural farmers from two regions in Ghana. An endogenous switching regression (ESR) model and probit models were employed to achieve the aims of the study. The results revealed that internet use was influenced by off-farm employment, education, access to credit, non-fixed asset (NFA), age, and perception variables. We found that internet use increased farm income and household income by 20.1% and 15.47%, respectively. Regarding heterogeneous impacts, the estimates showed that internet use reduced farm income by 18.12% for farm households that participated in off-farm activities but increased farm income by 14.66% for households that had access to NFA. The estimates also indicated that internet use increased household income by 31.77% for farm households that engaged in off-farm employment and by 15.33% for those that had access to NFA. Furthermore, internet use increased the household income for households that did not engage in off-farm activities by 24.85%. The findings of this study will contribute significantly to the existing literature on information communication technology (ICT) in developing countries by providing a new reference for improving rural development and solving the problem of poverty.
The Potential Role of Ammonia as Marine Fuel—Based on Energy Systems Modeling and Multi-Criteria Decision Analysis
Authors: Hansson Julia , Brynolf Selma , Fridell Erik , Lehtveer Mariliis
Published: April 2020

To reduce the climate impact of shipping, the introduction of alternative fuels is required. There is a range of different marine fuel options but ammonia, a potential zero carbon fuel, has recently received a lot of attention. The purpose of this paper is to assess the prospects for ammonia as a future fuel for the shipping sector in relation to other marine fuels. The assessment is based on a synthesis of knowledge in combination with: (i) energy systems modeling including the cost-effectiveness of ammonia as marine fuel in relation to other fuels for reaching global climate targets; and (ii) a multi-criteria decision analysis (MCDA) approach ranking marine fuel options while considering estimated fuel performance and the importance of criteria based on maritime stakeholder preferences. In the long-term and to reach global GHG reduction, the energy systems modeled indicate that the use of hydrogen represents a more cost-effective marine fuel option than ammonia. However, in the MCDA covering more aspects, we find that ammonia may be almost as interesting for shipping related stakeholders as hydrogen and various biomass-based fuels. Ammonia may to some extent be an interesting future marine fuel option, but many issues remain to be solved before large-scale introduction.
Analysis of the Impact of the Triple Helix on Sustainable Innovation Targets in Spanish Technology Companies
Authors: Luengo-Valderrey María-Jesús , Pando-García Julián , Periáñez-Cañadillas Iñaki , Cervera-Taulet Amparo
Published: April 2020

The establishment of broad-based networks, such as the Triple Helix, for innovation and sustainability is sufficiently corroborated. In this work we suggest that the information received from the Triple Helix has a significant and different impact on the objectives of sustainable innovation, depending on whether companies cooperate or not. To this end, an empirical analysis of a stratified sample of more than 5000 Spanish medium and high technology companies in 2010-2014-2015 was carried out. The results confirm that companies that do not cooperate place more importance on the information received from the Triple Helix to establish their sustainable innovation targets.