bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between Active H. pylori Infection and Iron Deficiency Assessed by Serum Hepcidin Levels in School-Age Children
Authors: Mendoza Eugenia , Duque Ximena , Hernández Franco Jordán I. , Reyes Maldonado Elba , Morán Segundo , Martínez Gloria , Salinas Rodríguez Aarón , Martínez Homero
Published: September 2019

Hepcidin regulates iron metabolism. Its synthesis increases in infection and decreases in iron deficiency. The aim of this study was to evaluate the relationship between H. pylori infection and iron deficiency by levels of hepcidin in children. A total of 350 school-age children participated in this cross-sectional study. Determinations of serum ferritin, hemoglobin, hepcidin, C-reactive protein, and α-1-acid-glycoprotein were done. Active H. pylori infection was performed with a 13C-urea breath test. In schoolchildren without H. pylori infection, hepcidin was lower in those with iron deficiency compared to children with normal iron status (5.5 ng/mL vs. 8.2 ng/mL, p = 0.017); while in schoolchildren with H. pylori infection the levels of hepcidin tended to be higher, regardless of the iron nutritional status. Using multivariate analysis, the association between H. pylori infection and iron deficiency was different by hepcidin levels. The association between H. pylori and iron deficiency was not significant for lower values of hepcidin (Odds Ratio = 0.17; 95% Confidence Interval [CI] 0.02–1.44), while the same association was significant for higher values of hepcidin (OR = 2.84; CI 95% 1.32–6.09). This joint effect is reflected in the adjusted probabilities for iron deficiency: Individuals with H. pylori infection and higher levels of hepcidin had a probability of 0.24 (CI 95% 0.14–0.34) for iron deficiency, and this probability was 0.24 (CI 95% 0.14–0.33) in children without H. pylori infection and lower levels of hepcidin. In children with H. pylori infection and iron deficiency, the hepcidin synthesis is upregulated. The stimulus to the synthesis of hepcidin due to H. pylori infection is greater than the iron deficiency stimulus.
Methylxanthines: Potential Therapeutic Agents for Glioblastoma
Authors: Pérez-Pérez Daniel , Reyes-Vidal Iannel , Chávez-Cortez Elda , Sotelo Julio , Magaña-Maldonado Roxana
Published: September 2019

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Currently, treatment is ineffective and the median overall survival is 20.9 months. The poor prognosis of GBM is a consequence of several altered signaling pathways that favor the proliferation and survival of neoplastic cells. One of these pathways is the deregulation of phosphodiesterases (PDEs). These enzymes participate in the development of GBM and may have value as therapeutic targets to treat GBM. Methylxanthines (MXTs) such as caffeine, theophylline, and theobromine are PDE inhibitors and constitute a promising therapeutic anti-cancer agent against GBM. MTXs also regulate various cell processes such as proliferation, migration, cell death, and differentiation; these processes are related to cancer progression, making MXTs potential therapeutic agents in GBM.
Twenty Years of Ferroportin Disease: A Review or An Update of Published Clinical, Biochemical, Molecular, and Functional Features
Authors: Vlasveld L. Tom , Janssen Roel , Bardou-Jacquet Edouard , Venselaar Hanka , Hamdi-Roze Houda , Drakesmith Hal , Swinkels Dorine W.
Published: September 2019

Iron overloading disorders linked to mutations in ferroportin have diverse phenotypes in vivo, and the effects of mutations on ferroportin in vitro range from loss of function (LOF) to gain of function (GOF) with hepcidin resistance. We reviewed 359 patients with 60 ferroportin variants. Overall, macrophage iron overload and low/normal transferrin saturation (TSAT) segregated with mutations that caused LOF, while GOF mutations were linked to high TSAT and parenchymal iron accumulation. However, the pathogenicity of individual variants is difficult to establish due to the lack of sufficiently reported data, large inter-assay variability of functional studies, and the uncertainty associated with the performance of available in silico prediction models. Since the phenotypes of hepcidin-resistant GOF variants are indistinguishable from the other types of hereditary hemochromatosis (HH), these variants may be categorized as ferroportin-associated HH, while the entity ferroportin disease may be confined to patients with LOF variants. To further improve the management of ferroportin disease, we advocate for a global registry, with standardized clinical analysis and validation of the functional tests preferably performed in human-derived enterocytic and macrophagic cell lines. Moreover, studies are warranted to unravel the definite structure of ferroportin and the indispensable residues that are essential for functionality.
A Multi-Label Learning Framework for Drug Repurposing
Authors: Mei Suyu , Zhang Kun
Published: September 2019

Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.
The Development of Gelatin/Hyaluronate Copolymer Mixed with Calcium Sulfate, Hydroxyapatite, and Stromal-Cell-Derived Factor-1 for Bone Regeneration Enhancement
Authors: Chang Yun-Liang , Hsieh Chia-Ying , Yeh Chao-Yuan , Lin Feng-Huei
Published: September 2019

In clinical practice, bone defects still remain a challenge. In recent years, apart from the osteoconductivity that most bone void fillers already provide, osteoinductivity has also been emphasized to promote bone healing. Stromal-cell-derived factor-1 (SDF-1) has been shown to have the ability to recruit mesenchymal stem cells (MSCs), which play an important role in the bone regeneration process. In this study, we developed a gelatin–hyaluronate (Gel-HA) copolymer mixed with calcium sulfate (CS), hydroxyapatite (HAP), and SDF-1 in order to enhance bone regeneration in a bone defect model. The composites were tested in vitro for biocompatibility and their ability to recruit MSCs after material characterization. For the in vivo test, a rat femoral condyle bone defect model was used. Micro computed tomography (Micro-CT), two-photon excitation microscopy, and histology analysis were performed to assess bone regeneration. As expected, enhanced bone regeneration was well observed in the group filled with Gel-HA/CS/HAP/SDF-1 composites compared with the control group in our animal model. Furthermore, detailed blood analysis of rats showed no obvious systemic toxicity or side effects after material implantation. In conclusion, the Gel-HA/CS/HAP/SDF-1 composite may be a safe and applicable material to enhance bone regeneration in bone defects.
Influence of the Conditions of Corotating Twin-Screw Extrusion for Talc-Filled Polypropylene on Selected Properties of the Extrudate
Authors: Sasimowski Emil , Majewski Łukasz , Grochowicz Marta
Published: September 2019

The aim of the study was to determine the effect of the application of processing screws with a modified test segment in a corotating twin-screw extruder on selected properties of talc-filled polypropylene extrudate. The test segment was built of trilobe kneading elements and its design modifications refered to changing the distance between the kneading elements and the angle of positions of kneading elements that are relative to each other. The performed tests included the production of extrudate with various degrees of talc-filling using five design solutions of the test segment and then measurements of selected properties, such as tensile strength, elongation at maximum tensile stress, and melt flow rate. Structural studies using scanning electron microscope (SEM) and differential scanning calorimetry (DSC) were also carried out. The study includes not only the description of experimental results but also the determination of empirical models describing the dependence of the properties of the obtained extrudate on the conditions of the extrusion process and the design features of the test segment.
Amine Responsive Poly(lactic acid) (PLA) and Succinic Anhydride (SAh) Graft-Polymer: Synthesis and Characterization
Authors: Lopera-Valle Adrián , Elias Anastasia
Published: September 2019

Amines are known to react with succinic anhydride (SAh), which in reactions near room temperature, undergoes a ring opening amidation reaction to form succinamic acid (succinic acid-amine). In this work, we propose to form an amine-responsive polymer by grafting SAh to a poly(lactic acid) (PLA) backbone, such that the PLA can provide chemical and mechanical stability for the functional SAh during the amidation reaction. Grafting is performed in a toluene solution at mass content from 10 wt% to 75 wt% maleic anhydride (MAh) (with respect to PLA and initiator), and films are then cast. The molecular weight and thermal properties of the various grafted polymers are measured by gel permeation chromatography and differential scanning calorimetry, and the chemical modification of these materials is examined using infrared spectroscopy. The efficiency of the grafting reaction is estimated with thermogravimetric analysis. The degree of grafting is determined to range from 5% to 42%; this high degree of grafting is desirable to engineer an amine-responsive material. The response of the graft-polymers to amines is characterized using X-ray photoelectron spectroscopy, infrared spectroscopy, and differential scanning calorimetry. Changes in the chemical and thermal properties of the graft-polymers are observed after exposure to the vapors from a 400 ppm methylamine solution. In contrast to these changes, control samples of neat PLA do not undergo comparable changes in properties upon exposure to methylamine vapor. In addition, the PLA-g-SAh do not undergo changes in structure when exposed to vapors from deionized water without amines. This work presents potential opportunities for the development of real-time amine sensors.
Sintering Reaction and Pyrolysis Process Analysis of Al/Ta/PTFE
Authors: Zhang Jun , Huang Junyi , Li Yuchun , Liu Qiang , Yu Zhongshen , Wu Jiaxiang , Gao Zhenru , Wu Shuangzhang , Kui Jiaying , Song Jiaxing
Published: September 2019

When the Al/Ta/PTFE reactive material was sintered at 360 °C in a vacuum sintering furnace, it was found that the material reacted to form a soft fluffy white substance and carbon black. To explore the reaction process further, powder samples of pure PTFE, Al/PTFE, Ta/PTFE and Al/Ta/PTFE, and molded cylindrical specimens were prepared. A TG-DSC test was carried out on the thermal reaction of four reactive materials, and XRD phase analysis was conducted on the white product, formed by the sintering reaction and the residue of the TG-DSC test sample, based on which of the pyrolysis processes and reaction mechanisms were analyzed. The results show that Ta and PTFE could have a chemical reaction at sintering temperature (360 °C) to form soft and fluffy white material TaF3 and carbon black, which can overflow the surface of the specimen and cause cracking of the specimen, which is tightly pressed. Since no obvious exothermic peak showed up on the TG-DSC curve, the composition of the residue of TG-DSC sample at different temperatures was tested and TaF3 was detected in the residue at 350 °C and 360 °C, indicating that Ta began to react with PTFE at a temperature range of 340–350 °C. According to the chemical properties and product formation of Ta, it could be speculated that the reaction mechanism between Ta and PTFE involves the PTFE decomposing first, then the fluorine-containing gas product reacting with metal Ta. According to the temperature range of the reaction, it is estimated that PTFE starts to decompose before 500 °C, but it is not detected effectively by TG-DSC, and the introduction of Ta could also affect the decomposition process of PTFE.
Intelligent Control Strategy for Transient Response of a Variable Geometry Turbocharger System Based on Deep Reinforcement Learning
Authors: Hu Bo , Yang Jie , Li Jiaxi , Li Shuang , Bai Haitao
Published: September 2019

Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of a variable geometry turbocharger (VGT)-equipped diesel engine is difficult mainly due to its strong coupling with an exhaust gas recirculation (EGR) system and large lag, resulting from time delay and hysteresis between the input and output dynamics of the engine’s gas exchange system. In this context, one of the latest model-free DRL algorithms, the deep deterministic policy gradient (DDPG) algorithm, was built in this paper to develop and finally form a strategy to track the target boost pressure under transient driving cycles. Using a fine-tuned proportion integration differentiation (PID) controller as a benchmark, the results show that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. In addition, the proposed strategy is able to adapt to the changing environment and hardware aging over time by adaptively tuning the algorithm in a self-learning manner on-line, making it attractive to real plant control problems whose system consistency may not be strictly guaranteed and whose environment may change over time.
Study of the Lamellar and Micellar Phases of Pluronic F127: A Molecular Dynamics Approach
Authors: Albano Juan , Grillo Damian , Facelli Julio , Ferraro Marta , Pickholz Mónica
Published: September 2019

In this work, we analyzed the behavior of Pluronic F127 through molecular dynamics simulations at the coarse-grain level, focusing on the micellar and lamellar phases. To this aim, two initial polymer conformations were considered, S-shape and U-shape, for both simulated phases. Through the simulations, we were able to examine the structural and mechanical properties that are difficult to access through experiments. Since no transition between S and U shapes was observed in our simulations, we inferred that all single co-polymers had memory of their initial configuration. Nevertheless, most copolymers had a more complex amorphous structure, where hydrophilic beads were part of the lamellar-like core. Finally, an overall comparison of the micellar a lamellar phases showed that the lamellar thickness was in the same order of magnitude as the micelle diameter (approx. 30 nm). Therefore, high micelle concentration could lead to lamellar formation. With this new information, we could understand lamellae as orderly packed micelles.
The Biomass Potential and GHG (Greenhouse Gas) Emissions Mitigation of Straw-Based Biomass Power Plant: A Case Study in Anhui Province, China
Authors: Li Hui , Min Xue , Dai Mingwei , Dong Xinju
Published: September 2019

Anhui Province (AHP), a typical agriculture-based province in China, has a significant amount of biomass resources for the development of biomass power plants. By the end of 2016, 23 straw based biomass power plants were established in AHP, aggregating to 6560 MW capacity, which is now ranked second in China. This paper presents the current development status and GHG (Greenhouse Gas) mitigation effect of the straw based biomass power plants in Anhui Province. Total biomass production in 2016 was calculated as 41.84 million tons. Although there is huge biomass potential in AHP, the distribution is heterogeneous with a gradually decreasing trend from north to south. Furthermore, the installed capacity of power generation is also unmatched with the biomass resources. Based on a calculation made in 2016, approximately 3.44 million tons of CO2-eq were mitigated from the biomass power plants in AHP. The large-scale development of biomass power plants remains a challenge for the future, especially in areas of AHP with a low biomass density.
Which Are the Tools Available for Scholars? A Review of Assisting Software for Authors during Peer Reviewing Process
Authors: Martínez-López J. Israel , Barrón-González Samantha , Martínez López Alejandro
Published: September 2019

There is a large amount of Information Technology and Communication (ITC) tools that surround scholar activity. The prominent place of the peer-review process upon publication has promoted a crowded market of technological tools in several formats. Despite this abundance, many tools are unexploited or underused because they are not known by the academic community. In this study, we explored the availability and characteristics of the assisting tools for the peer-reviewing process. The aim was to provide a more comprehensive understanding of the tools available at this time, and to hint at new trends for further developments. The result of an examination of literature assisted the creation of a novel taxonomy of types of software available in the market. This new classification is divided into nine categories as follows: (I) Identification and social media, (II) Academic search engines, (III) Journal-abstract matchmakers, (IV) Collaborative text editors, (V) Data visualization and analysis tools, (VI) Reference management, (VII) Proofreading and plagiarism detection, (VIII) Data archiving, and (IX) Scientometrics and Altmetrics. Considering these categories and their defining traits, a curated list of 220 software tools was completed using a crowdfunded database (AlternativeTo) to identify relevant programs and ongoing trends and perspectives of tools developed and used by scholars.
Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau
Authors: Gao Jinlong , Liang Tiangang , Yin Jianpeng , Ge Jing , Feng Qisheng , Wu Caixia , Hou Mengjing , Liu Jie , Xie Hongjie
Published: September 2019

The applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58–0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data
Authors: El-Nadry Maram , Li Wenzhao , El-Askary Hesham , Awad Mohamed A. , Mostafa Alaa Ramadan
Published: September 2019

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies.
Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES
Authors: Carr James L. , Wu Dong L. , Wolfe Robert E. , Madani Houria , Lin Guoqing (Gary) , Tan Bin
Published: September 2019

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.
Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests
Authors: Tompalski Piotr , Rakofsky Joseph , Coops Nicholas C. , White Joanne C. , Graham Alexander N. V. , Rosychuk Kyle
Published: September 2019

Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have both been demonstrated as reliable sources of information on forest stand inventory attributes. The increasing availability of both datasets provides a means for improving stand dynamics information over time; however, the cost of multi-temporal ALS can be prohibitive in some circumstances. As a result, a combination of ALS at an initial time step and subsequent updates using DAP has been proposed as a cost-effective alternative for maintaining forest inventories. In this study we used low density ALS and DAP point clouds acquired in 2007 and 2015, respectively, to quantify changes in forest structure, in a highly disturbed boreal mixedwood forest in Alberta, Canada. We examined the capacity of the two technologies to model changes in top height (H), volume (V), and basal area (BA) using both direct and indirect approaches for estimation. Results indicate that the proportion of explained variance (adjusted R2) for the models derived from the ALS (Time 1; T1) and DAP (Time 2; T2) data were highest for models predicting H at T1, and lowest for BA at T1 and T2 (R2 was 0.66–0.70). The indirect estimates of change in H, BA, and V were calculated by subtracting the T1 and T2 predictions. For the direct approach, separate regression models were developed that used the differences in point cloud metrics between T1 and T2 as predictors. Results indicated that the accuracy of the estimates generated using the indirect approach were markedly lower than the estimates generated using the direct approach, with especially poor results for ∆BA and ∆V. Best results were achieved for ∆H using the direct approach with an R2 coefficient of 0.65 and an root mean square error (RMSE)% of 190.06%. We found that the error associated with change estimates of H, BA and V increased with the increase or decrease in mortality. We conclude that forest managers should act carefully when applying the multi-temporal and multi-sensor analysis of forest growth if forest growth is slow and the level of mortality is high.
Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications
Authors: García-Haro Francisco Javier , Camacho Fernando , Martínez Beatriz , Campos-Taberner Manuel , Fuster Beatriz , Sánchez-Zapero Jorge , Gilabert María Amparo
Published: September 2019

The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed by vegetation (FAPAR) and the fractional vegetation cover (FVC), for the whole Meteosat disk at two temporal frequencies, daily and 10-days. The FVC algorithm relies on a novel stochastic spectral mixture model which addresses the variability of soils and vegetation types using statistical distributions whereas the LAI and FAPAR algorithms use statistical relationships general enough for global applications. An overview of the LSA SAF SEVIRI/MSG vegetation products, including expert knowledge and quality assessment of its internal consistency is provided. The climate data record (CDR) is freely available in the LSA SAF, offering more than fifteen years (2004-present) of homogeneous time series required for climate and environmental applications. The high frequency and good temporal continuity of SEVIRI products addresses the needs of near-real-time users and are also suitable for long-term monitoring of land surface variables. The study also evaluates the potential of the SEVIRI/MSG vegetation products for environmental applications, spanning from accurate monitoring of vegetation cycles to resolving long-term changes of vegetation.
UAV Imaging of a Martian Brine Analogue Environment in a Fluvio-Aeolian Setting
Authors: Bhardwaj Anshuman , Sam Lydia , Martín-Torres F. , Zorzano María-Paz , Ramírez Luque Juan
Published: September 2019

Understanding extraterrestrial environments and landforms through remote sensing and terrestrial analogy has gained momentum in recent years due to advances in remote sensing platforms, sensors, and computing efficiency. The seasonal brines of the largest salt plateau on Earth in Salar de Uyuni (Bolivian Altiplano) have been inadequately studied for their localized hydrodynamics and the regolith volume transport across the freshwater-brine mixing zones. These brines have recently been projected as a new analogue site for the proposed Martian brines, such as recurring slope lineae (RSL) and slope streaks. The Martian brines have been postulated to be the result of ongoing deliquescence-based salt-hydrology processes on contemporary Mars, similar to the studied Salar de Uyuni brines. As part of a field-site campaign during the cold and dry season in the latter half of August 2017, we deployed an unmanned aerial vehicle (UAV) at two sites of the Salar de Uyuni to perform detailed terrain mapping and geomorphometry. We generated high-resolution (2 cm/pixel) photogrammetric digital elevation models (DEMs) for observing and quantifying short-term terrain changes within the brines and their surroundings. The achieved co-registration for the temporal DEMs was considerably high, from which precise inferences regarding the terrain dynamics were derived. The observed average rate of bottom surface elevation change for brines was ~1.02 mm/day, with localized signs of erosion and deposition. Additionally, we observed short-term changes in the adjacent geomorphology and salt cracks. We conclude that the transferred regolith volume via such brines can be extremely low, well within the resolution limits of the remote sensors that are currently orbiting Mars, thereby making it difficult to resolve the topographic relief and terrain perturbations that are produced by such flows on Mars. Thus, the absence of observable erosion and deposition features within or around most of the proposed Martian RSL and slope streaks cannot be used to dismiss the possibility of fluidized flow within these features.
Error Analysis and Compensation of a Laser Measurement System for Simultaneously Measuring Five-Degree-of-Freedom Error Motions of Linear Stages
Authors: Cai Yindi , Sang Qi , Lou Zhi-Feng , Fan Kuang-Chao
Published: September 2019

A robust laser measurement system (LMS), consisting of a sensor head and a detecting part, for simultaneously measuring five-degree-of-freedom (five-DOF) error motions of linear stages, is proposed and characterized. For the purpose of long-travel measurement, all possible error sources that would affect the measurement accuracy are considered. This LMS not only integrates the merits of error compensations for the laser beam drift, beam spot variation, detector sensitivity variation, and non-parallelism of dual-beam that have been resolved by the author’s group before, but also eliminates the crosstalk errors among five-DOF error motions in this study. The feasibility and effectiveness of the designed LMS and modified measurement model are experimentally verified using a laboratory-built prototype. The experimental results show that the designed LSM has the capability of simultaneously measuring the five-DOF error motions of a linear stage up to one-meter travel with a linear error accuracy in sub-micrometer and an angular error accuracy in sub-arcsecond after compensation.
Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning
Authors: Zeng Junjie , Ju Rusheng , Qin Long , Hu Yue , Yin Quanjun , Hu Cong
Published: September 2019

In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot’s capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration commands.
A Novel Graphene Oxide-Based Aptasensor for Amplified Fluorescent Detection of Aflatoxin M1 in Milk Powder
Authors: Guo Xiaodong , Wen Fang , Qiao Qinqin , Zheng Nan , Saive Matthew , Fauconnier Marie-Laure , Wang Jiaqi
Published: September 2019

In this paper, a rapid and sensitive fluorescent aptasensor for the detection of aflatoxin M1 (AFM1) in milk powder was developed. Graphene oxide (GO) was employed to quench the fluorescence of a carboxyfluorescein-labelled aptamer and protect the aptamer from nuclease cleavage. Upon the addition of AFM1, the formation of an AFM1/aptamer complex resulted in the aptamer detaching from the surface of GO, followed by the aptamer cleavage by DNase I and the release of the target AFM1 for a new cycle, which led to great signal amplification and high sensitivity. Under optimized conditions, the GO-based detection of the aptasensor exhibited a linear response to AFM1 levels in a dynamic range from 0.2 to 10 μg/kg, with a limit of detection (LOD) of 0.05 μg/kg. Moreover, the developed aptasensor showed a high specificity towards AFM1 without interference from other mycotoxins. In addition, the technique was successfully applied for the detection of AFM1 in infant milk powder samples. The aptasensor proposed here offers a promising technology for food safety monitoring and can be extended to various targets.
Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models
Authors: Zhao Xia , Li Pengfei , Xiao Kaitai , Meng Xiangning , Han Lu , Yu Chongchong
Published: September 2019

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.
Development of Graphene Quantum Dots-Based Optical Sensor for Toxic Metal Ion Detection
Authors: Anas Nur Ain Asyiqin , Fen Yap Wing , Omar Nur Alia Sheh , Daniyal Wan Mohd Ebtisyam Mustaqim Mohd , Ramdzan Nur Syahira Md , Saleviter Silvan
Published: September 2019

About 71% of the Earth’s surface is covered with water. Human beings, animals, and plants need water in order to survive. Therefore, it is one of the most important substances that exist on Earth. However, most of the water resources nowadays are insufficiently clean, since they are contaminated with toxic metal ions due to the improper disposal of pollutants into water through industrial and agricultural activities. These toxic metal ions need to be detected as fast as possible so that the situation will not become more critical and cause more harm in the future. Since then, numerous sensing methods have been proposed, including chemical and optical sensors that aim to detect these toxic metal ions. All of the researchers compete with each other to build sensors with the lowest limit of detection and high sensitivity and selectivity. Graphene quantum dots (GQDs) have emerged as a highly potential sensing material to incorporate with the developed sensors due to the advantages of GQDs. Several recent studies showed that GQDs, functionalized GQDs, and their composites were able to enhance the optical detection of metal ions. The aim of this paper is to review the existing, latest, and updated studies on optical sensing applications of GQDs-based materials toward toxic metal ions and future developments of an excellent GQDs-based SPR sensor as an alternative toxic metal ion sensor.
Emerging Technologies for Future Sensor Networks—Selected Papers from ICGHIT 2019
Authors: Kim Byung-Seo , Kim Sung Won , Zhang Chi , Guo Yuanxiong , Umer Tariq
Published: September 2019

The International Conference on Green and Human Information Technology (ICGHIT) is an international conference focusing on green and information technologies oriented toward humanity [...]
Peptide Nanotube Encapsulated Enzyme Biosensor for Vapor Phase Detection of Malathion, an Organophosphorus Compound
Authors: Edwards Christopher , Duanghathaipornsuk Surachet , Goltz Mark , Kanel Sushil , Kim Dong-Shik
Published: September 2019

This study explores the use of a butyrylcholinesterase (BChE)-based, reversible reaction biosensor using screen-printed electrodes (SPEs) having a smaller working surface area than the single-use electrodes previously studied. Previous research demonstrated the prospective application of a single-use biosensor fabricated with an acetylcholinesterase (AChE) enzyme encapsulated in peptide nanotubes (PNTs) and enhanced with horseradish peroxidase (HRP) to detect organophosphorus compounds (OPCs) in aqueous and gas phases. In the current study, potential improvements to the biosensor are investigated. BChE-based biosensors were fabricated using PNTs, HRP, and Nafion in combination to increase the reactive surface area, enhance sensitivity, and maintain enzyme stability. Cyclic voltammetry (CV) was used along with the new modified sensor to measure malathion concentration in the gas phase. The results show that a BChE-based biosensor could reliably measure gas phase malathion concentrations between 6–25 ppbv by CV with the extent of inhibition linearly proportional to the malathion concentration (R2 = 0.941). This research demonstrated that fabricated BChE-based biosensors could be stored without cold storage requirement for up to six weeks with minimal performance degradation. Moreover, the sensor electrodes were each reused several times, and were still useable at the conclusion of the research. This research demonstrates the potential of fabricating a reusable, inexpensive biosensor that is capable of OPC detection with high sensitivity and a low detection limit without a long-term cold storage requirement.
Method of Evaluating the Positioning System Capability for Complying with the Minimum Accuracy Requirements for the International Hydrographic Organization Orders
Authors: Specht Mariusz
Published: September 2019

According to the IHO (International Hydrographic Organization) S-44 standard, hydrographic surveys can be carried out in four categories, the so-called orders—special, 1a, 1b, and 2—for which minimum accuracy requirements for the applied positioning system have been set out. These amount to, respectively: 2 m, 5 m, 5 m, and 20 m at a confidence level of 0.95. It is widely assumed that GNSS (Global Navigation Satellite System) network solutions with an accuracy of 2–5 cm (p = 0.95) and maritime DGPS (Differential Global Positioning System) systems with an error of 1–2 m (p = 0.95) are currently the two main positioning methods in hydrography. Other positioning systems whose positioning accuracy increases from year to year (and which may serve as alternative solutions) have been omitted. The article proposes a method that enables an assessment of any given navigation positioning system in terms of its compliance (or non-compliance) with the minimum accuracy requirements specified for hydrographic surveys. The method concerned clearly assesses whether a particular positioning system meets the accuracy requirements set out for a particular IHO order. The model was verified, taking into account both past and present research results (stationary and dynamic) derived from tests on the following systems: DGPS, EGNOS (European Geostationary Navigation Overlay Service), and multi-GNSS receivers (GPS/GLONASS/BDS/Galileo). The study confirmed that the DGPS system meets the requirements for all IHO orders and proved that the EGNOS system can currently be applied in measurements in the orders 1a, 1b, and 2. On the other hand, multi-GNSS receivers meet the requirements for order 2, while some of them meet the requirements for orders 1a and 1b as well.
Multipoint-Detection Strain Sensor with a Single Electrode Using Optical Ultrasound Generated by Carbon Nanotubes
Authors: Choi Won Young , Jo Hyeong Geun , Kwon Soo Won , Kim Young Hun , Pyun Joo Young , Park Kwan Kyu
Published: September 2019

With the development of wearable devices, strain sensors have attracted large interest for the detection of human motion, movement, and breathing. Various strain sensors consisting of stretchable conductive materials have been investigated based on resistance and capacitance differences according to the strain. However, this method requires multiple electrodes for multipoint detection. We propose a strain sensor capable of multipoint detection with a single electrode, based on the ultrasound pulse–echo method. It consists of several transmitters of carbon nanotubes (CNTs) and a single polyvinylidene fluoride receiver. The strain sensor was fabricated using CNTs embedded in stretchable polydimethylsiloxane. The received data are characterized by the different times of transmission from the CNTs of each point depending on the strain, i.e., the sensor can detect the positions of the CNTs. This study demonstrates the application of the multipoint strain sensor with a single electrode for measurements up to a strain of 30% (interval of 1%). We considered the optical and acoustic energy losses in the sensor design. In addition, to evaluate the utility of the sensor, finger bending with three-point CNTs and flexible phantom bending with six-point CNTs for the identification of an S-curve having mixed expansion and compression components were carried out.
Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
Authors: Ji Jie , Wu Guohua , Shuai Jinguo , Zhang Zhen , Wang Zhen , Ren Yizhi
Published: September 2019

The privacy and security of the Internet of Things (IoT) are emerging as popular issues in the IoT. At present, there exist several pieces of research on network analysis on the IoT network, and malicious network analysis may threaten the privacy and security of the leader in the IoT networks. With this in mind, we focus on how to avoid malicious network analysis by modifying the topology of the IoT network and we choose closeness centrality as the network analysis tool. This paper makes three key contributions toward this problem: (1) An optimization problem of removing k edges to minimize (maximize) the closeness value (rank) of the leader; (2) A greedy (greedy and simulated annealing) algorithm to solve the closeness value (rank) case of the proposed optimization problem in polynomial time; and (3)UpdateCloseness (FastTopRank)—algorithm for computing closeness value (rank) efficiently. Experimental results prove the efficiency of our pruning algorithms and show that our heuristic algorithms can obtain accurate solutions compared with the optimal solution (the approximation ratio in the worst case is 0.85) and outperform the solutions obtained by other baseline algorithms (e.g., choose k edges with the highest degree sum).
A Charging Algorithm for the Wireless Rechargeable Sensor Network with Imperfect Charging Channel and Finite Energy Storage
Authors: Tian Mengqiu , Jiao Wanguo , Liu Jiaming , Ma Siyuan
Published: September 2019

Recently, wireless energy transfer technology becomes a popular way to address energy shortage in wireless sensor networks. The capacity of the mobile wireless charging car (WCV) and the wireless channel between the WCV and the sensor are two important factors influencing the energy efficiency of the wireless sensor network, which has not been well considered. In this paper, we study the energy efficiency of a wireless rechargeable sensor network charged by a finite capacity WCV through an imperfect wireless channel. To estimate the energy efficiency, we first propose a new metric named waste rate, which is defined as a function of the charging channel quality. Then, energy efficiency optimization is modeled as minimizing the waste rate. Through optimizing the distance between the WCV and sensor nodes, the set of optimal charging sensor nodes is obtained. By using the Hamiltonian circle, the nearest neighbor algorithm is proposed to find the traveling path of the WCV. Furthermore, to avoid the untimely death of sensor nodes and the coverage hole, an extended node dynamic replacement strategy is proposed. The simulation results show that the proposed method can reduce the waste rate and the total charging time; i.e., the sum of traveling time and charging delay can be significantly reduced, which indicates that the proposed algorithm can improve the energy efficiency of the network.