bluuebunny/crossref_metadata_2025 huggingface dataset:
Element Accumulation Patterns of Native Plant Species under the Natural Geochemical Stress
Authors: Alekseenko Vladimir A. , Shvydkaya Natalya V. , Alekseenko Alexey V. , Machevariani Maria M. , Bech Jaume , Pashkevich Mariya A. , Puzanov Alexander V. , Nastavkin Aleksey V. , Roca Núria
Published: December 2020

A biogeochemical study of more than 20,000 soil and plant samples from the North Caucasus, Dzungarian Alatau, Kazakh Uplands, and Karatau Mountains revealed features of the chemical element uptake by the local flora. Adaptation of ore prospecting techniques alongside environmental approaches allowed the detection of geochemical changes in ecosystems, and the lessons learned can be embraced for soil phytoremediation. The data on the influence of phytogeochemical stress on the accumulation of more than 20 chemical elements by plants are considered in geochemical provinces, secondary fields of deposits, halos surrounding ore and nonmetallic deposits, zones of regional faults and schist formation, and over lithological contact lines of chemically contrasting rocks overlain by 5–20 m thick soils and unconsolidated cover. We have corroborated the postulate that the element accumulation patterns of native plants under the natural geochemical stress depend not only on the element content in soils and the characteristics of a particular species but also on the values of ionic radii and valences; with an increase in the energy coefficients of a chemical element, its plant accumulation decreases sharply. The contribution of internal factors to element uptake from solutions gives the way to soil phytoremediation over vast contaminated areas. The use of hyperaccumulating species for mining site soil treatment depends on several external factors that can strengthen or weaken the stressful situation, viz., the amount of bedrock exposure and thickness of unconsolidated rocks over ores, the chemical composition of ores and primary halos in ore-containing strata, the landscape and geochemical features of sites, and chemical element migration patterns in the supergene zone.
Mechanisms of Plant Antioxidants Action
Authors: Barreca Davide
Published: December 2020

The plant kingdom is a rich source of health-promoting compounds and has always played a fundamental role in the isolation, identification, and modification of compounds able to perform several properties on live organisms. Among them, the so-called “antioxidants” have a major potentiality to increase human wellness. Antioxidants are important components in the signaling and defense mechanisms in some plants, where they are precursors of compounds of greater complexity, the modulator of plant growth, and the defensive system against pathogenic organisms and predators. The extraordinary variety of chemical structure and substitution present in the different plant antioxidants make them an inestimable source of interesting compounds, with the ability to counter reactive oxygen/nitrogen species (ROS/RNS) and to stimulate the activation of signal cascade inside the cells. The mechanisms by which antioxidants detoxify these dangerous compounds are complex and involve either direct or indirect interaction with radicals. Antioxidants inhibit or quench free radical reactions mainly based on their reducing capacity or hydrogen atom-donating capacity, their solubility, and chelating properties. Moreover, their ability to modulate key metabolic enzymes and activate/block gene transcription also has remarkable importance.
Disinfection Treatments of Disposable Respirators Influencing the Bactericidal/Bacteria Removal Efficiency, Filtration Performance, and Structural Integrity
Authors: Jung Seojin , Hemmatian Tahmineh , Song Eugene , Lee Kyeongeun , Seo Dongwan , Yi Jehyung , Kim Jooyoun
Published: December 2020

In the outbreak of COVID-19, the extended wear of single-use, disposable respirators was inevitable due to limited supplies. As a respirator is front-line protection against particulate matter, including bioaerosol and droplets, a comprehensive understanding for the reuse strategy is needed. In this study, eight different disinfection methods commonly applied for the reuse of respirators were compared for their influence on the filtration and bactericidal/bacteria removal performance, with in-depth discussion on the cause of effects. Treatments including oven-dry, ultraviolet irradiation (UV), microwaving, laundering with and without detergent, and immersion in hypochlorite, isopropanol, and ethanol were performed to respirators. Immersion in ethanol or isopropanol was effective for inactivation and removal of bacteria, yet such a treatment significantly deteriorated the filtration efficiency in about 20–28%, dissipating the surface charges. Laundering, while effective in removing the attached bacteria, triggered physical damage, leading to a possible reduction of filtration performance. A short-term oven-dry, UV irradiation, and microwaving mostly preserved the filtration performance, yet the drawback lied in the incomplete bactericidal efficiency. This study would contribute to the public health and safety by providing scientific background on the effect of disinfection treatment methods for respirators.
Chitosan Woven Meshes: Influence of Threads Configuration on Mechanical, Morphological, and Physiological Properties
Authors: da Silva Henrique Nunes , da Silva Milena Costa , dos Santos Flavia Suzany Ferreira , da Silva Júnior José Alberto Campos , Barbosa Rossemberg Cardoso , Fook Marcus Vinícius Lia
Published: December 2020

This study aimed to develop meshes from the weaving of mono- and multifilament wet-spun chitosan (CS), for possible biomedical applications. In the wet-spinning process, CS solution (4% w/v) was extruded in a coagulation bath containing 70% sodium hydroxide solution (0.5 M), and 30% methanol was used. The multifilament thread was prepared by twisted of two and three monofilaments. CS threads obtained were characterized by tensile tests and scanning electron microscopy (SEM). Moreover, it was verified from the morphological tests that threads preserve the characteristics of the individual filaments and present typical “skin-core” microstructure obtained by wet spinning. CS woven meshes obtained were evaluated by optical microscopy (OM), tensile test, swelling degree, and in vitro enzymatic biodegradation. Mechanical properties, biodegradation rate, and amount of fluid absorbed of CS woven meshes were influenced by thread configuration. Hydrated CS meshes showed a larger elastic zone than the dry state. Therefore, CS woven meshes were obtained with modular properties from thread configuration used in weaving, suggesting potential applications in the biomedical field, like dressings, controlled drug delivery systems, or mechanical support.
Fabrication of a Plasmonic Nanoantenna Array Using Metal Deposition on Polymer Nanoimprinted Nanodots for an Enhanced Fluorescence Substrate
Authors: Kim Jun , Abbas Naseem , Lee Seongmin , Yeom Jeongwoo , Asgar Md Ali , Badshah Mohsin Ali , Lu Xun , Kim Young Kyu , Kim Seok-Min
Published: December 2020

A simple and cost-effective method is proposed herein for a plasmonic nanoantenna array (PNAA) for the fabrication of metal-enhanced fluorescence (MEF) substrates in which fluorophores interact with the enhanced electromagnetic field generated by a localized surface plasmon to provide a higher fluorescence signal. The PNAA is fabricated by the deposition of a silver (Ag) layer on an ultraviolet (UV) nanoimprinted nanodot array with a pitch of 400 nm, diameter of 200 nm, and height of 100 nm. During deposition, raised Ag nanodisks and a lower Ag layer are, respectively, formed on the top and bottom of the imprinted nanodot array, and the gap between these Ag layers acts as a plasmonic nanoantenna. Since the thickness of the gap within the PNAA is influenced by the thickness of Ag deposition, the effects of the latter upon the geometrical properties of the fabricated PNAA are examined, and the electromagnetic field intensity distributions of PNAAs with various Ag thicknesses are simulated. Finally, the fluorescence enhancement factor (FEF) of the fabricated PNAA MEF substrate is measured using spotted Cy5-conjugated streptavidin to indicate a maximum enhancement factor of ~22× for the PNAA with an Ag layer thickness of 75 nm. The experimental results are shown to match the simulated results.
Collective Motions and Mechanical Response of a Bulk of Single-Chain Nano-Particles Synthesized by Click-Chemistry
Authors: Maiz Jon , Verde-Sesto Ester , Asenjo-Sanz Isabel , Fouquet Peter , Porcar Lionel , Pomposo José A. , de Molina Paula Malo , Arbe Arantxa , Colmenero Juan
Published: December 2020

We investigate the effect of intra-molecular cross-links on the properties of polymer bulks. To do this, we apply a combination of thermal, rheological, diffraction, and neutron spin echo experiments covering the inter-molecular as well as the intermediate length scales to melts of single-chain nano-particles (SCNPs) obtained through ‘click’ chemistry. The comparison with the results obtained in a bulk of the corresponding linear precursor chains (prior to intra-molecular reaction) and in a bulk of SCNPs obtained through azide photodecomposition process shows that internal cross-links do not influence the average inter-molecular distances in the melt, but have a profound impact at intermediate length scales. This manifests in the structure, through the emergence of heterogeneities at nanometric scale, and also in the dynamics, leading to a more complex relaxation behavior including processes that allow relaxation of the internal domains. The influence of the nature of the internal bonds is reflected in the structural relaxation that is slowed down if bulky cross-linking agents are used. We also found that any residual amount of cross-links is critical for the rheological behavior, which can vary from an almost entanglement-free polymer bulk to a gel. The presence of such inter-molecular cross-links additionally hinders the decay of density fluctuations at intermediate length scales.
Microwave-Assisted Fabrication of Mesoporous Silica-Calcium Phosphate Composites for Dental Application
Authors: Szewczyk Adrian , Skwira Adrianna , Ginter Marta , Tajer Donata , Prokopowicz Magdalena
Published: December 2020

Herein, the microwave-assisted wet precipitation method was used to obtain materials consisting of mesoporous silica (SBA-15) and calcium orthophosphates (CaP). Composites were prepared through immersion of mesoporous silica in different calcification coating solutions and then exposed to microwave radiation. The composites were characterized in terms of molecular structure, crystallinity, morphology, chemical composition, and mineralization potential by Fourier-transform infrared spectroscopy (FTIR), powder X-ray diffraction (XRD), and scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (SEM-EDX). The application of microwave irradiation resulted in the formation of different types of calcium orthophosphates such as calcium deficient hydroxyapatite (CDHA), octacalcium phosphate (OCP), and amorphous calcium phosphate (ACP) on the SBA-15 surface, depending on the type of coating solution. The composites for which the progressive formation of hydroxyapatite during incubation in simulated body fluid was observed were further used in the production of final pharmaceutical forms: membranes, granules, and pellets. All of the obtained pharmaceutical forms preserved mineralization properties.
Insights into Thermal Degradation Behaviors and Reaction Kinetics of Medical Waste Infusion Bag and Nasal Oxygen Cannula
Authors: Zhang Lifan , Jiang Jiajia , Ma Tengkun , Pan Yong , Wang Yanjun , Jiang Juncheng
Published: December 2020

The thermal degradation behaviors and reaction kinetics of medical waste infusion bag (IB) and nasal oxygen cannula (NOC) were investigated under inert atmosphere with the heating rates of 5, 10, 15, and 25 K·min−1. Ozawa–Flynn–Wall (OFW), Kissinger–Akahira–Sunose (KAS), and Friedman were employed to estimate the activation energy. Coats–Redfern and Kennedy–Clark methods were adopted to predict the possible reaction mechanism. The results suggested that the reaction mechanism of IB pyrolysis was zero-order, and that of NOC pyrolysis was concluded that zero-order for the first stage and three-dimensional diffusion Jander equation for the second stage. Based on the kinetic compensation effect, the reconstructed reaction models for IB and NOC pyrolysis were elaborated by introducing adjustment functions. The results indicated that the reconstructed model fitted well with the experimental data. The results are helpful as a reference and provide guidance for the determination of IB and NOC degradation behaviors and the simulation of parameters.
Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges
Authors: Wu Xuerui , Ma Wenxiao , Xia Junming , Bai Weihua , Jin Shuanggen , Calabia Andrés
Published: December 2020

Soil moisture is the most active part of the terrestrial water cycle, and it is a key variable that affects hydrological, bio-ecological, and bio-geochemical processes. Microwave remote sensing is an effective means of monitoring soil moisture, but the existing conventional radiometers and single-station radars cannot meet the scientific needs in terms of temporal and spatial resolution. The emergence of GNSS-R (Global Navigation Satellite Systems Reflectometry) technology provides an alternative method with high temporal and spatial resolution. An important application field of GNSS-R is soil moisture monitoring, but it is still in the initial stage of research, and there are many uncertainties and open issues. Based on a review of the current state-of-the-art of soil moisture retrieval using GNSS-R, this paper points out the limitations of existing research in observation geometry, polarization, and coherent and non-coherent scattering. The smooth surface reflectivity model, the random rough surface scattering model, and the first-order radiation transfer equation model of the vegetation, which are in the form of bistatic and full polarization, are employed. Simulations and analyses of polarization, observation geometry (scattering zenith angle and scattering azimuth angle), Brewster angle, coherent and non-coherent component, surface roughness, and vegetation effects are carried out. The influence of the EIRP (Effective Isotropic Radiated Power) and the RFI (Radio Frequency Interference) on soil moisture retrieval is briefly discussed. Several important development directions for space-borne GNSS-R soil moisture retrieval are pointed out in detail based on the microwave scattering model.
Assessing the Natural Recovery of Mangroves after Human Disturbance Using Neural Network Classification and Sentinel-2 Imagery in Wunbaik Mangrove Forest, Myanmar
Authors: Maung Win Sithu , Sasaki Jun
Published: December 2020

In this study, we examined the natural recovery of mangroves in abandoned shrimp ponds located in the Wunbaik Mangrove Forest (WMF) in Myanmar using artificial neural network (ANN) classification and a change detection approach with Sentinel-2 satellite images. In 2020, we conducted various experiments related to mangrove classification by tuning input features and hyper-parameters. The selected ANN model was used with a transfer learning approach to predict the mangrove distribution in 2015. Changes were detected using classification results from 2015 and 2020. Naturally recovering mangroves were identified by extracting the change detection results of three abandoned shrimp ponds selected during field investigation. The proposed method yielded an overall accuracy of 95.98%, a kappa coefficient of 0.92, mangrove and non-mangrove precisions of 0.95 and 0.98, respectively, recalls of 0.96, and F1 scores of 0.96 for the 2020 classification. For the 2015 prediction, transfer learning improved model performance, resulting in an overall accuracy of 97.20%, a kappa coefficient of 0.94, mangrove and non-mangrove precisions of 0.98 and 0.96, respectively, recalls of 0.98 and 0.97, and F1 scores of 0.96. The change detection results showed that mangrove forests in the WMF slightly decreased between 2015 and 2020. Naturally recovering mangroves were detected at approximately 50% of each abandoned site within a short abandonment period. This study demonstrates that the ANN method using Sentinel-2 imagery and topographic and canopy height data can produce reliable results for mangrove classification. The natural recovery of mangroves presents a valuable opportunity for mangrove rehabilitation at human-disturbed sites in the WMF.
SARAL-AltiKa Wind and Significant Wave Height for Offshore Wind Energy Applications in the New England Region
Authors: Li Xinba , Mitsopoulos Panagiotis , Yin Yue , Peña Malaquias
Published: December 2020

The SARAL-AltiKa dataset was evaluated for refined offshore wind energy resources assessment and potential metocean monitoring capability in the Southern New England region. Surface wind speed and Significant Wave Height (Hs) products were assessed with corresponding variables from buoy observations for 2014–2019. To increase the sample size, this study analyzed and applied an approach to collect data around the reference buoys beyond the satellite footprint at the expense of a bias increment. The study corroborated the accuracy of the SARAL-AltiKa measurements for the offshore area of interest and added details for stations closer to the coast compared with past studies. A proportional bias with underestimation of high values of Hs was found in coastal sites. Wind speed estimates on the other hand appear to be less sensitive to the closeness to the coast. The empirical relationship between wind strength and Hs in the buoy observations is reproduced to a large extent by the AltiKa measurements in locations where land contamination is minimal. The histograms of surface wind and Hs are well described by the Weibull distribution and the shape and scale parameters closely resemble those of the histograms of the collocated in situ observations. We use these results to extrapolate the winds to a target domain with no in situ observations for wind energy resource estimation.
A Three-Stage Inter-Channel Calibration Approach for Passive Radar on Moving Platforms Exploiting the Minimum Variance Power Spectrum
Authors: Wojaczek Philipp , Cristallini Diego , O’Hagan Daniel W. , Colone Fabiola , Blasone Giovanni Paolo , Lombardo Pierfrancesco
Published: December 2020

Research in passive radar has moved its focus towards passive radar on moving platforms in recent years with the purpose of moving target indication and ground imaging via synthetic aperture radar. This is also fostered by the progress in hardware miniaturization, which alleviates the installation of the required hardware on moving platforms. Terrestrial transmitters, commonly known as illuminators of opportunity in the passive radar community, usually emit the signals in the Very High Frequency (VHF) or Ultra High Frequency (UHF) band. Due to the long wavelengths of the VHF/UHF band, there are constraints on the size of the used antenna elements, and therefore, the number of antenna elements to be employed is limited, especially as the platform carrying the passive radar system is intended to be small, potentially even an unmanned aerial vehicle. In order to detect moving targets hidden by Doppler shifted clutter returns, one common approach is to suppress the clutter returns by applying clutter suppression techniques that rely on spatial and temporal degrees of freedom, such as Displaced Phase Center Antenna (DPCA) or Space-Time Adaptive Processing. It has been shown that the DPCA approach is a meaningful technique to suppress the clutter if two antenna elements are employed. However, if the employed receiving channels are not carefully calibrated, the clutter suppression is shown to be not effective. Here, we suggest a three-stage calibration technique in order to perform the calibration of two receiving channels, which involves the exploitation of the direct signal, a data-adaptive amplitude calibration, and finally, a data-adaptive calibration of phase mismatches between both receiving channels by the estimation of the Minimum Variance Power Spectrum of the clutter. The validity of the proposed approach is shown with simulated data and demonstrated on real data from a fast ground moving platform, showing improved clutter cancellation capabilities.
Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence
Authors: Flores Thommas Kevin Sales , Villanueva Juan Moises Mauricio , Gomes Heber P. , Catunda Sebastian Y. C.
Published: December 2020

Indirect measurement can be used as an alternative to obtain a desired quantity, whose physical positioning or use of a direct sensor in the plant is expensive or not possible. This procedure can been improved by means of feedback control strategies of a secondary variable, which can be measured and controlled. Its main advantage is a new form of dynamic response, with improvements in the response time of the measurement of the quantity of interest. In water pumping networks, this methodology can be employed for measuring the flow indirectly, which can be advantageous due to the high price of flow sensors and the operational complexity to install them in pipelines. In this work, we present the use of artificial intelligence techniques in the implementation of the feedback system for indirect flow measurement. Among the contributions of this new technique is the design of the pressure controller using the Fuzzy logic theory, which rules out the need for knowing the plant model, as well as the use of an artificial neural network for the construction of nonlinear models with the purpose of indirectly estimating the flow. The validation of the proposed approach was carried out through experimental tests in a water pumping system, fully automated and installed at the Laboratory of Hydraulic and Energy Efficiency in Sanitation at the Federal University of Paraiba (LENHS/UFPB). The results were compared with an electromagnetic flow sensor present in the system, obtaining a maximum relative error of 10%.
Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation
Authors: Devine Jaime K. , Chinoy Evan D. , Markwald Rachel R. , Schwartz Lindsay P. , Hursh Steven R.
Published: December 2020

Traditional measures of sleep or commercial wearables may not be ideal for use in operational environments. The Zulu watch is a commercial sleep-tracking device designed to collect longitudinal sleep data in real-world environments. Laboratory testing is the initial step towards validating a device for real-world sleep evaluation; therefore, the Zulu watch was tested against the gold-standard polysomnography (PSG) and actigraphy. Eight healthy, young adult participants wore a Zulu watch and Actiwatch simultaneously over a 3-day laboratory PSG sleep study. The accuracy, sensitivity, and specificity of epoch-by-epoch data were tested against PSG and actigraphy. Sleep summary statistics were compared using paired samples t-tests, intraclass correlation coefficients, and Bland–Altman plots. Compared with either PSG or actigraphy, both the accuracy and sensitivity for Zulu watch sleep-wake determination were >90%, while the specificity was low (~26% vs. PSG, ~33% vs. actigraphy). The accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. The Zulu watch showed mixed results but performed well in determining total sleep time, sleep efficiency, sleep onset, and final awakening in healthy adults compared with PSG or actigraphy. The next step will be to test the Zulu watch’s ability to evaluate sleep in industrial operations.
Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation
Authors: Compaleo Jacob , Gupta Inder J.
Published: December 2020

A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.
An Adaptive Roller Speed Control Method Based on Monitoring Value of Real-Time Seed Flow Rate for Flute-Roller Type Seed-Metering Device
Authors: Liu Wei , Hu Jianping , Zhao Xingsheng , Yao Mengjiao , Lakhiar Imran Ali , Zhao Jun , Liu Jiaxin , Wang Wei
Published: December 2020

In order to obtain desirable crop yields, grain seeds need to be sown at the optimal seed amount per hectare with uniform distribution in the field. In previous grain sowing processes, the seeding rates are controlled by the rotational speed of the flute roller which significantly effects the uniform distribution of the seeds due to disturbances, such as the reduction of the seeds’ mass in the hopper and the change of working length of the flute roller. In order to overcome the above problem, we developed an adaptive roller speed control system based on the seed flow rate sensor. The developed system can monitor and feedback actual seeding rates. In addition, based on the monitoring value of the real-time seeding rates, an adaptive roller speed control method (ARSCM), which contains an algorithm for calculating the seeding rate with a compensation, was proposed. Besides, the seeding performance of the ARSCM and that of the conventional roller speed control method (CRSCM) were compared. The results of constant-velocity experiments demonstrated that the accuracy (SA) and the coefficient of variation (SCV) of the seeding rates controlled by the ARSCM were 94.12% and 6.77%, respectively. As for the CRSCM, the SA and SCV were 89.00% and 8.95%, respectively. Under variable-velocity conditions, the SA and SCV of the proposed system were 91.58% and 11.08%, respectively, while those of the CRSCM were 88.48% and 13.08%, respectively. Based on the above results, this study concluded that the ARSCM is able to replace the CRSCM in practical sowing processes for the optimal and uniform seed distribution in the field.
Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring
Authors: Lewandowski Marcin , Płaczek Bartłomiej , Bernas Marcin
Published: December 2020

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.
Locating and Imaging through Scattering Medium in a Large Depth
Authors: Zhu Shuo , Guo Enlai , Cui Qianying , Bai Lianfa , Han Jing , Zheng Dongliang
Published: December 2020

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.
Brazing Coupling Performance of Piezoelectric Waveguide Transducers for the Monitoring of High Temperature Components
Authors: Jia Jiu Hong , Wang Ze Hou , Yao Dai Feng , Tu Shan-Tung
Published: December 2020

Piezoelectric waveguide transducers possess great potential for the online monitoring of high temperature critical components, in order to improve their operational safety. Due to the use of a waveguide bar, the sensory device is not susceptible to high temperature environments, which enables the long-term service of the piezoelectric transducers. However, the coupling between the waveguide bar and the high-temperature component has been proven to be the most important part of the monitoring system. In order to effectively transmit waves through the junction of the waveguide bar and the monitoring target, it is necessary to research a reliable coupling method to connect the waveguide transducers with the host structure. In the present research, the feasibility of brazing coupling for wave propagation through the junction was investigated through experiments. Piezoelectric waveguide transducers were welded using various kinds of brazing filler metals. The experimental results indicate that the coupling effects of the brazing welding depend on the filler metals. At the same time, some filler metals for the effective coupling of the transducer and the target monitoring component were identified. The brazing coupling method was verified that it can non-dispersively and effectively propagate waves into the host structure with much better reliability than the conventional dry coupling approach. Moreover, the high-temperature experimental results show that the brazing-coupled waveguide bar system can work reliably and stably in high temperatures at 300 °C for a long time. This work strives to pave a solid foundation for the application of piezoelectric waveguide transducers for the structural health monitoring of high temperature critical components.
Extracting Effective Image Attributes with Refined Universal Detection
Authors: Yu Qiang , Xiao Xinyu , Zhang Chunxia , Song Lifei , Pan Chunhong
Published: December 2020

Recently, image attributes containing high-level semantic information have been widely used in computer vision tasks, including visual recognition and image captioning. Existing attribute extraction methods map visual concepts to the probabilities of frequently-used words by directly using Convolutional Neural Networks (CNNs). Typically, two main problems exist in those methods. First, words of different parts of speech (POSs) are handled in the same way, but non-nominal words can hardly be mapped to visual regions through CNNs only. Second, synonymous nominal words are treated as independent and different words, in which similarities are ignored. In this paper, a novel Refined Universal Detection (RUDet) method is proposed to solve these two problems. Specifically, a Refinement (RF) module is designed to extract refined attributes of non-nominal words based on the attributes of nominal words and visual features. In addition, a Word Tree (WT) module is constructed to integrate synonymous nouns, which ensures that similar words hold similar and more accurate probabilities. Moreover, a Feature Enhancement (FE) module is adopted to enhance the ability to mine different visual concepts in different scales. Experiments conducted on the large-scale Microsoft (MS) COCO dataset illustrate the effectiveness of our proposed method.
Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network
Authors: Lee Dongseok , Kwon Hyunbin , Son Dongyeon , Eom Heesang , Park Cheolsoo , Lim Yonggyu , Seo Chulhun , Park Kwangsuk
Published: December 2020

Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.
Vaginal Administration of Contraceptives
Authors: Jalalvandi Esmat , Jafari Hafez , Amorim Christiani A. , Petri Denise Freitas Siqueira , Nie Lei , Shavandi Amin
Published: December 2020

While contraceptive drugs have enabled many people to decide when they want to have a baby, more than 100 million unintended pregnancies each year in the world may indicate the contraceptive requirement of many people has not been well addressed yet. The vagina is a well-established and practical route for the delivery of various pharmacological molecules, including contraceptives. This review aims to present an overview of different contraceptive methods focusing on the vaginal route of delivery for contraceptives, including current developments, discussing the potentials and limitations of the modern methods, designs, and how well each method performs for delivering the contraceptives and preventing pregnancy.
Firefighting in Vulnerable Areas Based on the Connection between Fire Hydrants and Fire Brigade
Authors: Lee Yoon Ha , Kim Min Seok , Lee Ji Soo
Published: December 2020

A sufficient supply of water for firefighting is critical for effective responses to urban fires, thus reducing fire hazards. The aim of this study was the development of an analysis method for vulnerable areas with respect to firefighting activities that require the use of fire hydrants. In particular, a method was proposed for the determination of the operational range of firefighting activities based on the distribution of the fire hydrants and the roads that allow for the passage of firefighting vehicles. The proposed method, which employs a geographic information system (GIS), was applied to Buk-gu, Daegu City, South Korea. The research results revealed that the operational range of firefighting activities and vulnerable areas can be determined by studying the connection between the fire hydrant locations and the fire brigade in the analysis of the accessible areas. This study contributes to the development of GIS analysis methods for comprehensive vulnerability analyses of firefighting activities, including accessibility to fire hydrants.
The Impact of the COVID-19 Emergency on Local Vehicular Traffic and Its Consequences for the Environment: The Case of the City of Reggio Emilia (Italy)
Authors: Marinello Samuele , Lolli Francesco , Gamberini Rita
Published: December 2020

The COVID-19 health emergency has imposed the need to limit and/or stop non-essential economic and commercial activities and movement of people. The objective of this work is to report an assessment of the change in vehicle flows and in air quality of a specific study area in the north of Italy, comparing the periods February–May 2020 and February–May 2019. Circulating vehicles have been measured at nine characteristic points of the local road network of the city of Reggio Emilia (Italy), while atmospheric pollutant concentrations have been analysed using data extracted from the regional air quality monitoring network. The results highlight a rapid decline in the number of vehicles circulating in 2020 (with values of up to −82%). This has contributed to a reduction in air concentrations of pollutants, in particular for NO2 and CO (over 30% and over 22%, respectively). On the other hand, O3 has increased (by about +13%), but this is expected. Finally, the particulate matter grew (about 30%), with a behaviour similar to the whole regional territory. The empirical findings of this study provide some indications and useful information to assist in understanding the effects of traffic blocking in urban areas on air quality.
Impacts of Land-Use Change on Ecosystem Service Value of Mountain–Oasis–Desert Ecosystem: A Case Study of Kaidu–Kongque River Basin, Northwest China
Authors: Mamat Aynur , Wang Jianping , Ma Yuanxu
Published: December 2020

In this study, we used long time series Landsat data and ecosystem services coefficients for global and Chinese ecosystems during 1978–2018 to estimate the influences of land-use change on ecosystem services (ES) in Kaidu–Kongque River Basin (KKRB), where both socioeconomic progress and the arid, fragile natural environment have considerable affected land-use change. The results showed that (1) the total value of ES in the KKRB was about USD 8111.5, 7995.31, 8275.74, 8131.91, and 8016.38 million in 1978, 1988, 1998, 2008, and 2018, respectively. The net ecosystem service value (ESV) loss was about USD 116.19 million for 1978–1988, the net ESV profit was about USD 280.43 million for 1988–1998, and the net ESV loss was about USD 259.36 million for 1998–2018. (2) Water supply and waste treatment function were the top two ecological functions with high service value; their combined contribution rate was 59.3%, indicating that the regulating service function in this study area is higher than the provision services function. (3) The high to low ranking for each ecosystem function based on their contribution rate to overall ES value was water supply > waste treatment > climate regulation > biodiversity protection > recreation and culture > soil formation > gas regulation > food production > raw material. These results are significant for the continuation of the integrity and sustainability of the mountain–oasis–desert region ecosystem, where socioeconomic progress and the fragile characteristics of the natural ecosystem complement each other. The results of this study provide scientific evidence for governmental decision makers and local residents and offer a reference for environmental researchers in northwest China.
Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network
Authors: Li Zhuang , Hu Shenping , Gao Guoping , Xi Yongtao , Fu Shanshan , Yao Chenyang
Published: December 2020

Sustainable growth should not only be beneficial to the shipping industry in the future, but is also an urgent need to respond to resource and environmental crises and strengthen shipping governance. Maritime traffic in Arctic waters is prone to encounter dangerous ice conditions, and it is essential to study the mechanism of ice collision risk formation in relation to ice conditions. Taking the ship-ice collision risk in Arctic waters as the research object, we propose a dynamic assessment model of ship-ice collision risk under sea ice status dynamic association (SDA) effect. By constructing the standard paradigm of risk factor dynamic association (DA) effect, taking SDA as the key association factor. Combing with other risk factors that affect ship-ice collision accidents, the coupling relationship between risk factors were analyzed. Then, using the Bayesian network method to build a ship-ice collision accident dynamic risk assessment model and combing with the ice monitoring data in summer Arctic waters, we screen five ships’ position information on the trans-Arctic route in August. The risk behavior of ship-ice collision accidents on the selected route under SDA is analyzed by model simulation. The research reveal that the degree of SDA is a key related factor for the serious ice condition and the possibility of human error during ship’s navigation, which significantly affects the ship-ice collision risk. The traffic in Arctic waters requires extra vigilance of the SDA effect from no ice threat to ice threat, and continuous ice threat. According to the ship-ice collision risk analysis under the SDA effect and without SDA effect, the difference in risk reasoning results on the five stations of the selected route are 32.69%, −32.33%, −27.64%, −10.26%, and −30.13% respectively. The DA effect can optimize ship-ice collision risk inference problem in Arctic waters.
Food Security during the Pandemic and the Importance of the Bioeconomy in the New Era
Authors: Farcas Anca C. , Galanakis Charis M. , Socaciu Carmen , Pop Oana L. , Tibulca Dorin , Paucean Adriana , Jimborean Mirela A. , Fogarasi Melinda , Salanta Liana C. , Tofana Maria , Socaci Sonia A.
Published: December 2020

One of the biggest challenges in managing the food sector during a pandemic crisis is sustaining a robust food security system and adopting the right strategies in correlating the consumers’ needs and requirements with those of food safety, the producers, the distribution chain, the economic environment, and waste management. The restrictions on people’s global movement, commodities, and services and the measures taken to reduce the spread of COVID-19 have disrupted food environments around the world and forced us to collectively redesign and optimize our systems using existing resources from a more sustainable perspective. This paper offers an overview of the implications of COVID-19 for the food supply chain and discusses several potential strategies for tackling short- and long-term adverse effects resulting from the pandemic.
The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition
Authors: Wang Hsiuying
Published: December 2020

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.
Group Theoretical Approach to Pseudo-Hermitian Quantum Mechanics with Lorentz Covariance and c → ∞ Limit
Authors: Bedić Suzana , Kong Otto C. W. , Ting Hock King
Published: December 2020

We present the formulation of a version of Lorentz covariant quantum mechanics based on a group theoretical construction from a Heisenberg–Weyl symmetry with position and momentum operators transforming as Minkowski four-vectors. The basic representation is identified as a coherent state representation, essentially an irreducible component of the regular representation, with the matching representation of an extension of the group C*-algebra giving the algebra of observables. The key feature is that it is not unitary but pseudo-unitary, exactly in the same sense as the Minkowski spacetime representation. The language of pseudo-Hermitian quantum mechanics is adopted for a clear illustration of the aspect, with a metric operator obtained as really the manifestation of the Minkowski metric on the space of the state vectors. Explicit wavefunction description is given without any restriction of the variable domains, yet with a finite integral inner product. The associated covariant harmonic oscillator Fock state basis has all the standard properties in exact analog to those of a harmonic oscillator with Euclidean position and momentum operators. Galilean limit and the classical limit are retrieved rigorously through appropriate symmetry contractions of the algebra and its representation, including the dynamics described through the symmetry of the phase space.
Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection
Authors: AlDahoul Nouar , Abdul Karim Hezerul , Lye Abdullah Mohd Haris , Ahmad Fauzi Mohammad Faizal , Ba Wazir Abdulaziz Saleh , Mansor Sarina , See John
Published: December 2020

Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted features into two categories. However, existing methods were not able to detect the small-scale content of pornography and nudity in frames with diverse backgrounds. This limitation has led to a high false-negative rate (FNR) and misclassification of nude frames as normal ones. In order to address this matter, this paper explores the limitation of the existing convolutional-only approaches focusing the visual attention of CNN on the expected nude regions inside the frames to reduce the FNR. The You Only Look Once (YOLO) object detector was transferred to the pornography and nudity detection application to detect persons as regions of interest (ROIs), which were applied to CNN and SVM for nude/normal classification. Several experiments were conducted to compare the performance of various CNNs and classifiers using our proposed dataset. It was found that ResNet101 with random forest outperformed other models concerning the F1-score of 90.03% and accuracy of 87.75%. Furthermore, an ablation study was performed to demonstrate the impact of adding the YOLO before the CNN. YOLO–CNN was shown to outperform CNN-only in terms of accuracy, which was increased from 85.5% to 89.5%. Additionally, a new benchmark dataset with challenging content, including various human sizes and backgrounds, was proposed.