bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Gellan Gum and Xanthan Gum Synergistic Interactions and Plasticizers on Physical Properties of Plant-Based Enteric Polymer Films
Authors: Zhang Na , Li Xiaohui , Ye Jing , Yang Yucheng , Huang Yayan , Zhang Xueqin , Xiao Meitian
Published: January 2020

The mechanical and barrier properties of plant-based enteric polymer films were enhanced by synergistic interactions between binary gum mixtures and adding plasticizers. The results indicated that the best ratio of gellan gum (GG) and xanthan gum (XG) was 7:3 by comparing tensile strength, tensile elongation, transmittance, and water vapor permeability of plant-based enteric polymer films and rheological properties of solutions. Polyethylene glycol 400 (PEG-400) was an effective plasticizer in improving plasticity and water vapor barrier property of the plant-based enteric polymer film. Rheology measurement and different characterization methods, including Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, X-ray diffraction, and scanning electron microscopy, were used to explain interactions between GG and XG as well as PEG-400 and components of the film. The new mixed system, composed of GG/XG mixture with ratio of 7:3 as a novel gelling agent and PEG-400 as a plasticizer, was applied to prepare plant-based enteric hard capsules, which have potential applications in medicines and functional food preparations.
Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction
Authors: Afriyie Mensah Rhoda , Xiao Jie , Das Oisik , Jiang Lin , Xu Qiang , Okoe Alhassan Mohammed
Published: January 2020

The fire behavior of materials is usually modeled on the basis of fire physics and material composition. However, significant strides have been made recently in applying soft computing methods such as artificial intelligence in flammability studies. In this paper, multiple linear regression (MLR) was employed to test the degree of non-linearities in flammability parameter modeling by assessing the linear relationship between sample mass, heating rate, heat release capacity (HRC) and total heat release (THR). Adaptive neuro-fuzzy inference system (ANFIS) was then adopted to predict the HRC and THR of the extruded polystyrene measured from microscale combustion calorimetry experiments. The ANFIS models presented excellent predictions, showing very low mean training and testing errors as well as reasonable agreements between experimental and predicted datasets. Hence, it can be inferred that ANFIS can handle the non-linearities in flammability modeling, making it apt as a modeling technique for accurate and effective flammability assessments.
Enhanced Electrical Properties of Polyethylene-Graft-Polystyrene/LDPE Composites
Authors: Song Shuwei , Zhao Hong , Yao Zhanhai , Yan Zhiyu , Yang Jiaming , Wang Xuan , Zhao Xindong
Published: January 2020

Nanocomposite dielectrics show a great potential application in high voltage direct current cables for their obvious improvements in electrical properties. In the present manuscript, nanocomposite composed of low-density polyethylene and nanoscale polystyrene particles is studied by using low-density polyethylene grafted with polystyrene molecule. Fourier-transform infrared spectra reveal successful grafting of the polystyrene molecule onto the low-density polyethylene chain and the scanning electron microscope image shows the homogeneously dispersed nanoscale polystyrene particles. The presence of the polystyrene nanoparticles obviously improves the dielectric properties, such as the direct current breakdown strength and space charge inhibition. The conductivity and thermally stimulated current characteristics imply the deep traps in the composite increase obviously. Density functional theory calculation reveals that the grafted polystyrene can accommodate both shallow and deep electron carriers, and the depth of the hole traps are as deep as 2.07 eV.
Fabrication of Antibacterial Nanofibrous Membrane Infused with Essential Oil Extracted from Tea Tree for Packaging Applications
Authors: Lee Ji Yeon , Lee Joshua , Ko Sung Won , Son Byeong Cheol , Lee Jun Hee , Kim Cheol Sang , Park Chan Hee
Published: January 2020

Nanofibers made by electrospinning are being applied to an unlimited number of applications. In this paper, we propose the fabrication of antimicrobial functional nanofibers infused with essential oil for packaging applications that can extend the shelf-life of fruits. The morphology of nanofibers with different concentrations of essential oil was characterized by SEM and mechanical enhancement was confirmed via universal testing machine (UTM). The surface chemistry and crystalline of the nanofibers were investigated by FTIR and XRD, respectively. The CO2 reduction study was carried out using a hand-made experimental apparatus and nanofiber hydrophobicity, which can prevent moisture penetration from the outside, was evaluated by contact angle. Antimicrobial properties of the functional nanofibers were estimated by using Gram-negative/positive bacteria. The cytotoxicity of the functional nanofibers was studied using fibroblast cells. Furthermore, this study investigated how long the shelf-life of tomatoes was extended. The nanofibers could serve as a multifunctional packaging, as an emerging technology in agricultural products, and even contribute to a better quality of various distributed agricultural products.
Picosecond Laser Ablation of Polyhydroxyalkanoates (PHAs): Comparative Study of Neat and Blended Material Response
Authors: Ortiz Rocío , Basnett Pooja , Roy Ipsita , Quintana Iban
Published: January 2020

Polyhydroxyalkanoates (PHAs) have emerged as a promising biodegradable and biocompatible material for scaffold manufacturing in the tissue engineering field and food packaging. Surface modification is usually required to improve cell biocompatibility and/or reduce bacteria proliferation. Picosecond laser ablation was applied for surface micro structuring of short- and medium-chain length-PHAs and its blend. The response of each material as a function of laser energy and wavelength was analyzed. Picosecond pulsed laser modified the surface topography without affecting the material properties. UV wavelength irradiation showed halved ablation thresholds compared to visible (VIS) wavelength, revealing a greater photochemical nature of the ablation process at ultraviolet (UV) wavelength. Nevertheless, the ablation rate and, therefore, ablation efficiency did not show a clear dependence on beam wavelength. The different mechanical behavior of the considered PHAs did not lead to different ablation thresholds on each polymer at a constant wavelength, suggesting the interplay of the material mechanical parameters to equalize ablation thresholds. Blended-PHA showed a significant reduction in the ablation threshold under VIS irradiation respect to the neat PHAs. Picosecond ablation was proved to be a convenient technique for micro structuring of PHAs to generate surface microfeatures appropriate to influence cell behavior and improve the biocompatibility of scaffolds in tissue engineering.
Synthesis of Molecularly Imprinted Polymers for the Selective Extraction of Polymyxins from Environmental Water Samples
Authors: Song Xuqin , Turiel Esther , He Limin , Martín-Esteban Antonio
Published: January 2020

The emergence of colistin resistance gen has aroused public concern. It is significant to assess the concentrations of polymyxins residues in aquatic environment since resistant bacteria carrying colistin resistance gen are frequently isolated from wastewater; surface water and ground water. However; no literature on the determination of polymyxins in water is available; probably due to the absence of an efficient extraction method. Accordingly; molecularly imprinted polymers were synthesized by precipitation polymerization with colistin as the template. The polymers were successfully used as sorbents for the determination of polymyxins from water based on molecularly imprinted solid-phase extraction and high-performance liquid chromatography–ultraviolet detection. The molecularly imprinted cartridge showed excellent affinity and cross-reactivity to analytes in aqueous media. Recoveries obtained from water samples were between 65.9% and 90.1%, with relative standard deviations lower than 10.2%. Limits of detection were between 1.0 and 2.0 μg L−1 concentration levels. Compared with C18 cartridge; the molecularly imprinted cartridge could remove more interference from co-extracted matrices. This method is practical for the routine monitoring of polymyxin residues in environmental water; which will benefit studies on drug-resistance and occurrence of polymyxins in the environment.
Effects of a Macromolecule Spirocyclic Inflatable Flame Retardant on the Thermal and Flame Retardant Properties of Epoxy Resin
Authors: Song Kunpeng , Wang Yinjie , Ruan Fang , Liu Jiping , Li Nianhua , Li Xueli
Published: January 2020

A new strategy for the preparation of an integrated three-source intumescent flame retardant (IFR) has been developed to improve the flame-retardant and smoke suppression performance of epoxy resin (EP) with a synergistic flame retardant effect. Herein, the synthesis of a macromolecular spirocyclic phosphorus/nitrogen-containing IFR poly sulfonamide spirocyclic pentaerythritol bisphosphonate (SAPC) is reported via a two-step method that uses pentaerythritol, phosphorus oxychloride and sulfonamide (SAA) as raw materials. Subsequently, the SAPC was incorporated into EP to prepare the composite to investigate its thermal stability, flame retardancy, and smoke suppression performance. Herein, a differential scanning calorimetry (DSC) analysis showed that the addition of SAPC increased the glass transition temperature (Tg) of the composite. Cone test results indicated that the incorporation of 8 wt % SAPC significantly improved the flame-retardant performance for the composite, with a 43.45% decrease in peak of heat release rate, a 28.55% reduction in total heat release, and a 30.04% decrease in total smoke release. Additionally, the composite received the V-0 rating in a UL-94 vertical burning test, accompanied by the “blowout” phenomenon. After the addition of SAPC, the amount of flammable gas products from the EP composite decomposition was obviously suppressed, and the amount of non-flammable as was increased. All of this suggests a good dilution role of SAPC. There are enough reasons to believe that the enhanced flame-retardant and toxicity suppression performance for the EP composite can be attributed to the good coordination of carbonization agent, acid source, and blowing agent in the SAPC structure.
Improving the Electrochemical Performance and Stability of Polypyrrole by Polymerizing Ionic Liquids
Authors: Kesküla Arko , Heinmaa Ivo , Tamm Tarmo , Aydemir Nihan , Travas-Sejdic Jadranka , Peikolainen Anna-Liisa , Kiefer Rudolf
Published: January 2020

Polypyrrole (PPy) based electroactive materials are important building blocks for the development of flexible electronics, bio-sensors and actuator devices. As the properties and behavior of PPy depends strongly on the operating environment—electrolyte, solvent, etc., it is desirable to plant immobile ionic species into PPy films to ensure stable response. A premade ionic polymer is not optimal in many cases, as it enforces its own structure on the conducting polymer, therefore, polymerization during fabrication is preferred. Pyrrole (Py) was electropolymerized at low temperature together with a polymerizable ionic liquid (PIL) monomer in a one-step polymerization, to form a stable film on the working electrode. The structure and morphology of the PPyPIL films were investigated by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), Fourier-transform infrared (FTIR) spectroscopy and solid-state NMR (ssNMR) spectroscopy. The spectroscopy results confirmed the successful polymerization of Py to PPy and PIL monomer to PIL. The presence of (TFSI–) anions that balance the charge in PPyPIL was confirmed by EDX analysis. The electrical properties of PPyPIL in lithium bis(trifluoromethanesulfonyl)-imide (LiTFSI) aqueous and propylene carbonate solutions were examined with cyclic voltammetry (CV), chronoamperometry, and chronopotentiometry. The blend of PPyPIL had mixed electronic/ionic conductive properties that were strongly influenced by the solvent. In aqueous electrolyte, the electrical conductivity was 30 times lower and the diffusion coefficient 1.5 times higher than in the organic electrolyte. Importantly, the capacity, current density, and charge density were found to stay consistent, independent of the choice of solvent.
Tree Bark Phenols Regulate the Physiological and Biochemical Performance of Gladiolus Flowers
Authors: Elansary Hosam O.
Published: January 2020

The postharvest physiology of cut flowers is largely dependent on vase life, which is the maximum number of days before flower senescence. The use of tree bark extracts (major forest plant residues), as an eco-friendly and natural antioxidant preservative in holding solutions, is a novel tool for extending flower longevity. The morphological, physiological, biochemical, and genetic responses of Gladiolus grandiflorus cut spikes to Magnolia acuminata and Taxus cuspidata bark extracts as additives in holding solutions were investigated. G. grandiflorus subjected to bark extracts as well as catechin and protocatechuic acid (main phenols) displayed significant increased longevity (up to 18 days), an increased number of open florets, and increased floret fresh weight. Increases in the relative water content, leaf chlorophyll, carotenoids, soluble sugars, and protein content were observed in addition to a reduction in microbial growth in the cut spikes. Gas exchange parameters were higher in the bark extract treatments than in the controls. Higher antioxidant activities were detected and associated with increased superoxide dismutase and catalase enzyme activities and reduced H2O2 accumulation. The bark extract treatments associated with reduced expression of GgCyP1 (produces cysteine protease) and increased expression of both GgDAD1 (defends against apoptotic activity) and GgEXPA1 (regulates petal expansion). Several mechanisms were implicated in these effects, including maintenance of water content, enhanced management of reactive oxygen species (ROS), increased sugar and protein composition, and control of microbial growth. Thus, bark extracts and isolated phenols could be developed as an eco-friendly, non-toxic, and cost-effective natural preservative for cut gladiolus flowers.
Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice
Authors: Du Lin , Yang Jian , Chen Bowen , Sun Jia , Chen Biwu , Shi Shuo , Song Shalei , Gong Wei
Published: January 2020

Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs* Φ + GSIs* Φ , where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.
Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces
Authors: Thompson James O. , Ramsey Michael S.
Published: January 2020

Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over the full range of eruption temperatures and possible volcanic products. Here, four TIR datasets acquired over active lava surfaces are compared to quantify the uncertainty, accuracy, and variability in derived surface radiance, emissivity, and kinetic temperature. These data were acquired at Kīlauea volcano in Hawai’i, USA, in January/February 2017 and 2018. The analysis reveals that spatial resolution strongly limits the accuracy of the derived surface thermal properties, resulting in values that are significantly below the expected values for molten basaltic lava at its liquidus temperature. The surface radiance is ~2400% underestimated in the orbital data compared to only ~200% in ground-based data. As a result, the surface emissivity is overestimated and the kinetic temperature is underestimated by at least 30% and 200% in the airborne and orbital datasets, respectively. A thermal mixed pixel separation analysis is conducted to extract only the molten fraction within each pixel in an attempt to mitigate this complicating factor. This improved the orbital and airborne surface radiance values to within 15% of the expected values and the derived emissivity and kinetic temperature within 8% and 12%, respectively. It is, therefore, possible to use moderate spatial resolution TIR data to derive accurate and reliable emissivity and kinetic temperatures of a molten lava surface that are comparable to the higher resolution data from airborne and ground-based instruments. This approach, resulting in more accurate kinetic temperature and emissivity of the active surfaces, can improve estimates of flow hazards by greatly improving lava flow propagation models that rely on these data.
Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem
Authors: Hunter Frederick D.L. , Mitchard Edward T.A. , Tyrrell Peter , Russell Samantha
Published: January 2020

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.
Responses of Water Use Efficiency to Drought in Southwest China
Authors: Zhao Jingxue , Xu Tongren , Xiao Jingfeng , Liu Shaomin , Mao Kebiao , Song Lisheng , Yao Yunjun , He Xinlei , Feng Huaize
Published: January 2020

Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg−1H2O) > shrublands (2.00 gC kg−1H2O) > croplands (1.76 gC kg−1H2O) > grasslands (1.04 gC kg−1H2O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.
LTCC Flow Sensor with RFID Interface
Authors: Węglarski Mariusz , Jankowski-Mihułowicz Piotr , Pitera Grzegorz , Jurków Dominik , Dorczyński Mateusz
Published: January 2020

The idea of battery-less flow sensors and their implementation in wireless measurement systems is presented in this research article. The authors take advantage of their latest achievements in the Low Temperature Co-fired Ceramic (LTCC) technology, RadioFrequency Identification (RFID) technique, and increasing availability of low power electronics in order to get rid of the need to use electrochemical cells in a power supply unit of the elaborated device. To reach this assumption, special care has to be put on the energy balance in such an autonomous sensor node. First of all, the new concept of an electromagnetic LTCC turbine transducer with a signal conditioner which only draws a current of around 15 µA, is proposed for measuring a flow rate of fluids. Next, the autonomy of the device is showed; measured data are gathered in a microcontroller memory and sent to a control unit via an RFID interface which enables both information exchange and power transfer. The energy harvested from the electromagnetic field is used to conduct a data transmission, but also its excess can be accumulated, so the proposed sensor operates as a semi-passive transponder. The total autonomy of the device is achieved by implementing a second harvester that continually gathers energy from the environmental electromagnetic field of common active radio systems (e.g., Global System for Mobile Communications (GSM), wireless network Wi-Fi).
Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device
Authors: Baty Florent , Boesch Maximilian , Widmer Sandra , Annaheim Simon , Fontana Piero , Camenzind Martin , Rossi René M. , Schoch Otto D. , Brutsche Martin H.
Published: January 2020

Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea–hypopnea events per hour of sleep (apnea–hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7–40] h − 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.
Channel Prediction Based on BP Neural Network for Backscatter Communication Networks
Authors: Zhao Jumin , Tian Hao , Li Deng-ao
Published: January 2020

Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.
A High-Performance 2.5 μm Charge Domain Global Shutter Pixel and Near Infrared Enhancement with Light Pipe Technology
Authors: Mizuno Ikuo , Tsutsui Masafumi , Yokoyama Toshifumi , Hirata Tatsuya , Nishi Yoshiaki , Veinger Dmitry , Birman Adi , Lahav Assaf
Published: January 2020

We developed a new 2.5 μm global shutter (GS) pixel using a 65 nm process with an advanced light pipe (LP) structure. This is the world’s smallest charge domain GS pixel reported so far. This new developed pixel platform is a key enabler for ultra-high resolution sensors, industrial cameras with wide aperture lenses, and low form factors optical modules for mobile applications. The 2.5 μm GS pixel showed excellent optical performances: 68% quantum efficiency (QE) at 530 nm, ±12.5 degrees angular response (AR), and quite low parasitic light sensitivity (PLS)—10,400 1/PLS with the F#2.8 lens. In addition, we achieved an extremely low memory node (MN) dark current 13 e−/s at 60 °C by fully pinned MN. Furthermore, we studied how the LP technology contributes to the improvement of the modulation transfer function (MTF) in near infrared (NIR) enhanced GS pixel. The 2.8 μm GS pixel using a p-substrate showed 109 lp/mm MTF@50% at 940 nm, which is 1.6 times better than that without an LP. The MTF can be more enhanced by the combination of the LP and the deep photodiode (PD) electrically isolated from the substrate. We demonstrated the advantage of using LP technology and our advanced stacked deep photodiode (SDP) technology together. This unique combination showed an improvement of more than 100% in NIR QE while maintaining an MTF that is close to the theoretical Nyquist limit (MTF @50% = 156 lp/mm).
Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training
Authors: Zhao Di , Zhao Feng , Gan Yongjin
Published: January 2020

Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data.
A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors
Authors: Mohino-Herranz Inma , Gil-Pita Roberto , García-Gómez Joaquín , Rosa-Zurera Manuel , Seoane Fernando
Published: January 2020

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.
Integrated Piezoelectric AlN Thin Film with SU-8/PDMS Supporting Layer for Flexible Sensor Array
Authors: Yeo Hong Goo , Jung Joontaek , Sim Minkyung , Jang Jae Eun , Choi Hongsoo
Published: January 2020

This research focuses on the development of a flexible tactile sensor array consisting of aluminum nitride (AlN) based on micro-electro-mechanical system (MEMS) technology. A total of 2304 tactile sensors were integrated into a small area of 2.5 × 2.5 cm2. Five hundred nm thick AlN film with strong c-axis texture was sputtered on Cr/Au/Cr (50/50/5 nm) layers as the sacrificial layer coated on a Si wafer. To achieve device flexibility, polydimethylsiloxane (PDMS) polymer and SU-8 photoresist layer were used as the supporting layers after etching away a release layer. Twenty-five mM (3-mercaptopropyl) trimethoxysilane (MPTMS) improves the adhesion between metal and polymers due to formation of a self-assembled monolayer (SAM) on the surface of the top electrode. The flexible tactile sensor has 8 × 8 channels and each channel has 36 sensor elements with nine SU-8 bump blocks. The tactile sensor array was demonstrated to be flexible by bending 90 degrees. The tactile sensor array was demonstrated to show clear spatial resolution through detecting the distinct electrical response of each channel under local mechanical stimulus.
Measuring the Water Content in Wood Using Step-Heating Thermography and Speckle Patterns-Preliminary Results
Authors: Madruga Francisco J. , Sfarra Stefano , Perilli Stefano , Pivarčiová Elena , López-Higuera José M.
Published: January 2020

The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive evaluation (NDE) of the amount of moisture in wood materials allows to define, e.g., the restoration procedures of buildings or artworks. In this work, an integrated study of two non-contact techniques is presented. Infrared thermography (IRT) was able to retrieve thermal parameters of the wood related to the amount of water added to the samples, while the interference pattern generated by speckles was used to quantify the expansion and contraction of wood that can be related to the amount of water. In twenty-seven wooded samples, a known quantity of water was added in a controlled manner. By applying advanced image processing to thermograms and specklegrams, it was possible to determine fundamental values controlling both the absorption of water and the main thermophysical parameters that link the samples. On the one hand, results here shown should be considered preliminary because the experimental values obtained by IRT need to be optimized for low water contents introduced into the samples. On the other hand, speckle interferometry by applying an innovative procedure provided robust results for both high and low water contents.
A Machine Condition Monitoring Framework Using Compressed Signal Processing
Authors: Rani Meenu , Dhok Sanjay , Deshmukh Raghavendra
Published: January 2020

The vibration monitoring of ball bearings of a rotating machinery is a crucial aspect for smooth functioning and sustainability of plants. The wireless vibration monitoring using conventional Nyquist sampling techniques is costly in terms of power consumption, as it generates lots of data that need to be processed. To overcome this issue, compressive sensing (CS) can be employed, which directly acquires the signal in compressed form and hence reduces power consumption. The compressive measurements so generated can easily be transmitted to the base station and the original signal can be recovered there using CS reconstruction algorithms to diagnose the faults. However, the CS reconstruction is very costly in terms of computational time and power. Hence, this conventional CS framework is not suitable for diagnosing the machinery faults in real time. In this paper, a bearing condition monitoring framework is presented based on compressed signal processing (CSP). The CSP is a newer research area of CS, in which inference problems are solved without reconstructing the original signal back from compressive measurements. By omitting the reconstruction efforts, the proposed method significantly improves the time and power cost. This leads to faster processing of compressive measurements for solving the required inference problems for machinery condition monitoring. This gives a way to diagnose the machinery faults in real-time. A comparison of proposed scheme with the conventional method shows that the proposed scheme lowers the computational efforts while simultaneously achieving the comparable fault classification accuracy.
Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
Authors: Wang Xiaodong , Liu Feng
Published: January 2020

Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.
Corporate Social Responsibility Information Disclosure and Innovation Sustainability: Evidence from China
Authors: Hu Wenxiu , Du Jinzhu , Zhang Weiguo
Published: January 2020

We selected the Chinese A-share listed companies during period of 2007 to 2017 as the research subject, and from the perspective of information and reputation effects, we examined the relationship between corporate social responsibility (CSR) information disclosure and innovation sustainability. The results show that CSR information disclosure has a significant positive relationship with innovation sustainability. Analysis of the effects channel suggests that the information effect plays a dominant role; CSR information disclosure can alleviate the information asymmetry between managers and investors, controlling shareholders and minority shareholders, and alleviate the financing constraint problems, thereby improving innovation sustainability. Our findings support the information hypothesis but not the reputation hypothesis. The relationship between CSR information disclosure and innovation sustainability is more significant in non-state-owned companies. The moderating effect shows that managerial stock incentives can strengthen the positive relationship between CSR information disclosure and innovation sustainability. A series of robustness test results show that the conclusions are reliable. The research is important for promoting the fulfillment of CSR, improving corporate innovation, and promoting the healthy development of the capital market.
Older Travelers’ E-Loyalty: The Roles of Service Convenience and Social Presence in Travel Websites
Authors: Bi Yahua , Kim Insin
Published: January 2020

The research aims to examine what service convenience factors drive customer satisfaction in travel websites from the perspective of older travelers, and if older travelers’ satisfaction enhances e-loyalty. Additionally, drawing on socioemotional selectivity theory, this study argues that the social presence in travel websites plays a significant moderating role in increasing older travelers’ satisfaction. To empirically verify the conceptual model, an online survey was conducted targeting older travelers aged over 50 in the USA who have purchased products via travel websites. The data from 308 older travelers were analyzed, and the results revealed four dimensions of service convenience positively influence satisfaction. Unexpectedly, access convenience and transaction convenience do not influence older travelers’ satisfaction. The older travelers’ satisfaction with travel websites has a positive impact on e-loyalty. Additionally, social presence amplifies the effect of post-purchase convenience on satisfaction. The current research makes a significant contribution to understanding older travelers’ perceptions and behaviors for using e-commerce service in the field of tourism and provides practitioners with effective ways to attract older travelers for sustainable management of travel websites.
Selected Papers From 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS 2019)
Authors: Meen Teen-Hang , Matsumoto Yusuke , Wang Ming-Shyan
Published: January 2020

The 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare, and Sustainability (IEEE ECBIOS 2019) was held in Okinawa, Japan on 31 May–3 June 2019, and provided a unified communication platform for researchers in the fields of biomedical engineering, healthcare, and sustainability. Recently, healthcare has undergone a sector-wide transformation thanks to advances in computing, networking technologies, big data, and artificial intelligence. Healthcare is not only changing from being reactive and hospital-centered to preventive and personalized, but it is also changing from being disease focused to well-being centered. Healthcare systems, as well as fundamental medicine research, are becoming smarter and enabled in biomedical engineering. Furthermore, with cutting edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower cost. This Special Issue on “Selected Papers from the 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare, and Sustainability (IEEE ECBIOS 2019)” is expected to select excellent papers that were presented in IEEE ECBIOS 2019 on the topic of sustainability. It will link several disciplines, including the environmental, cultural, economic, and social sustainability of human beings, which provide an advanced forum for studies related to sustainability and sustainable development. Our aim is to encourage scientists to publish their experimental and theoretical research relating to natural sciences, social sciences, and humanities in as much detail as possible in order to promote scientific predictions and impact assessments of global change and development.
Agricultural Technology Transfer Preferences of Smallholder Farmers in Tunisia’s Arid Regions
Authors: Dhehibi Boubaker , Rudiger Udo , Moyo Hloniphani Peter , Dhraief Mohamed Zied
Published: January 2020

The objective of this research study was to assess the sources of information on two improved agricultural and livestock technologies (barley variety and feed blocks) as well as the efficacy of numerous agricultural technology diffusion means introduced in the livestock–barley system in semi-arid Tunisia. The research used primary data collected from 671 smallholder farmers. A descriptive statistical analysis was conducted, and Kendall’s W-test and the chi-squared distribution test were deployed to categorize and evaluate the efficacy of the different methods of technology diffusion used by the Tunisian extension system. To address farmers’ perceived opinions and classify the changes from the use of the improved technologies, a qualitative approach based on the Stapel scale was used. Farmer training, demonstration, and farmer-to-farmer interactions were perceived as the most effective agricultural extension methods. The access to technology, know-how, adoption cost of that technology, and labor intensity for adoption influenced its adoption level. Farmers’ opinions about the changes resulting from the adoption of both technologies revealed that yield and resistance to drought were the most important impacts of the two technologies. The study recommends empowering the national extension system through both conventional and non-conventional technologies (ICT, video, mobile phones, etc.), given the cost-effectiveness and their impact on the farmers’ adoption decisions.
Novel Test Bench for the Active Reduction of Biomass Particulate Matter Emissions
Authors: Pérez-Orozco Raquel , Patiño David , Porteiro Jacobo , Míguez José Luís
Published: January 2020

This paper introduces an experimental plant specifically designed to challenge the main operating issues related to modern biomass combustion systems (mainly NOx, particulate matter, and deposition phenomena). The prototype is an 11–18 kW overfed fixed-bed burner with a modular configuration, and the design considers the implementation of certain strategies for improving combustion: (1) a complete refrigeration system that also includes the fuel bed; and (2) an air injection control through flue gas recirculation. First, the stability and repeatability of the facility were successfully tested, establishing the duration of transient periods in the phase of experiment design. The results revealed similar effects in temperature and particulate emissions when comparing the use of the cooling bed and recirculation techniques. Reductions of 15% and up to 70% were achieved for the exhaust temperature and particulate matter concentration, respectively. Otherwise, the refrigeration considerably reduced the bed temperature, especially in its core, which enhanced the condensation of volatile salts and therefore the fouling phenomena. Although the viability of using both techniques as temperature control methods is demonstrated, further studies are needed to clarify the specific effects of each technology and to clarify the possible significance of a hybrid solution that combines both strategies.
Smartphone Use and Academic Performance of University Students: A Mediation and Moderation Analysis
Authors: Ahmed Rizwan Raheem , Salman Faryal , Malik Shahab Alam , Streimikiene Dalia , Soomro Riaz Hussain , Pahi Munwar Hussain
Published: January 2020

The purpose of the undertaken study is to examine the influence of smartphones on the performance of university students in Pakistan. This paper also investigates the functions of a smartphone as exogenous predictors such as smartphone applications, multimedia messaging service (MMS), short message service (SMS), warp-speed processing, and entertainment on the academic performance of a student. This paper also addresses the impact of electronic word of mouth (eWOM) and attitude as mediating variables between exogenous and endogenous variables. Finally, we incorporated technology and addiction as moderating variables between independent variables and the outcome variable to measure the influence of moderating variables. We have taken 684 responses from seven universities in Pakistan and employed the SEM-based multivariate approach for the analysis of the data. The findings of this paper demonstrate that smartphone functions have a significant influence on students’ academic performance, and moderating and mediating variables also have a significant influence on exogenous and endogenous variables. The practical implications have provided a guideline for university teachers, parents, and decision-makers of how a smartphone could be used to improve student academic performance inside and outside university campuses.