bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Interplay between RNA Viruses and Non-Coding RNA in Mammals
Authors: Damas Nkerorema Djodji , Fossat Nicolas , Scheel Troels K. H.
Published: January 2019

Exploring virus–host interactions is key to understand mechanisms regulating the viral replicative cycle and any pathological outcomes associated with infection. Whereas interactions at the protein level are well explored, RNA interactions are less so. Novel sequencing methodologies have helped uncover the importance of RNA–protein and RNA–RNA interactions during infection. In addition to messenger RNAs (mRNAs), mammalian cells express a great number of regulatory non-coding RNAs, some of which are crucial for regulation of the immune system whereas others are utilized by viruses. It is thus becoming increasingly clear that RNA interactions play important roles for both sides in the arms race between virus and host. With the emerging field of RNA therapeutics, such interactions are promising antiviral targets. In this review, we discuss direct and indirect RNA interactions occurring between RNA viruses or retroviruses and host non-coding transcripts upon infection. In addition, we review RNA virus derived non-coding RNAs affecting immunological and metabolic pathways of the host cell typically to provide an advantage to the virus. The relatively few known examples of virus–host RNA interactions suggest that many more await discovery.
Emerging Role of Vitamins D and K in Modulating Uremic Vascular Calcification: The Aspect of Passive Calcification
Authors: Hou Yi-Chou , Lu Chien-Lin , Zheng Cai-Mei , Chen Ruei-Ming , Lin Yuh-Feng , Liu Wen-Chih , Yen Tzung-Hai , Chen Remy , Lu Kuo-Cheng
Published: January 2019

Vascular calcification is a critical complication in patients with chronic kidney disease (CKD) because it is predictive of cardiovascular events and mortality. In addition to the traditional mechanisms associated with endothelial dysfunction and the osteoblastic transformation of vascular smooth muscle cells (VSMCs), the regulation of calcification inhibitors, such as calciprotein particles (CPPs) and matrix vesicles plays a vital role in uremic vascular calcification in CKD patients because of the high prevalence of vitamin K deficiency. Vitamin K governs the gamma-carboxylation of matrix Gla protein (MGP) for inhibiting vascular calcification, and the vitamin D binding protein receptor is related to vitamin K gene expression. For patients with chronic kidney disease, adequate use of vitamin D supplements may play a role in vascular calcification through modulation of the calciprotein particles and matrix vesicles (MVs).
Sweet and Umami Taste Perception Differs with Habitual Exercise in Males
Authors: Feeney Emma L. , Leacy Laura , O’Kelly Mark , Leacy Niamh , Phelan Abbie , Crowley Leah , Stynes Emily , de Casanove Aude , Horner Katy
Published: January 2019

Taste is influenced by several factors. However, whether habitual exercise level is associated with differences in taste perception has received little investigation. The aim of this study was to determine if habitual exercise is associated with differences in taste perception in men. Active (n = 16) and inactive (n = 14) males, between ages 18–55, underwent two days of sensory testing, using prototypical taste stimuli of high and low concentrations for sweet, salt, bitter, sour, umami, and carbohydrate (maltodextrin). Mean perceived intensity and hedonic ratings were recorded. Eating behaviour was assessed by the three factor eating questionnaire and food intake by EPIC food frequency questionnaire (FFQ). There were moderate to large differences between the two groups in perceived intensity for sweet taste at the high concentration and umami taste at both high and low concentrations, with active males recording a higher perceived intensity (p < 0.05 for all). The active group also recorded a greater dislike for umami low and carbohydrate low concentration (p < 0.01). Salt, bitter and sour perception did not significantly differ between the two groups. FFQ analysis showed no difference in % energy from macronutrients between the groups. Eating behaviour traits correlated with sweet taste intensity and umami taste liking, independent of activity status. Results indicated that sweet and umami taste perception differ in active compared to inactive males. Habitual exercise level should be considered in taste perception research and in product development. Whether differences in taste perception could be one factor influencing food intake and thus energy balance with habitual exercise warrants further investigation.
White Sweet Potato as Meal Replacement for Overweight White-Collar Workers: A Randomized Controlled Trial
Authors: Shih Chun-Kuang , Chen Chiao-Ming , Hsiao Tun-Jen , Liu Ching-Wen , Li Sing-Chung
Published: January 2019

Overweight and obesity are a global concern. Meal replacements (MRs) are portion- and calorie-controlled meals, which make the food environment part of an individual’s weight loss regimen. White sweet potato (WSP; Ipomoea batatas L.), used in traditional medicine in Brazil, Japan, and Taiwan, is a healthy carbohydrate source. In this randomized controlled trial, we assessed the effects of a WSP formula on body weight management in 58 white-collar workers through MR to elucidate the effects of this WSP-MR on factors leading to overweight. The participants consumed either two packs a day for a total of 132 g of WSP (WSP-MR group) or a normal diet daily (non-WSP group) for eight weeks. After eight weeks, body weight, body fat, body mass index, wrist circumference, thigh circumference, calf circumference, mid-arm circumference, and triceps skinfolds decreased significantly in both the groups. Moreover, the WSP-MR group demonstrated a 5% decrease in body weight, body fat, body mass index, and mid-arm circumference and a 3.5% decrease in glycated hemoglobin levels (p < 0.05). The treatment was well tolerated, without side effects or adverse events. Thus, our WSP formula as an MR can facilitate individual weight loss and thus has commercial application in the food industry.
Evaluation of a Brief Sodium Screener in Two Samples
Authors: Tangney Christy C. , Rasmussen Heather E. , Richards Candace , Li Michelle , Appelhans Bradley M.
Published: January 2019

The Sodium Screener© (SS©), as developed by NutritionQuest (Berkeley, CA, USA), was designed to reduce the burden of repeated dietary or urinary sodium measurements, but the accuracy of daily sodium intake estimates has not been reported. Associations were examined between sodium intakes derived from the SS© scores and repeated 24-h recalls (24DR) in two studies with different administration modes. In one study, 102 registered dietitians (RD) completed three Automated Self-Administered 24DRs (ASA24©), version 2014, followed by the SS©; both were self-administered and web-based. In the second sample, (the Study of Household Purchasing Patterns, Eating, and Recreation or SHoPPER), trained dietitians conducted 24DR interviews with 69 community-dwelling adults in their homes; all the community adults then completed a paper-based SS© at the final visit. In the RD study, SS© -predicted sodium intakes were 2604 ± 990 (mean ± Standard deviation (SD)), and ASA24© sodium intakes were 3193 ± 907 mg/day. In the SHoPPER sample, corresponding values were 3338 ± 1310 mg/day and 2939 ± 1231 mg/day, respectively. SS©-predicted and recall sodium estimates were correlated in the RD study (r = 0.381, p = 0.0001) and in the SHoPPER (r = 0.430, p = 0.0002). Agreement between the SS© and 24-h recalls was poor when classifying individuals as meeting the dietary sodium guidelines of 2300 mg/day or not (RD study: kappa = 0.080, p = 0.32; SHoPPER: kappa = 0.207, p = 0.08). Based on repeated 24DR either in person or self-reported online as the criterion for estimating daily sodium intakes, the SS© may require additional modifications.
Fatigue Life Prediction of Reinforced Concrete Beams Strengthened with CFRP: Study Based on an Accumulative Damage Model
Authors: Guo Xin-Yan , Wang Yi-Lin , Huang Pei-Yan , Zheng Xiao-Hong , Yang Yi
Published: January 2019

With the prestressed carbon fiber reinforced polymer (CFRP) strengthening technique widely used in reinforced concrete (RC) structures, it is more and more important to study the fatigue performance of RC structures. Since the fracture of a tensile steel bar at the main cracked section is the leading reason for the failure of RC beams reinforced by prestressed CFRP, a fatigue life prediction model of RC beams reinforced by prestressed CFRP was developed based on an accumulative damage model. Moreover, gradual degradation of the performance of the concrete was considered in the fatigue life prediction model. An experimental study was also conducted to research the fatigue behavior of RC beams reinforced by prestressed or non-prestressed carbon fiber laminate (CFL). During the tests, fatigue crack patterns were captured using a digital image correlation (DIC) technique, and the fatigue lives of a total of 30 beams were recorded. The results showed that the predicted main crack propagation curves and the fatigue lives were close to the experimental data. This study also exhibited that the prestressed CFRP could reduce the stress of main steel bars in RC beams and effectively improve the fatigue performance of the RC beams.
Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries
Authors: Son Jeongeun , Du Yuncheng
Published: January 2019

The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.
Cryogenic Resources: Ice, Snow, and Permafrost in Traditional Subsistence Systems in Russia
Authors: Fedorov Roman
Published: January 2019

This article is devoted to the study of the role of natural cryogenic resources in the traditional subsistence systems of the people of Russia. The main source of the actual information and the empirical basis reflecting the features of traditional ecological knowledge of the ethnic groups considered in the article are the scientific publications and ethnographic descriptions made by Russian researchers in the second half of the 19th century through to the beginning of the 21st century, and the results of our modern field research in the territory of Siberia and the Far East of Russia. The methodology of the study lies in the field of ethnoecology, and contains comparative and typological approaches, which have allowed for the detection and systematization of the main spheres of using natural cryogenic resources in traditional subsistence systems by the people of Russia, which include using the environment for indigenous subsistence, building materials, food preservation, obtaining potable water, the irrigation of crops, etc. In conclusion, some of the prospective for the ethnoecological examination of the role of the natural cryogenic resources in traditional subsistence systems were designated, and adaptations to modern innovative technologies based on the rational use of natural resources were also examined.
Impact of Land Cover Change on Ecosystem Services in a Tropical Forested Landscape
Authors: Sharma Roshan , Rimal Bhagawat , Baral Himlal , Nehren Udo , Paudyal Kiran , Sharma Sunil , Rijal Sushila , Ranpal Surendra , Acharya Ram Prasad , Alenazy Amer A. , Kandel Prashid
Published: January 2019

Ecosystems provide a wide range of goods, services or ecosystem services (ES) to society. Estimating the impact of land use and land cover (LULC) changes on ES values (ESV) is an important tool to support decision making. This study used remote sensing and GIS tools to analyze LULC change and transitions from 2001 to 2016 and assess its impact on ESV in a tropical forested landscape in the southern plains of Nepal. The total ESV of the landscape for the year 2016 is estimated at USD 1264 million year−1. As forests are the dominant land cover class and have high ES value per hectare, they have the highest contribution in total ESV. However, as a result of LULC change (loss of forests, water bodies, and agricultural land), the total ESV of the landscape has declined by USD 11 million year−1. Major reductions come from the loss in values of climate regulation, water supply, provision of raw materials and food production. To halt the ongoing loss of ES and maintain the supply and balance of different ES in the landscape, it is important to properly monitor, manage and utilize ecosystems. We believe this study will inform policymakers, environmental managers, and the general public on the ongoing changes and contribute to developing effective land use policy in the region.
An Object-Oriented Bayesian Framework for the Detection of Market Drivers
Authors: De Giuli Maria Elena , Greppi Alessandro , Resta Marina
Published: January 2019

We use Object Oriented Bayesian Networks (OOBNs) to analyze complex ties in the equity market and to detect drivers for the Standard & Poor’s 500 (S&P 500) index. To such aim, we consider a vast number of indicators drawn from various investment areas (Value, Growth, Sentiment, Momentum, and Technical Analysis), and, with the aid of OOBNs, we study the role they played along time in influencing the dynamics of the S&P 500. Our results highlight that the centrality of the indicators varies in time, and offer a starting point for further inquiries devoted to combine OOBNs with trading platforms.
Earth Observations-Based Evapotranspiration in Northeastern Thailand
Authors: Zheng Chaolei , Jia Li , Hu Guangcheng , Lu Jing
Published: January 2019

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.
Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar
Authors: Yuan Fei , Zhang Limin , Soe Khin Min Wun , Ren Liliang , Zhao Chongxu , Zhu Yonghua , Jiang Shanhu , Liu Yi
Published: January 2019

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.
Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector
Authors: Ukaegbu Ikechukwu K. , Gamage Kelum A. A. , Aspinall Michael D.
Published: January 2019

This study reports on the combination of data from a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive depth estimation of buried radioactive sources. The use of the GPR was to enable the estimation of the material density required for the calculation of the depth of the source from the radiation data. Four different models for bulk density estimation were analysed using three materials, namely: sand, gravel and soil. The results showed that the GPR was able to estimate the bulk density of the three materials with an average error of 4.5%. The density estimates were then used together with gamma ray measurements to successfully estimate the depth of a 658 kBq ceasium-137 radioactive source buried in each of the three materials investigated. However, a linear correction factor needs to be applied to the depth estimates due to the deviation of the estimated depth from the measured depth as the depth increases. This new application of GPR will further extend the possible fields of application of this ubiquitous geophysical tool.
Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images
Authors: Ma Wenping , Yang Hui , Wu Yue , Xiong Yunta , Hu Tao , Jiao Licheng , Hou Biao
Published: January 2019

In this paper, a novel change detection approach based on multi-grained cascade forest (gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detects the changed and unchanged areas of the images by using the well-trained gcForest. Most existing change detection methods need to select the appropriate size of the image block. However, the single size image block only provides a part of the local information, and gcForest cannot achieve a good effect on the image representation learning ability. Therefore, the proposed approach chooses different sizes of image blocks as the input of gcForest, which can learn more image characteristics and reduce the influence of the local information of the image on the classification result as well. In addition, in order to improve the detection accuracy of those pixels whose gray value changes abruptly, the proposed approach combines gradient information of the difference image with the probability map obtained from the well-trained gcForest. Therefore, the image edge information can be enhanced and the accuracy of edge detection can be improved by extracting the image gradient information. Experiments on four data sets indicate that the proposed approach outperforms other state-of-the-art algorithms.
High-Resolution Mass Trends of the Antarctic Ice Sheet through a Spectral Combination of Satellite Gravimetry and Radar Altimetry Observations
Authors: Sasgen Ingo , Konrad Hannes , Helm Veit , Grosfeld Klaus
Published: January 2019

Time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) missions and satellite altimetry measurements from CryoSat-2 enable independent mass balance estimates of the Earth’s glaciers and ice sheets. Both approaches vary in terms of their retrieval principles and signal-to-noise characteristics. GRACE/GRACE-FO recovers the gravity disturbance caused by changes in the mass of the entire ice sheet with a spatial resolution of 300 to 400 km. In contrast, CryoSat-2measures travel times of a radar signal reflected close to the ice sheet surface, allowing changes of the surface topography to be determined with about 5 km spatial resolution. Here, we present a method to combine observations from the both sensors, taking into account the different signal and noise characteristics of each satellite observation that are dependent on the spatial wavelength. We include uncertainties introduced by the processing and corrections, such as the choice of the re-tracking algorithm and the snow/ice volume density model for CryoSat-2, or the filtering of correlated errors and the correction for glacial-isostatic adjustment (GIA) for GRACE. We apply our method to the Antarctic ice sheet and the time period 2011–2017, in which GRACE and CryoSat-2 were simultaneously operational, obtaining a total ice mass loss of 178 ± 23 Gt yr−1. We present a map of the rate of mass change with a spatial resolution of 40 km that is evaluable across all spatial scales, and more precise than estimates based on a single satellite mission.
A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data
Authors: Scarth Peter , Armston John , Lucas Richard , Bunting Peter
Published: January 2019

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.
Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm
Authors: Yang Guanci , Chen Zhanjie , Li Yang , Su Zhidong
Published: January 2019

In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.
Dopamine/2-Phenylethylamine Sensitivity of Ion-Selective Electrodes Based on Bifunctional-Symmetrical Boron Receptors
Authors: Durka Martyna , Durka Krzysztof , Adamczyk-Woźniak Agnieszka , Wróblewski Wojciech
Published: January 2019

Piperazine-based compounds bearing two phenylboronic acid or two benzoxaborole groups (PBPA and PBBB) were applied as dopamine receptors in polymeric membranes (PVC/DOS) of ion-selective electrodes. The potentiometric sensitivity and selectivity of the sensors towards dopamine were evaluated and compared with the results obtained for 2-phenylethylamine. Since the developed electrodes displayed strong interference from 2-phenylethylamine, single-molecule geometry optimizations were performed using the density functional theory (DFT) method in order to investigate the origin of dopamine/2-phenylethylamine selectivity. The results indicated that phenylboronic acid and benzoxaborole receptors bind dopamine mainly through the dative B–N bond (like 2-phenylethylamine) and the potentiometric selectivity is mainly governed by the higher lipophilicity of 2-phenylethylamine.
Polymer Optical Fiber Tip Mass Production Etch Mechanism to Achieve CPC Shape for Improved Biosensor Performance
Authors: Hassan Hafeez Ul , Bang Ole , Janting Jakob
Published: January 2019

We report on a simple chemical etching method that enables nonlinear tapering of Polymer Optical Fiber (POF) tips to manufacture Compound Parabolic Concentrator (CPC) fiber tips. We show that, counter-intuitively, nonlinear tapering can be achieved by first etching the core and not the cladding. The etching mechanism is modelled and etched tips are characterized both geometrically and optically in a fluorescence glucose sensor chemistry. A Zemax model of the CPC tipped sensor predicts an optimal improvement in light capturing efficiency of a factor of 3.96 compared to the conventional sensor with a plane-cut fiber tip. A batch of eight CPC fiber tips has been manufactured by the chemical etching method. The batch average showed an increase of a factor of 3.16, which is only 20% less than the predicted value. The method is reproducible and can be up-scaled for mass production.
Prelinearized Class-B Power Amplifier for Piezoelectric Transducers and Portable Ultrasound Systems
Authors: Choi Hojong
Published: January 2019

Portable ultrasound systems typically suffer from unwanted heat and limited battery life, resulting in reduced system performance or the applicable number of piezoelectric transducer elements. This can be a bottleneck in widely used portable ultrasound systems. Class-A power amplifiers are typically used in portable ultrasound systems. However, unwanted heat dissipation needs to be reduced by using large cooling fans and heat pipe structures. To reduce unwanted heat, class-B power amplifiers may be a possible solution. However, the non-linearity of class-B power amplifiers could limit their integration with piezoelectric transducers because non-linearity in the high-voltage output of the power amplifiers deteriorates the sensitivity of portable ultrasound systems. To improve the linearity of the power amplifier, we developed prelinearized class-B power amplifiers for piezoelectric transducers and portable ultrasound systems. To verify our proposed method, we compared the performances of class-B and prelinearized class-B power amplifiers in their pulse-echo responses. Therefore, prelinearized class-B power amplifiers are a possible solution to produce better echo signal performance in piezoelectric transducers and portable ultrasound systems.
A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing
Authors: Lin Lin , Wang Bin , Qi Jiajin , Chen Lingling , Huang Nantian
Published: January 2019

The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified.
Obstacle Avoidance of Two-Wheel Differential Robots Considering the Uncertainty of Robot Motion on the Basis of Encoder Odometry Information
Authors: Jin Jiyong , Chung Woojin
Published: January 2019

It is important to overcome different types of uncertainties for the safe and reliable navigation of mobile robots. Uncertainty sources can be categorized into recognition, motion, and environmental sources. Although several challenges of recognition uncertainty have been addressed, little attention has been paid to motion uncertainty. This study shows how the uncertainties of robot motions can be quantitatively modeled through experiments. Although the practical motion uncertainties are affected by various factors, this research focuses on the velocity control performance of wheels obtained by encoder sensors. Experimental results show that the velocity control errors of practical robots are not negligible. This paper proposes a new motion control scheme toward reliable obstacle avoidance by reflecting the experimental motion uncertainties. The presented experimental results clearly show that the consideration of the motion uncertainty is essential for successful collision avoidance. The presented simulation results show that a robot cannot move through narrow passages owing to a risk of collision when the uncertainty of motion is high. This research shows that the proposed method accurately reflects the motion uncertainty and balances the collision safety with the navigation efficiency of the robot.
3D Affine: An Embedding of Local Image Features for Viewpoint Invariance Using RGB-D Sensor Data
Authors: Sahloul Hamdi , Shirafuji Shouhei , Ota Jun
Published: January 2019

Local image features are invariant to in-plane rotations and robust to minor viewpoint changes. However, the current detectors and descriptors for local image features fail to accommodate out-of-plane rotations larger than 25°–30°. Invariance to such viewpoint changes is essential for numerous applications, including wide baseline matching, 6D pose estimation, and object reconstruction. In this study, we present a general embedding that wraps a detector/descriptor pair in order to increase viewpoint invariance by exploiting input depth maps. The proposed embedding locates smooth surfaces within the input RGB-D images and projects them into a viewpoint invariant representation, enabling the detection and description of more viewpoint invariant features. Our embedding can be utilized with different combinations of descriptor/detector pairs, according to the desired application. Using synthetic and real-world objects, we evaluated the viewpoint invariance of various detectors and descriptors, for both standalone and embedded approaches. While standalone local image features fail to accommodate average viewpoint changes beyond 33.3°, our proposed embedding boosted the viewpoint invariance to different levels, depending on the scene geometry. Objects with distinct surface discontinuities were on average invariant up to 52.8°, and the overall average for all evaluated datasets was 45.4°. Similarly, out of a total of 140 combinations involving 20 local image features and various objects with distinct surface discontinuities, only a single standalone local image feature exceeded the goal of 60° viewpoint difference in just two combinations, as compared with 19 different local image features succeeding in 73 combinations when wrapped in the proposed embedding. Furthermore, the proposed approach operates robustly in the presence of input depth noise, even that of low-cost commodity depth sensors, and well beyond.
Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System
Authors: Fan Qigao , Zhang Hai , Pan Peng , Zhuang Xiangpeng , Jia Jie , Zhang Pengsong , Zhao Zhengqing , Zhu Gaowen , Tang Yuanyuan
Published: January 2019

Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment.
An Analog-Front ROIC with On-Chip Non-Uniformity Compensation for Diode-Based Infrared Image Sensors
Authors: Ye Mao , Zhao Gongyuan , Li Yao , Zhao Yiqiang
Published: January 2019

This paper proposes a CMOS front-end readout-integrated circuit (ROIC) with on-chip non-uniformity compensation technique for a diode-based uncooled infrared image sensor. Two techniques are adopted to achieve on-chip non-uniformity compensation: a reference dummy metal line is introduced to alleviate the dominant non-uniformity with IR-drop presented in large pixel array, and a current splitting architecture-based variable current source for diode bias is proposed to compensate other residual non-uniformity. A differential integrator is chosen as the main amplifier of readout circuit for its superior noise performance. For low power design, a pulse-powered row buffer is designed in this work. The proposed ROIC for 384 × 288 diode-based detector array is fabricated with a 0.35- μ m CMOS process. It occupies an area of 4.4 mm × 15 mm, and the power consumption is 180 mW. The measured result shows that with the proposed on-chip non-uniformity compensation, the output voltage variation is greatly reduced from 2.5 V to 60 mV.
Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
Authors: Ateeq Muhammad , Ishmanov Farruh , Afzal Muhammad Khalil , Naeem Muhammad
Published: January 2019

Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (EC) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals—not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (PDR) and EC can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like PDR and EC in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both PDR and EC based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both PDR and EC predictions. These prediction results can help configure communication parameters taking into account the performance goals.
Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm
Authors: Giernacki Wojciech , Horla Dariusz , Báča Tomáš , Saska Martin
Published: January 2019

The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.
Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach
Authors: Gao Pengbo , Zhang Yan , Zhang Linhuan , Noguchi Ryozo , Ahamed Tofael
Published: January 2019

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.
Carbon Nanotube/Graphene Nanoplatelet Hybrid Film as a Flexible Multifunctional Sensor
Authors: Huang JianRen , Yang XiaoXiang , Her Shiuh-Chuan , Liang Yuan-Ming
Published: January 2019

A flexible hybrid film consisting of graphene nanoplatelets (GNPs) and multi-walled carbon nanotubes (MWCNTs) was prepared and employed as a multifunctional sensor to monitor temperature and liquid leakage, based on the piezoresistive effect. The influences of the GNP content on the mechanical, thermal, and sensing properties were investigated. Experimental results showed that both the hardness and Young’s modulus of the hybrid film were decreased with an increasing GNP content, while the thermal conductivity exhibited in an opposite trend. The electrical resistance of the hybrid film decreased was linearly with an increase in temperature. The resistance change increased linearly with an increase of the solvent adsorption. These features demonstrated the potential applications of the hybrid film in the detection of temperature, and liquid leakage. The sensitivity of leakage detection is increasing with the increase of the GNP loading, while temperature sensitivity is in the opposite trend.
Acknowledgement to Reviewers of Societies in 2018
Authors:   Societies Editorial Office
Published: January 2019

Rigorous peer review is the cornerstone of high-quality academic publishing [...]
Normative Shifts in the Global Conception of Climate Change: The Growth of Climate Justice
Authors: Gach Evan
Published: January 2019

While climate change has been framed as an environmental issue from the very beginning of United Nations Framework Convention on Climate Change (UNFCCC) negotiations, over the years the concept has expanded to further emphasize it as a fundamental issue of human rights and global justice. This paper examines the evolution of the conception of climate change since 2009, arguing that the issue framing utilized by UNFCCC member states has increasingly trended toward some aspects of the climate justice frame, including disparities in vulnerability to climate change (loss and damage), human rights impacts, and social inequalities. This shift also extends to the framing adopted by civil society organizations in the form of the Climate Action Network (CAN International), in which a larger focus on issues of climate justice can be seen in recent years. These trends are then reviewed alongside the objectives, mechanisms, and language of the ratified text of the Paris Agreement in order to evaluate the status of the growing international norm of climate justice.