bluuebunny/crossref_metadata_2025 huggingface dataset:
Hesperidin Alleviates Methotrexate-Induced Memory Deficits via Hippocampal Neurogenesis in Adult Rats
Authors: Naewla Salinee , Sirichoat Apiwat , Pannangrong Wanassanan , Chaisawang Pornthip , Wigmore Peter , Welbat Jariya Umka
Published: April 2019

Methotrexate (MTX), a folic acid antagonist, is widely used in cancer treatment. However, treatment with MTX reduces hippocampal neurogenesis, leading to memory deficits. Hesperidin (Hsd) is a flavonoid glycoside that promotes anti-inflammation, acts as an antioxidant, and has neuroprotective properties. Consumption of Hsd enhances learning and memory. In the present study, we investigated the protective effects of Hsd against MTX-induced impairments of memory and neurogenesis; male Sprague Dawley rats were administered with a single dose of MTX (75 mg/kg) by intravenous (i.v.) injection on days 8 and 15 or Hsd (100 mg/kg) by oral gavage for 21 days. Memory was tested using novel object location (NOL) and novel object recognition (NOR) tasks. Immunofluorescence staining of Ki-67, bromodeoxyuridine (BrdU), and doublecortin (DCX) was performed to assess cell proliferation, survival, and immature neurons. The data showed that Hsd and MTX did not disable locomotor ability. The MTX animals exhibited memory deficits in both memory tests. There were significant decreases in the numbers of cell proliferation, survival, and immature neurons in the MTX animals. However, co-administration with MTX and Hsd alleviated memory loss and neurogenesis decline. These results revealed that Hsd could protect against MTX side effects in the animals in this study.
Caffeinated Gel Ingestion Enhances Jump Performance, Muscle Strength, and Power in Trained Men
Authors: Venier Sandro , Grgic Jozo , Mikulic Pavle
Published: April 2019

We aimed to explore the effects of caffeinated gel ingestion on neuromuscular performance in resistance-trained men. The participants (n = 17; mean ± standard deviation (SD): age 23 ± 2 years, height 183 ± 5 cm, body mass 83 ± 11 kg) completed two testing conditions that involved ingesting a caffeinated gel (300 mg of caffeine) or placebo. The testing outcomes included: (1) vertical jump height in the squat jump (SJ) and countermovement jump (CMJ); (2) knee extension and flexion peak torque and average power at angular velocities of 60°·s−1 and 180°·s−1; (3) barbell velocity in the bench press with loads corresponding to 50%, 75%, and 90% of one-repetition maximum (1RM); and (4) peak power output in a test on a rowing ergometer. Compared to the placebo, caffeine improved: (1) SJ (p = 0.039; Cohen’s d effect size (d) = 0.18; +2.9%) and CMJ height (p = 0.011; d = 0.18; +3.3%); (2) peak torque and average power in the knee extensors at both angular velocities (d ranged from 0.21 to 0.37; percent change from +3.5% to +6.9%), peak torque (p = 0.034; d = 0.24; +4.6%), and average power (p = 0.015; d = 0.32; +6.7%) at 60°·s−1 in the knee flexors; (3) barbell velocity at 50% 1RM (p = 0.021; d = 0.33; +3.5%), 75% 1RM (p < 0.001; d = 0.42; +5.4%), and 90% 1RM (p < 0.001; d = 0.59, +12.0%). We conclude that the ingestion of caffeinated gels may acutely improve vertical jump performance, strength, and power in resistance-trained men.
Effect of High Fiber Cereal Intake on Satiety and Gastrointestinal Symptoms during Ramadan
Authors: Jarrar Amjad H. , Beasley Jeannette M. , Ohuma Eric O. , Cheikh Ismail Leila , Qeshta Dina A. , Mohamad Maysm N. , Al Dhaheri Ayesha S.
Published: April 2019

(1) Background: Fasting during Ramadan involves large changes in daily eating patterns which strongly impacts the daily biorhythm and challenges the regular function of the digestive tract. The aim of this study was to assess satiety, bowel habits, body composition, blood glycaemia, and blood lipidemia after the consumption of high fiber cereal at dawn (Sohor) during the month of Ramadan; (2) Methods: A two-arm randomized, controlled, single-blinded, parallel-design study was conducted in Ramadan month. Participants were randomized to consume either 90 g of high fiber cereal (11 g fiber/90 g) at Sohor for 20 consecutive days (intervention group, n = 45) or to maintain their habitual diet intake (control group; n = 36); (3) Results: The intervention group reported higher satiety rating scores, improved bowel habits and reduced bloating frequency after the 20-day intervention. Significantly higher intake of carbohydrates and dietary fiber were observed in the intervention group. Total cholesterol and low density lipoprotein (LDL) cholesterol were significantly lower among the intervention group compared to the control group (p-value = 0.043, and p-value = 0.033, respectively) at the end of the intervention. No significant differences in body weight, body fat percentage, waist circumference, body mass index, blood glucose, high density lipoprotein (HDL) cholesterol, and triglycerides were observed between the two groups; (4) Conclusions: Consuming high fiber cereal had a positive effect on health and well-being during the month of Ramadan with better satiety, improved bowel functions, and improved blood lipids.
Soluble CD163-Associated Dietary Patterns and the Risk of Metabolic Syndrome
Authors: Hu Tzu-Yu , Lee Shin-Yng , Shih Chun-Kuang , Chou Meng-Jung , Wu Meng-Chieh , Teng I-Chun , Bai Chyi-Huey , Sabrina Nindy , Tinkov Alexey A. , Skalny Anatoly V. , Chang Jung-Su
Published: April 2019

Elevated soluble cluster of differentiation 163 (sCD163) concentrations, a marker of macrophage activation, are associated with obesity. Weight reduction decreases circulating CD163 levels, and changes in sCD163 levels are associated with improved metabolic dysfunction. Currently, the relationship between sCD163 and diet remains unclear. This study investigated dietary patterns associated with sCD163 concentrations and its predictive effect on metabolic syndrome (MetS). Data on anthropometrics, blood biochemistry, and a food frequency questionnaire were collected from 166 Taiwanese adults. sCD163 levels independently predicted MetS (odds ratio (OR): 5.35; 95% confidence interval (CI): 2.13~13.44, p < 0.001), non-alcoholic fatty liver disease (OR: 2.19; 95% CI: 1.03~4.64, p < 0.001), and central obesity (OR: 3.90; 95% CI: 1.78~8.55, p < 0.001), after adjusting for age and sex. An adjusted linear regression analysis revealed strong correlations between levels of sCD163 and aspartate transaminase (AST) (β = 0.250 (0.023~0.477), p < 0.05) and red blood cell aggregation (β = 0.332 (0.035~0.628), p < 0.05). sCD163-associated dietary pattern scores (high frequencies of consuming noodles and desserts, and eating at home, and a low intake frequency of steamed/boiled/raw food, white/light-green-colored vegetables, orange/red/purple-colored vegetables, dairy products, seafood, dark-green leafy vegetables, and soy products) were positively correlated with MetS, liver injury biomarkers, and sCD163 levels (all p for trend < 0.05). Individuals with the highest dietary pattern scores (tertile 3) had a 2.37-fold [OR: 2.37; 95% CI: 1.04~5.37, p < 0.05] higher risk of MetS compared to those with the lowest scores (tertile 1). Overall, the study findings suggest the importance of a healthy dietary pattern in preventing elevated sCD163 levels and diet-related chronic disease such as MetS.
Race/Ethnicity, Socioeconomic Status, and Polypharmacy among Older Americans
Authors: Assari Shervin , Bazargan Mohsen
Published: April 2019

Background: Very few studies with nationally representative samples have investigated the combined effects of race/ethnicity and socioeconomic position (SEP) on polypharmacy (PP) among older Americans. For instance, we do not know if prevalence of PP differs between African Americans (AA) and white older adults, whether this difference is due to a racial gap in SEP, or whether racial and ethnic differences exist in the effects of SEP indicators on PP. Aims: We investigated joint effects of race/ethnicity and SEP on PP in a national household sample of American older adults. Methods: The first wave of the University of Michigan National Poll on Healthy Aging included a total of 906 older adults who were 65 years or older (80 AA and 826 white). Race/ethnicity, SEP (income, education attainment, marital status, and employment), age, gender, and PP (using 5+ medications) were measured. Logistic regression was applied for data analysis. Results: Race/ethnicity, age, marital status, and employment did not correlate with PP; however, female gender, low education attainment, and low income were associated with higher odds of PP among participants. Race/ethnicity interacted with low income on odds of PP, suggesting that low income might be more strongly associated with PP in AA than white older adults. Conclusions: While SEP indicators influence the risk of PP, such effects may not be identical across diverse racial and ethnic groups. That is, race/ethnicity and SEP have combined/interdependent rather than separate/independent effects on PP. Low-income AA older adults particularly need to be evaluated for PP. Given that race and SEP have intertwined effects on PP, racially and ethnically tailored interventions that address PP among low-income AA older adults may be superior to universal interventions and programs that ignore the specific needs of diverse populations. The results are preliminary and require replication in larger sample sizes, with PP measured directly without relying on individuals’ self-reports, and with joint data collected on chronic disease.
Casuistic Reasoning, Standards of Evidence, and Expertise on Elite Athletes’ Nutrition
Authors: Jukola Saana
Published: April 2019

This paper assesses the epistemic challenges of giving nutrition advice to elite athletes in light of recent philosophical discussion concerning evidence-based practice. Our trust in experts largely depends on the assumption that their advice is based on reliable evidence. In many fields, the evaluation of the reliability of evidence is made on the basis of standards that originate from evidence-based medicine. I show that at the Olympic or professional level, implementing nutritional plans in real-world competitions requires contextualization of knowledge in a way that contravenes the tenets of evidence-based thinking. Nutrition experts need to be able to combine and apply evidence from multiple sources, including the previous successes and failures of particular athletes. I argue that in this sense, the practice of elite sport nutrition embodies casuistic reasoning.
The Use of Lanthanum Ions and Chitosan for Boron Elimination from Aqueous Solutions
Authors: Kluczka Joanna , Dudek Gabriela , Kazek-Kęsik Alicja , Gnus Małgorzata , Krzywiecki Maciej , Mitko Krzysztof , Krukiewicz Katarzyna
Published: April 2019

Boron is an essential element for plants and living organisms; however, it can be harmful if its concentration in the environment is too high. In this paper, lanthanum(III) ions were introduced to the structure of chitosan via an encapsulation technique and the obtained hydrogel (La-CTS) was used for the elimination of the excess of B(III) from modelling solutions. The reaction between boric acid and hydroxyl groups bound to the lanthanum coordinated by chitosan active centres was the preponderant mechanism of the bio-adsorption removal process. The results demonstrated that La-CTS removed boric acid from the aqueous solution more efficiently than either lanthanum hydroxide or native chitosan hydrogel, respectively. When the initial boron concentration was 100 mg/dm3, the maximum adsorption capacity of 11.1 ± 0.3 mg/g was achieved at pH 5 and the adsorption time of 24 h. The successful introduction of La(III) ions to the chitosan backbone was confirmed by Scanning Electron Microscopy with Energy Dispersive X-Ray Spectroscopy, Fourier-Transform Infrared Spectroscopy, X-Ray Diffraction, X-ray Photoelectron Spectroscopy, and Inductively Coupled Plasma Optical Emission Spectroscopy. Due to its high-performance boron adsorption-desorption cycle and convenient form, La-CTS seems to be a promising bio-adsorbent for water treatment.
Fuzzy Optimization on the Synthesis of Chitosan-Graft-Polyacrylic Acid with Montmorillonite as Filler Material: A Case Study
Authors: Choi Angelo Earvin Sy , Futalan Cybelle Morales , Yee Jurng-Jae
Published: April 2019

In this paper, the synthesis of a chitosan–montmorillonite nanocomposite material grafted with acrylic acid is presented based on its function in a case study analysis. Fuzzy optimization is used for a multi-criteria decision analysis to determine the best desirable swelling capacity (YQ) of the material synthesis at its lowest possible variable cost. For YQ, the integrating the result’s cumulative uncertainty is an essential element to investigate the feasibility of the developed model equation. The Pareto set analysis is able to set the appropriate boundary limits for YQ and the variable cost. Two case studies are presented in determining the lowest possible cost: Case 1 for maximum YQ, and Case 2 for minimum YQ. These boundary limits were used in the fuzzy optimization to determine its global optimum results that achieved the overall satisfaction ratings of 67.2% (Case 1) and 52.3% (Case 2). The synthesis of the polyacrylic acid/chitosan material for Case 1 resulted in 305 g/g YQ and 10.8 USD/kg, while Case 2 resulted in 97 g/g YQ and 12.3 USD/kg. Thus, the fuzzy optimization approach proves to be a practical method for examining the best possible compromise solution based on the desired function to adequately synthesize a material.
Pluronic F127-Folate Coated Super Paramagenic Iron Oxide Nanoparticles as Contrast Agent for Cancer Diagnosis in Magnetic Resonance Imaging
Authors: Vu-Quang Hieu , Vinding Mads Sloth , Nielsen Thomas , Ullisch Marcus Görge , Nielsen Niels Chr. , Nguyen Dinh-Truong , Kjems Jørgen
Published: April 2019

Contrast agents have been widely used in medicine to enhance contrast in magnetic resonance imaging (MRI). Among them, super paramagnetic iron oxide nanoparticles (SPION) have been reported to have low risk in clinical use. In our study, F127-Folate coated SPION was fabricated in order to efficiently target tumors and provide imaging contrast in MRI. SPION alone have an average core size of 15 nm. After stabilizing with Pluronic F127, the nanoparticles reached a hydrodynamic size of 180 nm and dispersed well in various kinds of media. The F127-Folate coated SPION were shown to specifically target folate receptor expressing cancer cells by flow cytometry analysis, confocal laser scanning microscope, as well as in vitro MRI. Furthermore, in vivo MRI images have shown the enhanced negative contrast from the F127-Folate coated SPION in tumor-bearing mice. In conclusion, our F127-Folate coated SPION have shown great potential as a contrast agent in MRI, as well as in the combination with drug delivery for cancer therapy.
Static Light Scattering Monitoring and Kinetic Modeling of Polyacrylamide Hydrogel Synthesis
Authors: Gomes Catarina , Dias Rolando C.S. , Costa Mário Rui P.F.N.
Published: April 2019

A kinetic model describing aqueous acrylamide homopolymerization and copolymerization of acrylamide with methylene bisacrylamide, leading to hydrogel formation, is presented and applied in the simulation of these reaction processes. This modeling approach is based on population balances of generating functions and, besides the crosslinking mechanisms inherent to network formation, other specific kinetic steps important in acrylamide polymerization (e.g., branching due to backbiting) are considered in the simulation tool developed. The synthesis of acrylamide polymers and hydrogels was performed at 26 °C and at 40 °C using two different initiation systems. The formation of such materials was monitored using in-line static light scattering (SLS), and the spatial inhomogeneity of the final hydrogels was also measured using this experimental technique. It is shown that the simulations are helpful in describing information provided by SLS in-line monitoring, namely in the early stages of polymerization with the transition from dilute to semi-dilute regime. Indeed, it finds a plausible match between the critical overlap polymer concentration and gelation, this later leading to the observed spatial heterogeneity of the hydrogels. Usefulness of the kinetic model for defining operation conditions (initial composition, semi-batch feed policies, chain transfer, etc.) in making the shift from gelation to the semi-dilute regime is discussed, and the extension of this approach to processes enabling a higher control of gelation (e.g., controlled radical polymerization) is also prospected.
Hydrogen Production and Subsequent Adsorption/Desorption Process within a Modified Unitized Regenerative Fuel Cell
Authors: Kapoor Diksha , Oberoi Amandeep Singh , Nijhawan Parag
Published: April 2019

For sustainable and incremental growth, mankind is adopting renewable sources of energy along with storage systems. Storing surplus renewable energy in the form of hydrogen is a viable solution to meet continuous energy demands. In this paper the concept of electrochemical hydrogen storage in a solid multi-walled carbon nanotube (MWCNT) electrode integrated in a modified unitized regenerative fuel cell (URFC) is investigated. The method of solid electrode fabrication from MWCNT powder and egg white as an organic binder is disclosed. The electrochemical testing of a modified URFC with an integrated MWCNT-based hydrogen storage electrode is performed and reported. Galvanostatic charging and discharging was carried out and results analyzed to ascertain the electrochemical hydrogen storage capacity of the fabricated electrode. The electrochemical hydrogen storage capacity of the porous MWCNT electrode is found to be 2.47 wt%, which is comparable with commercially available AB5-based hydrogen storage canisters. The obtained results prove the technical feasibility of a modified URFC with an integrated MWCNT-based hydrogen storage electrode, which is the first of its kind. This is surelya step forward towards building a sustainable energy economy.
Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks
Authors: Silva Waldeyr M. C. da , Andersen Jakob L. , Holanda Maristela T. , Walter Maria Emília M. T. , Brigido Marcelo M. , Stadler Peter F. , Flamm Christoph
Published: April 2019

Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases that admit different cyclizations and rearrangements to yield a blend of sesquiterpenes. Here, we investigate to what extent sesquiterpene biosynthesis metabolic pathways can be reconstructed just from the knowledge of the final product and the reaction mechanisms catalyzed by sesquiterpene synthases. We use the software package MedØlDatschgerl (MØD) to generate chemical networks and to elucidate pathways contained in them. As examples, we successfully consider the reachability of the important plant sesquiterpenes β -caryophyllene, α -humulene, and β -farnesene. We also introduce a graph database to integrate the simulation results with experimental biological evidence for the selected predicted sesquiterpenes biosynthesis.
Isolation and Characterization of Small Extracellular Vesicles from Porcine Blood Plasma, Cerebrospinal Fluid, and Seminal Plasma
Authors: Kupcova Skalnikova Helena , Bohuslavova Bozena , Turnovcova Karolina , Juhasova Jana , Juhas Stefan , Rodinova Marie , Vodicka Petr
Published: April 2019

Extracellular vesicles (EVs) are a highly attractive subject of biomedical research as possible carriers of nucleic acid and protein biomarkers. EVs released to body fluids enable indirect access to inner organs by so-called “liquid biopsies”. Obtaining a high-quality EV sample with minimum contaminants is crucial for proteomic analyses using LC–MS/MS or other techniques. However, the EV content in various body fluids largely differs, which may hamper subsequent analyses. Here, we present a comparison of extracellular vesicle yields from blood plasma, cerebrospinal fluid, and seminal plasma using an experimental pig model. Pigs are widely used in biomedical research as large animal models with anatomy and physiology close to those of humans and enable studies (e.g., of the nervous system) that are unfeasible in humans. EVs were isolated from body fluids by differential centrifugation followed by ultracentrifugation. EVs were characterized according to protein yields and to the quality of the isolated vesicles (e.g., size distribution, morphology, positivity for exosome markers). In our experimental setting, substantial differences in EV amounts were identified among body fluids, with the seminal plasma being the richest EV source. The yields of pellet proteins from ultracentrifugation of 1 mL of porcine body fluids may help to estimate body fluid input volumes to obtain sufficient samples for subsequent proteomic analyses.
Evolution of a Service Management Framework: Spotlight at Stanford as a Use Case
Authors: Aster Catherine
Published: April 2019

Service management—the entirety of activities undertaken by an organization to design, plan, deliver, operate, and control information technology (IT) services offered to stakeholders—has long been applied successfully by the government and commercial sectors. In this article, service management is discussed in the context of open-source software developed in an academic library setting, by exploring the creation and growth of the Spotlight at Stanford service framework. First, service management is briefly introduced as a guiding principle and philosophy, within the Stanford Libraries context. Second, the Spotlight at Stanford software is described. Third, people who are key players in both the delivery and use of the software are discussed. Fourth, processes including goals and activities of the Spotlight at Stanford service team are reviewed. Fifth, various accomplishments are listed, including how the service team has contributed to the successful adoption and development of the web application at Stanford University. Finally, lessons learned are discussed and directions are shared for the future development of the Spotlight at Stanford service framework.
What Does It Mean To Be a Badly Behaved Animal? An Answer from the Devadatta Stories of the Pāli Jātakas
Authors: Appleton Naomi
Published: April 2019

The many animals that appear in the Pāli Jātakatthavaṇṇanā often mirror human predicaments, society and language, and this has prompted largely allegorical readings of the stories. In addition, in many cases the animals are identified as past lives of important human characters, potentially diminishing their animality further. In particular, the Buddha’s repeated rebirth as a range of virtuous and wise animals tells us plenty about the Buddha, but arguably little about animals. Nonetheless, in this article I argue that the jātakas are able to tell us interesting things about the capabilities of animals. By using stories of another key animal character—namely Devadatta, the Buddha’s nemesis—I explore what might be distinctive about the ability of animals to misbehave. Since Devadatta appears 28 times as an animal and 46 as a human, he allows us to probe whether or not the text’s compilers saw a difference between human and animal capacities for evil. In the process, I raise questions about how we should view animal tales in the Jātakas more broadly, and highlight the productive tension between animals as unfortunate fellow travellers in the cycle of rebirth, and animals as literary devices that shed light on human behaviour.
Determining Health Index of Transmission Line Asset using Condition-Based Method
Authors: Hashim Ruqayyah , Usman Fathoni , Baharuddin Intan Nor Zuliana
Published: April 2019

Assessment of overhead transmission lines is a crucial task in the asset management of electric power infrastructures. Any assets have different life spans and require proper assessment and maintenance actions. Disruption of the power supply may cause national problems. Therefore, it is essential to ensure that the distribution and transmission of electric power from the power plant to end consumers is achieved without fail. This paper presents a proposed framework of health index of the transmission line using a condition-based method. This study refers to previous methods in determining the health index of electrical power assets, mainly transformer and transmission line. Three main indicators contributed and need to be considered in determining the health index. The indicators are structural, electrical and environmental aspects. The health index of these three indicators with 14 items was calculated, and the overall health index of the transmission line determined. From the case study conducted for this study, the specific location, tower and item can be acknowledged that cause the failure and the service interruption of energy supply to the consumer. It is found that the implementation of the health index calculation gives a more accurate description of the health status of a transmission line. The health index can be used for the prioritizing of maintenance, refurbishment or replacement to avoid disruption.
Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake
Authors: Shao Xiaoyi , Ma Siyuan , Xu Chong , Zhang Pengfei , Wen Boyu , Tian Yingying , Zhou Qing , Cui Yulong
Published: April 2019

The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km2. Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.
Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory
Authors: Schumann Guy J.-P. , Muhlhausen Joseph , Andreadis Konstantinos M.
Published: April 2019

Unmanned Aerial Vehicle (UAV) platforms have rapidly developed as tools for remote mapping at very high spatial resolutions. They have recently gained in popularity in many application fields owing to the versatility of platforms and sensors, ease of deployment, and a steady increase in computational power. Obtaining highly detailed topography data over very small scales is one of the more typical application domains. Here, we demonstrate this application using Structure from Motion (SfM) processing over a small river floodplain in Howard County (Maryland, USA). Evaluation of the derived bare-earth terrain model with state-of-the art LiDAR shows a trivial bias of 1.6 cm and a root mean square deviation (RMSD) of 39 cm. We then applied this terrain model to extract floodplain and river cross-section geometries of a small stream, important during high-magnitude urban flash flood events, with the aim to assess its value for floodplain inundation mapping and first order characterization of in-channel hydraulics. Initial findings agree with traditional stream and floodplain classification theory and thus show very promising results for this type of UAV usage. We expect this type of application to gain more momentum in the near future with the ever-growing importance of more detailed data in order to increase resilience to flood risk, especially in urban areas.
Ten Priority Science Gaps in Assessing Climate Data Record Quality
Authors: Nightingale Joanne , Mittaz Jonathan P.D. , Douglas Sarah , Dee Dick , Ryder James , Taylor Michael , Old Christopher , Dieval Catherine , Fouron Celine , Duveau Guillaume , Merchant Christopher
Published: April 2019

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union’s Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons. This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described. The development of the EQC framework has highlighted cross-domain as well as ECV specific science knowledge gaps in relation to addressing the quality of climate data sets derived from satellite and in situ observations. We discuss 10 common priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and provide users with the range of information necessary to confidently select relevant products for their specific application.
Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
Authors: Jiang Nan , Xu Tianhe , Xu Yan , Xu Guochang , Schuh Harald
Published: April 2019

Inter-system bias (ISB) will affect accuracy and processing time in integrated precise point positioning (PPP), and ISB stochastic models will largely determine the quality of ISB estimation. Thus, the impacts of four different stochastic models of ISB processing will be assessed and studied in detail to further reveal the influence of ISB in positioning. They are ISB-PW considering ISB as a piece-wise constant, ISB-RW considering ISB as random walk, ISB-AD considering ISB as an arc-dependent constant, and ISB-WN considering ISB as white noise. Together with the model without introducing ISB called ISB-OFF, i.e., five different schemes, ISB-OFF, ISB-PW, ISB-RW, ISB-AD, and ISB-WN, will be designed and tested in this study. From the results of pseudorange residuals, it can be noticed that when considering ISB, the Root-Mean-Square (RMS) of ionosphere-free combined pseudorange residuals are much smaller than without ISB (ISB-OFF). The results of convergence time and positioning accuracy analysis show that PPP performance with ISB-AD is even worse than ISB-OFF, when using the precise products from the German Research Centre for Geosciences (GFZ) named as GBM products here; while the strategies of ISB-RW, and ISB-WN achieve the best results. For the products from Wuhan University called WUM products, a completely different result is achieved. PPP with the stochastic models of ISB-PW and ISB-AD perform best. The most likely reason is the ISB stochastic models applied by the analysis centers are consistent with those used in the PPP on the user side. So, ISB-RW, or ISB-WN is recommended when GBM products are used, and for the WUM products, ISB-PW, or ISB-AD is chosen. From the statistics of PPP precision during the convergence period, it can be concluded that considering ISB also has a great improvement on combined PPP accuracy during the initialization phase.
Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators
Authors: Irulappa-Pillai-Vijayakumar Dinesh Babu , Renaud Jean-Pierre , Morneau François , McRoberts Ronald E. , Vega Cédric
Published: April 2019

Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter.
Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface
Authors: Tinoco Varela David , Gudiño Peñaloza Fernando , Villaseñor Rodelas Carolina Jeanette
Published: April 2019

Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or physical features of a user. In this sense, it has been sought out the development of small interfaces to avoid any type of user annoyance. In this paper, bioelectric signals have been analyzed and characterized in order to propose a more natural human-machine interaction system. The proposed scheme is controlled by electromyographic signals that a person can create through arm movements. Such arm signals have been analyzed and characterized by a back-propagation neural network, and by a wavelet analysis, in this way control commands were obtained from such arm electromyographic signals. The developed interface, uses Extensible Messaging and Presence Protocol (XMPP) to send control commands remotely. In the experiment, it manipulated a vehicle that was approximately 52 km away from the user, with which it can be showed that a characterized electromyographic signal can be sufficient for controlling embedded devices such as a Raspberri Pi, and in this way we can use the neural network and the wavelet analysis to generate control words which can be used inside the Internet of Things too. A Tiva-C board has been used to acquire data instead of more popular development boards, with an adequate response. One of the most important aspects related to the proposed interface is that it can be used by almost anyone, including people with different abilities and even illiterate people. Due to the existence of individual efforts to characterize different types of bioelectric signals, we propose the generation of free access Bioelectric Control Dictionary, to define and consult each characterized biosignal.
The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras
Authors: Albakri Ghazel , Alghowinem Sharifa
Published: April 2019

Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.
IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics
Authors: Xiong Lu , Xia Xin , Lu Yishi , Liu Wei , Gao Letian , Song Shunhui , Han Yanqun , Yu Zhuoping
Published: April 2019

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.
A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018
Authors: Pham Tien Dat , Xia Junshi , Ha Nam Thang , Bui Dieu Tien , Le Nga Nhu , Tekeuchi Wataru
Published: April 2019

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.
Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
Authors: Wang Haiyuan , Tian Hongxian
Published: April 2019

Low-Power Wide-Area Network (LPWAN) is the technology that the Internet-of-Things (IoT) uses in long-distance, wide-coverage scenarios. As one of the ultra-narrowband (UNB) modulation techniques, M-ary position phase shift keying (MPPSK) modulation can provide high coverage and high reliability for LPWAN. This paper proposes a multipath separation method based on MPPSK modulation, which aims to eliminate the influence of multipath on the main path without increasing the spectrum overhead and system complexity. Specifically, the modulation parameter of the system is adjusted according to the delay value, so that the phase jump of the multipath signal falls outside the phase jump of the main path symbol to achieve separation of the multipath from the main path. Moreover, a normalized symbol joint decision method is proposed in order to reduce the introduced noise while using multipath information for decisions. The simulation results indicate that the multipath separation conditions given in this paper can meet the requirements of multipath separation of MPPSK signals. Compared with the existing mainstream decision scheme, the normalized symbol joint decision improves the demodulation performance of the system.
Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End
Authors: Qiu Xiaochen , Zhang Hai , Fu Wenxing , Zhao Chenxu , Jin Yanqiong
Published: April 2019

The research field of visual-inertial odometry has entered a mature stage in recent years. However, unneglectable problems still exist. Tradeoffs have to be made between high accuracy and low computation for users. In addition, notation confusion exists in quaternion descriptions of rotation; although not fatal, this may results in unnecessary difficulties in understanding for researchers. In this paper, we develop a visual-inertial odometry which gives consideration to both precision and computation. The proposed algorithm is a filter-based solution that utilizes the framework of the noted multi-state constraint Kalman filter. To dispel notation confusion, we deduced the error state transition equation from scratch, using the more cognitive Hamilton notation of quaternion. We further come up with a fully linear closed-form formulation that is readily implemented. As the filter-based back-end is vulnerable to feature matching outliers, a descriptor-assisted optical flow tracking front-end was developed to cope with the issue. This modification only requires negligible additional computation. In addition, an initialization procedure is implemented, which automatically selects static data to initialize the filter state. Evaluations of proposed methods were done on a public, real-world dataset, and comparisons were made with state-of-the-art solutions. The experimental results show that the proposed solution is comparable in precision and demonstrates higher computation efficiency compared to the state-of-the-art.
Finite-Time Disturbance Observer for Robotic Manipulators
Authors: Cao Pengfei , Gan Yahui , Dai Xianzhong
Published: April 2019

Robotic manipulators may be subject to different types of disturbances such as unknown payloads, unmodeled dynamics, and environment interaction forces. Observing these unknown disturbances in robotic manipulators is fundamental in many robotic applications such as disturbance rejection and sensorless force control. In this paper, a novel disturbance observer (DOB) is introduced based on the insights from the finite-time observer (FTO) and robot dynamics. Different from the traditional DOBs, this new observer can provide the capability to track the disturbance within a finite time. The performance of the presented observer is verified by two kinds of typical disturbances for a two-link manipulator with a comparison with several existing DOBs. The simulation results show the rapidity and accuracy of the proposed FTO.
Towards Automatic UAS-Based Snow-Field Monitoring for Microclimate Research
Authors: Gabrlik Petr , Janata Premysl , Zalud Ludek , Harcarik Josef
Published: April 2019

This article presents unmanned aerial system (UAS)-based photogrammetry as an efficient method for the estimation of snow-field parameters, including snow depth, volume, and snow-covered area. Unlike similar studies employing UASs, this method benefits from the rapid development of compact, high-accuracy global navigation satellite system (GNSS) receivers. Our custom-built, multi-sensor system for UAS photogrammetry facilitates attaining centimeter- to decimeter-level object accuracy without deploying ground control points; this technique is generally known as direct georeferencing. The method was demonstrated at Mapa Republiky, a snow field located in the Krkonose, a mountain range in the Czech Republic. The location has attracted the interest of scientists due to its specific characteristics; multiple approaches to snow-field parameter estimation have thus been employed in that area to date. According to the results achieved within this study, the proposed method can be considered the optimum solution since it not only attains superior density and spatial object accuracy (approximately one decimeter) but also significantly reduces the data collection time and, above all, eliminates field work to markedly reduce the health risks associated with avalanches.